# TnsAI Framework — Full Documentation

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---

# Advanced Agent Features

URL: https://tnsai.dev/docs/agents/advanced
Description: Beyond the basic Agent lifecycle (create, chat, stop), TnsAI.Core provides specialized subsystems for cognitive support, streaming, ensemble execution, hierarchy management, and chat orchestration. These are internal components extracted from the Agent class for cohesion; most are accessed through the Agent public API rather than directly.

import { Callout } from 'fumadocs-ui/components/callout'

## AgentCognitiveSupport

`com.tnsai.agents.AgentCognitiveSupport` holds planning, reasoning, evaluation, environment perception, and feedback collection for an Agent. It is created internally during agent construction and accessed through `AgentCapabilities`.

### Evaluation Hooks

Eval hooks fire at lifecycle points (before/after chat, before/after tool call, on error, on agent stop) and record metrics into an `EvalContext`.

```java
// Add an evaluation hook
agent.addEvalHook(myHook);

// Enable/disable evaluation
agent.setEvalEnabled(true);

// Record goal completion
agent.recordGoalCompletion("goal-1", true, Map.of("steps", 3));
```

Key methods on `AgentCognitiveSupport`:

| Method                                                                                 | Description                                         |
| -------------------------------------------------------------------------------------- | --------------------------------------------------- |
| `addEvalHook(EvalHook hook)`                                                           | Register an eval hook                               |
| `removeEvalHook(EvalHook hook)`                                                        | Unregister an eval hook                             |
| `clearEvalContext()`                                                                   | Create a fresh `EvalContext` with new session ID    |
| `setEvalEnabled(boolean)`                                                              | Enable or disable all eval hooks                    |
| `isEvalEnabled()`                                                                      | Check if evaluation is active                       |
| `recordGoalCompletion(String goalId, boolean success, Map<String, Object> details)`    | Record a goal outcome                               |
| `fireOnBeforeChat(String message)`                                                     | Fire pre-chat eval event                            |
| `fireOnAfterChat(String result, long latencyMs)`                                       | Fire post-chat eval event                           |
| `fireOnBeforeToolCall(String toolName, Map<String, Object> arguments)`                 | Fire pre-tool eval event                            |
| `fireOnAfterToolCall(String toolName, Object result, boolean success, long latencyMs)` | Fire post-tool eval event                           |
| `fireOnAgentStop(String status)`                                                       | Fire agent stop event and complete the eval context |

### Planning (BDI)

Planning uses a `PlannerHandle` (SPI, provided by tnsai-intelligence) for GOAP/HTN planning.

```java
// Set a planner
agent.setPlannerHandle(myPlanner);

// Plan for all goals
List<PlannerHandle.PlanStep> steps = agent.plan();

// Plan for a specific goal
List<PlannerHandle.PlanStep> steps = agent.planForGoal("deliverPackage");

// Execute a plan
PlannerHandle.PlanResult result = agent.executePlan(steps);
```

Key methods on `AgentCognitiveSupport`:

| Method                | Signature                                                        |
| --------------------- | ---------------------------------------------------------------- |
| `plan`                | `List<PlanStep> plan(List<Role> roles)`                          |
| `planForGoal`         | `List<PlanStep> planForGoal(String goalName, List<Role> roles)`  |
| `executePlan`         | `PlanResult executePlan(List<PlanStep> steps, List<Role> roles)` |
| `extractCurrentState` | `Map<String, Object> extractCurrentState(List<Role> roles)`      |
| `setPlannerHandle`    | `void setPlannerHandle(PlannerHandle handle)`                    |
| `getPlannerHandle`    | `Optional<PlannerHandle> getPlannerHandle()`                     |
| `isPlanningEnabled`   | `boolean isPlanningEnabled()`                                    |

### Reasoning Strategy

Reasoning strategies (ReAct, Tree-of-Thought, etc.) are pluggable via `ReasoningStrategyHandle`.

```java
agent.setReasoningStrategy(reactStrategy);

// After chat, inspect reasoning
Optional<ReasoningResult> result = agent.getLastReasoningResult();
```

When reasoning is enabled, `AgentOrchestrator.chat()` routes through the strategy instead of direct LLM invocation.

### Environment Perception

Agents can perceive and act on environments (BDI perception-to-belief pipeline).

```java
agent.setEnvironment(myEnvironment);

Percept percept = agent.perceive();
ActionOutcome outcome = agent.actOnEnvironment("moveForward", Map.of("speed", 5));
```

Environment changes trigger `onEnvironmentChangeWithRoles`, which checks for unsatisfied goals if a planner is configured.

### Feedback Collection

`FeedbackCollector` (SPI, provided by tnsai-intelligence) records tool outcomes for preference learning.

```java
agent.setFeedbackCollector(myCollector);
FeedbackCollector collector = agent.getFeedbackCollector();
```

Tool outcomes are recorded automatically via `recordToolOutcome(toolName, result, success, latencyMs)`.

## AgentEnsembleExecutor

`com.tnsai.agents.ensemble.AgentEnsembleExecutor` provides multi-LLM ensemble operations accessed via `agent.getEnsembleExecutor()`.

### Patterns

| Method             | Signature                                                                                                  | Description                                |
| ------------------ | ---------------------------------------------------------------------------------------------------------- | ------------------------------------------ |
| `parallelChat`     | `ParallelResults parallelChat(String message, List<LLMClient> llms)`                                       | Fan out to all LLMs, collect all responses |
| `raceChat`         | `Optional<LLMResult> raceChat(String message, List<LLMClient> llms, long timeoutSeconds)`                  | First successful response wins             |
| `consensusChat`    | `Optional<LLMResult> consensusChat(String message, List<LLMClient> llms, Function<String, Double> scorer)` | Score responses, pick highest              |
| `majorityVoteChat` | `Optional<LLMResult> majorityVoteChat(String message, List<LLMClient> llms)`                               | Most common response pattern               |
| `ensembleChat`     | `Optional<String> ensembleChat(String message, List<LLMClient> llms, LLMClient aggregator)`                | Synthesize responses with aggregator LLM   |

```java
// Fan-out to multiple LLMs
ParallelLLM.ParallelResults results = agent.getEnsembleExecutor()
    .parallelChat("Explain quantum computing", List.of(gpt4, claude, gemini));

// Race: first response wins
Optional<ParallelLLM.LLMResult> fastest = agent.getEnsembleExecutor()
    .raceChat("Quick question", List.of(gpt4, claude), 10);

// Consensus with scoring
Optional<ParallelLLM.LLMResult> best = agent.getEnsembleExecutor()
    .consensusChat("Review this code", llms, response -> scoreQuality(response));

// Ensemble synthesis
Optional<String> synthesized = agent.getEnsembleExecutor()
    .ensembleChat("Complex analysis", List.of(gpt4, claude), aggregatorLLM);
```

## AgentHierarchyManager

`com.tnsai.agents.hierarchy.AgentHierarchyManager` manages parent-child relationships between agents, including bidirectional consistency and task delegation/escalation.

### Hierarchy Setup

```java
Agent supervisor = new SupervisorAgent();
Agent developer = new DeveloperAgent();
Agent tester = new TesterAgent();

// Bidirectional parent-child links
supervisor.addChild(developer);
supervisor.addChild(tester);

// developer.getParent() == supervisor
// supervisor.getChildren() contains both

// Add multiple at once
supervisor.addChildren(developer, tester, designer);
```

### Task Delegation and Escalation

```java
// Supervisor delegates to child
supervisor.delegateToChild(developer.id(), "writeCode", Map.of(
    "task", "Implement authentication",
    "language", "Java"
));

// Developer escalates to supervisor
developer.escalateToParent("requestApproval", Map.of(
    "reason", "Production deployment requires sign-off"
));
```

Both methods use `AgentCommunicationManager` to send `TaskMessageType.REQUEST` messages. `delegateToChild` throws `NoSuchElementException` if the child is not found; `escalateToParent` throws `IllegalStateException` if no parent is set.

### HierarchyContext Interface

The manager uses a `HierarchyContext` interface (implemented by `Agent`) to access internals:

```java
public interface HierarchyContext {
    String getAgentId();
    Agent getAgentRef();
    Agent getParentRef();
    void setParentRef(Agent parent);
    List<Agent> getChildrenSnapshot();
    boolean hasChild(Agent child);
    boolean addChildDirect(Agent child);
    boolean removeChildDirect(Agent child);
    Agent findChildById(String childId);
    AgentCommunicationManager getCommunicationManager();
}
```

## AgentStreamingSupport

`com.tnsai.agents.streaming.AgentStreamingSupport` handles streaming and event-based chat operations.

### Token Streaming

```java
// Stream tokens as a Java Stream
Stream<String> tokens = agent.streamChat("Tell me about Java");
tokens.forEach(System.out::print);
```

### Streaming with Tool Calls

The `streamChatWithTools` method combines streaming with multi-turn tool execution (up to 10 iterations):

```java
agent.streamChatWithTools("Search for TnsAI docs", chunk -> {
    if (chunk.isContent()) {
        System.out.print(chunk.getContent());
    } else if (chunk.isToolCall()) {
        System.out.println("Tool: " + chunk.getToolCall().get().getName());
    }
});
```

Flow: stream LLM response -\\> if tool calls received, execute them -\\> send results back to LLM -\\> repeat until final text or max iterations (10).

### Event-Based Chat (AG-UI)

```java
// With direct event consumer
agent.chatWithEvents("Hello", event -> {
    if (event instanceof TextDeltaEvent delta) {
        System.out.print(delta.getText());
    } else if (event instanceof ToolCallStartEvent tc) {
        System.out.println("Calling: " + tc.getToolName());
    }
});

// With session-based publisher
agent.chatWithEvents("Hello", sessionId);
```

Events emitted: `RunStartEvent`, `StatusEvent`, `TextDeltaEvent`, `ToolCallStartEvent`, `ToolCallEndEvent`, `ErrorEvent`, `RunEndEvent`.

## AgentChatOrchestrator

`com.tnsai.agents.chat.AgentChatOrchestrator` handles LLM invocation, response processing, structured output, and RAG augmentation.

### LLM Invocation

```java
// Full control
Object response = chatOrchestrator.invokeLLM(message, useHistory, useTools, trace);

// Process response (handles text and tool calls)
String result = chatOrchestrator.processLLMResponse(response, useHistory);
```

### Structured Output (Guardrails Pattern)

```java
// With custom parser
MyOutput output = agent.chatWithStructure(
    "Extract the key points",
    new MyOutputParser(),
    3  // maxRetries for correction
);

// With format detection (JSON, YAML, TOML)
MyRecord record = agent.chatWithFormat("Generate config", MyRecord.class, 2);

// With explicit format
MyRecord record = agent.chatWithFormat("Generate config", MyRecord.class, OutputFormat.YAML, 2);
```

The orchestrator detects output format from `@OutputFormatSpec` on the target class or agent class, defaulting to JSON.

### RAG (Knowledge Base Augmentation)

When a `KnowledgeBase` is set on the agent, the orchestrator automatically augments user messages with relevant knowledge results before sending to the LLM:

```java
agent.setKnowledgeBase(myKnowledgeBase);
agent.setKnowledgeBaseTopK(5);

// Messages are now automatically augmented with knowledge context
agent.chat("What is our refund policy?");
```

## AgentOrchestrator

`com.tnsai.agents.orchestration.AgentOrchestrator` is the top-level coordinator that wires together `AgentToolExecutor`, `AgentChatOrchestrator`, `AgentStreamingSupport`, and `AgentEnsembleExecutor`. It implements the context interfaces for all three sub-delegates so they can access agent internals without circular dependencies.

### Key Public Methods

| Method                                                                        | Description                                                     |
| ----------------------------------------------------------------------------- | --------------------------------------------------------------- |
| `chat(String message, boolean useHistory, boolean useTools)`                  | Main chat entry point with eval hooks and context graph tracing |
| `streamChat(String message)`                                                  | Token-by-token streaming                                        |
| `streamChatWithTools(String message, Consumer<ChatChunk> handler)`            | Streaming with tool calling loop                                |
| `chatWithEvents(String message, Consumer<TnsAIEvent> consumer)`               | Event-based chat                                                |
| `chatWithEvents(String message, String sessionId)`                            | Event-based chat with session publisher                         |
| `executeActionPublic(String name, Map<String, Object> params)`                | Execute a named action                                          |
| `executeActionTyped(ActionRequest request)`                                   | Execute with typed request/response                             |
| `executeActionOnRole(String roleId, String name, Map<String, Object> params)` | Execute on a specific role                                      |
| `setToolCallFilter(ToolCallFilter filter)`                                    | Set permission control for tool calls                           |
| `setToolCallListener(ToolCallListener listener)`                              | Set progress callbacks                                          |
| `setKnowledgeBase(KnowledgeBase kb)`                                          | Set RAG knowledge base                                          |
| `shutdown()`                                                                  | End context conversations                                       |

### Context Graph Integration

When context graph is enabled, the orchestrator automatically:

- Starts/ends context conversations around chat sessions
- Creates snapshots at conversation boundaries
- Records `DecisionTrace` entries for each chat (success or failure)

## AgentCapabilities

`com.tnsai.agents.capabilities.AgentCapabilities` is the capability facade that bridges cognitive support, resilience, variants, and context graphs. Agent delegates to this class for all capability operations.

### Variant Selection

```java
agent.setVariant(AgentVariant.HIGH);
AgentVariant current = agent.getVariant();

// Auto-resolve based on task
AgentVariant resolved = agent.resolveVariant("Complex refactoring task");

// Custom selector
agent.setVariantSelector(mySelector);
```

### Context Graph

```java
agent.enableContextGraph(snapshotStore, decisionTraceStore);

Optional<ContextManagerHandle> cm = agent.getContextManager();
boolean enabled = agent.isContextGraphEnabled();
```

### Resilience

```java
AgentHealthState health = agent.getHealthState();
ResilienceExecutor executor = agent.getResilienceExecutor();
RetryPolicy policy = agent.getDefaultRetryPolicy();
DeadLetterQueue dlq = agent.getDeadLetterQueue();
agent.clearRecoveryState();
```

## Related Documentation

- [Streaming](/docs/agents/behavior/streaming) -- streaming basics and ChatChunk
- [Tools](/docs/capabilities/tools/registration) -- tool registration and the Tool interface
- [Roles](/docs/agents/fundamentals/roles) -- role-based action discovery
- [Events](/docs/agents/fundamentals/events) -- TnsAI event system
- [Resilience](/docs/agents/reliability/resilience) -- retry, circuit breaker, recovery
- [Variants](/docs/agents/behavior/variants) -- AgentVariant cost/quality tiers

---

# Behavior

URL: https://tnsai.dev/docs/agents/behavior
Description: How your agent talks, streams, and remembers.

import { Callout } from 'fumadocs-ui/components/callout'

## Pages

- [Prompt Strategies](/docs/agents/behavior/prompt-strategies) — System prompt templates, role composition, example injection.
- [Output Parsing](/docs/agents/behavior/output-parsing) — Structured output with `chatWithFormat`, retries, validation.
- [Streaming](/docs/agents/behavior/streaming) — `streamChatWithTools`, chunk types, event flow.
- [Variants](/docs/agents/behavior/variants) — Quality, speed, cost tiers on a single agent.
- [Memory](/docs/agents/behavior/memory) — Conversation history, window management, summarization.

---

# Memory

URL: https://tnsai.dev/docs/agents/behavior/memory
Description: TnsAI.Core provides a pluggable memory system for agent conversation history. The MemoryStore interface defines storage, retrieval, pruning, and search operations. Four implementations cover different persistence and sharing requirements. The AgentBuilder.memoryStore() method wires a store into an agent.

import { Callout } from 'fumadocs-ui/components/callout'

## MemoryStore Interface

`MemoryStore` is the contract that all memory implementations must follow. It defines how an agent saves, retrieves, prunes, and searches its conversation history. You pick an implementation based on your persistence needs (in-memory for development, file-based for production, shared for multi-agent setups).

```java
public interface MemoryStore {
    void init(String agentId);
    void addMessage(String role, String content);
    void addMessage(Map<String, Object> message);
    List<Map<String, Object>> getHistory();
    List<Map<String, Object>> getRecentHistory(int limit);
    void clear();
    void prune(int maxTokens);
    default List<String> search(String query, int limit);
}
```

| Method                                    | Description                                                                                                              |
| ----------------------------------------- | ------------------------------------------------------------------------------------------------------------------------ |
| `init(String agentId)`                    | Initializes the store for a specific agent. Triggers history loading for persistent stores.                              |
| `addMessage(String role, String content)` | Adds a simple message with role (`user`, `assistant`, `system`) and content.                                             |
| `addMessage(Map<String, Object> message)` | Adds a structured message (e.g., with tool calls, metadata). The map is defensively copied.                              |
| `getHistory()`                            | Returns the full conversation history as an unmodifiable list.                                                           |
| `getRecentHistory(int limit)`             | Returns the last N messages.                                                                                             |
| `clear()`                                 | Removes all messages.                                                                                                    |
| `prune(int maxTokens)`                    | Removes oldest messages until the estimated token count is within the limit. System prompts are preserved when possible. |
| `search(String query, int limit)`         | Semantic search over history. Default returns empty list. `AbstractMemoryStore` provides a TF-IDF implementation.        |

## AbstractMemoryStore

If you want to create your own memory store, extend `AbstractMemoryStore` instead of implementing `MemoryStore` from scratch. It handles thread safety, pruning, and search out of the box -- you only need to define how history is loaded and what happens after it changes.

All built-in implementations extend `AbstractMemoryStore`, which provides:

- **Thread safety** via `ReentrantLock` (Virtual Thread friendly, avoids carrier thread pinning)
- **LinkedList** backing for O(1) removal during pruning
- **TF-IDF search** using log-normalized term frequency and smooth inverse document frequency
- **Extension hooks**: `onHistoryChanged()` and `loadHistory()`

Subclasses only need to override two methods:

```java
// Called after any modification (addMessage, clear, prune)
protected void onHistoryChanged() { }

// Called during init() to load persisted history
protected void loadHistory() { }
```

Additional utility method:

```java
// Returns current history size
public int size();
```

## InMemoryStore

The simplest memory store -- it keeps conversation history in a plain list in memory. This is the default when you do not configure any memory store on your agent. It is fast and requires zero setup, but all data is lost when the process exits.

```java
public class InMemoryStore extends AbstractMemoryStore {
    public InMemoryStore();
}
```

All functionality is inherited from `AbstractMemoryStore` with no persistence hooks. Suitable for:

- Development and testing
- Short-lived conversations
- Scenarios where persistence is not required

This is the default store used by `AgentBuilder` when no store is explicitly configured.

## FileMemoryStore

When you need conversations to survive restarts, use `FileMemoryStore`. It writes each agent's history to a JSON file on disk after every change and reloads it automatically when the agent initializes.

```java
public class FileMemoryStore extends AbstractMemoryStore {
    public FileMemoryStore();                    // uses default dir: .tnsai/memory/
    public FileMemoryStore(String storageDirPath);
    public Path getAgentFile();                  // path to agent's JSON file
}
```

Each agent's history is stored in a separate file: `{storageDirPath}/{agentId}.json`.

Suitable for:

- Production environments requiring conversation persistence
- Long-running agents that need to resume conversations
- Multi-session scenarios

Example:

```java
FileMemoryStore store = new FileMemoryStore("./data/memory");
store.init("agent-1");

store.addMessage("user", "Hello");
// Automatically saved to ./data/memory/agent-1.json

// On restart, history is loaded from file
FileMemoryStore restored = new FileMemoryStore("./data/memory");
restored.init("agent-1");
List<Map<String, Object>> history = restored.getHistory(); // Contains previous messages
```

## SharedMemoryStore

In multi-agent systems, you often want several agents to read and write the same conversation history. `SharedMemoryStore` wraps any other `MemoryStore` and adds an access-control layer so only authorized agents can interact with the shared memory.

```java
public class SharedMemoryStore implements MemoryStore {
    public SharedMemoryStore(MemoryStore delegate, String namespace);
    public void grantAccess(String agentId);
    public void revokeAccess(String agentId);
    public boolean hasAccess(String agentId);
    public String getNamespace();
}
```

Thread safety uses `ReentrantReadWriteLock` for high-throughput concurrent reads with exclusive writes. Only the first authorized agent to call `init()` initializes the underlying store.

Calling `init()` with an unauthorized agent ID throws `IllegalStateException`.

Example:

```java
MemoryStore base = new InMemoryStore();
SharedMemoryStore shared = new SharedMemoryStore(base, "team-chat");
shared.grantAccess("agent-a");
shared.grantAccess("agent-b");

// First agent initializes the shared memory
shared.init("agent-a");

// Both agents can read and write
shared.addMessage("assistant", "Hello from agent-a");
List<Map<String, Object>> history = shared.getHistory(); // visible to all authorized agents
```

## SharedMemoryRegistry

When you have many shared memory namespaces across your application, `SharedMemoryRegistry` acts as a central lookup. It ensures each namespace has exactly one `SharedMemoryStore` instance, creating one on demand if it does not exist yet. The framework uses this internally when processing `@Memory(shared = true, shareWith = {...})` annotations.

```java
public final class SharedMemoryRegistry {
    public static SharedMemoryRegistry getInstance();
    public SharedMemoryStore getOrCreate(String namespace);
    public SharedMemoryStore getOrCreate(String namespace, MemoryStore baseStore);
    public Optional<SharedMemoryStore> find(String namespace);
    public boolean remove(String namespace);
    public void clear();
    public int size();
}
```

`getOrCreate(String namespace)` creates a `SharedMemoryStore` wrapping an `InMemoryStore` if the namespace does not exist. Use the two-argument overload to provide a custom base store (e.g., `FileMemoryStore` for persistence).

Example:

```java
SharedMemoryRegistry registry = SharedMemoryRegistry.getInstance();

// Get or create a shared namespace
SharedMemoryStore shared = registry.getOrCreate("project-alpha");
shared.grantAccess("researcher");
shared.grantAccess("writer");

// With custom persistence
SharedMemoryStore persistent = registry.getOrCreate(
    "persistent-namespace",
    new FileMemoryStore("./shared-memory")
);

// Look up existing namespace
Optional<SharedMemoryStore> found = registry.find("project-alpha");

// Clean up
registry.remove("project-alpha");
```

## MemoryStoreFactory

If you use the `@MemorySpec` annotation to configure memory declaratively, `MemoryStoreFactory` is what turns that annotation into an actual `MemoryStore` instance at runtime. It reads the annotation's fields and automatically wraps the base store with decorators for capacity limits, token budgets, or summarization as needed.

```java
public final class MemoryStoreFactory {
    public static MemoryStore create(MemorySpec config);
    public static MemoryStore createDefault();  // returns new InMemoryStore()
}
```

Supported persistence types: `IN_MEMORY`, `FILE`, `REDIS` (via SPI), `DATABASE` (via SPI).

The factory automatically wraps the base store with decorators based on configuration:

- **CapacityAwareMemoryStore** -- enforces message count limits with configurable prune strategy
- **SummarizingMemoryStore** -- summarizes old messages instead of deleting them when capacity is exceeded
- **TokenAwareMemoryStore** -- enforces token limits by pruning after each message

## Integration with AgentBuilder

The most common way to configure memory is through the `AgentBuilder`. Call `.memoryStore()` to plug in any `MemoryStore` implementation. If you skip this step, the agent defaults to `InMemoryStore` (volatile, no persistence).

```java
// Default (in-memory, volatile)
Agent agent = AgentBuilder.create()
    .model("claude-sonnet-4")
    .build();

// Persistent file storage
Agent agent = AgentBuilder.create()
    .model("claude-sonnet-4")
    .memoryStore(new FileMemoryStore("./data/memory"))
    .build();

// Shared memory between agents
SharedMemoryStore shared = SharedMemoryRegistry.getInstance()
    .getOrCreate("team-namespace");
shared.grantAccess("agent-1");
shared.grantAccess("agent-2");

Agent agent1 = AgentBuilder.create()
    .model("claude-sonnet-4")
    .memoryStore(shared)
    .build();

Agent agent2 = AgentBuilder.create()
    .model("gpt-4o")
    .memoryStore(shared)
    .build();
```

## Code Examples

These examples demonstrate typical memory usage patterns, from basic conversation persistence to semantic search and token-aware pruning.

### Conversation with History Management

This example creates an agent with file-based memory so conversations survive restarts. You can also access the memory store directly to inspect recent history.

```java
Agent agent = AgentBuilder.create()
    .model("claude-sonnet-4")
    .memoryStore(new FileMemoryStore("./memory"))
    .build();

// Conversation persists across restarts
agent.chat("What is the capital of France?");
agent.chat("What about Germany?");

// Access history directly
MemoryStore memory = agent.getMemoryStore();
List<Map<String, Object>> recent = memory.getRecentHistory(5);
```

### Semantic Search over History

The built-in TF-IDF search lets you find past messages by meaning rather than scrolling through the full history. This is useful for agents that need to recall earlier context from a long conversation.

```java
MemoryStore store = new InMemoryStore();
store.init("search-agent");

store.addMessage("user", "Tell me about Java generics");
store.addMessage("assistant", "Java generics provide compile-time type safety...");
store.addMessage("user", "How do I create a REST API?");
store.addMessage("assistant", "You can use Spring Boot or Javalin...");

// TF-IDF search finds relevant messages
List<String> results = store.search("generics type safety", 2);
// Returns messages about Java generics ranked by relevance
```

### Token-Aware Pruning

LLMs have a maximum context window size. When your conversation history grows too large, call `prune()` to remove the oldest messages until the estimated token count fits within the model's limit. System prompts are preserved when possible.

```java
MemoryStore store = new InMemoryStore();
store.init("pruning-agent");

// Add many messages
for (int i = 0; i < 1000; i++) {
    store.addMessage("user", "Message " + i + " with some content");
    store.addMessage("assistant", "Response to message " + i);
}

// Prune to fit context window
store.prune(4096);
// Oldest messages are removed, keeping history within token limit
```

## Advanced Memory

The `com.tnsai.memory.advanced` package provides production-grade memory capabilities for agents that need more than simple history management. These include vector-based semantic search, keyword-based BM25 indexing, hybrid retrieval that combines both, skill persistence, importance-based pruning, staleness detection, and full session serialization.

### VectorMemoryStore

When you need to find past messages based on meaning (not just keywords), `VectorMemoryStore` converts each message into an embedding vector and uses cosine similarity to find the closest matches. This powers true semantic retrieval over conversation history.

```java
VectorMemoryStore vectorStore = VectorMemoryStore.builder()
    .embeddingClient(embeddingClient)
    .dimensions(1536)
    .build();

vectorStore.init("agent-1");
vectorStore.addMessage("user", "The deployment uses Kubernetes with 3 replicas");

// Semantic search -- finds relevant messages even with different wording
List<String> results = vectorStore.search("container orchestration setup", 5);
```

### BM25Index

While vector search excels at finding semantically similar content, sometimes you need exact keyword matching. `BM25Index` uses the BM25 scoring algorithm (the same approach behind traditional search engines) to rank documents by how well they match specific terms.

```java
BM25Index index = new BM25Index();
index.addDocument("doc-1", "Kubernetes deployment configuration");
index.addDocument("doc-2", "Database connection pooling settings");

List<BM25Index.ScoredDocument> results = index.search("Kubernetes", 5);
```

### HybridMemoryRetriever

For the best retrieval quality, combine both approaches. `HybridMemoryRetriever` runs a vector search and a BM25 keyword search in parallel, then merges the results using Reciprocal Rank Fusion (RRF). This captures both semantic meaning and exact keyword matches in a single query.

```java
HybridMemoryRetriever retriever = HybridMemoryRetriever.builder()
    .vectorStore(vectorStore)
    .bm25Index(bm25Index)
    .vectorWeight(0.6)
    .keywordWeight(0.4)
    .fusionK(60)
    .build();

List<String> results = retriever.search("deployment configuration", 10);
```

RRF fusion merges the ranked lists from both retrieval methods, giving high-quality results that capture both semantic meaning and exact keyword matches.

### SkillMemoryStore

Agents can learn reusable procedures and remember them across sessions. `SkillMemoryStore` saves these skills as Markdown files on disk, making them human-readable, easy to version-control with Git, and editable by hand if needed.

```java
SkillMemoryStore skillStore = new SkillMemoryStore(".tnsai/skills/");

skillStore.saveSkill("deploy-k8s", SkillEntry.builder()
    .name("deploy-k8s")
    .description("Deploy application to Kubernetes cluster")
    .steps(List.of("Build Docker image", "Push to registry", "Apply manifests"))
    .tags(Set.of("devops", "kubernetes"))
    .build());

Optional<SkillEntry> skill = skillStore.loadSkill("deploy-k8s");
List<SkillEntry> devopsSkills = skillStore.findByTag("devops");
```

### MemoryImportanceScorer

When memory grows too large and needs pruning, not all entries are equally valuable. `MemoryImportanceScorer` assigns an importance score to each entry based on recency, access frequency, semantic distinctiveness, and user-marked importance, so the pruning process can keep the most valuable memories.

```java
MemoryImportanceScorer scorer = new MemoryImportanceScorer();

double importance = scorer.score(memoryEntry);
// Factors: recency, access frequency, semantic distinctiveness, user-marked importance
```

### StalenessDetector

Over time, memory accumulates near-duplicate or outdated entries that waste context window space. `StalenessDetector` uses Jaccard similarity to find entries that are too similar to each other, and age thresholds to flag entries that are too old, so you can clean them up.

```java
StalenessDetector detector = StalenessDetector.builder()
    .similarityThreshold(0.85)    // entries above 85% similarity are stale
    .maxAge(Duration.ofDays(30))  // entries older than 30 days are candidates
    .build();

List<String> staleIds = detector.detectStale(memoryEntries);
```

### SessionSerializer / SessionRestorer

If you need to save and restore an agent's complete session state (not just conversation history, but all runtime state), these utilities serialize the session to bytes and restore it later. This is useful for long-running agents that need to survive process restarts without losing their place.

```java
// Save session state
SessionSerializer serializer = new SessionSerializer();
byte[] data = serializer.serialize(agent.getSession());
Files.write(Path.of("session-backup.bin"), data);

// Restore session state
SessionRestorer restorer = new SessionRestorer();
AgentSession restored = restorer.restore(Files.readAllBytes(Path.of("session-backup.bin")));
agent.restoreSession(restored);
```

---

# Output Parsing & Serialization

URL: https://tnsai.dev/docs/agents/behavior/output-parsing
Description: TnsAI provides type-safe output parsing for converting raw LLM responses into structured Java objects, and a multi-format serialization system for producing structured output.

import { Callout } from 'fumadocs-ui/components/callout'

## OutputParser\<T> Interface

LLMs return free-form text, but your application usually needs structured Java objects. The `OutputParser<T>` interface defines the contract for converting raw LLM text into a typed object of your choice. It also provides prompt instructions you can include in your LLM call so the model knows what format to produce.

| Method                                          | Description                                                |
| ----------------------------------------------- | ---------------------------------------------------------- |
| `parse(String)`                                 | Returns a `ParseResult<T>` (success or error)              |
| `parseOrThrow(String)`                          | Returns `T` or throws `ParseException`                     |
| `parseOptional(String)`                         | Returns `Optional<T>`                                      |
| `getTargetType()`                               | Returns the `Class<T>` this parser produces                |
| `getFormatInstruction()`                        | Prompt text guiding the LLM to produce the expected format |
| `getSchemaDescription()`                        | Schema string (e.g. JSON Schema) for the target type       |
| `getErrorCorrectionPrompt(failedOutput, error)` | Generates a retry prompt when parsing fails                |

```java
OutputParser<WeatherResponse> parser = JsonOutputParser.forType(WeatherResponse.class);

ParseResult<WeatherResponse> result = parser.parse(llmOutput);
if (result.isSuccess()) {
    WeatherResponse weather = result.get();
}
```

## JsonOutputParser\<T>

The most commonly used parser. It extracts JSON from LLM responses -- even when the model wraps JSON in markdown code blocks or surrounds it with explanatory text -- and deserializes it into your Java class using Jackson.

**Features:**

- Extracts JSON from ` ```json ` code blocks
- Falls back to raw `{...}` or `[...]` detection
- Supports Java Records and POJOs
- Field validation via `OutputValidator`
- Auto-generates schema descriptions from target type reflection

### Factory method

The simplest way to create a `JsonOutputParser` is with the static `forType()` factory. It sets up sensible Jackson defaults and auto-generates schema descriptions from your target class.

```java
JsonOutputParser<Person> parser = JsonOutputParser.forType(Person.class);
```

### Builder

For more control, use the builder to supply a custom Jackson `ObjectMapper`, enable strict mode (which rejects unknown JSON properties), or plug in a custom validator.

```java
JsonOutputParser<Person> parser = JsonOutputParser.builder(Person.class)
    .objectMapper(customMapper)     // custom Jackson ObjectMapper
    .strictMode(true)               // fail on unknown properties
    .validator(customValidator)     // custom OutputValidator
    .build();
```

### Parsing LLM output with embedded JSON

This example demonstrates the parser's ability to extract a JSON block from an LLM response that includes surrounding explanatory text. The parser automatically locates the JSON within the markdown code fence and deserializes it.

````java
record Person(String name, int age) {}

JsonOutputParser<Person> parser = JsonOutputParser.forType(Person.class);

ParseResult<Person> result = parser.parse("""
    Here's the person data:
    ```json
    {"name": "John", "age": 30}
    ```
    """);

Person person = result.get(); // Person[name=John, age=30]
````

Default ObjectMapper settings:

- `FAIL_ON_UNKNOWN_PROPERTIES = false`
- `ACCEPT_SINGLE_VALUE_AS_ARRAY = true`
- `INDENT_OUTPUT = true`
- `NON_NULL` property inclusion

## RetryableParser\<T>

LLMs occasionally produce malformed output -- missing fields, broken JSON, or wrong structure. `RetryableParser` wraps any parser and handles this automatically: when parsing fails, it sends the LLM an error-correction prompt explaining what went wrong and asks it to try again, up to a configurable number of retries.

### Wrapping a parser

To add retry behavior, wrap your existing parser with `RetryableParser.wrap()`. You can optionally configure the maximum number of retries (default is 3).

```java
JsonOutputParser<Person> baseParser = JsonOutputParser.forType(Person.class);

RetryableParser<Person> parser = RetryableParser.wrap(baseParser)
    .maxRetries(3)   // default is 3
    .build();
```

### Manual retry flow

If you want to control the retry loop yourself (for example, to use a different LLM for corrections), you can get the correction prompt and send it manually.

```java
ParseResult<Person> result = parser.parse(llmOutput);

if (result.isFailure()) {
    String correctionPrompt = parser.getCorrectionPrompt(llmOutput, result.getError());
    // Send correctionPrompt to LLM, then parse the new response
}
```

### Automatic retry with LLM function

The easiest approach: pass `parseWithRetry` a function that calls your LLM. It will automatically loop -- sending correction prompts and re-parsing -- up to `maxRetries` times until parsing succeeds or retries are exhausted.

```java
ParseResult<Person> result = parser.parseWithRetry(initialOutput, prompt -> {
    return llmClient.chat(prompt);  // your LLM call
});

if (result.isSuccess()) {
    Person person = result.get();
}
```

### Attempt tracking

The retryable parser records every attempt so you can inspect what happened during the retry loop -- useful for debugging and monitoring parse success rates.

```java
parser.getAttemptCount();        // total attempts made
parser.getAttempts();            // List<ParseAttempt> (output, success, error)
parser.getLastAttempt();         // most recent attempt
parser.clearAttempts();          // reset history
```

## ParseResult\<T>

Every parser returns a `ParseResult<T>` instead of throwing exceptions or returning null. It is a monadic result type (similar to Rust's `Result` or Scala's `Either`) that always tells you whether parsing succeeded or failed, and gives you safe access to the value or the error message.

### Construction

You create `ParseResult` instances through static factory methods rather than constructors. Parsers return these automatically, but you can also create them yourself for testing or custom parsing logic.

| Factory method                                                      | Description                    |
| ------------------------------------------------------------------- | ------------------------------ |
| `ParseResult.success(value, rawOutput, parseTimeMs)`                | Successful parse with timing   |
| `ParseResult.success(value)`                                        | Successful parse (shorthand)   |
| `ParseResult.failure(error, rawOutput)`                             | Failed parse                   |
| `ParseResult.validationFailure(error, validationErrors, rawOutput)` | Failed validation with details |

### Querying

These methods let you inspect the result, extract the parsed value, or get error details without risking null pointer exceptions.

```java
result.isSuccess();            // true if parsed
result.isFailure();            // true if error
result.get();                  // value or throws IllegalStateException
result.getOrElse(defaultVal);  // value or fallback
result.getError();             // error message (null on success)
result.getValidationErrors();  // List<String> validation details
result.getRawOutput();         // original LLM text
result.getParseTimeMs();       // parse duration in ms
result.toOptional();           // Optional<T>
```

### Transformations

Like `Optional` or `Stream`, `ParseResult` supports `map` and `flatMap` so you can transform the parsed value without unwrapping it first. Failures pass through unchanged.

```java
// Map to a different type
ParseResult<String> nameResult = result.map(User::name);

// FlatMap to another ParseResult
ParseResult<Address> addr = result.flatMap(user -> parseAddress(user.addressJson()));
```

### Side effects

Use these methods to run an action only when the result is a success or a failure, without needing an `if` statement. This keeps your code concise and readable.

```java
// Conditional actions
result.ifSuccess(user -> save(user));
result.ifFailure(error -> log.warn(error));

// Handle both cases
result.ifSuccessOrElse(
    user -> System.out.println("Parsed: " + user),
    error -> System.err.println("Error: " + error)
);
```

## OutputSerializer Interface

While `OutputParser` converts LLM text into Java objects, `OutputSerializer` does the reverse: it converts Java objects into structured text formats (JSON, YAML, etc.) and back again. This is useful when you need to produce output in a specific format, or when re-serializing a parsed result into a different format for downstream consumers.

| Method                                         | Description                                      |
| ---------------------------------------------- | ------------------------------------------------ |
| `getFormat()`                                  | The `OutputFormat` this serializer handles       |
| `serialize(data, prettyPrint)`                 | Serialize an object to string                    |
| `serialize(data)`                              | Serialize with pretty print (default)            |
| `serializeList(items, itemClass, prettyPrint)` | Serialize a typed list                           |
| `deserialize(data, targetClass)`               | Deserialize string to object                     |
| `deserializeList(data, itemClass)`             | Deserialize string to typed list                 |
| `getFormatInstructions(targetClass)`           | LLM prompt instructions for single-object output |
| `getListFormatInstructions(itemClass)`         | LLM prompt instructions for list output          |
| `supportsType(dataClass)`                      | Check if format supports a data structure        |

### OutputFormat enum

TnsAI supports five output formats. JSON and YAML are standard, while TOON and TONL are custom token-optimized formats that significantly reduce token usage when sending structured data to LLMs.

| Format | Extension | MIME Type            | Nesting | Token Efficiency      |
| ------ | --------- | -------------------- | ------- | --------------------- |
| `JSON` | `.json`   | `application/json`   | Yes     | Baseline              |
| `YAML` | `.yaml`   | `application/x-yaml` | Yes     | \~10-15% fewer tokens |
| `TOON` | `.toon`   | `text/x-toon`        | Yes     | \~40% fewer tokens    |
| `TONL` | `.tonl`   | `text/x-tonl`        | Yes     | \~32-50% fewer tokens |
| `TEXT` | `.txt`    | `text/plain`         | No      | N/A                   |

### Implementations

Each format has a dedicated serializer class. You rarely need to use these directly -- the `OutputSerializerRegistry` provides a simpler API for accessing them.

- **JsonOutputSerializer** -- Jackson-based JSON with configurable pretty printing.
- **YamlOutputSerializer** -- Zero-dependency YAML with multi-line string support and flow-style compact lists.
- **ToonOutputSerializer** -- Token-Optimized Object Notation for uniform arrays.
- **TonlOutputSerializer** -- Token-Optimized Notation Language with schema support.
- **TextOutputSerializer** -- Plain `toString()` serialization. Deserialization limited to `String` and basic primitives.

### OutputSerializerRegistry

The registry is a one-stop shop for serialization. It holds all built-in serializers and provides convenience methods so you do not need to look up or instantiate serializers yourself. You can also register custom serializers here.

```java
OutputSerializerRegistry registry = OutputSerializerRegistry.getInstance();

// Get a specific serializer
OutputSerializer jsonSerializer = registry.getSerializer(OutputFormat.JSON);
String json = jsonSerializer.serialize(myObject);

// Convenience methods
String yaml = registry.serialize(myObject, OutputFormat.YAML);
Person person = registry.deserialize(jsonString, OutputFormat.JSON, Person.class);

// LLM prompt instructions
String instructions = registry.getFormatInstructions(OutputFormat.JSON, Person.class);

// Register a custom serializer
registry.register(OutputFormat.JSON, new CustomJsonSerializer());

// Reset to defaults
registry.reset();
```

## Full Example

This end-to-end example shows the complete workflow: defining an output type as a Java record, creating a parser with retry support, including format instructions in the prompt, parsing the LLM response with automatic correction, handling the result, and re-serializing to a different format.

```java
// 1. Define output type
record AnalysisResult(String summary, List<String> issues, double score) {}

// 2. Create parser with retry
JsonOutputParser<AnalysisResult> baseParser = JsonOutputParser.forType(AnalysisResult.class);
RetryableParser<AnalysisResult> parser = RetryableParser.wrap(baseParser)
    .maxRetries(2)
    .build();

// 3. Include format instructions in the prompt
String prompt = "Analyze this code.\n\n" + baseParser.getFormatInstruction();

// 4. Parse LLM response with automatic retry
String llmResponse = llmClient.chat(prompt);
ParseResult<AnalysisResult> result = parser.parseWithRetry(llmResponse, llmClient::chat);

// 5. Handle result
result.ifSuccessOrElse(
    analysis -> {
        System.out.println("Score: " + analysis.score());
        analysis.issues().forEach(issue -> System.out.println("- " + issue));
    },
    error -> System.err.println("Parse failed after retries: " + error)
);

// 6. Re-serialize to a different format
if (result.isSuccess()) {
    OutputSerializerRegistry registry = OutputSerializerRegistry.getInstance();
    String yaml = registry.serialize(result.get(), OutputFormat.YAML);
    System.out.println(yaml);
}
```

---

# Prompt Strategies

URL: https://tnsai.dev/docs/agents/behavior/prompt-strategies
Description: TnsAI includes a prompt enhancement system that applies proven prompting techniques to improve LLM response quality. The system is built around the PromptStrategy enum, PromptEnhancer builder, and EnhancedPrompt output.

import { Callout } from 'fumadocs-ui/components/callout'

**Package:** `com.tnsai.prompt.strategy`

## PromptStrategy Enum

Prompt strategies are research-backed techniques that improve LLM response quality by structuring how the model approaches a problem. Instead of writing complex system prompts by hand, you pick one or more strategies and the framework generates the right instructions automatically. Twelve predefined strategies are available, based on techniques from OpenAI, Anthropic, and Google AI research.

| Strategy                | Description                                                                                                                                                                                                              | Multi-Pass | Post-Processing |
| ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | :--------: | :-------------: |
| `CHAIN_OF_THOUGHT`      | Step-by-step reasoning before final answer. Best for math, logic, multi-step analysis.                                                                                                                                   |     No     |        No       |
| `CHAIN_OF_VERIFICATION` | Self-verification with generated questions. Initial answer, then verify, then refine. Reported 60% to 92% accuracy improvement on complex queries.                                                                       |     Yes    |       Yes       |
| `CONFIDENCE_WEIGHTED`   | Includes confidence score (0--100%), key assumptions, and alternatives when confidence is below threshold.                                                                                                               |     No     |        No       |
| `STRUCTURED_THINKING`   | Four-phase protocol: UNDERSTAND, ANALYZE, STRATEGIZE, EXECUTE.                                                                                                                                                           |     No     |        No       |
| `MULTI_PERSPECTIVE`     | Examines problem from Technical, Business, User Experience, and Risk perspectives, then synthesizes a balanced recommendation.                                                                                           |     No     |        No       |
| `CONSTRAINT_FIRST`      | Separates hard constraints (must satisfy) from soft preferences (nice to have) before proceeding.                                                                                                                        |     No     |        No       |
| `ITERATIVE_REFINEMENT`  | Multi-pass generation: Draft, Critique, Refine, Review.                                                                                                                                                                  |     Yes    |       Yes       |
| `CONTEXT_BOUNDARIES`    | Clear separation of Context, Focus, Task, and Constraints. Explicitly flags insufficient information.                                                                                                                    |     No     |        No       |
| `FEW_SHOT_EXAMPLES`     | Learns from positive and negative examples to guide response format and quality.                                                                                                                                         |     No     |        No       |
| `META_PROMPTING`        | AI designs the optimal prompt for the task first, then responds to it.                                                                                                                                                   |     Yes    |        No       |
| `SIX_PART_ANATOMY`      | Comprehensive structure: Role, Objective, Request, Process, Output, Stop Condition.                                                                                                                                      |     No     |        No       |
| `ATOM_OF_THOUGHT`       | Decomposes problems into independent atoms solved in parallel, then synthesizes. Unlike CoT, errors are isolated per atom. +30--40% accuracy on complex reasoning, +20--30% token usage. Best for 70B+ parameter models. |     Yes    |        No       |

### Strategy Methods

Each strategy enum value provides these methods to retrieve its system instruction text and to check whether it requires multiple LLM passes or post-processing.

| Method                     | Return Type | Description                                                                                         |
| -------------------------- | ----------- | --------------------------------------------------------------------------------------------------- |
| `getSystemInstruction()`   | `String`    | The full system instruction text injected for this strategy                                         |
| `getDescription()`         | `String`    | Short human-readable description                                                                    |
| `requiresPostProcessing()` | `boolean`   | `true` for `CHAIN_OF_VERIFICATION` and `ITERATIVE_REFINEMENT`                                       |
| `isMultiPass()`            | `boolean`   | `true` for `CHAIN_OF_VERIFICATION`, `ITERATIVE_REFINEMENT`, `META_PROMPTING`, and `ATOM_OF_THOUGHT` |

## PromptEnhancer Builder

`PromptEnhancer` is where you assemble your prompting configuration. It is a fluent builder that lets you combine multiple strategies, set a role and objective, define constraints, provide few-shot examples, and choose an output format. When you call `.enhance()`, it compiles everything into an `EnhancedPrompt` ready to send to the LLM.

### Quick Construction

If you only need a single strategy with no extra configuration, use the shorthand factory method.

```java
// Single strategy
PromptEnhancer enhancer = PromptEnhancer.withStrategy(PromptStrategy.CHAIN_OF_THOUGHT);
EnhancedPrompt prompt = enhancer.enhance("Solve: 2x + 5 = 15");
```

### Full Builder API

For more control, use the builder. You can combine multiple strategies, set a role and objective, add constraints and preferences, provide positive and negative examples, define process steps, and choose an output format.

```java
PromptEnhancer enhancer = PromptEnhancer.builder()
    .strategy(PromptStrategy.CHAIN_OF_THOUGHT)
    .strategy(PromptStrategy.CONFIDENCE_WEIGHTED)
    .role("Expert mathematician")
    .objective("Solve algebra problems accurately")
    .constraint("Show all work")
    .constraint("Verify answer by substitution")
    .constraints(List.of("Use standard notation", "Simplify"))
    .softPreference("Explain in simple terms")
    .softPreferences(List.of("Use examples", "Keep it concise"))
    .positiveExample("2x = 10", "x = 5", "Correct division by 2")
    .positiveExample("3x + 1 = 7", "x = 2")
    .negativeExample("2x = 10", "x = 10", "Forgot to divide")
    .processStep("Parse the equation")
    .processStep("Isolate the variable")
    .process(List.of("Simplify", "Verify"))
    .outputFormat(OutputFormat.STRUCTURED)
    .stopCondition("Stop after verification pass")
    .verificationQuestions(5)       // CoVe: number of verification questions
    .refinementPasses(3)            // Iterative Refinement: number of passes
    .confidenceThreshold(0.7f)      // Confidence Weighted: threshold for alternatives
    .build();
```

### Builder Methods

The complete list of builder methods. All setter methods are chainable and can be called in any order.

| Method                                    | Parameter                  | Description                                                    |
| ----------------------------------------- | -------------------------- | -------------------------------------------------------------- |
| `strategy(PromptStrategy)`                | strategy                   | Adds a prompting strategy (chainable, multiple allowed)        |
| `role(String)`                            | role                       | Sets the role/persona (e.g., "Expert researcher")              |
| `objective(String)`                       | objective                  | Sets the high-level goal                                       |
| `constraint(String)`                      | constraint                 | Adds a single hard constraint                                  |
| `constraints(List<String>)`               | constraints                | Adds multiple hard constraints                                 |
| `softPreference(String)`                  | preference                 | Adds a single soft preference                                  |
| `softPreferences(List<String>)`           | preferences                | Adds multiple soft preferences                                 |
| `positiveExample(String, String)`         | input, output              | Adds a good example                                            |
| `positiveExample(String, String, String)` | input, output, explanation | Adds a good example with reasoning                             |
| `negativeExample(String, String, String)` | input, badOutput, whyBad   | Adds a bad example with explanation                            |
| `processStep(String)`                     | step                       | Adds a single numbered process step                            |
| `process(List<String>)`                   | steps                      | Adds multiple process steps                                    |
| `outputFormat(OutputFormat)`              | format                     | Sets the response output format                                |
| `stopCondition(String)`                   | condition                  | Sets the completion/stop condition                             |
| `verificationQuestions(int)`              | count                      | Number of verification questions (for `CHAIN_OF_VERIFICATION`) |
| `refinementPasses(int)`                   | passes                     | Number of refinement passes (for `ITERATIVE_REFINEMENT`)       |
| `confidenceThreshold(float)`              | threshold                  | Threshold for showing alternatives (for `CONFIDENCE_WEIGHTED`) |

### OutputFormat Enum

This enum tells the LLM what format to use in its response. The framework appends the corresponding instruction to the system prompt so the model produces output in the desired structure.

`PromptEnhancer.OutputFormat` controls the response format instruction.

| Value              | Instruction                                         |
| ------------------ | --------------------------------------------------- |
| `TEXT`             | Provide your response as plain text.                |
| `JSON`             | Provide your response as valid JSON.                |
| `MARKDOWN`         | Format your response using Markdown.                |
| `MARKDOWN_TABLE`   | Format your response as a Markdown table.           |
| `BULLET_POINTS`    | Use bullet points for your response.                |
| `NUMBERED_LIST`    | Use a numbered list for your response.              |
| `STRUCTURED`       | Use clear sections with headers for your response.  |
| `CODE`             | Provide code with comments explaining each section. |
| `COMPARISON_TABLE` | Create a comparison table with pros/cons.           |

### Example Record

When using the `FEW_SHOT_EXAMPLES` strategy, you teach the LLM by showing it input/output pairs. The `Example` record holds one such pair, with an optional explanation of why the output is correct (or incorrect for negative examples).

```java
// record Example(String input, String output, String explanation)
new PromptEnhancer.Example("2x = 10", "x = 5", "Divided both sides by 2");
new PromptEnhancer.Example("2x = 10", "x = 5");  // explanation is optional (null)
```

### Enhancer Instance Methods

Once built, the `PromptEnhancer` instance provides these methods. The main one is `enhance()`, which takes a user message and returns a fully assembled `EnhancedPrompt`.

| Method                        | Return Type            | Description                                                        |
| ----------------------------- | ---------------------- | ------------------------------------------------------------------ |
| `enhance(String userMessage)` | `EnhancedPrompt`       | Applies all configured strategies and produces the enhanced prompt |
| `getStrategies()`             | `List<PromptStrategy>` | Returns the configured strategies                                  |
| `requiresMultiPass()`         | `boolean`              | `true` if any strategy is multi-pass                               |

## EnhancedPrompt

`EnhancedPrompt` is what you get after calling `enhance()`. It holds the assembled system prompt (with all strategy instructions baked in), the original user message, and metadata about which strategies are active. Pass it directly to your `LLMClient` to make the enhanced call.

### Methods

These methods let you access the prompt content and check which capabilities the enhanced prompt expects from the LLM response.

| Method                        | Return Type              | Description                                                                                         |
| ----------------------------- | ------------------------ | --------------------------------------------------------------------------------------------------- |
| `getSystemPrompt()`           | `String`                 | The full system prompt with all strategy instructions                                               |
| `getUserMessage()`            | `String`                 | The original user message                                                                           |
| `getSystemPromptOptional()`   | `Optional<String>`       | System prompt wrapped in `Optional` for LLMClient convenience                                       |
| `getStrategies()`             | `List<PromptStrategy>`   | List of applied strategies                                                                          |
| `getOutputFormat()`           | `Optional<OutputFormat>` | The output format, if specified                                                                     |
| `requiresMultiPass()`         | `boolean`                | `true` if any applied strategy is multi-pass                                                        |
| `requiresPostProcessing()`    | `boolean`                | `true` if any strategy needs post-processing                                                        |
| `expectsConfidenceScore()`    | `boolean`                | `true` if `CONFIDENCE_WEIGHTED` is applied                                                          |
| `expectsStructuredThinking()` | `boolean`                | `true` if `STRUCTURED_THINKING` is applied                                                          |
| `expectsVerification()`       | `boolean`                | `true` if `CHAIN_OF_VERIFICATION` is applied                                                        |
| `getCombinedPrompt()`         | `String`                 | System prompt + user message in a single string (for models without separate system prompt support) |
| `getEstimatedOverhead()`      | `int`                    | Estimated additional tokens from enhancement (\~4 chars/token)                                      |

### Using EnhancedPrompt with LLMClient

Here is how to pass the enhanced prompt to your LLM client. Most providers accept a separate system prompt; for those that do not, use `getCombinedPrompt()` to get a single string.

```java
EnhancedPrompt prompt = enhancer.enhance("What causes inflation?");

// With separate system prompt support
ChatResponse response = client.chat(
    prompt.getUserMessage(),
    prompt.getSystemPromptOptional(),
    Optional.empty(),
    Optional.empty()
);

// Without system prompt support
String combined = prompt.getCombinedPrompt();
```

## Integration with AgentBuilder

You can wire prompt strategies directly into an agent through the `AgentBuilder`, so every message the agent processes is automatically enhanced. There are three approaches: adding individual strategies, adding a list of strategies, or providing a fully configured `PromptEnhancer`.

```java
// Add individual strategies
Agent agent = AgentBuilder.create()
    .llm(llmClient)
    .role(myRole)
    .promptStrategy(PromptStrategy.CHAIN_OF_THOUGHT)
    .promptStrategy(PromptStrategy.CONFIDENCE_WEIGHTED)
    .build();

// Add multiple strategies at once
Agent agent = AgentBuilder.create()
    .llm(llmClient)
    .promptStrategies(List.of(
        PromptStrategy.STRUCTURED_THINKING,
        PromptStrategy.MULTI_PERSPECTIVE
    ))
    .build();

// Use a fully configured PromptEnhancer
PromptEnhancer enhancer = PromptEnhancer.builder()
    .role("Expert researcher")
    .objective("Provide accurate information")
    .strategy(PromptStrategy.CHAIN_OF_VERIFICATION)
    .constraint("Always cite sources")
    .positiveExample("Question", "Good answer", "Why it's good")
    .build();

Agent agent = AgentBuilder.create()
    .llm(llmClient)
    .promptEnhancer(enhancer)
    .build();
```

| AgentBuilder Method                       | Description                      |
| ----------------------------------------- | -------------------------------- |
| `.promptStrategy(PromptStrategy)`         | Adds a single strategy           |
| `.promptStrategies(List<PromptStrategy>)` | Adds multiple strategies at once |
| `.promptEnhancer(PromptEnhancer)`         | Sets a fully configured enhancer |

## Code Examples

These examples show how to apply different strategies to real-world use cases. Each one demonstrates a different prompting technique suited to the task at hand.

### Chain-of-Thought for Math

Chain-of-Thought prompting asks the model to show its step-by-step reasoning before giving a final answer. This significantly improves accuracy on math, logic, and multi-step analysis tasks.

```java
PromptEnhancer enhancer = PromptEnhancer.builder()
    .strategy(PromptStrategy.CHAIN_OF_THOUGHT)
    .role("Mathematics tutor")
    .outputFormat(OutputFormat.STRUCTURED)
    .build();

EnhancedPrompt prompt = enhancer.enhance(
    "A train travels 120km in 2 hours. It then travels 180km in 3 hours. " +
    "What is its average speed for the entire journey?"
);
```

### Chain-of-Verification for Fact Checking

Chain-of-Verification (CoVe) makes the model generate an initial answer, then create verification questions to check its own claims, and finally refine the answer based on what it finds. This is a multi-pass strategy that dramatically reduces factual errors.

```java
PromptEnhancer enhancer = PromptEnhancer.builder()
    .strategy(PromptStrategy.CHAIN_OF_VERIFICATION)
    .verificationQuestions(5)
    .constraint("Each claim must be independently verifiable")
    .build();

EnhancedPrompt prompt = enhancer.enhance("What were the causes of World War I?");

if (prompt.requiresMultiPass()) {
    // Handle multi-pass verification flow
}
```

### Atom-of-Thought for Complex Reasoning

Atom-of-Thought decomposes a complex problem into independent "atoms" that can be solved in parallel, then synthesizes the results. Unlike Chain-of-Thought, errors in one atom do not cascade to others. This works best with large models (70B+ parameters) and is combined here with confidence scoring.

```java
PromptEnhancer enhancer = PromptEnhancer.builder()
    .strategy(PromptStrategy.ATOM_OF_THOUGHT)
    .strategy(PromptStrategy.CONFIDENCE_WEIGHTED)
    .confidenceThreshold(0.7f)
    .objective("Analyze system architecture tradeoffs")
    .build();

EnhancedPrompt prompt = enhancer.enhance(
    "Compare microservices vs monolith for a 10-person startup " +
    "building a real-time analytics platform"
);
// Estimated overhead: prompt.getEstimatedOverhead() tokens
```

### Few-Shot with Examples

Few-shot prompting teaches the model by example. You provide a few input/output pairs (both good and bad), and the model learns the expected format and quality from them. This is especially effective for classification, formatting, and style-matching tasks.

```java
PromptEnhancer enhancer = PromptEnhancer.builder()
    .strategy(PromptStrategy.FEW_SHOT_EXAMPLES)
    .positiveExample(
        "The food was great",
        "Sentiment: POSITIVE (0.95)",
        "Clear positive language"
    )
    .positiveExample(
        "Terrible service, never again",
        "Sentiment: NEGATIVE (0.98)",
        "Strong negative indicators"
    )
    .negativeExample(
        "The food was great",
        "positive",
        "Missing confidence score and proper format"
    )
    .outputFormat(OutputFormat.TEXT)
    .build();

EnhancedPrompt prompt = enhancer.enhance("The product works but could be better");
```

---

# Streaming

URL: https://tnsai.dev/docs/agents/behavior/streaming
Description: TnsAI supports three streaming modes for real-time token delivery from LLM providers.

import { Callout } from 'fumadocs-ui/components/callout'

## Token Streaming

Returns text tokens as they are generated — simplest mode:

```java
Stream<String> tokens = agent.streamChat("Explain relativity");
tokens.forEach(System.out::print);
```

## ChatChunk Streaming

Returns typed chunks with metadata (token counts, finish reason, tool calls):

```java
llmClient.streamChatWithSpec(request).forEach(chunk -> {
    switch (chunk.getType()) {
        case START -> System.out.println("Stream started: " + chunk.getModel());
        case CONTENT -> System.out.print(chunk.getContent());
        case TOOL_CALL -> handleToolCall(chunk.getToolCall().orElseThrow());
        case DONE -> System.out.println("\nTokens: " + chunk.getTokenCount());
        case ERROR -> System.err.println("Error: " + chunk.getContent());
    }
});
```

### Chunk Types

Each `ChatChunk` has a type that tells you what kind of data it carries. Your code should handle each type to respond appropriately as the stream progresses.

| Type        | Description                           |
| ----------- | ------------------------------------- |
| `START`     | Stream initialization with model info |
| `CONTENT`   | Text content delta                    |
| `TOOL_CALL` | Tool/function invocation request      |
| `DONE`      | Stream complete with finish reason    |
| `ERROR`     | Error occurred during streaming       |

### Finish Reasons

When a stream ends, the `DONE` chunk includes a finish reason that explains why the LLM stopped generating. This helps you decide what to do next -- for example, if the reason is `TOOL_CALLS`, you need to execute the requested tool and feed the result back.

| Reason           | Description             |
| ---------------- | ----------------------- |
| `STOP`           | Natural completion      |
| `LENGTH`         | Max tokens reached      |
| `TOOL_CALLS`     | LLM wants to call tools |
| `CONTENT_FILTER` | Content was filtered    |
| `ERROR`          | Error during generation |

## Handler-Based Streaming

Callback pattern with full tool-call loop — ideal for UI integration:

```java
llmClient.streamChatWithHandler(request, chunk -> {
    if (chunk.isContent()) {
        System.out.print(chunk.getContent());
    } else if (chunk.isToolCall()) {
        // Framework handles tool execution automatically
    } else if (chunk.isDone()) {
        System.out.println("\nFinish reason: " + chunk.getFinishReason());
    }
});
```

## Convenience Methods

`ChatChunk` provides static factory methods so you can create chunks without calling constructors directly. These are useful when you build custom streaming pipelines or write tests that simulate LLM output.

```java
// ChatChunk factory methods
ChatChunk.start(model, requestId);
ChatChunk.content("Hello", tokenCount, index);
ChatChunk.content("Hello");
ChatChunk.toolCall(toolCallObject);
ChatChunk.done(FinishReason.STOP, totalTokens);
ChatChunk.error("Something went wrong");
```

## Which Mode to Use?

TnsAI offers three streaming modes at different levels of abstraction. Pick the simplest one that meets your needs.

| Mode                 | Use When                                            |
| -------------------- | --------------------------------------------------- |
| **Token Stream**     | Simple text display, CLI output                     |
| **ChatChunk Stream** | Need metadata (tokens, model), manual tool handling |
| **Handler-Based**    | UI integration, automatic tool execution loop       |

## Async Execution

The `AsyncAgent` interface (`com.tnsai.agents.async.AsyncAgent`) provides non-blocking chat operations with multiple consumption patterns.

### Methods

`AsyncAgent` exposes several ways to consume responses. Choose based on whether you need simple text, typed events, or reactive backpressure control.

| Method                        | Return Type                 | Description                                                 |
| ----------------------------- | --------------------------- | ----------------------------------------------------------- |
| `chatAsync(message)`          | `CompletableFuture<String>` | Async chat, completes with full response                    |
| `chatAsync(message, options)` | `CompletableFuture<String>` | Async chat with `ChatOptions`                               |
| `chatStream(message)`         | `Stream<String>`            | Streaming tokens as a Java Stream                           |
| `chatEventStream(message)`    | `Stream<ChatEvent>`         | Typed event stream (tokens, tool calls, etc.)               |
| `chatPublisher(message)`      | `Flow.Publisher<ChatEvent>` | Reactive Streams publisher for backpressure-aware consumers |
| `cancel()`                    | `void`                      | Cancels any ongoing async operation                         |
| `isProcessing()`              | `boolean`                   | True if an async operation is in progress                   |
| `getProgress()`               | `double`                    | Execution progress (0.0 - 1.0)                              |

### CompletableFuture

The simplest async pattern. `chatAsync` returns a `CompletableFuture` that completes with the full response string once the LLM finishes generating. Use this when you do not need to show partial results to the user.

```java
AsyncAgent agent = new MyAsyncAgent();

agent.chatAsync("Tell me about Java")
     .thenAccept(response -> System.out.println(response))
     .exceptionally(e -> { e.printStackTrace(); return null; });
```

### Token Stream

Returns a `Stream<String>` that emits each text token as it arrives. This lets you print tokens to the console (or a UI) incrementally instead of waiting for the full response.

```java
agent.chatStream("Tell me a story")
     .forEach(token -> System.out.print(token));
```

### Typed Event Stream

`ChatEvent` subtypes distinguish tokens from tool calls and other events:

```java
agent.chatEventStream("Complex task")
     .forEach(event -> {
         if (event instanceof ChatEvent.Token t) {
             System.out.print(t.content());
         } else if (event instanceof ChatEvent.ToolCall tc) {
             System.out.println("Calling tool: " + tc.toolName());
         }
     });
```

### Reactive Publisher

For backpressure-aware consumers using `java.util.concurrent.Flow`:

```java
agent.chatPublisher("Generate a report")
     .subscribe(new Flow.Subscriber<>() {
         private Flow.Subscription subscription;

         @Override
         public void onSubscribe(Flow.Subscription s) {
             this.subscription = s;
             s.request(1);
         }

         @Override
         public void onNext(ChatEvent event) {
             process(event);
             subscription.request(1);
         }

         @Override
         public void onError(Throwable t) { t.printStackTrace(); }

         @Override
         public void onComplete() { System.out.println("Done"); }
     });
```

### Cancellation

You can cancel a running async operation at any time. This is useful for timeout handling or when the user navigates away from a page before the response finishes.

```java
CompletableFuture<String> future = agent.chatAsync("Long running task");

// Cancel if still running
if (agent.isProcessing()) {
    agent.cancel();
}
```

---

# Agent Variants

URL: https://tnsai.dev/docs/agents/behavior/variants
Description: Agent variants let you trade off between response quality, execution speed, and token cost. A single agent can switch variants at runtime -- per task or per action.

import { Callout } from 'fumadocs-ui/components/callout'

## AgentVariant Enum

The four variant tiers represent different quality/speed/cost tradeoffs. Pick the one that matches your task, or use `AUTO` to let the framework decide at runtime based on task complexity.

Defined in `com.tnsai.enums.AgentVariant`. Four tiers:

| Variant  | Quality        | Speed        | Cost         | Best For                                           |
| -------- | -------------- | ------------ | ------------ | -------------------------------------------------- |
| `HIGH`   | Max (1.0)      | Slow (0.3)   | High (1.0)   | Complex refactoring, security review, architecture |
| `MEDIUM` | Balanced (0.7) | Normal (0.6) | Medium (0.5) | Regular development, feature implementation        |
| `MINI`   | Basic (0.4)    | Fast (1.0)   | Low (0.2)    | Quick fixes, typo corrections, simple queries      |
| `AUTO`   | Adaptive       | Adaptive     | Optimal      | Production environments with varied workloads      |

### Helper methods

These convenience methods let you check what a variant prioritizes without comparing enum values directly.

```java
variant.isQualityFocused();  // true for HIGH, MEDIUM
variant.isSpeedFocused();    // true for MINI
variant.isCostOptimized();   // true for MINI, AUTO
```

### Task-based suggestion

If you are not sure which variant to use, `forTask()` analyzes the task description and suggests one based on keyword matching. This is a simple heuristic -- for smarter auto-selection, use `VariantManager` with auto mode enabled.

```java
AgentVariant.forTask("Refactor the auth system");  // HIGH
AgentVariant.forTask("Fix a typo in README");       // MINI
AgentVariant.forTask("Implement login page");       // MEDIUM
```

Keywords that push toward HIGH: `refactor`, `architect`, `complex`, `critical`, `security`, `review`.
Keywords that push toward MINI: `typo`, `fix`, `simple`, `quick`, `minor`, `small`.

## VariantSpec

Each variant tier has a `VariantSpec` that defines its concrete settings: which LLM model to use, token limits, available tools, timeout, retry count, and temperature. You can use the built-in specs or build a custom one for your specific models and requirements.

### Predefined specs

The built-in specs for each tier ship with sensible defaults for model selection, token limits, and timeouts. These are what you get when you use a variant without customization.

|                       | HIGH                      | MEDIUM                       | MINI                                 |
| --------------------- | ------------------------- | ---------------------------- | ------------------------------------ |
| **Preferred model**   | `claude-opus-4`           | `claude-sonnet-4`            | `claude-haiku-3`                     |
| **Fallback models**   | `gpt-4`, `gemini-1.5-pro` | `gpt-4o`, `gemini-1.5-flash` | `gpt-4o-mini`, `gemini-1.5-flash-8b` |
| **Max input tokens**  | 128,000                   | 64,000                       | 32,000                               |
| **Max output tokens** | 16,384                    | 8,192                        | 4,096                                |
| **Tool set**          | ALL                       | STANDARD                     | MINIMAL                              |
| **Timeout**           | 10 min                    | 5 min                        | 2 min                                |
| **Max retries**       | 3                         | 2                            | 1                                    |
| **Temperature**       | 0.7                       | 0.5                          | 0.3                                  |
| **Streaming**         | Yes                       | Yes                          | No                                   |

`AUTO` defaults to the MEDIUM spec and adjusts dynamically at runtime.

### Using predefined specs

Retrieve the built-in spec for a variant tier with `VariantSpec.forVariant()` and query its settings.

```java
VariantSpec highSpec = VariantSpec.forVariant(AgentVariant.HIGH);
String model = highSpec.getPreferredModel();       // "claude-opus-4"
int inputTokens = highSpec.getMaxInputTokens();    // 128000
Duration timeout = highSpec.getTimeout();           // PT10M
```

### Building a custom spec

When the built-in specs do not match your environment (different models, different limits), build a custom one. Custom specs are immutable -- once built, they cannot be changed.

```java
VariantSpec custom = VariantSpec.builder()
    .variant(AgentVariant.HIGH)
    .preferredModel("claude-opus-4")
    .fallbackModels(List.of("gpt-4", "gemini-1.5-pro"))
    .maxInputTokens(128000)
    .maxOutputTokens(16384)
    .toolSet(VariantSpec.ToolSet.ALL)
    .timeout(Duration.ofMinutes(10))
    .maxRetries(3)
    .temperature(0.7)
    .enableStreaming(true)
    .addSetting("customKey", "value")
    .build();
```

### Model resolution

When the preferred model is unavailable (API outage, not provisioned), `getEffectiveModel` automatically falls back to the next available model from the fallback list.

```java
Set<String> available = Set.of("gpt-4o", "claude-haiku-3");
String model = highSpec.getEffectiveModel(available);  // "gpt-4o" (fallback)
```

### Immutable copies

Since specs are immutable, changing a field returns a new `VariantSpec` instance. The original is not modified.

```java
VariantSpec modified = spec.withVariant(AgentVariant.MEDIUM);
VariantSpec remodeled = spec.withModel("gpt-4-turbo");
```

### ToolSet levels

The `ToolSet` controls which tools are available to the agent in a given variant. Lower tiers restrict tool access to reduce cost and latency.

| Level      | Description          |
| ---------- | -------------------- |
| `ALL`      | All available tools  |
| `STANDARD` | Most common tools    |
| `MINIMAL`  | Essential tools only |
| `NONE`     | No tools             |

## VariantManager

The `VariantManager` handles variant switching at runtime. It can operate in manual mode (you choose the variant) or auto mode (it analyzes each task and picks the best tier). It also tracks usage statistics per variant, so you can see how often each tier is used and how well it performs.

### Creating a manager

Create a `VariantManager` with a default variant. If not specified, it defaults to `MEDIUM`.

```java
VariantManager manager = new VariantManager();                    // defaults to MEDIUM
VariantManager manager = new VariantManager(AgentVariant.HIGH);   // explicit initial
```

### Manual switching

Explicitly set the variant when you know what quality level the next task needs.

```java
manager.setVariant(AgentVariant.HIGH);
AgentVariant current = manager.getCurrentVariant();   // HIGH
VariantSpec spec = manager.getCurrentSpec();           // VariantSpec for HIGH
```

### Auto mode

In auto mode, the manager analyzes each task description and automatically switches to the most appropriate variant. This is ideal for production environments where tasks vary in complexity.

```java
manager.setAutoMode(true);

// Analyzes task complexity (0-10 score) and switches automatically
AgentVariant suggested = manager.suggestVariant("Refactor the authentication system");
// suggested = HIGH, manager now using HIGH spec
```

Complexity scoring adds/subtracts from a base score of 5. Score `>= 7` returns HIGH, score `<= 3` returns MINI, otherwise MEDIUM. Task length is also considered (`>200` chars adds 1, `<50` chars subtracts 1).

### Custom specs per variant

Override the default spec for any variant tier. This is useful when you want to use a different model or different token limits for a specific tier in your environment.

```java
VariantSpec custom = VariantSpec.builder()
    .variant(AgentVariant.HIGH)
    .preferredModel("my-custom-model")
    .maxInputTokens(200000)
    .build();

manager.setVariantSpec(AgentVariant.HIGH, custom);
```

### Change listeners

Register callbacks to be notified whenever the variant changes, whether manually or through auto mode. This is useful for logging, metrics, or adjusting other system behavior based on the active variant.

```java
// Register
VariantManager.Registration reg = manager.onVariantChange(event -> {
    System.out.printf("Variant: %s -> %s (reason: %s)%n",
        event.previous(), event.current(), event.reason());
});

// Unregister
reg.unregister();
```

The `VariantChangeEvent` record contains `previous`, `current`, and `reason` (either `"manual"` or `"auto:<task summary>"`).

### Usage statistics

The manager tracks task count, success rate, and timing per variant. Use this data to understand your cost distribution and identify variants that are underperforming.

```java
manager.recordTask(AgentVariant.HIGH, 1500, true);

VariantManager.VariantStats stats = manager.getStats(AgentVariant.HIGH);
stats.getTaskCount();          // total tasks
stats.getSuccessRate();        // 0.0 - 1.0
stats.getAverageDurationMs();  // average task time
stats.getMinDurationMs();
stats.getMaxDurationMs();

// All stats
Map<AgentVariant, VariantManager.VariantStats> all = manager.getAllStats();
```

## @Variant Annotation

Some actions always need a specific quality level regardless of the agent's current setting -- a security audit should always use HIGH, while a text formatter can always use MINI. The `@Variant` annotation locks an action method to a specific variant tier. The framework temporarily switches to that variant for the duration of the action, then restores the previous one.

| Attribute     | Type           | Default    | Description                                        |
| ------------- | -------------- | ---------- | -------------------------------------------------- |
| `value`       | `AgentVariant` | (required) | Variant to use                                     |
| `reason`      | `String`       | `""`       | Documentation for variant choice                   |
| `recordStats` | `boolean`      | `true`     | Track usage statistics                             |
| `fallback`    | `AgentVariant` | `MEDIUM`   | Fallback if primary variant's model is unavailable |

```java
// Force HIGH for security-critical action
@ActionSpec(type = ActionType.LLM, description = "Security analysis")
@Variant(AgentVariant.HIGH)
public String analyzeSecurityRisks(String code) {
    return "Analyze security: " + code;
}

// Use MINI for a quick utility
@ActionSpec(type = ActionType.LOCAL, description = "Format text")
@Variant(AgentVariant.MINI)
public String formatText(String text) {
    return text.trim();
}

// Let the framework auto-select based on input
@ActionSpec(type = ActionType.LLM, description = "Code review")
@Variant(value = AgentVariant.AUTO, reason = "Complexity varies by input size")
public String reviewCode(String code) {
    return "Review: " + code;
}
```

Actions without `@Variant` use the agent's current variant. The annotation only affects the specific method it decorates.

## Full Example

This end-to-end example shows how to set up an agent with auto variant selection, log all variant switches, run tasks of varying complexity, override the variant at runtime, and check usage statistics afterward.

```java
// Configure agent with variant support
VariantManager variantManager = new VariantManager(AgentVariant.AUTO);
variantManager.setAutoMode(true);

// Log all variant switches
variantManager.onVariantChange(event ->
    log.info("Variant {} -> {} ({})", event.previous(), event.current(), event.reason()));

Agent agent = AgentBuilder.create()
    .withVariant(AgentVariant.AUTO)
    .llm(new OpenAIClient())
    .build();

// Simple task -- framework auto-selects MINI
agent.chat("Fix the typo in line 42");

// Complex task -- framework auto-selects HIGH
agent.chat("Refactor the authentication module for OAuth2 support");

// Override at runtime
agent.setVariant(AgentVariant.HIGH);
agent.chat("Critical security audit of payment processing");

// Check statistics
VariantManager.VariantStats highStats = variantManager.getStats(AgentVariant.HIGH);
System.out.printf("HIGH: %d tasks, %.0f%% success, avg %dms%n",
    highStats.getTaskCount(),
    highStats.getSuccessRate() * 100,
    highStats.getAverageDurationMs());
```

---

# Action System

URL: https://tnsai.dev/docs/agents/fundamentals/action-system
Description: The action system is the execution backbone of TnsAI. When an LLM decides to call a function, or an agent needs to perform work, the request flows through ActionExecutor, which routes it to the appropriate executor based on the action's ActionType.

import { Callout } from 'fumadocs-ui/components/callout'

## Architecture Overview

```
Agent.executeAction(name, params)
    |
    v
ActionExecutor.execute(action, role, params, context)
    |
    +-- 1. Validate inputs
    +-- 2. Check @ApprovalRequired
    +-- 3. Route by ActionType:
    |       LOCAL      -> Reflection invocation on Role
    |       WEB_SERVICE -> WebServiceExecutor (HTTP)
    |       LLM        -> LLMRoleExecutor (single-shot LLM call)
    |       MCP_TOOL   -> McpToolExecutor (MCP protocol)
    +-- 4. Return result or wrap in ActionExecutionException
```

Tool dispatch — when the LLM emits a tool call during an `LLM` action — is handled separately by the agent's `ToolMethodDispatcher`, which is built from every POJO and dynamic tool registered with `AgentBuilder`.

## ActionType Enum

`com.tnsai.enums.ActionType` defines the four execution methods:

| Value         | Executor             | Description                                                                |
| ------------- | -------------------- | -------------------------------------------------------------------------- |
| `LOCAL`       | Reflection           | Direct Java method invocation on the Role                                  |
| `WEB_SERVICE` | `WebServiceExecutor` | HTTP REST API calls                                                        |
| `LLM`         | `LLMRoleExecutor`    | Single-shot LLM call; tool dispatch via the agent's `ToolMethodDispatcher` |
| `MCP_TOOL`    | `McpToolExecutor`    | Model Context Protocol server calls                                        |

## ActionExecutor

`com.tnsai.actions.ActionExecutor` is the central, thread-safe dispatcher.

### Construction

```java
// Built by AgentBuilder using the agent's tool registry
ActionExecutor executor = new ActionExecutor(toolMethodDispatcher);
```

Default executors are registered automatically: `WebServiceExecutor` for `WEB_SERVICE`, `LLMRoleExecutor` for `LLM`, and `McpToolExecutor` for `MCP_TOOL` (if tnsai-mcp is on classpath). The `ToolMethodDispatcher` is built from `AgentBuilder.builtInTools(...)`, `.toolPojos(...)`, and `.dynamicTool(...)` registrations.

### Execution Flow

1. **Validation** -- null checks on action, role, parameters
2. **Approval check** -- if the action method has `@ApprovalRequired`, verifies `_approval_token` is present in parameters
3. **Routing** -- `LOCAL` actions are invoked via reflection; others delegate to `TypedActionExecutor`
4. **Error wrapping** -- all exceptions become `ActionExecutionException` with a category (`PARAMETER`, `INVOCATION`, `NETWORK`, `UNKNOWN`)

### Approval-Required Actions

```java
// In a Role class
@ApprovalRequired
@ActionSpec(description = "Deploy to production")
public String deploy(String target) { ... }

// Calling with approval token
Map<String, Object> params = Map.of(
    "target", "production",
    "_approval_token", approvalService.getToken()
);
agent.executeAction("deploy", params);
```

If the token is missing, `ApprovalRequiredException` is thrown.

## TypedActionExecutor Interface

`com.tnsai.actions.executors.TypedActionExecutor` is the extension point for custom execution strategies.

```java
public interface TypedActionExecutor {
    Object execute(
        ActionMetadata action,
        Role role,
        Map<String, Object> parameters,
        Map<String, Object> context
    );
}
```

The `context` map provides runtime data: `"llm"` (LLMClient), `"agent"` (Agent reference), `"mcpToolName"` (for MCP routing).

## Executor Types

### WebServiceExecutor

Handles `WEB_SERVICE` actions by making HTTP calls using OkHttp.

Features:

- URL template variables: `{city}`, `{id}` in endpoints are replaced from parameters
- Parameter types: `QUERY` (URL params), `PATH` (URL path segments), `BODY` (JSON payload)
- Authentication: `BEARER` and `BASIC` via environment variables
- Custom headers via `@Header` annotations
- Configurable per-action timeout

```java
@ActionSpec(
    type = ActionType.WEB_SERVICE,
    endpoint = "https://api.weather.com/v1/forecast/{city}",
    httpMethod = HttpMethod.GET,
    paramType = ParamType.QUERY,
    auth = AuthType.BEARER,
    authToken = "WEATHER_API_KEY",
    timeout = 5000
)
@Header(key = "Accept", value = "application/json")
public Object getWeather(String city) { return null; }
```

### LLMRoleExecutor

Handles `LLM` actions with a single-shot LLM call. The action's prompt comes from the method body's return value (the convention is to return a String describing what the LLM should do); the executor sends that prompt to the agent's LLM client and returns the raw response.

Tool dispatch is **not** done by this executor. If the LLM emits a tool call in its response, dispatch flows through the agent's `ToolMethodDispatcher`, which is built once from the `AgentBuilder.builtInTools(...)` / `.toolPojos(...)` / `.dynamicTool(...)` registrations and shared across every `LLM` action.

Per-action overrides:

| `@ActionSpec` field | Effect                                                                                                   |
| ------------------- | -------------------------------------------------------------------------------------------------------- |
| `llmSystemPrompt`   | Prepended as the system message of the chat request, overriding the LLM client's default for this action |
| `llmTemperature`    | When `>= 0`, sets the chat temperature; `-1.0f` (default) means "fall back to the LLM client's default"  |

### McpToolExecutor

Handles `MCP_TOOL` actions by connecting to remote MCP servers.

Features:

- Automatic tool discovery via `tools/list`
- Connection caching per endpoint
- API key support via environment variables
- Reflection-based MCP client creation (no compile-time dependency on tnsai-mcp)

```java
@ActionSpec(
    type = ActionType.MCP_TOOL,
    serverUrl = "https://mcp.api.coingecko.com/mcp",
    description = "Access cryptocurrency data"
)
public String cryptoData(String query) { return null; }
```

Key methods:

| Method                                             | Description                        |
| -------------------------------------------------- | ---------------------------------- |
| `discoverTools(String endpoint, String apiKeyEnv)` | Discover tools from MCP server     |
| `getEndpointForTool(String toolName)`              | Find which endpoint handles a tool |
| `close()`                                          | Disconnect all cached MCP clients  |

## ActionContract

`com.tnsai.actions.contracts.ActionContract` provides optional pre/post condition validation for roles.

```java
public interface ActionContract {
    default void validatePreConditions(ActionMetadata action, Map<String, Object> parameters)
        throws ValidationException { }

    default void validatePostConditions(ActionMetadata action, Object result)
        throws ValidationException { }

    default void validateInvariants() throws ValidationException { }
}
```

A role implements this interface to enforce contracts:

```java
public class OrderRole extends Role implements ActionContract {
    @Override
    public void validatePreConditions(ActionMetadata action, Map<String, Object> params)
            throws ValidationException {
        if ("placeOrder".equals(action.getName())) {
            if (!params.containsKey("items")) {
                throw new ValidationException("items parameter required");
            }
        }
    }
}
```

## TypeConverter

`com.tnsai.actions.TypeConverter` handles automatic parameter type conversion in action invocations.

### Supported Conversions

| Source                | Target Types                                      |
| --------------------- | ------------------------------------------------- |
| `String`              | `int`, `long`, `double`, `float`, `boolean`, Enum |
| `Number`              | `int`, `long`, `double`, `float`                  |
| `Map<String, Object>` | Any POJO/record (via Jackson)                     |

```java
// String to int
Object result = TypeConverter.convert("42", int.class);  // 42

// Number to int
Object result = TypeConverter.convert(42L, int.class);    // 42

// Map to POJO
record UserDto(String name, int age) {}
Map<String, Object> params = Map.of("name", "Alice", "age", 30);
UserDto user = TypeConverter.convertMapToPojo(params, UserDto.class);
```

Utility methods:

| Method                                       | Description                                 |
| -------------------------------------------- | ------------------------------------------- |
| `convert(Object value, Class<?> targetType)` | Convert a value to the target type          |
| `convertMapToPojo(Map, Class<T>)`            | Convert a map to a POJO via Jackson         |
| `isPrimitiveOrWrapper(Class<?>)`             | Check if type is primitive or wrapper       |
| `isSimpleType(Class<?>)`                     | Check if type does not need POJO conversion |

Enum conversion is case-insensitive: `TypeConverter.convert("get", HttpMethod.class)` matches `HttpMethod.GET`.

## ActionRequest and ActionResponse

Typed records that replace raw `Map<String, Object>` and untyped `Object` returns.

### ActionRequest

`com.tnsai.actions.model.ActionRequest` -- immutable request record.

```java
// With parameters
ActionRequest request = ActionRequest.of("searchWeb", Map.of(
    "query", "Java frameworks",
    "maxResults", 10
));

// Without parameters
ActionRequest request = ActionRequest.of("getStatus");
```

Fields: `actionName` (required, non-blank), `parameters` (never null, defensively copied).

### ActionResponse

`com.tnsai.actions.model.ActionResponse` -- immutable response record.

```java
// Success
ActionResponse response = ActionResponse.success(resultData);

// Failure
ActionResponse response = ActionResponse.failure("Connection timeout");

// Failure with partial result
ActionResponse response = ActionResponse.failure("Partial data received", partialData);
```

Fields: `value` (result object), `success` (boolean), `error` (String, null on success).

### Usage with Agent

```java
ActionRequest request = ActionRequest.of("searchWeb", Map.of("query", "TnsAI"));
ActionResponse response = agent.executeAction(request);

if (response.success()) {
    Object data = response.value();
} else {
    logger.error("Failed: {}", response.error());
}
```

## Related Documentation

- [Roles](/docs/agents/fundamentals/roles) -- defining roles with `@ActionSpec` annotations
- [Capabilities](/docs/agents/fundamentals/capabilities) -- reusable body-less `@ActionSpec` contracts via `@Capability` interfaces
- [Tools](/docs/capabilities/tools/registration) -- registering POJO toolkits the LLM can call
- [Advanced Tools](/docs/capabilities/tools/registration-advanced) -- filters, listeners, and dispatcher introspection
- [SPI Reference](/docs/reference/spi) -- SPI interfaces for cross-module extensibility

---

# Capabilities

URL: https://tnsai.dev/docs/agents/fundamentals/capabilities
Description: Capabilities are reusable, body-less action contracts. A @Capability interface carries one or more @ActionSpec-annotated methods that describe what the capability does; the framework dispatches the call at runtime. A role gains the capability by implements-ing the interface — without writing any method bodies.

import { Callout } from 'fumadocs-ui/components/callout'

This page covers the pattern, composition, override rules, validation, and migration from the legacy `return null;` style.

*Available from 0.3.1. The legacy pattern (concrete methods with `return null;` bodies) was removed in 0.5.0.*

## The Problem

Before capabilities, every dispatched `@ActionSpec` method required a `return null;` body:

```java
@ActionSpec(
    type = ActionType.LLM,
    description = "Summarise the text in one short paragraph",
    llmSystemPrompt = "You are concise.",
    llmTemperature = 0.2f
)
public String summarize(String text) {
    return null;   // body unused — LLMRoleExecutor handles dispatch
}
```

The body is dead code: [`ActionExecutor`](/docs/agents/fundamentals/action-system) skips method invocation entirely for `LLM`/`MCP_TOOL`/`WEB_SERVICE` actions when there is no `ActionResult` parameter. Yet the method declaration forces three problems:

- **Misleading signature** — static analysis marks the method as "always returns null"; downstream `@NonNull` checks inferred from this are bogus.
- **Silent failure on bypass** — anyone who invokes the method outside the dispatch path (direct `role.summarize(x)`, test code, misconfigured filter) gets `null`. This looks identical to a genuine LLM failure and makes debugging painful.
- **Duplicated specification** — every role that wants the same capability copy-pastes the `@ActionSpec` annotation. Drift is inevitable.

## The Pattern

Move the method into a `@Capability` interface. The interface's `default` body throws `Actions.dispatchedByFramework()` — a loud, framework-owned marker that never executes in the normal dispatch path:

```java
@Capability
public interface Summarizer {
    @ActionSpec(
        type = ActionType.LLM,
        description = "Summarise the text in one short paragraph",
        llmSystemPrompt = "You are concise.",
        llmTemperature = 0.2f
    )
    default String summarize(String text) {
        throw Actions.dispatchedByFramework();
    }
}
```

A role picks up the capability by implementing the interface — with no method bodies of its own:

```java
@RoleIdentity(name = "Editor", goal = "Produce clean, readable articles")
public class EditorRole extends Role implements Summarizer {
    // State, lifecycle, and other non-capability methods live here.
    // No `summarize` body — inherited from Summarizer.
}
```

When the LLM calls `summarize`, `ActionExecutor` discovers the method through the capability interface and routes dispatch through the `LLM` executor — exactly as it would for a concrete `@ActionSpec` method. The default `throw` body is never executed in the normal path; it only fires if someone bypasses dispatch and invokes the method directly, producing a clear `DispatchedByFrameworkException` instead of a silent `null`.

## Composition

A role can implement any number of capability interfaces. Each contributes its actions to the role:

```java
@Capability
public interface Translator {
    @ActionSpec(type = ActionType.LLM, description = "Translate the text to the target language")
    default String translate(String text, String targetLanguage) {
        throw Actions.dispatchedByFramework();
    }
}

@Capability
public interface Classifier {
    @ActionSpec(type = ActionType.LLM, description = "Classify input as POSITIVE / NEGATIVE / NEUTRAL")
    default String classifySentiment(String text) {
        throw Actions.dispatchedByFramework();
    }
}

@RoleIdentity(name = "Assistant", goal = "Handle ad-hoc requests")
public class AssistantRole extends Role implements Summarizer, Translator, Classifier {
    // Three capabilities, zero bodies. All actions ready for LLM dispatch.
}
```

`ActionDiscovery` walks the role's interface chain — including super-interfaces of capabilities (`MultilingualSummarizer extends Summarizer`) — so every capability contributes its methods regardless of whether it arrives via direct implementation or transitive extension.

## Override — Role Declaration Wins

If a role declares the same signature as a capability's default method, the role's version is what ends up in the discovered action list. Use this to drop dispatch entirely for a specific role and provide a deterministic local implementation:

```java
public class StrictEditor extends Role implements Summarizer {
    // Replaces Summarizer.summarize's LLM dispatch with a deterministic local impl
    @Override
    @ActionSpec(type = ActionType.LOCAL, description = "Truncate-first summarise (deterministic)")
    public String summarize(String text) {
        return text.length() <= 60 ? text : text.substring(0, 60) + "...";
    }
}
```

Discovery sees `summarize` declared on the concrete class first, records its signature, and then skips the capability's default when walking the interface chain. Exactly one `summarize` action ends up in the role's metadata, and its type is `LOCAL` rather than `LLM`.

## Validation

Two rules are enforced at action-discovery time; violations throw `IllegalStateException` with a message naming the offending interface and method.

### Capability methods must be `default`

An abstract method on a `@Capability` interface would force every adopting role to write a body — defeating the point of the annotation. The error message points at the correct body:

```text
@Capability interface com.example.Summarizer method summarize must be a default method.
Abstract capability methods force every adopting role to write a body, defeating the
purpose of the annotation. Use `default { throw Actions.dispatchedByFramework(); }` as the body.
```

### Capability methods must not declare an `ActionResult` parameter

Capabilities are pure dispatch. Post-processing (reading the LLM's raw response, running it through custom logic before returning) belongs on the concrete role class where per-role logic makes sense. The validator rejects the mixed case:

```text
@Capability interface com.example.Summarizer method summarize must not declare an
ActionResult parameter. Capabilities are pure dispatch — move post-processing
(ActionResult-based) to a concrete method on the role class itself.
```

If you want post-processing, keep that specific method off the capability interface and declare it directly on the role (the legacy pattern). Capability-dispatched and role-owned methods coexist on the same role without interference.

## Migration

To migrate a legacy role:

1. For each dispatched `@ActionSpec` method (LLM / MCP / WEB\_SERVICE) whose body is `return null;`, extract it to a `@Capability` interface.
2. Give the interface method a `default { throw Actions.dispatchedByFramework(); }` body.
3. On the role class, remove the original method entirely and add `implements YourCapability`.
4. Methods that have a meaningful body — typically because they declare an `ActionResult` parameter for post-processing — stay on the role class unchanged.

### Before

```java
@RoleIdentity(name = "Editor", goal = "Produce clean articles")
public class EditorRole extends Role {

    @ActionSpec(type = ActionType.LLM, description = "Summarise the text",
                llmSystemPrompt = "You are concise.", llmTemperature = 0.2f)
    public String summarize(String text) {
        return null;
    }

    @ActionSpec(type = ActionType.LLM, description = "Translate to the target language")
    public String translate(String text, String targetLanguage) {
        return null;
    }
}
```

### After

```java
@Capability
public interface Summarizer {
    @ActionSpec(type = ActionType.LLM, description = "Summarise the text",
                llmSystemPrompt = "You are concise.", llmTemperature = 0.2f)
    default String summarize(String text) {
        throw Actions.dispatchedByFramework();
    }
}

@Capability
public interface Translator {
    @ActionSpec(type = ActionType.LLM, description = "Translate to the target language")
    default String translate(String text, String targetLanguage) {
        throw Actions.dispatchedByFramework();
    }
}

@RoleIdentity(name = "Editor", goal = "Produce clean articles")
public class EditorRole extends Role implements Summarizer, Translator {
    // No capability bodies.
}
```

The first time `Summarizer` is used by another role, the duplication problem is already solved — update the prompt once, every adopter gets the change.

## When Not to Use Capabilities

- **`ActionType.LOCAL` methods** — these have real bodies that the framework invokes via reflection. They are not "framework-dispatched" and do not have the `return null;` problem. Leave them on the concrete role class.
- **Methods that declare an `ActionResult` parameter** — they are validated-out of capability interfaces by design (see above). Keep them on the role.
- **One-off methods used by a single role** with no prospect of reuse — extracting a capability interface for one consumer adds indirection without benefit. Capabilities shine when two or more roles share the same `@ActionSpec`.

## Related

- [Action System](/docs/agents/fundamentals/action-system) — routing, `ActionType` enum, executor types.
- [Roles](/docs/agents/fundamentals/roles) — role identity, responsibilities, lifecycle.

## Implementation References

- `com.tnsai.capabilities.Capability` — the marker annotation (`@Target(TYPE)`, `@Retention(RUNTIME)`).
- `com.tnsai.actions.Actions.dispatchedByFramework()` — helper returning `DispatchedByFrameworkException` (subtype of `UnsupportedOperationException`).
- `com.tnsai.actions.ActionDiscovery` — two-pass discovery: role class first, then the capability interface chain.

---

# Event System

URL: https://tnsai.dev/docs/agents/fundamentals/events
Description: The event system provides full observability into the agent lifecycle. Events use a sealed interface hierarchy with 20+ event types, enabling type-safe pattern matching.

import { Callout } from 'fumadocs-ui/components/callout'

## Subscribing to Events

To listen to what your agent is doing, pass an event callback to `chatWithEvents`. The callback receives every event the agent fires during the run, and you can use Java's pattern matching to handle only the ones you care about.

```java
String response = agent.chatWithEvents("Do research on AI", event -> {
    switch (event) {
        case RunStartEvent e -> log.info("Agent run started");
        case ActionStartEvent e -> log.info("Action: {}", e.actionName());
        case ToolCallStartEvent e -> log.info("Tool: {}", e.toolName());
        case ToolCallEndEvent e -> log.info("Result: {}", e.result());
        case ErrorEvent e -> log.error("Error: {}", e.message());
        case RunEndEvent e -> log.info("Run completed");
        default -> {} // Other events
    }
});
```

## Event Types

Events are grouped into categories based on what part of the agent they relate to. Each event carries contextual data you can inspect in your handler.

### Lifecycle Events

These events tell you when the agent starts and stops processing, and when it transitions between states. Use them for logging, timing, or coordinating external systems.

| Event                    | When                              |
| ------------------------ | --------------------------------- |
| `RunStartEvent`          | Agent begins processing a message |
| `RunEndEvent`            | Agent finishes processing         |
| `AgentStateChangedEvent` | Agent state transitions           |

### Action Events

Actions are the high-level steps an agent takes (for example, "search the web" or "write a file"). These events let you track when each action starts and finishes.

| Event              | When                       |
| ------------------ | -------------------------- |
| `ActionStartEvent` | Action execution begins    |
| `ActionEndEvent`   | Action execution completes |

### Tool Events

Tools are the concrete functions an agent can call (like an HTTP client or a file reader). These events fire each time a tool is invoked, so you can monitor tool usage, measure latency, or build dashboards.

| Event                | When                      |
| -------------------- | ------------------------- |
| `ToolCallStartEvent` | Tool invocation begins    |
| `ToolCallEndEvent`   | Tool invocation completes |

### Communication Events

These events cover messages flowing to and from the agent, as well as any custom events you emit yourself using the `@EventEmitter` annotation.

| Event               | When                                     |
| ------------------- | ---------------------------------------- |
| `MessageEvent`      | Agent sends or receives a message        |
| `EventEmitterEvent` | Custom event emitted via `@EventEmitter` |

### Error Events

When something goes wrong during a run, these events let you react immediately. `ErrorEvent` signals a hard failure, while `WarningEvent` signals a recoverable issue the agent can continue past.

| Event          | When                              |
| -------------- | --------------------------------- |
| `ErrorEvent`   | An error occurs during processing |
| `WarningEvent` | A non-fatal issue is detected     |

## Event Publisher

If you need to fire your own events from inside agent roles or custom logic, grab the publisher from the agent and call `publish`. Any registered handler will receive your custom event just like a built-in one.

```java
TnsAIEventPublisher publisher = agent.getEventPublisher();
publisher.publish(new CustomEvent("data"));
```

## Event Handler Registration

Instead of handling all events in one big switch block, you can register a handler for a single event type. This is useful for focused concerns like metrics collection or audit logging.

```java
EventHandlerRegistry registry = agent.getEventHandlerRegistry();
registry.register(ToolCallStartEvent.class, event -> {
    metrics.increment("tool.calls");
});
```

## Annotation-Based Handlers

For the cleanest approach, annotate a method with `@EventHandler` and TnsAI will wire it up automatically. No manual registry calls needed -- just declare the event type you want and write your logic.

```java
@EventHandler(ToolCallEndEvent.class)
public void onToolComplete(ToolCallEndEvent event) {
    log.info("Tool {} took {}ms", event.toolName(), event.duration());
}
```

---

# Fundamentals

URL: https://tnsai.dev/docs/agents/fundamentals
Description: The core moving parts of a single agent. This page covers the Agent class itself — construction, chat, memory, lifecycle. See the other pages in this section for Roles, the Action System, Capabilities, and Events.

import { Callout } from 'fumadocs-ui/components/callout'

An `Agent` is the top-level orchestrator in TnsAI. It owns an LLM client, one or more roles, a memory store, and an event system. Agents handle the full chat loop: receiving a message, consulting their roles for available actions, calling the LLM, executing tool calls, and returning a response.

## Quick Start

The fastest way to create an agent is with `AgentBuilder`:

```java
Agent agent = AgentBuilder.create()
    .llm(LLMClientFactory.create("openai", "gpt-4o", 0.7f))
    .role(RoleBuilder.create()
        .name("Assistant")
        .goal("Help users with their questions")
        .build())
    .build();

String response = agent.chat("What is BDI architecture?");
```

For more control, extend the `Agent` class directly:

```java
@AgentSpec(name = "ResearchAgent", description = "Conducts research")
public class ResearchAgent extends Agent {

    @Override
    protected LLMClient getLLM() {
        return LLMClientFactory.create("anthropic", "claude-sonnet-4-20250514", 0.7f);
    }

    @Override
    protected List<Role> getRoles() {
        return List.of(Role.create(ResearchRole.class));
    }
}
```

## Creating Agents

There are two ways to create an agent: programmatically with `AgentBuilder`, or declaratively by extending the `Agent` class and using annotations. Use the builder when you want quick, inline setup. Use annotations when you want a reusable agent class with its configuration baked in.

### With AgentBuilder (programmatic)

`AgentBuilder` lets you configure an agent in a single fluent chain. This is the best approach for simple agents or when you want to assemble an agent dynamically at runtime.

```java
Agent agent = AgentBuilder.create()
    .id("agent-001")
    .llm(new OpenAIClient("gpt-4o"))
    .role(myRole)
    .roles(List.of(role1, role2))
    .builtInTools(BuiltInTool.WEB_SEARCH_TOOLS, BuiltInTool.UTILITY_TOOLS)
    .toolPojos(new MyDomainTools())
    .memoryStore(new InMemoryStore())
    .maxContextTokens(8192)
    .build();
```

### With Annotations (declarative)

If you prefer a class-per-agent design, extend `Agent` and use `@AgentSpec` and `@LLMSpec` annotations. This keeps configuration next to the code and makes agents easy to discover in your project.

```java
@AgentSpec(name = "Analyst", description = "Data analysis agent")
@LLMSpec(provider = "openai", model = "gpt-4o", temperature = 0.3f)
public class AnalystAgent extends Agent {

    @Override
    protected List<Role> getRoles() {
        return List.of(Role.create(AnalystRole.class));
    }
}
```

## Chat Methods

Once you have an agent, you interact with it through chat methods. TnsAI provides several variants depending on whether you need conversation history, streaming output, or visibility into tool calls happening inside the agent loop.

```java
// Simple chat — single turn, uses conversation history
String response = agent.chat("Explain quantum computing");

// Chat without history
String response = agent.chat("Translate this to French", false);

// Streaming — returns tokens as they arrive
Stream<String> tokens = agent.streamChat("Write a poem about Java");
tokens.forEach(System.out::print);

// Event-driven chat — full visibility into the agent loop
String response = agent.chatWithEvents("Research AI safety", event -> {
    switch (event) {
        case ToolCallStartEvent e -> System.out.println("Calling: " + e.toolName());
        case ToolCallEndEvent e -> System.out.println("Result: " + e.result());
        case ErrorEvent e -> System.err.println("Error: " + e.message());
        default -> {}
    }
});
```

## Memory Management

Agents automatically track conversation history so the LLM has context across turns. You can also inspect, modify, or prune this history directly when you need to manage token usage or reset a conversation.

```java
// Get conversation history
List<Map<String, Object>> history = agent.getConversationHistory();

// Clear all history
agent.clearHistory();

// Add a message manually
agent.addToHistory("user", "Remember this context");

// Prune memory to fit within a token limit (removes oldest messages first)
agent.getMemoryStore().prune(4096);
```

## Lifecycle

Agents have a start/stop lifecycle. Call `start()` to initialize the agent and `shutdown()` to release its resources. You can check whether an agent is active or inspect its health state at any time.

```java
agent.start();
boolean running = agent.isRunning();
AgentHealthState health = agent.getHealthState();
agent.shutdown();
```

## Configuration Summary

This table lists every property you can set on an agent through `AgentBuilder`. Only `llm` and at least one `role` are required; everything else has sensible defaults.

| Property          | Builder Method                    | Default          | Description                                                      |
| ----------------- | --------------------------------- | ---------------- | ---------------------------------------------------------------- |
| ID                | `.id(String)`                     | Auto-generated   | Unique agent identifier                                          |
| LLM               | `.llm(LLMClient)`                 | Optional         | Language model client (omit for traditional agents)              |
| Roles             | `.role(Role)`                     | Required         | Agent roles                                                      |
| Built-in toolkits | `.builtInTools(BuiltInTool...)`   | Empty            | Shipped POJO toolkits from `tnsai-tools`                         |
| Custom toolkits   | `.toolPojos(Object...)`           | Empty            | Your own POJOs with `@Tool` methods                              |
| Runtime tools     | `.dynamicTool(DynamicToolMethod)` | Empty            | Tools whose identity is only known at runtime (e.g. MCP proxies) |
| Memory            | `.memoryStore(MemoryStore)`       | `InMemoryStore`  | Conversation memory                                              |
| Context limit     | `.maxContextTokens(int)`          | Provider default | Max context window                                               |
| Knowledge base    | `.knowledgeBase(KnowledgeBase)`   | None             | RAG knowledge source                                             |
| Prompt strategy   | `.promptStrategy(PromptStrategy)` | Default          | Prompt enhancement                                               |
| Reasoning         | `.reasoningStrategy(String)`      | None             | Reasoning strategy name                                          |

## SPI Extension Points

The Core module defines SPI interfaces that other modules implement. Extensions are discovered automatically via `ServiceLoader`:

| SPI Interface          | Purpose                            | Implementing Module |
| ---------------------- | ---------------------------------- | ------------------- |
| `MessageBroker`        | Agent communication routing        | Coordination        |
| `ResilienceStrategy`   | Resilience pattern implementations | Quality             |
| `CognitiveModel`       | Cognitive processing models        | Intelligence        |
| `CheckpointerFactory`  | State checkpointing                | Custom              |
| `CheckpointerProvider` | Checkpoint storage backends        | Custom              |

Register an SPI implementation by adding a file to `META-INF/services/`:

```
# META-INF/services/com.tnsai.spi.MessageBroker
com.example.MyCustomMessageBroker
```

## Next in this Section

- [Roles](/docs/agents/fundamentals/roles) — Bundling capabilities into `Role` classes.
- [Action System](/docs/agents/fundamentals/action-system) — `@ActionSpec` routing and executor types.
- [Capabilities](/docs/agents/fundamentals/capabilities) — Reusable body-less action contracts via `@Capability` interfaces.
- [Events](/docs/agents/fundamentals/events) — The agent's lifecycle event bus.

---

# Roles

URL: https://tnsai.dev/docs/agents/fundamentals/roles
Description: A Role defines what an agent can do. Each role has an identity (name, goal, domain), a set of responsibilities, and discoverable actions. Roles generate the system prompt that instructs the LLM. Actions are methods annotated with @ActionSpec — they are discovered at runtime via reflection and routed to one of four executor types.

import { Callout } from 'fumadocs-ui/components/callout'

## Creating Roles

There are two ways to create a role: programmatically with `RoleBuilder`, or declaratively with annotations. Pick whichever style fits your project -- they produce the same result.

### With RoleBuilder (programmatic)

Use `RoleBuilder` when you want to define a role inline -- for example in tests, scripts, or when the role configuration is loaded dynamically at runtime.

```java
Role role = RoleBuilder.create()
    .name("Researcher")
    .goal("Find and synthesize information from academic sources")
    .domain("academic-research")
    .duty("Search papers", "Users need access to recent research")
    .duty("Summarize findings")
    .mustNever("Fabricate citations", "Academic integrity")
    .mustAlways("Include source references", "Traceability")
    .llm(new AnthropicClient("claude-sonnet-4-20250514"))
    .build();
```

### With Annotations (declarative)

Use `@RoleSpec` when you want the role definition to live directly on the class. This is the preferred approach for production roles because everything -- name, capabilities, LLM config -- is visible at a glance.

```java
@RoleSpec(
    name = "Researcher",
    description = "Finds and synthesizes academic information",
    beliefs = {"research_context", "available_sources"},
    desires = {"find_papers", "synthesize_findings"},
    intentions = {"search_database", "analyze_paper"},
    capabilities = {"search", "analysis", "summarization"},
    domains = {"academic", "research"},
    responsibilities = {
        @Responsibility(
            name = "Paper Search",
            description = "Search academic databases",
            actions = {"searchPapers", "filterResults"}
        )
    },
    llm = @LLMConfig(provider = "anthropic", model = "claude-sonnet-4-20250514")
)
public class ResearchRole extends Role {

    @Override
    public RoleIdentity getIdentity() {
        return new RoleIdentity("Researcher", "Find papers", "academic");
    }

    @Override
    public List<Responsibility> getResponsibilities() {
        return List.of(
            new CoreDuty("Search papers", "Find relevant research"),
            new CoreDuty("Analyze findings", "Extract key insights")
        );
    }
}
```

## Actions

An `Action` is a method annotated with `@ActionSpec` on a Role class. Actions are routed to one of four executor types based on their `ActionType`:

| Type          | Executor              | Description                                                                |
| ------------- | --------------------- | -------------------------------------------------------------------------- |
| `LOCAL`       | `TypedActionExecutor` | Direct method invocation via reflection                                    |
| `WEB_SERVICE` | `WebServiceExecutor`  | HTTP REST API calls                                                        |
| `LLM`         | `LLMRoleExecutor`     | Single-shot LLM call; tool dispatch via the agent's `ToolMethodDispatcher` |
| `MCP_TOOL`    | `McpToolExecutor`     | Model Context Protocol tools                                               |

### Defining Actions

Annotate any method on your Role class with `@ActionSpec` to expose it as an action. The `type` field tells the framework which executor handles the call.

```java
@ActionSpec(
    name = "searchPapers",
    description = "Search for academic papers on a topic",
    type = ActionType.LLM
)
public String searchPapers(@LLMParam("The search query") String query) {
    // Implementation
}
```

## ActionResult

When an action delegates to an external system (HTTP call, LLM tool, MCP tool), the framework executes the call and makes the raw result available as an `ActionResult`. There are two usage patterns:

### Pure Delegate (Abstract, No Body)

If the method has no body (abstract or the framework handles it entirely), the framework executes the action and returns the result directly. No `ActionResult` parameter is needed:

```java
@ActionSpec(
    type = ActionType.WEB_SERVICE,
    endpoint = "https://api.example.com/users/{id}"
)
public abstract Object getUser(String id);
```

### Post-Process with ActionResult

Add an `ActionResult` parameter to receive the raw execution result and transform it before returning:

```java
@ActionSpec(
    type = ActionType.WEB_SERVICE,
    endpoint = "https://api.example.com/data/{id}"
)
public Object getData(String id, ActionResult result) {
    // Return as-is
    return result;

    // Or extract a field
    Map<String, Object> json = result.asMap();
    return json.get("name");
}
```

### ActionResult API

`ActionResult` wraps the raw value returned by the external system and provides convenience methods for common conversions like JSON parsing and type deserialization.

| Method         | Return type           | Description                                               |
| -------------- | --------------------- | --------------------------------------------------------- |
| `getValue()`   | `Object`              | Raw result value                                          |
| `asString()`   | `String`              | Value as String (JSON-serialized if not already a String) |
| `asMap()`      | `Map<String, Object>` | Value as Map (parsed from JSON if needed)                 |
| `asList()`     | `List<Object>`        | Value as List (parsed from JSON if needed)                |
| `asJson()`     | `JsonNode`            | Jackson `JsonNode` for flexible JSON traversal            |
| `as(Class<T>)` | `T`                   | Deserialize to a specific type                            |
| `isNull()`     | `boolean`             | True if the underlying value is null                      |
| `isEmpty()`    | `boolean`             | True if null, empty String, empty Map, or empty List      |

### Example -- Transforming a Web Service Response

This example shows a common pattern: calling a weather API and reshaping the JSON response into a human-readable string before returning it to the agent.

```java
@ActionSpec(
    type = ActionType.WEB_SERVICE,
    endpoint = "https://api.weather.com/forecast/{city}"
)
public String getForecast(String city, ActionResult result) {
    JsonNode json = result.asJson();
    String temp = json.path("main").path("temp").asText();
    String desc = json.path("weather").get(0).path("description").asText();
    return String.format("Temperature: %s, Conditions: %s", temp, desc);
}
```

## Role Accessors

Once you have a `Role` instance, these methods let you inspect its identity, actions, safety constraints, and generated system prompt. This is useful for debugging, logging, or building tooling around roles.

```java
RoleIdentity identity = role.identity();
String name = role.getName();
String goal = role.getGoal();
String domain = role.getDomain();

List<ActionMetadata> actions = role.getActions();
int count = role.getActionCount();
boolean has = role.hasAction("searchPapers");
Optional<ActionMetadata> action = role.getAction("searchPapers");

List<SafetyProperty> mustNever = role.getMustNeverConstraints();
List<SafetyProperty> mustAlways = role.getMustAlwaysConstraints();

String systemPrompt = role.getSystemPrompt();
String minimalPrompt = role.getMinimalPrompt();
```

## BDI Model

TnsAI implements the Belief-Desire-Intention (BDI) architecture for agent reasoning:

```java
Agent agent = AgentBuilder.create()
    .llm(llm)
    .role(role)
    .identity(new AgentIdentity("ResearchBot", "AI Research Assistant"))
    .belief(new Belief("domain", "artificial intelligence"))
    .belief(new Belief("max_papers", 10))
    .desire(new Desire("find_papers", "Locate relevant research papers"))
    .desire(new Desire("synthesize", "Create comprehensive summaries"))
    .intention(new Intention("search", "Search academic databases"))
    .intention(new Intention("analyze", "Analyze paper contents"))
    .capability(new Capability("search", "Academic database search"))
    .capability(new Capability("summarization", "Text summarization"))
    .build();
```

| Concept        | Class        | Purpose                                      |
| -------------- | ------------ | -------------------------------------------- |
| **Belief**     | `Belief`     | What the agent knows (key-value pairs)       |
| **Desire**     | `Desire`     | What the agent wants to achieve (goals)      |
| **Intention**  | `Intention`  | How the agent plans to act (committed plans) |
| **Capability** | `Capability` | What the agent can do (skills)               |
| **Plan**       | `Plan`       | Structured plan for achieving desires        |

---

# Agents

URL: https://tnsai.dev/docs/agents
Description: Everything about building a single agent — from the first Agent instance to advanced cognitive composition.

import { Callout } from 'fumadocs-ui/components/callout'

## Sections

- [Fundamentals](/docs/agents/fundamentals) — Agents, roles, actions, and the event bus. Start here.
- [Behavior](/docs/agents/behavior) — Prompts, output parsing, streaming, memory, variants.
- [Reliability](/docs/agents/reliability) — Resilience, error handling, schema identity.
- [Advanced](/docs/agents/advanced) — Cognitive support, planner/reasoner handles, ensemble execution.

## Related

- [Multi-Agent](/docs/multi-agent) — When one agent isn't enough.
- [Capabilities](/docs/capabilities) — Tools, intelligence, RAG, LLM.

---

# Error Handling

URL: https://tnsai.dev/docs/agents/reliability/error-handling
Description: TnsAI.Core provides a structured exception hierarchy rooted in TnsAIException. Every exception carries an error code, retryability flag, and suggested retry parameters, enabling automated recovery decisions across the framework.

import { Callout } from 'fumadocs-ui/components/callout'

## TnsAIException (Base Class)

All TnsAI exceptions extend `TnsAIException`, which itself extends `RuntimeException` (unchecked).

```java
public class TnsAIException extends RuntimeException {
    public boolean isRetryable();
    public String getErrorCode();
    public long getSuggestedRetryDelayMs();
    public int getMaxRetryAttempts();
}
```

| Method                       | Description                                                                                      |
| ---------------------------- | ------------------------------------------------------------------------------------------------ |
| `isRetryable()`              | `true` for transient errors (network, rate limits, server errors)                                |
| `getErrorCode()`             | Auto-derived code in format `TNSAI-CLASSNAME` (e.g., `TNSAI-NETWORK`, `TNSAI-RATELIMIT`)         |
| `getSuggestedRetryDelayMs()` | Default `1000ms` for retryable, `0` for non-retryable. Subclasses override with specific delays. |
| `getMaxRetryAttempts()`      | Default `3` for retryable, `0` for non-retryable                                                 |

The error code is derived from the class name using `Locale.ROOT`:

```java
// TnsAIException -> "TNSAI-TNSAI"
// LLMException   -> "TNSAI-LLM"
// NetworkException -> "TNSAI-NETWORK"
```

## LLMException

When something goes wrong during a call to an LLM provider (invalid API key, context too long, server outage), the framework throws an `LLMException`. Each exception carries an `ErrorType` that tells you exactly what happened and whether it makes sense to retry.

```java
public class LLMException extends TnsAIException {
    public String getModel();
    public ErrorType getErrorType();
    public long getSuggestedRetryDelayMs();
}
```

### ErrorType Enum

The `ErrorType` enum classifies the root cause of an LLM failure. Use it to decide how your application should react -- for example, retrying a transient `SERVER_ERROR` but surfacing a permanent `AUTHENTICATION_FAILED` to the user.

| Value                   | Retryable | Description                                        |
| ----------------------- | --------- | -------------------------------------------------- |
| `MODEL_NOT_FOUND`       | No        | Model not found or unavailable                     |
| `AUTHENTICATION_FAILED` | No        | Invalid API key or auth failure                    |
| `CONTENT_FILTERED`      | No        | Content policy violation                           |
| `INVALID_REQUEST`       | No        | Invalid request format                             |
| `MODEL_OVERLOADED`      | Yes       | Model overloaded, try again                        |
| `CONTEXT_TOO_LONG`      | No        | Context length exceeded                            |
| `SERVER_ERROR`          | Yes       | Generic server error                               |
| `MALFORMED_TOOL_CALL`   | No        | Bad JSON or missing fields in tool call from model |
| `CAPABILITY_MISMATCH`   | No        | Model does not support a required capability       |
| `UNKNOWN`               | Yes       | Unknown error                                      |

Retry delays are error-type specific:

- `MODEL_OVERLOADED` -- 5000ms
- `SERVER_ERROR` -- 2000ms
- All others -- 1000ms

### Factory Methods

Instead of calling the constructor directly, use these static factory methods to create `LLMException` instances with the correct `ErrorType` and retry parameters already set.

```java
LLMException.modelNotFound("gpt-5")
LLMException.authenticationFailed("claude-sonnet-4", "Invalid API key")
LLMException.contentFiltered("gpt-4o", "Violates content policy")
LLMException.contextTooLong("gpt-4o-mini", 128000, 150000)
LLMException.modelOverloaded("claude-sonnet-4")
LLMException.serverError("gemini-2.5-flash", cause)
LLMException.malformedToolCall("gpt-4o", "search", "Invalid JSON in arguments", cause)
LLMException.malformedToolCall("gpt-4o", "search", "Missing required field 'query'")
```

## RateLimitException

LLM providers enforce request quotas and return HTTP 429 ("Too Many Requests") when you exceed them. `RateLimitException` wraps these responses and is always retryable, carrying the provider-suggested wait time so your code can back off automatically.

```java
public class RateLimitException extends TnsAIException {
    public Long getRetryAfterMs();
    public String getService();
    public Integer getRemainingQuota();
    public long getSuggestedRetryDelayMs();  // Uses retryAfterMs, defaults to 60000ms
    public int getMaxRetryAttempts();        // Returns 5
}
```

### Factory Methods

These factory methods create `RateLimitException` instances from common rate-limit scenarios, automatically setting the correct retry delay and max retry count.

```java
// Parse HTTP 429 Retry-After header (seconds -> ms conversion)
RateLimitException.fromHttp429("openai", "30")

// LLM quota exceeded (defaults to 300000ms / 5 minutes)
RateLimitException.llmQuotaExceeded("claude-sonnet-4")

// API endpoint rate limit with explicit delay
RateLimitException.apiRateLimit("/api/v1/chat", 10000L)
```

## ActionExecutionException

When an agent action fails at runtime (a web service call times out, a parameter is missing, an MCP tool returns an error), the framework throws an `ActionExecutionException`. It includes the action name, type, and error category so you can programmatically decide whether to retry, fix parameters, or escalate.

```java
public class ActionExecutionException extends TnsAIException {
    public String getActionName();
    public ActionType getActionType();
    public ErrorCategory getCategory();
    public String getDetailedMessage();
}
```

### ErrorCategory Enum

Each `ActionExecutionException` is tagged with an `ErrorCategory` that groups the failure by root cause. This makes it straightforward to write a `switch` block that handles transient network errors differently from permanent validation errors.

| Category       | Retryable (default) | Description                     |
| -------------- | ------------------- | ------------------------------- |
| `NETWORK`      | Yes                 | Connection timeout, DNS failure |
| `PARAMETER`    | No                  | Missing parameter, wrong type   |
| `CLIENT_ERROR` | No                  | HTTP 4xx status codes           |
| `SERVER_ERROR` | Yes                 | HTTP 5xx status codes           |
| `VALIDATION`   | No                  | Contract violations             |
| `LLM`          | Yes                 | Model errors, quota exceeded    |
| `MCP`          | Yes                 | MCP tool errors                 |
| `INVOCATION`   | No                  | Reflection, method not found    |
| `UNKNOWN`      | No                  | Unclassified errors             |

The `getDetailedMessage()` method produces a structured log line:

```
[WEB_SERVICE] Action 'fetchWeather' failed: Network error | Category: Network error | Retryable: true | Cause: SocketTimeoutException (Connect timed out)
```

### Factory Methods

Use these static factories to create `ActionExecutionException` instances with the correct category, retryability flag, and detailed message already populated.

```java
ActionExecutionException.fromNetworkError("fetchWeather", ActionType.WEB_SERVICE, ioException)
ActionExecutionException.fromParameterError("search", ActionType.LOCAL, "query is required", cause)
ActionExecutionException.fromApiError("createIssue", ActionType.WEB_SERVICE, 503, "Service Unavailable", cause)
ActionExecutionException.fromLLMError("summarize", ActionType.LLM, llmException)
ActionExecutionException.fromMCPError("mcp-tool-name", mcpException)
ActionExecutionException.fromInvocationError("calculate", ActionType.LOCAL, reflectionException)
```

## Other Exceptions

Beyond the main exception types above, TnsAI provides several specialized exceptions for network failures, timeouts, validation errors, capability mismatches, and control-flow signals. The table below summarizes their retry behavior and key fields.

| Exception                       | Retryable | Retry Delay              | Max Retries | Key Fields                                 |
| ------------------------------- | --------- | ------------------------ | ----------- | ------------------------------------------ |
| `NetworkException`              | Yes       | 2000ms                   | 5           | `host`, `port`                             |
| `TimeoutException`              | Yes       | `min(timeoutMs/2, 5000)` | 3           | `timeoutMs`, `operation`                   |
| `ValidationException`           | No        | --                       | --          | --                                         |
| `ApprovalRequiredException`     | No        | --                       | --          | `actionName`, `reason`                     |
| `TaskCompleteException`         | No        | --                       | --          | `summary`, `result`, `success`, `metadata` |
| `LLMCapabilityException`        | No        | --                       | --          | `provider`, `capability`                   |
| `ToolCallNotSupportedException` | No        | --                       | --          | `model`, `provider`                        |

### NetworkException Factories

Create `NetworkException` instances for common connectivity failures like refused connections, DNS resolution problems, and connection timeouts.

```java
NetworkException.connectionRefused("api.example.com", 443)
NetworkException.dnsResolutionFailed("api.example.com", cause)
NetworkException.connectionTimeout("api.example.com", 443, 5000L)
```

### TimeoutException Factories

Create `TimeoutException` instances for operations that exceed their time budget, whether that is an LLM call, an HTTP request, or an action execution.

```java
TimeoutException.llmTimeout(30000L)
TimeoutException.httpTimeout("https://api.example.com/v1/chat", 10000L)
TimeoutException.actionTimeout("fetchData", 5000L)
```

### LLMCapabilityException Factories

Thrown when you request a feature (streaming, vision, structured output) that the selected model or provider does not support. These are never retryable because the model simply lacks the capability.

```java
LLMCapabilityException.streamingNotSupported("phi", "Ollama")
LLMCapabilityException.visionNotSupported("gpt-3.5-turbo", "OpenAI")
LLMCapabilityException.structuredOutputNotSupported("llama-2", "Ollama")
```

### TaskCompleteException (Control Flow)

`TaskCompleteException` is not an error -- it is a control flow signal used to indicate that a task has been completed and the agent loop should terminate.

```java
// Simple completion
throw new TaskCompleteException("Analysis complete");

// With result data
throw TaskCompleteException.withResult("Task done", Map.of("filesCreated", 5));

// Failed completion
throw TaskCompleteException.failed("Could not complete", "API unavailable");

// With metadata
throw TaskCompleteException.withMetadata("Done", Map.of("duration", "45s"));
```

Handling in the agent loop:

```java
try {
    agent.run(task);
} catch (TaskCompleteException e) {
    System.out.println("Summary: " + e.getSummary());
    System.out.println("Success: " + e.isSuccess());
    if (e.hasResult()) {
        MyResult result = e.getResultAs(MyResult.class);
    }
}
```

## Code Examples

These examples show common patterns for handling TnsAI exceptions in your application code.

### Catching and Classifying Errors

The recommended approach is to catch exceptions from most specific to least specific. This lets you handle rate limits, LLM-specific errors, and generic TnsAI errors each in the most appropriate way.

```java
try {
    String response = agent.chat("Analyze this data");
} catch (RateLimitException e) {
    // Wait for the provider-specified delay
    Thread.sleep(e.getSuggestedRetryDelayMs());
    // Retry...
} catch (LLMException e) {
    if (e.getErrorType() == LLMException.ErrorType.CONTEXT_TOO_LONG) {
        // Truncate context and retry
    } else if (e.isRetryable()) {
        // Retry with backoff
    } else {
        // Log and fail
        logger.error("LLM error [{}]: {}", e.getErrorCode(), e.getMessage());
    }
} catch (TnsAIException e) {
    if (e.isRetryable()) {
        logger.warn("Retryable error [{}], retrying in {}ms",
            e.getErrorCode(), e.getSuggestedRetryDelayMs());
    } else {
        logger.error("Non-retryable error [{}]: {}", e.getErrorCode(), e.getMessage());
    }
}
```

### Handling Action Execution Errors

When an action fails, you can use the error category to decide on recovery. Transient errors (network, server) can be retried with backoff, while parameter or validation errors need to be fixed before retrying.

```java
try {
    executor.execute(action, params);
} catch (ActionExecutionException e) {
    logger.error(e.getDetailedMessage());

    switch (e.getCategory()) {
        case NETWORK, SERVER_ERROR -> {
            // Transient -- retry with backoff
            Thread.sleep(e.getSuggestedRetryDelayMs());
        }
        case PARAMETER, VALIDATION -> {
            // Fix parameters and retry
            logger.warn("Fix parameters for action: {}", e.getActionName());
        }
        case LLM -> {
            // Check nested LLMException for details
            if (e.getCause() instanceof LLMException llm) {
                logger.warn("LLM error type: {}", llm.getErrorType());
            }
        }
        default -> throw e;
    }
}
```

### Using Error Codes for Monitoring

Every `TnsAIException` carries a stable error code (like `TNSAI-NETWORK` or `TNSAI-RATELIMIT`) that you can use as a metric tag in your monitoring system. This example shows how to increment a counter on each failure for dashboards and alerting.

```java
try {
    agent.chat("query");
} catch (TnsAIException e) {
    metrics.counter("tnsai.errors",
        "code", e.getErrorCode(),
        "retryable", String.valueOf(e.isRetryable())
    ).increment();
    throw e;
}
```

---

# Reliability

URL: https://tnsai.dev/docs/agents/reliability
Description: Making agents survive the real world.

import { Callout } from 'fumadocs-ui/components/callout'

## Pages

- [Resilience](/docs/agents/reliability/resilience) — Retry, fallback, health state, circuit breaker.
- [Error Handling](/docs/agents/reliability/error-handling) — Error types, propagation, recovery.
- [Long-Running Runs](/docs/agents/reliability/long-running-runs) — Checkpoint, resume, idempotent retry, runtime cost ceiling for hour-scale executions.
- [Schema Identity](/docs/agents/reliability/schema-identity) — Deterministic schema hashes across LLM providers.

---

# Long-Running Runs

URL: https://tnsai.dev/docs/agents/reliability/long-running-runs
Description: Multi-hour and multi-day agent executions need a categorically different runtime than minute-scale interactive sessions. Process crashes, runtime upgrades, transient API failures, and operator-initiated pauses all happen — the framework's reliability layer makes them survivable rather than catastrophic.

import { Callout } from 'fumadocs-ui/components/callout'

The `com.tnsai.reliability` package in `tnsai-core` ships the primitives:

- **`Checkpoint`** + **`CheckpointStore`** — durable state at step boundaries
- **`ResumableRun`** + **`DefaultResumableRun`** — execution driver that consumes the stores
- **`Progress`** + **`ProgressSink`** — observable mid-run state for live dashboards
- **`RunConfig`** — duration / retries / cost / progress-timeout policy bundle
- **`Outcome`** + **`AbortReason`** — terminal-result sealed type

Pairs with the [idempotency layer](/docs/capabilities/tools/idempotency) (`tnsai-core.idempotency`) so side-effecting steps don't double-execute on retry, and with [cost governance](/docs/security/cost-governance) so a runaway loop hits a configured ceiling instead of burning unbounded spend.

## When to use

You probably want this if your agent run:

- takes longer than the longest interactive session you'd let the user wait on (rule of thumb: \\> 5 minutes)
- calls external systems with non-trivial side effects (creates issues, posts messages, runs payments, kicks off CI builds)
- has a per-run budget worth enforcing during execution rather than after the fact
- needs to be inspectable from a separate process / dashboard while it's running

You probably don't need it for:

- single-LLM-call request handlers
- read-only research agents that always finish in seconds
- one-shot CLI utilities without resumption needs

## Quick start

```java
import com.tnsai.reliability.*;
import com.tnsai.idempotency.InMemoryIdempotencyStore;
import java.time.Duration;
import java.math.BigDecimal;

// 1. Pick stores. InMemory for tests; Filesystem / SQLite for persistence.
CheckpointStore checkpoints  = new FilesystemCheckpointStore(Path.of("/var/tns/checkpoints"));
var idempotency               = new InMemoryIdempotencyStore();
ProgressSink progress         = new InMemoryProgressSink();

// 2. Define a state codec — encode/decode whatever your run threads
//    through its steps. JSON is the recommended default; here we use UTF-8
//    bytes for a string state.
DefaultResumableRun.StateCodec<String> codec =
    new DefaultResumableRun.StateCodec<>() {
        public byte[] encode(String s) { return s.getBytes(StandardCharsets.UTF_8); }
        public String decode(byte[] b) { return new String(b, StandardCharsets.UTF_8); }
    };

// 3. Compose the run. Each step is a UnaryOperator<S> with a name and a
//    side-effecting flag. Side-effecting steps consult the idempotency
//    store so a retry doesn't double-execute.
DefaultResumableRun<String> run = DefaultResumableRun.<String>builder()
    .codec(codec)
    .checkpointStore(checkpoints)
    .idempotencyStore(idempotency)
    .progressSink(progress)
    .step(DefaultResumableRun.Step.pure("plan", state -> state + "\n[plan]"))
    .step(DefaultResumableRun.Step.sideEffect("post-pr",
            state -> { githubClient.createPr(...); return state + "\n[posted]"; }))
    .step(DefaultResumableRun.Step.pure("verify", state -> state + "\n[verified]"))
    .build();

// 4. Execute with a config bound by duration / retries / cost / progress timeout.
RunConfig config = RunConfig.builder()
    .maxDuration(Duration.ofHours(8))
    .costCeilingUSD(new BigDecimal("5.00"))
    .checkpointInterval(Duration.ofMinutes(5))
    .maxRetries(3)
    .progressTimeout(Duration.ofMinutes(10))
    .build();

Outcome<String> result = run.execute("start", config);
```

The orchestrator returns one of four [`Outcome`](#outcome-shape) variants when the run terminates.

## Outcome shape

`Outcome<T>` is a sealed interface — pattern-match exhaustively:

| Variant                | When                                                                                                                                    |
| ---------------------- | --------------------------------------------------------------------------------------------------------------------------------------- |
| `Outcome.Completed<T>` | Run reached the end of its step list                                                                                                    |
| `Outcome.Failed<T>`    | A step exhausted its retry budget; carries the throwable                                                                                |
| `Outcome.Aborted<T>`   | Policy stopped the run (`AbortReason`); user-requested, budget exhausted, max-duration exceeded, progress timeout, retry-limit exceeded |
| `Outcome.Suspended<T>` | Run yielded cleanly at a step boundary; resumable via the carried `checkpointId`                                                        |

```java
Outcome<String> result = run.execute(input, config);
String final = switch (result) {
    case Outcome.Completed<String> c -> c.result();
    case Outcome.Failed<String> f -> { logger.error("Failed: {}", f.reason()); yield ""; }
    case Outcome.Aborted<String> a -> { alert("Aborted: " + a.reason()); yield ""; }
    case Outcome.Suspended<String> s -> { /* resume later via run.resume(s.runId()) */ yield ""; }
};
```

## Resume after a crash

After a process crash, runtime upgrade, or `OOMError`, the run is recoverable as long as the configured `CheckpointStore` survives:

```java
// New process, same store backend.
DefaultResumableRun<String> run = /* ... same builder, same store paths ... */;

Outcome<String> result = run.resume(savedRunId);
// Resume loads the latest checkpoint, decodes the state via the codec,
// and continues from the next step.
```

If `run.resume(runId)` finds no checkpoint for the id, it returns `Outcome.Failed` with a clear "no such run" message rather than silently starting a fresh run — confusing one for the other was the bug pattern this primitive prevents.

## Side-effect safety: the kill-and-resume invariant

The unit test that defines the contract:

> A side-effecting step that runs once and crashes mid-execution must not run a second time on resume — its cached result is replayed instead.

Implementation: `DefaultResumableRun` keys side-effecting steps by `runId:stepIndex:stepName` against the configured `IdempotencyStore`. On the original run the orchestrator records the step's return value via `IdempotencyEntry.forSuccess(...)`; on resume the orchestrator checks the store before invoking the body. Cached hit → replay; miss → execute + record.

Concretely: a step that posts a GitHub PR, captured the PR id in its return state, then crashed before saving its checkpoint, will on resume:

- find the cached PR id in the idempotency store under `<runId>:<stepIndex>:post-pr`
- skip the body (no second `POST /pulls` to GitHub)
- thread the cached state forward into the next step

This is what makes hour-scale runs against rate-limited / cost-bearing external APIs feasible.

## CheckpointStore selection

| Store                       | When                                                                                                                                                                                                                                                      |
| --------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `InMemoryCheckpointStore`   | Tests, single-process workloads, "checkpointing-on" baseline. Doesn't survive restarts.                                                                                                                                                                   |
| `FilesystemCheckpointStore` | Single-process production. JSON files under `<root>/<runId>/<stepIndex>.json`. Atomic write-tmp-then-rename with `fsync`.                                                                                                                                 |
| `SqliteCheckpointStore`     | Personal-fleet workloads (one binary on a laptop / NAS). Single SQLite file, durable across restarts. Optional `xerial:sqlite-jdbc` dep — pull only when used.                                                                                            |
| `RedisCheckpointStore`      | Multi-process fleet sharing a Redis instance. Per-checkpoint blob + per-run sorted set for O(log N) latest / range queries. Reuses the existing Jedis dep (no new artefact).                                                                              |
| `S3CheckpointStore`         | Distributed deployments where checkpoints must outlive a single host — runs started on box A may resume on box B / region 2. JSON object per checkpoint at `s3://<bucket>/<keyPrefix><runId>/<stepIndex>.json`. Optional `software.amazon.awssdk:s3` dep. |

### Picking between distributed stores

| Concern             | Redis                        | S3                                               |
| ------------------- | ---------------------------- | ------------------------------------------------ |
| **Latency**         | sub-ms intra-region          | tens of ms (eventually-consistent regions: more) |
| **Durability**      | RDB / AOF — operator-tunable | 11 nines — managed                               |
| **Cost**            | proportional to memory + ops | proportional to storage + requests               |
| **Operations**      | needs a Redis cluster        | managed by AWS                                   |
| **Region affinity** | typically per-region cluster | cross-region replication available               |

Use Redis for fast resume cycles (operator-paused runs picked up within minutes); use S3 for archival / cross-region durability. The two are not mutually exclusive — a layered stack with a Redis hot path and an S3 cold path is a reasonable production shape, but ships as a consumer-side composite rather than a built-in.

## Cost ceiling enforcement

`DefaultResumableRun.Builder#costMonitor` is optional. Provide one and pair it with `RunConfig.costCeilingUSD` to make the orchestrator check spend before every step:

```java
// Wire to the cost-governance store from tnsai-quality.
CostBudgetStore costStore = ...; // your spend ledger
DefaultResumableRun.CostMonitor monitor = () ->
    Optional.of(costStore.currentSpend(
        CostScope.tenant(tenantId), Duration.ofHours(24)));

DefaultResumableRun<S> run = DefaultResumableRun.<S>builder()
    // ...
    .costMonitor(monitor)
    .build();

run.execute(input, RunConfig.builder()
    .costCeilingUSD(new BigDecimal("10.00"))   // $10 cap
    .build());
```

When `currentSpend ≥ ceiling`, the orchestrator:

1. Saves a final checkpoint at the current state
2. Publishes `Progress.RunAborted(BUDGET_EXHAUSTED)`
3. Returns `Outcome.Aborted`

The consumer can resume the run on a higher ceiling once it's available — the saved checkpoint preserves work-done-so-far.

## Progress events

`Progress` is a sealed interface with eight variants. Subscribe to a run's events for live dashboards or progress-timeout enforcement:

```java
ProgressSink.Subscription sub = sink.subscribe(runId, event -> {
    switch (event) {
        case Progress.StepStarted s   -> dashboard.markStepRunning(s.stepIndex());
        case Progress.StepCompleted s -> dashboard.markStepDone(s.stepIndex(), s.took());
        case Progress.CheckpointSaved c -> dashboard.checkpointAt(c.checkpointId());
        case Progress.CostUpdate c    -> dashboard.spendTick(c.spentUSD());
        case Progress.Heartbeat h     -> dashboard.heartbeat(h.currentStep());
        case Progress.RunCompleted r  -> dashboard.markDone();
        case Progress.RunFailed r     -> dashboard.markFailed(r.reason());
        case Progress.RunAborted r    -> dashboard.markAborted(r.reason());
    }
});
// Cancel when the dashboard tab closes.
sub.cancel();
```

`InMemoryProgressSink` is the default. `KafkaProgressSink` and similar fan-out adapters are tracked as follow-ups; the SPI accepts third-party impls today.

## RunConfig defaults

| Field                | Default | Notes                                                          |
| -------------------- | ------- | -------------------------------------------------------------- |
| `maxDuration`        | 8h      | Hard wall-clock cap; `MAX_DURATION_EXCEEDED` on breach         |
| `costCeilingUSD`     | empty   | Optional; without it, runtime cost-ceiling checks are skipped  |
| `checkpointInterval` | 5 min   | Caps worst-case replay window after a crash                    |
| `maxRetries`         | 3       | Per-step retry budget before `Outcome.Failed`                  |
| `progressTimeout`    | 10 min  | "stuck" detector — abort if no `Progress` event in this window |

Use `RunConfig.defaults()` for an overnight-run-friendly starting point, or `RunConfig.builder()` for finer control.

## Tool annotations

Tools that participate in resumable runs benefit from declaring their effect classification + idempotency expectation. Two annotations were added to `@Tool` for this:

```java
import com.tnsai.annotations.*;

public class GithubTools {

    @Tool(name = "github.create_pr",
          description = "Open a pull request on a GitHub repo",
          sideEffect = SideEffect.EXTERNAL,
          idempotencyHint = IdempotencyHint.REQUIRED)
    public PullRequest createPr(@ToolParam("title") String title, ...) { ... }

    @Tool(name = "github.list_branches",
          description = "List branches on a GitHub repo",
          sideEffect = SideEffect.READ)            // idempotencyHint defaults to NONE
    public List<Branch> listBranches(...) { ... }
}
```

`SideEffect`:

| Value      | Meaning                                                                |
| ---------- | ---------------------------------------------------------------------- |
| `NONE`     | Pure function. Default.                                                |
| `READ`     | Reads external state, doesn't mutate.                                  |
| `WRITE`    | Mutates state inside the framework or a system the framework controls. |
| `EXTERNAL` | Calls a third-party system whose effect we can't reverse.              |

`IdempotencyHint`:

| Value      | Meaning                                                                                                    |
| ---------- | ---------------------------------------------------------------------------------------------------------- |
| `NONE`     | No tracking. Default.                                                                                      |
| `OPTIONAL` | Track when caller supplies a key, otherwise dispatch unguarded.                                            |
| `REQUIRED` | Refuse to dispatch without an idempotency key. Reserve for payments / public posts / irreversible deletes. |

Both fields default to safe values so existing tools retain their current behaviour without modification — the annotations are purely additive metadata.

## What's not in this layer

Out of scope for the framework primitives, tracked separately:

- **Distributed transaction coordination** (sagas) — checkpointing is local; cross-system consistency is a different problem.
- **Run inspection UI** — the `ProgressSink` API is enough for now; UI is downstream.
- **Backfill for runs without checkpointing** — additive feature, not retroactive.
- **Harness execution-loop refactor** — the existing `AgentExecutor` will move onto `ResumableRun` as part of the harness work (TNS-291).

## See Also

- **[Idempotency](/docs/capabilities/tools/idempotency)** — the SPI consumed by side-effecting steps
- **[Cost Governance](/docs/security/cost-governance)** — the spend ledger the cost monitor wires into
- **[Resilience](/docs/agents/reliability/resilience)** — companion retry / fallback / circuit-breaker layer for individual operations
- **[Error Handling](/docs/agents/reliability/error-handling)** — how step exceptions surface and propagate

---

# Resilience

URL: https://tnsai.dev/docs/agents/reliability/resilience
Description: TnsAI.Core provides a declarative resilience framework built on top of Resilience4j. The @Resilience annotation configures retry, circuit breaker, rate limiting, bulkhead isolation, timeout, and fallback policies for actions and roles. The ResilienceExecutor applies these policies in a layered pipeline and tracks terminal failures in a dead-letter queue.

import { Callout } from 'fumadocs-ui/components/callout'

## @Resilience Annotation

The `@Resilience` annotation is the single entry point for declaring all resilience policies on an action or role. Apply it to a method to configure that specific action, or to a class to set defaults for all actions in the role. Method-level annotations override type-level ones.

```java
@Documented
@Retention(RetentionPolicy.RUNTIME)
@Target({ElementType.METHOD, ElementType.TYPE})
public @interface Resilience {
    Retry retry() default @Retry();
    CircuitBreaker circuitBreaker() default @CircuitBreaker();
    RateLimit rateLimit() default @RateLimit();
    int timeout() default 0;             // milliseconds, 0 = no timeout
    String fallback() default "";         // fallback method name (same signature)
    boolean bulkhead() default false;     // thread pool isolation
    int maxConcurrent() default 10;       // max concurrent calls for bulkhead
}
```

### @Retry

When a transient error occurs (network timeout, server 500), retrying after a short delay often succeeds. The `@Retry` sub-annotation configures automatic retries with exponential backoff (each retry waits longer) and optional jitter (randomized delays to avoid thundering herd problems).

```java
@interface Retry {
    int maxAttempts() default 0;                            // 0 = no retries
    int backoffMs() default 1000;                           // initial delay
    double multiplier() default 2.0;                        // backoff multiplier
    int maxBackoffMs() default 30000;                       // max delay cap
    Class<? extends Throwable>[] retryOn() default {};      // empty = all
    Class<? extends Throwable>[] noRetryOn() default {};    // exclusions
    boolean jitter() default true;                          // add randomization
}
```

Example:

```java
@Resilience(retry = @Retry(
    maxAttempts = 3,
    backoffMs = 500,
    multiplier = 2.0,
    maxBackoffMs = 10000,
    retryOn = {NetworkException.class, RateLimitException.class},
    jitter = true
))
public String fetchData(String query) { ... }
```

With the default `multiplier = 2.0` and `jitter = true`, delays are approximately: 500ms, 1000ms, 2000ms (with random jitter applied to each).

### @CircuitBreaker

If a downstream service is down, retrying every request wastes resources and can cascade failures across your system. A circuit breaker tracks failures and, after a threshold is reached, "opens" the circuit to reject all calls immediately. After a cooldown period, it allows a few test calls through ("half-open") to check if the service has recovered.

```java
@interface CircuitBreaker {
    boolean enabled() default false;
    int failureThreshold() default 5;        // failures before opening
    int failureWindowMs() default 60000;     // window for counting failures
    int resetTimeoutMs() default 30000;      // wait before half-open
    int successThreshold() default 3;        // successes needed in half-open
    int failureRateThreshold() default 0;    // percentage (0-100), alternative to count
}
```

Example:

```java
@Resilience(circuitBreaker = @CircuitBreaker(
    enabled = true,
    failureThreshold = 5,
    failureWindowMs = 60000,
    resetTimeoutMs = 30000,
    successThreshold = 3
))
public String callExternalApi() { ... }
```

States: **Closed** (normal) -\\> **Open** (reject all calls) -\\> **Half-Open** (allow `successThreshold` test calls) -\\> **Closed** (if tests pass).

### @RateLimit

Rate limiting prevents your application from overwhelming a downstream service or exceeding API quotas. The `@RateLimit` sub-annotation lets you cap the number of requests in a time window, with four different strategies for how that limit is enforced.

```java
@interface RateLimit {
    boolean enabled() default false;
    int maxRequests() default 100;
    int windowMs() default 60000;                        // 1 minute
    Strategy strategy() default Strategy.SLIDING_WINDOW;
}
```

## Rate Limit Strategies

The four strategies differ in how smoothly they distribute requests over time. Choose based on whether your use case tolerates bursts or needs strict even pacing.

| Strategy         | Description                                                                                      |
| ---------------- | ------------------------------------------------------------------------------------------------ |
| `FIXED_WINDOW`   | Counts requests in fixed time windows. Simple but can allow bursts at window boundaries.         |
| `SLIDING_WINDOW` | Counts requests in a sliding time window. Smoother rate control than fixed window.               |
| `TOKEN_BUCKET`   | Tokens are added at a fixed rate. Each request consumes one token. Allows controlled bursts.     |
| `LEAKY_BUCKET`   | Requests are processed at a fixed rate. Excess requests queue or are rejected. Smoothest output. |

Example:

```java
@Resilience(rateLimit = @RateLimit(
    enabled = true,
    maxRequests = 60,
    windowMs = 60000,
    strategy = RateLimit.Strategy.TOKEN_BUCKET
))
public String queryLlm(String prompt) { ... }
```

## Combining Policies

In production, you typically want multiple resilience layers working together. A single `@Resilience` annotation can configure retry, circuit breaker, rate limiting, timeout, bulkhead isolation, and a fallback method all at once.

```java
@Resilience(
    retry = @Retry(maxAttempts = 3, backoffMs = 1000),
    circuitBreaker = @CircuitBreaker(enabled = true, failureThreshold = 5, resetTimeoutMs = 30000),
    rateLimit = @RateLimit(enabled = true, maxRequests = 100, windowMs = 60000),
    timeout = 5000,
    bulkhead = true,
    maxConcurrent = 10,
    fallback = "fetchDataFallback"
)
public Result fetchData(String query) { ... }

// Fallback must have same signature
public Result fetchDataFallback(String query) {
    return Result.cached(query);
}
```

Type-level defaults apply to all actions in a role unless overridden:

```java
@Resilience(
    retry = @Retry(maxAttempts = 2),
    timeout = 10000
)
public class ApiRole extends Role {

    @Resilience(retry = @Retry(maxAttempts = 5))  // overrides type-level retry
    public String criticalCall() { ... }

    // Uses type-level defaults: 2 retries, 10s timeout
    public String normalCall() { ... }
}
```

## ResilienceExecutor

The `ResilienceExecutor` is the engine that applies all your resilience policies at runtime. It builds a layered pipeline where each policy wraps the next, using Resilience4j under the hood. You can also use it programmatically (without annotations) for ad-hoc resilient operations.

### Pipeline Order

Policies are applied in a specific order, from outermost to innermost. Rate limiting is checked first (to avoid unnecessary work), then bulkhead isolation, then the circuit breaker, and finally retries wrap the actual operation.

```
Rate Limit -> Bulkhead -> Circuit Breaker -> Retry -> Operation
```

Each layer wraps the next. When an operation fails and exhausts all resilience layers, the failure is recorded in the dead-letter queue.

### Construction

You can create a `ResilienceExecutor` with defaults, inject a custom dead-letter queue, or take full control over all Resilience4j registries.

```java
// Default registries + in-memory DLQ
ResilienceExecutor executor = new ResilienceExecutor();

// Custom dead-letter queue
ResilienceExecutor executor = new ResilienceExecutor(myDlq);

// Full control over all registries
ResilienceExecutor executor = new ResilienceExecutor(
    circuitBreakerRegistry,
    bulkheadRegistry,
    rateLimiterRegistry,
    deadLetterQueue
);
```

### Programmatic Usage with ResilienceRequest

When you need resilience for a one-off operation that is not tied to an annotated action method, use `ResilienceRequest` to define the operation and its policies inline.

```java
ResilienceExecutor executor = new ResilienceExecutor();

String result = executor.execute(
    ResilienceRequest.<String>builder()
        .operationId("fetch-weather")
        .operation(() -> httpClient.get("https://api.weather.com/current"))
        .retryPolicy(RetryPolicy.defaultPolicy())
        .rateLimit(60, 60000)           // 60 requests per minute
        .bulkhead(5)                    // max 5 concurrent calls
        .timeout(5000)                  // 5 second timeout
        .fallbackStrategy(ex -> "Weather data unavailable")
        .build()
);
```

### FallbackStrategy

When all retries are exhausted and the operation still fails, a fallback strategy provides a default value instead of throwing an exception. This is useful for returning cached data or a graceful degradation response.

```java
@FunctionalInterface
public interface FallbackStrategy<T> {
    T fallback(Exception exception);

    // Default: accepts all exceptions
    default boolean supports(Exception exception) { return true; }
}
```

Custom fallback with selective exception handling:

```java
FallbackStrategy<String> fallback = new FallbackStrategy<>() {
    @Override
    public String fallback(Exception exception) {
        return "Cached result";
    }

    @Override
    public boolean supports(Exception exception) {
        return exception instanceof NetworkException;
    }
};
```

### Exception Handling

When a resilience policy rejects a request (rather than the underlying operation failing), the executor catches these Resilience4j-specific exceptions so you know which layer blocked the call.

| Exception                   | Meaning                       |
| --------------------------- | ----------------------------- |
| `CallNotPermittedException` | Circuit breaker is open       |
| `BulkheadFullException`     | Max concurrent calls exceeded |
| `RequestNotPermitted`       | Rate limit exceeded           |
| `TimeoutException`          | Operation timed out           |

All failures are recorded in the dead-letter queue before being re-thrown.

## DeadLetterQueue

When an operation fails even after retries, circuit breaker bypass, and fallback attempts, the failure is not silently discarded. Instead, it is recorded in a `DeadLetterQueue` (DLQ) so you can monitor terminal failures, alert on them, or replay the operations later.

### Interface

The DLQ interface is intentionally simple: enqueue failed entries, and query them by operation ID or in bulk.

```java
public interface DeadLetterQueue {
    void enqueue(DeadLetterEntry entry);
    List<DeadLetterEntry> getEntries();
    List<DeadLetterEntry> getEntries(String operationId);
    int size();
}
```

### DeadLetterEntry

Each DLQ entry captures everything you need to understand and potentially replay a failed operation: which operation failed, what exception occurred, when it happened, and any additional context as metadata.

```java
DeadLetterEntry entry = DeadLetterEntry.builder()
    .operationId("fetch-weather")
    .exceptionType("NetworkException")
    .exceptionMessage("Connection timed out")
    .timestamp(Instant.now())
    .metadata(Map.of("host", "api.weather.com", "port", 443))
    .build();
```

Fields:

- `id` -- auto-generated UUID
- `operationId` -- identifies the operation that failed
- `exceptionType` -- exception class name
- `exceptionMessage` -- exception message
- `timestamp` -- when the failure occurred
- `metadata` -- additional context (immutable map)

### Accessing the DLQ

You can query the DLQ through the executor to get all failures, filter by operation ID, or check the total failure count.

```java
ResilienceExecutor executor = new ResilienceExecutor();

// After operations...
DeadLetterQueue dlq = executor.getDeadLetterQueue();

// All failures
List<DeadLetterEntry> allFailures = dlq.getEntries();

// Failures for a specific operation
List<DeadLetterEntry> weatherFailures = dlq.getEntries("fetch-weather");

// Total failure count
int failureCount = dlq.size();
```

The default implementation (`InMemoryDeadLetterQueue`) stores entries in memory. Implement the `DeadLetterQueue` interface for persistent storage (database, Redis, etc.) and pass it to the `ResilienceExecutor` constructor.

## Code Examples

These examples show resilience in practice, from a fully annotated action to DLQ monitoring.

### Action with Full Resilience

This example shows a real-world action with all resilience layers active: retries for network and rate-limit errors, a circuit breaker to stop calling a failing service, rate limiting to stay within API quotas, a timeout, and a fallback that returns cached data.

```java
@ActionSpec(type = ActionType.WEB_SERVICE, name = "fetchStockPrice")
@Resilience(
    retry = @Retry(
        maxAttempts = 3,
        backoffMs = 1000,
        multiplier = 2.0,
        retryOn = {NetworkException.class, RateLimitException.class}
    ),
    circuitBreaker = @CircuitBreaker(
        enabled = true,
        failureThreshold = 5,
        resetTimeoutMs = 30000
    ),
    rateLimit = @RateLimit(
        enabled = true,
        maxRequests = 120,
        windowMs = 60000,
        strategy = RateLimit.Strategy.SLIDING_WINDOW
    ),
    timeout = 10000,
    fallback = "fetchStockPriceFallback"
)
public double fetchStockPrice(String symbol) {
    return stockApi.getPrice(symbol);
}

public double fetchStockPriceFallback(String symbol) {
    return cacheStore.getLastKnownPrice(symbol);
}
```

### Monitoring Failures via DLQ

In production, you want to know when operations are permanently failing. This example sets up a periodic check that logs all DLQ entries, which you can hook into your alerting system.

```java
ResilienceExecutor executor = new ResilienceExecutor();

// Periodic monitoring
ScheduledExecutorService scheduler = Executors.newSingleThreadScheduledExecutor();
scheduler.scheduleAtFixedRate(() -> {
    DeadLetterQueue dlq = executor.getDeadLetterQueue();
    int count = dlq.size();
    if (count > 0) {
        logger.warn("Dead letter queue has {} entries", count);
        for (DeadLetterEntry entry : dlq.getEntries()) {
            logger.warn("  Failed: {} - {} at {}",
                entry.getOperationId(),
                entry.getExceptionType(),
                entry.getTimestamp());
        }
    }
}, 0, 1, TimeUnit.MINUTES);
```

---

# Schema, Identity, and Enums

URL: https://tnsai.dev/docs/agents/reliability/schema-identity
Description: TnsAI.Core provides typed schemas for LLM tool definitions, agent identity and communication style modeling, decentralized identifiers (DIDs), and a set of enums that control agent behavior.

import { Callout } from 'fumadocs-ui/components/callout'

## ToolDefinition

`com.tnsai.schema.ToolDefinition` is a typed record representing an LLM tool/function definition. It replaces untyped `Map<String, Object>` schemas with compile-time safety.

### Record Fields

```java
public record ToolDefinition(
    String name,            // required, non-blank
    String description,     // defaults to ""
    Map<String, Object> parameters,  // JSON Schema object, never null
    String endpoint,        // optional API endpoint, defaults to ""
    String apiKeyEnvVar     // optional env var name for API key
)
```

### Creating Definitions

```java
// Builder (recommended)
ToolDefinition tool = ToolDefinition.builder()
    .name("search_weather")
    .description("Get weather for a city")
    .addParameter("city", "string", "City name", true)
    .addParameter("unit", "string", "Temperature unit", false)
    .addEnumParameter("format", "Output format", List.of("json", "text"), false)
    .endpoint("https://api.weather.com/v1")
    .apiKeyEnvVar("WEATHER_API_KEY")
    .build();

// Simple (no parameters)
ToolDefinition simple = ToolDefinition.of("get_time", "Returns current UTC time");

// From OpenAI-format map
ToolDefinition parsed = ToolDefinition.fromMap(existingMap);
```

### Conversion

```java
// To OpenAI function calling format
Map<String, Object> map = tool.toMap();
// {"type": "function", "function": {"name": ..., "description": ..., "parameters": ...}}

// Batch conversions
List<ToolDefinition> defs = ToolDefinition.fromMaps(listOfMaps);
List<Map<String, Object>> maps = ToolDefinition.toMaps(listOfDefs);
```

### Query Methods

| Method                 | Return                | Description                         |
| ---------------------- | --------------------- | ----------------------------------- |
| `hasParameters()`      | `boolean`             | True if properties map is non-empty |
| `requiredParameters()` | `List<String>`        | Names of required parameters        |
| `properties()`         | `Map<String, Object>` | Parameter property schemas          |
| `requiresApiKey()`     | `boolean`             | True if `apiKeyEnvVar` is set       |

### Builder API

| Method                                                  | Description                      |
| ------------------------------------------------------- | -------------------------------- |
| `name(String)`                                          | Set tool name                    |
| `description(String)`                                   | Set description                  |
| `addParameter(name, type, description, required)`       | Add a parameter                  |
| `addEnumParameter(name, description, values, required)` | Add enum-constrained parameter   |
| `parameters(Map<String, Object>)`                       | Set raw parameters schema        |
| `endpoint(String)`                                      | Set API endpoint URL             |
| `apiKeyEnvVar(String)`                                  | Set API key environment variable |
| `noApiKeyRequired()`                                    | Clear API key requirement        |

## ToolSchemaGenerator

`com.tnsai.schema.ToolSchemaGenerator` generates tool/function schemas for LLM function calling from roles and tools.

### Generating Schemas

```java
ToolSchemaGenerator generator = new ToolSchemaGenerator();

// Typed ToolDefinition records (preferred)
List<ToolDefinition> defs = generator.generateToolDefinitions(roles);

// Single action
ToolDefinition def = generator.generateToolDefinition(action);

// From a standalone Tool
ToolDefinition def = generator.generateToolDefinition(tool);

// Legacy Map format
List<Map<String, Object>> schemas = generator.generateToolSchemas(roles);
Map<String, Object> schema = generator.generateToolSchema(action);

// Human-readable description for system prompts
String desc = generator.generateToolsDescription(roles);
```

### Configuration

```java
// Enable/disable example inclusion in schemas
generator.withExamples(true);
```

### Java-to-JSON-Schema Type Mapping

| Java Type                                         | JSON Schema Type                    |
| ------------------------------------------------- | ----------------------------------- |
| `String`, `char`, `Character`                     | `"string"`                          |
| `int`, `Integer`, `long`, `Long`, `short`, `byte` | `"integer"`                         |
| `double`, `Double`, `float`, `Float`              | `"number"`                          |
| `boolean`, `Boolean`                              | `"boolean"`                         |
| Arrays, `List`                                    | `"array"`                           |
| Enums                                             | `"string"` (with `enum` constraint) |
| Other                                             | `"object"`                          |

## AgentIdentity

`com.tnsai.models.agent.AgentIdentity` represents a personality trait or characteristic that shapes agent behavior. Identities are included in the system prompt to influence communication style.

```java
public final class AgentIdentity {
    private final String name;        // required, non-blank
    private final String description; // required, non-blank
}
```

### Usage

```java
AgentIdentity analytical = new AgentIdentity(
    "analytical",
    "Takes a data-driven approach to problem solving"
);

AgentIdentity empathetic = new AgentIdentity(
    "empathetic",
    "Shows understanding and consideration for user emotions"
);

// In an Agent subclass
@Override
protected List<AgentIdentity> getIdentities() {
    return List.of(
        new AgentIdentity("expert", "Deep knowledge in software engineering"),
        new AgentIdentity("patient", "Takes time to explain concepts clearly"),
        new AgentIdentity("thorough", "Considers all aspects before responding")
    );
}
```

### Common Identity Types

| Category   | Examples                                   |
| ---------- | ------------------------------------------ |
| Cognitive  | analytical, creative, logical, intuitive   |
| Social     | friendly, professional, empathetic, direct |
| Behavioral | proactive, thorough, efficient, cautious   |
| Domain     | expert, specialist, generalist             |

The class is immutable and thread-safe. It supports Jackson JSON serialization via `@JsonCreator`/`@JsonProperty`.

## Communication (Style)

`com.tnsai.models.agent.Communication` is a record that defines how an agent expresses itself.

```java
public record Communication(
    Tone tone,
    Formality formality,
    Verbosity verbosity
)
```

### Usage

```java
Communication style = new Communication(
    Tone.FRIENDLY,
    Formality.CASUAL,
    Verbosity.CONCISE
);

// Default style
Communication defaultStyle = Communication.defaultStyle();
// Tone.PROFESSIONAL, Formality.NEUTRAL, Verbosity.MODERATE

// Get description
String desc = style.getDescription();
// "Tone: Warm and approachable, Formality: ..., Verbosity: ..."

// Generate prompt section for LLM
String promptSection = style.generatePromptSection();
```

Null values default to: `Tone.PROFESSIONAL`, `Formality.NEUTRAL`, `Verbosity.MODERATE`.

## DID (Decentralized Identifier)

`com.tnsai.identity.DID` implements W3C DID Core for agent identification.

Format: `did:<method>:<method-specific-id>`

```java
// Parse from string
DID did = DID.parse("did:wba:example.com:agent-123");

// Create from components
DID did = new DID("wba", "example.com:agent-123");

// Factory methods
DID wba = DID.createWba("example.com", "agent-123");
// did:wba:example.com:agent-123

DID web = DID.createWeb("example.com");
// did:web:example.com
```

### Methods

| Method                  | Return    | Description                                  |
| ----------------------- | --------- | -------------------------------------------- |
| `getMethod()`           | `String`  | DID method (e.g., `"wba"`, `"web"`, `"key"`) |
| `getMethodSpecificId()` | `String`  | Method-specific identifier                   |
| `getDidString()`        | `String`  | Full DID string                              |
| `asString()`            | `String`  | Alias for `getDidString()`                   |
| `toURI()`               | `URI`     | DID as a `java.net.URI`                      |
| `isWba()`               | `boolean` | Check if `did:wba`                           |
| `isWeb()`               | `boolean` | Check if `did:web`                           |

Validation: method must be lowercase alphanumeric (`[a-z0-9]+`). Invalid format throws `IllegalArgumentException`.

## Core Enums

### ActionType

`com.tnsai.enums.ActionType` -- execution method for actions.

| Value         | Description                   |
| ------------- | ----------------------------- |
| `LOCAL`       | Direct Java method invocation |
| `WEB_SERVICE` | HTTP API calls                |
| `LLM`         | LLM with tool selection       |
| `MCP_TOOL`    | Model Context Protocol        |

### AgentVariant

`com.tnsai.enums.AgentVariant` -- quality/speed/cost tiers.

| Variant  | Quality  | Speed    | Cost    | Description            |
| -------- | -------- | -------- | ------- | ---------------------- |
| `HIGH`   | MAX      | SLOW     | HIGH    | Complex/critical tasks |
| `MEDIUM` | BALANCED | NORMAL   | MEDIUM  | Regular development    |
| `MINI`   | BASIC    | FAST     | LOW     | Quick fixes            |
| `AUTO`   | ADAPTIVE | ADAPTIVE | OPTIMAL | Task-based selection   |

```java
agent.setVariant(AgentVariant.HIGH);

// Auto-select based on task keywords
AgentVariant suggested = AgentVariant.forTask("Complex refactoring");  // HIGH
AgentVariant suggested = AgentVariant.forTask("Fix typo");             // MINI
```

Key methods: `isQualityFocused()`, `isSpeedFocused()`, `isCostOptimized()`, `forTask(String)`.

Sub-enums: `Quality` (MAX/BALANCED/BASIC/ADAPTIVE), `Speed` (SLOW/NORMAL/FAST/ADAPTIVE), `Cost` (HIGH/MEDIUM/LOW/OPTIMAL).

### Tone

`com.tnsai.enums.agent.Tone` -- communication tone.

| Value          | Description                     |
| -------------- | ------------------------------- |
| `ANALYTICAL`   | Analytical and logical          |
| `EMPATHETIC`   | Understanding and compassionate |
| `ASSERTIVE`    | Direct and confident            |
| `FRIENDLY`     | Warm and approachable           |
| `PROFESSIONAL` | Formal and business-like        |
| `CREATIVE`     | Imaginative and innovative      |

### Formality

`com.tnsai.enums.agent.Formality` -- language formality level.

### Verbosity

`com.tnsai.enums.agent.Verbosity` -- response length preference.

### AuthType

`com.tnsai.enums.AuthType` -- authentication types for web service actions: `NO_AUTH`, `BEARER`, `BASIC`.

### HttpMethod

`com.tnsai.enums.HttpMethod` -- HTTP methods: `GET`, `POST`, `PUT`, `PATCH`, `DELETE`, `HEAD`, `OPTIONS`.

## Related Documentation

- [Action System](/docs/agents/fundamentals/action-system) -- how ActionType routes to executors
- [Tools](/docs/capabilities/tools/registration) -- the Tool interface that ToolDefinition describes
- [Variants](/docs/agents/behavior/variants) -- detailed variant configuration
- [Advanced Agent Features](/docs/agents/advanced) -- how identities and variants are used
- [Roles](/docs/agents/fundamentals/roles) -- role-based action discovery with @ActionSpec

---

# Capabilities

URL: https://tnsai.dev/docs/capabilities
Description: Pluggable building blocks that give an agent power beyond raw LLM calls.

import { Callout } from 'fumadocs-ui/components/callout'

## Sections

- [Tools](/docs/capabilities/tools) — Built-in catalog (62 POJO toolkits, \~206 `@Tool` methods, 29 categories), custom tools, registration.
- [Skills](/docs/capabilities/skills) — On-demand modular knowledge (Claude Code's Skills layer), `SKILL.md` parser, resolver policies, per-skill tool scope.
- [Intelligence](/docs/capabilities/intelligence) — Planning (GOAP/HTN), reasoning (ReAct/ToT), FSM, context, learning.
- [RAG](/docs/capabilities/rag) — Knowledge bases, retrieval strategies, production pipelines.
- [LLM](/docs/capabilities/llm) — Providers, routing, caching, cost tracking, audio.

## Related

- [Agents](/docs/agents) — Where capabilities get composed.
- [Deploy & Integrate](/docs/integrate) — Where capabilities reach the outside world.

---

# Advanced Intelligence Patterns

URL: https://tnsai.dev/docs/capabilities/intelligence/advanced
Description: Advanced cognitive capabilities in TnsAI.Intelligence for reasoning, memory consolidation, output validation, and iterative refinement.

import { Callout } from 'fumadocs-ui/components/callout'

## Reasoning Strategies

TnsAI.Intelligence provides three advanced reasoning executors that go beyond simple prompt-response patterns. Each implements a different strategy for exploring solution spaces.

### ReActExecutor

The `ReActExecutor` implements the ReAct (Reason + Act + Observe) pattern where an LLM explicitly reasons about what to do, takes an action, observes the result, and repeats until the goal is achieved. This produces structured Thought/Action/Observation traces for interpretability and decision auditing.

Based on "ReAct: Synergizing Reasoning and Acting in Language Models" (Yao et al., 2022).

```java
ReActExecutor react = ReActExecutor.builder()
    .llm(client)
    .actionHandler((name, input) -> {
        if ("search".equals(name)) return Optional.of(searchService.search(input));
        if ("calculate".equals(name)) return Optional.of(calculator.eval(input));
        return Optional.empty();
    })
    .maxSteps(10)
    .thoughtFormat(ThoughtFormat.STRUCTURED)
    .stopCondition(StopCondition.finalAnswerDetected())
    .availableActions("search, calculate")
    .timeout(Duration.ofMinutes(5))
    .build();

ReActResult result = react.execute("Find the population of Tokyo and compare it to NYC");

System.out.println("Answer: " + result.getFinalAnswer());
System.out.println("Status: " + result.getStatus());    // SUCCESS, TIMEOUT, ERROR, MAX_STEPS_REACHED, STOPPED
System.out.println("Steps: " + result.getSteps().size());
System.out.println("LLM calls: " + result.getTotalLLMCalls());
System.out.println("Duration: " + result.getDuration());

// Inspect individual reasoning steps
for (ReActStep step : result.getSteps()) {
    System.out.println("Step " + step.getStepNumber());
    System.out.println("  Thought: " + step.getThought());
    System.out.println("  Action: " + step.getAction());
    System.out.println("  Observation: " + step.getObservation());
}
```

**ThoughtFormat options**: `STRUCTURED` (formal Thought/Action/Observation format) or `FREE_FORM` (open-ended reasoning). The `ActionHandler` is a `@FunctionalInterface` that takes an action name and input, returning `Optional<String>` (empty means action not available).

**ReActStatus values**: `SUCCESS`, `TIMEOUT`, `ERROR`, `MAX_STEPS_REACHED`, `STOPPED`.

### TreeOfThoughtsExecutor

The `TreeOfThoughtsExecutor` explores multiple reasoning paths by generating candidate thoughts at each step, evaluating them for promise, pruning low-quality branches, and continuing on promising paths.

Based on "Tree of Thoughts: Deliberate Problem Solving with Large Language Models" (Yao et al., 2023).

```java
TreeOfThoughtsExecutor tot = TreeOfThoughtsExecutor.builder()
    .llm(client)
    .evaluator(BranchEvaluator.llm(evalClient))
    .pruning(PruningStrategy.BEAM_SEARCH)
    .beamWidth(3)
    .maxDepth(5)
    .branchingFactor(3)
    .pruneThreshold(0.3)
    .timeout(Duration.ofMinutes(10))
    .build();

ToTResult result = tot.explore("Design a REST API for a todo app");

System.out.println("Best path: " + result.getBestPath());
System.out.println("Total nodes: " + result.getTotalNodes());
System.out.println("Pruned: " + result.getPrunedNodes());
System.out.println("Max depth reached: " + result.getMaxDepthReached());
```

**PruningStrategy options**:

| Strategy        | Behavior                                          |
| --------------- | ------------------------------------------------- |
| `BEAM_SEARCH`   | Keep top-K nodes by score at each level (default) |
| `BEST_FIRST`    | Sort by cumulative score, keep top-K              |
| `GREEDY`        | Keep only the single best node                    |
| `DEPTH_LIMITED` | No score-based pruning, only depth limit          |
| `EXHAUSTIVE`    | No pruning at all                                 |

### GraphOfThoughtsExecutor

The `GraphOfThoughtsExecutor` extends Tree of Thoughts by allowing cycles and merging of thought branches. Better for problems where partial solutions can be combined.

Based on "Graph of Thoughts" (Besta et al., 2023).

```java
GraphOfThoughtsExecutor got = GraphOfThoughtsExecutor.builder()
    .llm(client)
    .evaluator(BranchEvaluator.llm(client))
    .operations(List.of(GoTOperation.GENERATE, GoTOperation.AGGREGATE, GoTOperation.REFINE))
    .maxNodes(20)
    .branchingFactor(3)
    .timeout(Duration.ofMinutes(10))
    .build();

GoTResult result = got.explore("Design a database schema for e-commerce");

System.out.println("Best thought: " + result.getBestThought());
System.out.println("Aggregated insight: " + result.aggregatedInsight());
System.out.println("Total nodes: " + result.totalNodes());
System.out.println("Merges performed: " + result.mergeCount());
System.out.println("Duration: " + result.duration());
```

**GoTOperation types**: `GENERATE` (create child thoughts), `AGGREGATE` (merge top-scoring nodes into a unified solution), `REFINE` (improve existing thoughts), `SCORE` (evaluate nodes).

The executor uses a frontier-based exploration: each iteration generates children, refines promising nodes (score \\> 0.3), and aggregates top nodes from different branches. The frontier is pruned to the top `branchingFactor` nodes each round.

> **Cross-reference**: For basic reasoning configuration, see [Reasoning](/docs/capabilities/intelligence/reasoning).

## Atom of Thought (AoT)

The `AotProcessor` implements the Atom of Thought reasoning technique that decomposes complex problems into independent "atoms" solved in parallel. Unlike Chain-of-Thought where errors in early steps propagate, AoT isolates atoms so one bad result does not ruin the answer.

Based on "Atom of Thoughts for Markov LLM Test-Time Scaling" (arXiv, Feb 2025).

### Benefits and Trade-offs

- +30-40% accuracy improvement on complex reasoning tasks
- Error isolation between atoms
- Parallel execution of independent atoms
- Per-atom confidence tracking
- Trade-off: +20-30% token usage due to multiple LLM calls

### Usage

```java
// Simple usage
AotProcessor aot = new AotProcessor(llmClient);
AotResult result = aot.process("Calculate compound interest for $1000 at 5% for 3 years");

System.out.println("Answer: " + result.getAnswer());
System.out.println("Confidence: " + result.getConfidence() + "%");

// With configuration
AotProcessor aot = AotProcessor.builder()
    .llmClient(llmClient)
    .maxAtoms(8)
    .parallelExecution(true)
    .minConfidence(40f)
    .complexityThreshold(50)
    .synthesisStrategy(AtomSynthesizer.SynthesisStrategy.WEIGHTED_COMBINATION)
    .build();

AotResult result = aot.process(complexQuestion);
System.out.println(result.getDetailedTrace());

// Force AoT even for simple questions
AotResult forced = aot.processForced("What is 2+2?");

// Check question complexity before processing
int score = aot.getComplexityScore("Compare Python and Java for web development");
```

### How It Works

1. **Complexity check**: The processor scores the question for complexity (multi-step indicators, calculation keywords, comparison keywords). Below the threshold, it processes as a single atom.
2. **AtomGenerator**: Decomposes the problem into atoms. Tries heuristic patterns first (calculation, comparison, list), then falls back to LLM-based decomposition. Each atom has an `id`, `description`, `prompt`, `type` (REASONING, RETRIEVAL, COMPUTATION, VALIDATION, SYNTHESIS), `dependencies`, and `priority`.
3. **AtomSolver**: Solves atoms in parallel with dependency resolution. Groups atoms by level (independent atoms first, then dependent ones). Includes dependency context in prompts and extracts confidence scores from LLM responses.
4. **AtomSynthesizer**: Combines atom solutions into a final answer using the configured synthesis strategy, weighting by confidence.

Call `aot.shutdown()` to release the executor thread pool when done.

## Memory Consolidation

The `MemoryConsolidationPipeline` automates the transition from short-term conversation memory to long-term knowledge storage.

### Pipeline Steps

1. Extract knowledge from the session (via `KnowledgeExtractor`)
2. Filter by confidence threshold
3. Apply forgetting curve to older knowledge
4. Store high-value knowledge (via `KnowledgeStore`)
5. Compact conversation context if compactor provided

```java
MemoryConsolidationPipeline pipeline = MemoryConsolidationPipeline.builder()
    .knowledgeExtractor(extractor)
    .contextCompactor(compactor)
    .knowledgeStore(store)
    .forgettingCurve(ForgettingCurve.EXPONENTIAL)
    .compactionConfig(compactionConfig)
    .minKnowledgeConfidence(0.5)
    .build();

ConsolidationResult result = pipeline.consolidate(session);
System.out.println("Extracted: " + result.knowledgeCount() + " knowledge items");
System.out.println("Original turns: " + result.originalCount());
System.out.println("Consolidated turns: " + result.consolidatedCount());
System.out.println("Summary: " + result.summary());
System.out.println("Duration: " + result.duration());
```

### ForgettingCurve

Controls how memory importance decays over time when not accessed. Knowledge that decays below 0.1 is automatically deleted from the store.

| Curve         | Formula                           | Behavior                                                     |
| ------------- | --------------------------------- | ------------------------------------------------------------ |
| `NONE`        | No decay                          | All memories retain full importance forever                  |
| `LINEAR`      | `score * max(0, 1 - days * 0.01)` | Gradual linear decline, reaches zero after \~100 days        |
| `EXPONENTIAL` | `score * e^(-0.05 * days)`        | Ebbinghaus-inspired rapid initial decay that slows over time |

```java
double decayed = ForgettingCurve.EXPONENTIAL.decay(0.9, Duration.ofDays(7));
```

> **Cross-reference**: For context management fundamentals, see [Context](/docs/capabilities/intelligence/context).

## Context Compaction

The `ContextCompactor` interface provides strategies for reducing conversation context size when approaching the token limit.

### ContextCompactor Interface

```java
public interface ContextCompactor {
    CompactionResult compact(List<Map<String, Object>> messages, CompactionConfig config);
    boolean shouldCompact(int currentTokens, int maxTokens, CompactionConfig config);
    int estimateTokens(List<Map<String, Object>> messages);
}
```

### TwoPhaseCompactor

Combines truncation and LLM summarization in two phases for optimal context reduction.

- **Phase 1** (`TruncatingCompactor`): Truncates large tool-call arguments and tool-result content in older messages. Fast, requires no LLM call.
- **Phase 2** (`LLMContextCompactor`): Summarizes older messages using an LLM. Only runs if phase 1 alone is insufficient.

```java
LLMClient summarizer = LLMClientFactory.create("openai", "gpt-4o-mini");
TwoPhaseCompactor compactor = new TwoPhaseCompactor(
    new TruncatingCompactor(200),       // max 200 chars per truncated field
    new LLMContextCompactor(summarizer)
);

CompactionConfig config = CompactionConfig.builder()
    .thresholdRatio(0.85)       // trigger compaction at 85% capacity
    .preserveLastN(5)           // keep last 5 messages intact
    .build();

if (compactor.shouldCompact(currentTokens, maxTokens, config)) {
    CompactionResult result = compactor.compact(messages, config);
    String summary = result.summary();
    int savedTokens = result.originalTokenCount() - result.compactedTokenCount();
}
```

## Structured Output Validation

The `StructuredOutputExecutor` ensures LLM outputs conform to an expected schema with automatic retry on validation failure.

```java
StructuredOutputExecutor<OrderSummary> executor = StructuredOutputExecutor.<OrderSummary>builder()
    .llm(client)
    .targetType(OrderSummary.class)
    .outputFormat(OutputFormat.JSON)
    .rules(List.of(
        ValidationRule.notNull("orderId"),
        ValidationRule.range("total", 0, 100000),
        ValidationRule.pattern("email", "^[\\w.-]+@[\\w.-]+$")
    ))
    .maxRetries(3)
    .systemPrompt("You are an order processing assistant.")
    .build();

StructuredOutputExecutor.StructuredOutputResult<OrderSummary> result =
    executor.generate("Summarize this order: ...");

if (result.success()) {
    OrderSummary order = result.value();
    System.out.println("Parsed in " + result.attempts() + " attempts");
} else {
    System.out.println("Failed after " + result.attempts() + " attempts");
    System.out.println("Errors: " + result.errors());
}
```

### ValidationRule

Built-in validation rules for common checks:

| Factory Method                                  | Behavior                                |
| ----------------------------------------------- | --------------------------------------- |
| `ValidationRule.notNull(fieldName)`             | Field must exist and be non-null        |
| `ValidationRule.range(fieldName, min, max)`     | Numeric field must be within range      |
| `ValidationRule.pattern(fieldName, regex)`      | String field must match regex           |
| `ValidationRule.custom(description, predicate)` | Custom predicate on the entire data map |

On validation failure, the executor sends a correction prompt containing the specific errors and re-requests a valid response, up to `maxRetries` times.

## NormEngine

The `NormEngine` evaluates and enforces behavioral norms (obligations, prohibitions, permissions) on agent actions at runtime. Norms can be defined via `@Norm` annotations on role classes or programmatically.

### Annotation-Based Norms

```java
@Norms({
    @Norm(type = NormType.OBLIGATION, action = "logActivity",
          description = "Must log all activities", priority = 10),
    @Norm(type = NormType.PROHIBITION, action = "shareData",
          condition = "isConfidential",
          description = "Must not share confidential data", priority = 20),
    @Norm(type = NormType.PERMISSION, action = "readPublicData",
          description = "May read public data")
})
public class AnalystRole { }

NormEngine engine = NormEngine.fromAnnotations(AnalystRole.class);
```

### Programmatic Norms

```java
NormEngine engine = NormEngine.of(
    new NormEntry(NormType.PROHIBITION, "isConfidential", "shareData",
                  "Must not share confidential data", 20),
    new NormEntry(NormType.OBLIGATION, "", "logActivity",
                  "Must log all activities", 10)
);

// Add norms dynamically at runtime
engine.addNorm(new NormEntry(NormType.PERMISSION, "", "readData", "May read data", 5));
```

### Checking Actions

```java
// Check if an action is permitted (evaluates prohibitions)
NormEngine.CheckResult result = engine.checkAction("shareData",
    condition -> condition.equals("isConfidential") && context.isConfidential());

if (result.isViolation()) {
    for (NormViolation v : result.violations()) {
        System.out.println("Violated: " + v.norm().description());
    }
}

// Check for unfulfilled obligations
NormEngine.CheckResult obligations = engine.checkObligations(
    Set.of("readData"),       // actions already performed
    condition -> true          // condition evaluator
);

// Query active norms for current context
List<NormEntry> activeObligations = engine.getActiveObligations(cond -> true);
List<NormEntry> activeProhibitions = engine.getActiveProhibitions(cond -> true);
List<NormEntry> activePermissions = engine.getActivePermissions(cond -> true);
List<NormEntry> allNorms = engine.getAllNorms();
```

### NormEntry Record

Fields: `type()` (NormType), `condition()`, `action()`, `description()`, `priority()`. The `hasCondition()` method returns true if the norm has a non-blank condition expression.

## RefinementLoop

The `RefinementLoop` iteratively refines LLM outputs until they meet predefined quality standards. Inspired by Claude's Ralph plugin, it repeatedly evaluates output against completion criteria and re-prompts for corrections.

```java
RefinementLoop loop = RefinementLoop.builder()
    .task("Convert Python to TypeScript")
    .completionCriteria(CompletionCriteria.builder()
        .compilerCheck("tsc --noEmit")
        .testCommand("npm test")
        .mustNotContain("def ", "import ")
        .mustContain("interface", "type")
        .customCheck("no-any", code -> !code.contains(": any"))
        .build())
    .maxIterations(10)
    .timeout(Duration.ofMinutes(30))
    .stopHook(StopHook.onAllTestsPass())
    .onIteration(iter -> log.info("Iteration {} score: {}",
        iter.iterationNumber(), iter.evaluation().overallScore()))
    .build();

RefinementResult result = loop.execute(agent, pythonCode);    // or loop.execute(llmClient, input)

System.out.println("Final output: " + result.getFinalOutput());
System.out.println("Iterations: " + result.getIterationCount());
System.out.println("Status: " + result.getStatus());         // SUCCESS, TIMEOUT, MAX_ITERATIONS, STOPPED, ERROR
System.out.println("Duration: " + result.getDuration());
System.out.println("Metrics: " + result.getMetrics());       // totalIterations, totalDurationMs, totalTokens, scoreProgression, finalScore
```

### CompletionCriteria

Defines when the refinement loop should stop. Combines multiple check types:

```java
CompletionCriteria criteria = CompletionCriteria.builder()
    .withLLM(llmClient)                        // required for llmCheck
    .compilerCheck("tsc --noEmit")             // shell command (exit 0 = pass)
    .testCommand("npm test")                   // shell command
    .lintCheck("eslint .")                     // shell command
    .mustContain("interface", "type")          // required content
    .mustNotContain("def ", "import ")         // forbidden content
    .matchesPattern("export default .*")       // regex match
    .wordCount(50, 5000)                       // word count range
    .minLines(10)                              // minimum line count
    .validJson()                               // valid JSON check
    .customCheck("no-any", code -> !code.contains(": any"))
    .llmCheck("Is this idiomatic TypeScript?", 0.8)  // LLM-based quality check
    .build();

EvaluationResult eval = criteria.evaluate(output);
boolean done = eval.allCriteriaMet();
double score = eval.overallScore();          // 0.0 - 1.0
List<CheckResult> passed = eval.passed();
List<CheckResult> failed = eval.failed();
List<String> reasons = eval.getFailureReasons();
```

### RefinementStatus

Possible terminal states: `SUCCESS` (all criteria met), `TIMEOUT`, `MAX_ITERATIONS`, `STOPPED` (stop hook triggered), `ERROR`.

The refinement prompt includes failed checks as issues to fix and passed checks as elements to preserve, guiding the LLM toward incremental improvement.

---

# Context Management

URL: https://tnsai.dev/docs/capabilities/intelligence/context
Description: Context window management, decision tracing, session history, knowledge extraction, automatic memory consolidation, and auto-summarization. These components help agents operate effectively within token limits and learn from past interactions.

import { Callout } from 'fumadocs-ui/components/callout'

## Auto-Summarization (Context Compaction)

LLMs have a fixed context window, and long conversations will eventually exceed it. Compaction strategies automatically shrink the conversation history to fit within the token budget while preserving as much important information as possible. Package: `com.tnsai.context.compaction`.

### TruncatingCompactor

The simplest strategy: removes oldest messages (preserving system prompts) until the token budget is met. Zero cost -- no LLM calls required.

```java
ContextCompactor compactor = new TruncatingCompactor();
List<Map<String, Object>> compacted = compactor.compact(messages, 4096);
```

### TwoPhaseCompactor

Two-phase strategy that first truncates tool call arguments, then uses an LLM to summarize older conversation turns. Preserves more semantic information than pure truncation.

```java
TwoPhaseCompactor compactor = TwoPhaseCompactor.builder()
    .llm(llmClient)
    .argTruncationThreshold(500)   // truncate tool args over 500 chars
    .summaryRatio(0.3)             // summarize oldest 30% of messages
    .build();

List<Map<String, Object>> compacted = compactor.compact(messages, 8192);
```

**Phase 1 -- Argument Truncation**: Tool call arguments and results exceeding the threshold are truncated to their keys and types. This often reclaims significant tokens without losing conversational context.

**Phase 2 -- LLM Summarization**: If Phase 1 does not reduce the context enough, older messages are sent to the LLM for summarization. The summary replaces the original messages as a single system message.

| Compactor             | Cost             | Information Loss                  | Best For                                 |
| --------------------- | ---------------- | --------------------------------- | ---------------------------------------- |
| `TruncatingCompactor` | Free             | High (oldest context lost)        | Short conversations, cost-sensitive      |
| `TwoPhaseCompactor`   | Low (1 LLM call) | Low (summary preserves key facts) | Long conversations, multi-turn reasoning |

## LLMContextCompactor

A standalone LLM-based compactor that gives you fine-grained control over when compaction triggers, how many recent messages to preserve, and how the summary is generated. Use this when you need precise configuration beyond what TwoPhaseCompactor offers.

```java
LLMClient summarizer = LLMClientFactory.create("openai", "gpt-4o-mini");
LLMContextCompactor compactor = new LLMContextCompactor(summarizer);

CompactionConfig config = CompactionConfig.builder()
    .thresholdRatio(0.80)
    .preserveLastN(5)
    .preserveSystemPrompt(true)
    .minMessagesForCompaction(10)
    .summarizerModel("gpt-4o-mini")
    .maxSummaryTokens(500)
    .build();

CompactionResult result = compactor.compact(messages, config);
```

### CompactionConfig

These settings control when compaction fires and how much context is preserved versus summarized.

| Parameter                  | Default  | Description                                      |
| -------------------------- | -------- | ------------------------------------------------ |
| `thresholdRatio`           | 0.80     | Token ratio at which compaction triggers         |
| `preserveLastN`            | 5        | Recent messages to keep unmodified               |
| `preserveSystemPrompt`     | true     | Always keep system prompt                        |
| `minMessagesForCompaction` | 10       | Minimum messages before compaction               |
| `summarizerModel`          | null     | Model for summarization (null = agent's default) |
| `summaryPromptTemplate`    | built-in | Custom template (use `{messages}` placeholder)   |
| `maxSummaryTokens`         | 500      | Max tokens for the generated summary             |

### Compaction Process

The compactor follows a four-step process that ensures the system prompt and recent messages are always preserved, while older messages are summarized into a compact form.

1. Separate system prompt (if preserved)
2. Identify last N messages to preserve
3. Summarize remaining messages via LLM
4. Reconstruct: \[system prompt] + \[summary as system message] + \[preserved messages]

## AgentContextManager

The central hub for observing and recording what your agent does during a conversation. It captures state snapshots at key moments, traces the reasoning behind each decision, tracks entities mentioned in the conversation, and manages conversation lifecycle. This is essential for debugging, auditing, and building agents that can learn from their own history.

```java
AgentContextManager contextManager = new AgentContextManager(
    "agent-001",
    new InMemorySnapshotStore(),
    new InMemoryDecisionTraceStore()
);
```

### Context Snapshots

A snapshot captures the agent's complete state (beliefs, desires, intentions) at a specific moment. By taking snapshots before and after decisions, you can see exactly what changed and why.

```java
ContextSnapshot snapshot = contextManager.createSnapshot(
    SnapshotTrigger.PRE_DECISION,
    agent.getBeliefs(),
    agent.getDesires(),
    agent.getIntentions()
);

List<ContextSnapshot> recent = contextManager.getRecentSnapshots(10);
Optional<SnapshotDiff> diff = contextManager.diffSnapshots(beforeId, afterId);
```

### Decision Tracing

Wrap any agent operation in a decision trace to automatically record what the agent was thinking before and after the decision, what it decided, and whether it succeeded. This creates an audit trail of every significant action.

```java
// Full version with BDI suppliers
String response = contextManager.executeWithTrace(
    "Process discount request",
    () -> agent.chat("Should we approve 20% discount?"),
    agent::getBeliefs,
    agent::getDesires,
    agent::getIntentions
);

// Simplified version (without BDI)
String result = contextManager.executeWithTrace(
    "Summarize report",
    () -> agent.chat("Summarize this...")
);
```

This creates pre/post snapshots, records the full decision trace, and handles errors transparently.

### Querying Decisions

Once decisions are recorded, you can query them by various criteria -- recent decisions, failures, successes, or by action name. The manager also supports finding similar past decisions and generating human-readable explanations.

```java
List<DecisionTrace> recent = contextManager.getRecentDecisions(10);
List<DecisionTrace> failures = contextManager.findFailedDecisions();
List<DecisionTrace> successes = contextManager.findSuccessfulDecisions();
List<DecisionTrace> byAction = contextManager.findDecisionsByAction("approve");

Optional<String> explanation = contextManager.explainDecision(traceId);
List<DecisionTrace> precedents = contextManager.findPrecedents(referenceTrace, 0.7);
List<String> summaries = contextManager.summarizeRecentDecisions(5);
```

### Entity Tracking

Register external entities (users, resources, tasks) so they are included in context snapshots. This helps the agent maintain awareness of what objects it is working with.

```java
contextManager.trackEntity(customerEntity);
contextManager.untrackEntity("entity-123");
Collection<Entity> tracked = contextManager.getTrackedEntities();
```

### Context Variables

Store arbitrary key-value metadata that gets included in every snapshot. Use this for session-level information like the current user, project, or priority level.

```java
contextManager.setVariable("sessionType", "support");
contextManager.setVariable("priority", 5);
Optional<Integer> typed = contextManager.getVariable("priority", Integer.class);
```

### Conversation Lifecycle

Group related decisions into a conversation so you can query all decisions that happened during a specific interaction.

```java
String convId = contextManager.startConversation();
// ... process messages ...
List<DecisionTrace> inConv = contextManager.findDecisionsInCurrentConversation();
contextManager.endConversation();
```

### Cleanup

Remove old traces and snapshots to prevent unbounded storage growth. Pass a cutoff time to delete everything older than that point.

```java
Instant cutoff = Instant.now().minus(Duration.ofDays(30));
int deletedTraces = contextManager.cleanupOldTraces(cutoff);
int deletedSnapshots = contextManager.cleanupOldSnapshots(cutoff);
```

## BDIContextMapper

An internal utility that converts the agent's BDI (Belief-Desire-Intention) state into the context graph structures used by `AgentContextManager`. You typically do not need to use this directly -- the context manager handles the mapping automatically.

## AgentSession

A session records the full conversation between a user and an agent: every message, tool call, and result, along with metadata and outcome. Sessions are the building blocks for history search, knowledge extraction, and learning from past interactions.

```java
AgentSession session = AgentSession.start("code-assistant", "/path/to/project");

session.addUserMessage("Fix the authentication bug");
session.addAssistantMessage("I'll analyze the auth module...");
session.addToolCall("readFile", Map.of("path", "auth.java"));
session.addToolResult("readFile", "public class Auth { ... }");

session.addMetadata("model", "gpt-4o");
session.addTags("security", "bug-fix");

session.success();
// or: session.fail("Compilation error");
// or: session.cancel();

session.getTurnCount();                           // 4
session.getDuration();                             // Duration since start
session.getTurnsByType(TurnType.TOOL_CALL);       // Tool call turns only
session.getFirstUserMessage();                     // "Fix the authentication bug"
session.isComplete();                              // true
session.isSuccess();                               // true
```

### SessionOutcome

Tracks how the session ended. This is useful for filtering sessions during analysis -- for example, finding all failed sessions to understand common failure modes.

| Value         | Description            |
| ------------- | ---------------------- |
| `IN_PROGRESS` | Session is active      |
| `SUCCESS`     | Completed successfully |
| `FAILED`      | Ended with error       |
| `CANCELLED`   | User cancelled         |

## SessionStore / FileSessionStore

Persist agent sessions to disk so they survive application restarts. `FileSessionStore` saves each session as a JSON file and provides query methods to find sessions by agent, project, tag, outcome, or date range.

```java
SessionStore store = SessionStore.file(".tnsai/history/");

store.save(session);
Optional<AgentSession> loaded = store.load("session-123");

store.findRecent(10);
store.findByAgent("code-assistant");
store.findByProject("/my/project");
store.findByTag("security");
store.findByOutcome(SessionOutcome.SUCCESS);
store.findByDateRange(LocalDate.now().minusDays(7), LocalDate.now());
store.count();
```

## SearchableSessionStore / SessionQuery

When you need to search session contents (not just metadata), use `SearchableSessionStore`. It supports full-text search combined with filters for agent, project, tags, outcome, and date range.

```java
SessionQuery query = SessionQuery.builder()
    .text("authentication bug")
    .agent("code-assistant")
    .project("/my/project")
    .tags("security", "urgent")
    .outcome(SessionOutcome.SUCCESS)
    .dateRange(LocalDate.now().minusDays(7), LocalDate.now())
    .searchMode(SessionQuery.SearchMode.ALL)
    .limit(20)
    .offset(0)
    .build();

List<SessionSearchResult> results = searchableStore.search(query);
```

### SessionQuery Convenience

Shortcut factory methods for common query patterns so you do not need to use the builder for simple cases.

```java
SessionQuery recent = SessionQuery.recent(10);
SessionQuery textSearch = SessionQuery.text("database migration");
SessionQuery lastWeek = SessionQuery.builder().lastDays(7).build();
```

### SearchMode

Controls how the text search matches against session content.

| Mode    | Description              |
| ------- | ------------------------ |
| `ANY`   | Match any word (default) |
| `ALL`   | Match all words          |
| `EXACT` | Match exact phrase       |
| `REGEX` | Regular expression match |

## AutoConsolidationManager

Monitors conversation state and triggers memory consolidation automatically based on configurable thresholds.

```java
AutoConsolidationManager manager = AutoConsolidationManager.builder()
    .pipeline(consolidationPipeline)
    .triggers(Set.of(
        ConsolidationTrigger.MESSAGE_COUNT,
        ConsolidationTrigger.TOKEN_LIMIT,
        ConsolidationTrigger.ON_SESSION_END))
    .messageCountThreshold(50)
    .tokenBudget(8192)
    .tokenThresholdPercent(80)
    .build();

// Call on each message
ConsolidationResult result = manager.onMessageAdded(session, estimatedTokens);
if (result != null) {
    System.out.println("Consolidated: " + result.knowledgeCount() + " items");
}

// Call when session ends
manager.onSessionEnd(session);
```

### ConsolidationTrigger

These triggers define when automatic consolidation runs. You can enable one or more triggers depending on your needs.

| Trigger          | Default       | Fires When                        |
| ---------------- | ------------- | --------------------------------- |
| `MESSAGE_COUNT`  | 50 messages   | Counter reaches threshold         |
| `TOKEN_LIMIT`    | 80% of budget | Estimated tokens exceed threshold |
| `ON_SESSION_END` | always        | `onSessionEnd()` is called        |

Consolidation is thread-safe: concurrent triggers are skipped if one is already in progress.

## LLMKnowledgeExtractor

Uses an LLM to analyze session conversations and extract reusable knowledge.

```java
KnowledgeExtractor extractor = new LLMKnowledgeExtractor(llmClient, 0.5);

List<ExtractedKnowledge> knowledge = extractor.extract(session);

// Filter by type
knowledge.stream()
    .filter(k -> k.type() == ExtractionType.SOLUTION)
    .filter(k -> k.isHighConfidence())
    .forEach(k -> System.out.println(k.summary()));

// Extract specific types only
List<ExtractedKnowledge> decisions = extractor.extract(session,
    EnumSet.of(ExtractionType.DECISION, ExtractionType.LEARNING));

// Adjust confidence threshold
KnowledgeExtractor strict = extractor.withMinConfidence(0.8);
```

### ExtractionType

Five categories of knowledge that can be extracted from session conversations. Each type has a different practical use -- solutions help with similar problems in the future, patterns provide reusable templates, and antipatterns warn about approaches to avoid.

| Type          | Key                | Description                              |
| ------------- | ------------------ | ---------------------------------------- |
| `SOLUTION`    | `problem-solution` | A problem and how it was solved          |
| `PATTERN`     | `pattern`          | A reusable code or design pattern        |
| `DECISION`    | `decision`         | An architectural decision with rationale |
| `LEARNING`    | `learning`         | A lesson learned or insight              |
| `ANTIPATTERN` | `antipattern`      | An approach to avoid                     |

Each `ExtractedKnowledge` item includes content, optional problem description, code snippets, related files, tags, and a confidence score (0.0-1.0).

### Knowledge Storage

Persist extracted knowledge for later retrieval. Two implementations are provided: in-memory for testing and file-based for production.

```java
KnowledgeStore store = new InMemoryKnowledgeStore();
// Or persist to files:
KnowledgeStore store = new FileKnowledgeStore(".tnsai/knowledge/");

store.save(knowledge);
List<ExtractedKnowledge> solutions = store.findByType(ExtractionType.SOLUTION);
```

---

# Finite State Machine

URL: https://tnsai.dev/docs/capabilities/intelligence/fsm
Description: Deterministic state machine for bounded agent autonomy. Provides guard-based transitions, entry/exit actions, automatic transitions, event payloads, listeners, and visualization to Mermaid and Graphviz DOT.

import { Callout } from 'fumadocs-ui/components/callout'

## Quick Start

This example builds a simple review workflow with five states and guarded transitions. The machine starts in IDLE, moves through PROCESSING and REVIEW, and ends in either APPROVED or REJECTED based on a confidence score.

```java
State idle = State.initial("IDLE");
State processing = State.of("PROCESSING");
State review = State.of("REVIEW");
State approved = State.terminal("APPROVED");
State rejected = State.terminal("REJECTED");

StateMachine sm = StateMachine.builder("ReviewWorkflow")
    .states(idle, processing, review, approved, rejected)
    .transition(Transition.from(idle).to(processing).on("START"))
    .transition(Transition.from(processing).to(review).on("SUBMIT"))
    .transition(Transition.from(review).to(approved)
        .on("REVIEW_COMPLETE")
        .when(Guard.greaterThan("confidence", 0.9)))
    .transition(Transition.from(review).to(rejected)
        .on("REVIEW_COMPLETE")
        .when(Guard.lessThan("confidence", 0.5)))
    .maxTransitions(100)
    .build();

sm.start();
sm.fire(Event.of("START"));
sm.fire(Event.of("SUBMIT"));
sm.getContext().set("confidence", 0.95);
sm.fire(Event.of("REVIEW_COMPLETE"));

System.out.println(sm.getCurrentState().getName()); // "APPROVED"
System.out.println(sm.isInTerminalState());          // true
```

## State

States can be initial, terminal, or intermediate. Each supports entry/exit actions, timeouts, and metadata.

### Factory Methods

Use these to create states quickly. Every state machine needs exactly one initial state, and at least one terminal state where the machine stops.

| Method                 | Description                            |
| ---------------------- | -------------------------------------- |
| `State.initial(name)`  | Entry point -- exactly one per machine |
| `State.of(name)`       | Regular intermediate state             |
| `State.terminal(name)` | End state -- machine completes here    |

### Builder

For more control, the builder lets you attach entry/exit actions (code that runs when a state is entered or left), set timeouts, and store metadata on the state.

```java
State processing = State.builder("PROCESSING")
    .initial()                                         // Mark as initial
    .onEntry(ctx -> ctx.set("startTime", System.currentTimeMillis()))
    .onExit(ctx -> {
        long duration = System.currentTimeMillis() - (long) ctx.get("startTime");
        ctx.set("processingDuration", duration);
    })
    .timeout(30_000)                                   // 30 second timeout (ms)
    .metadata("retryable", true)
    .build();

processing.isInitial();        // true
processing.isTerminal();       // false
processing.hasTimeout();       // true
processing.getTimeoutMs();     // 30000
processing.getSpec("retryable"); // true
```

States are identified by name. Two states with the same name are considered equal.

## Transition

Transitions define movement between states, triggered by events with optional guards, actions, and priority.

```java
// Simple transition
Transition t1 = Transition.from(idle).to(processing).on("START").build();

// With guard and priority
Transition t2 = Transition.from(review).to(approved)
    .on("REVIEW_COMPLETE")
    .when(Guard.greaterThan("confidence", 0.9))
    .priority(10)
    .build();

// With transition action
Transition t3 = Transition.from(review).to(rejected)
    .on("REVIEW_COMPLETE")
    .when(Guard.lessThan("confidence", 0.5))
    .action((ctx, from, to) -> ctx.set("reason", "Low confidence"))
    .build();

// Automatic transition (no event required, fires when guard passes)
Transition auto = Transition.from(processing).to(review)
    .automatic()
    .when(Guard.equals("processed", true))
    .build();

// Internal (self-loop) transition
Transition internal = Transition.internal(processing)
    .on("RETRY")
    .action((ctx, from, to) -> {
        int count = ctx.getOrDefault("retries", 0);
        ctx.set("retries", count + 1);
    })
    .build();
```

When multiple transitions match the same event from the same state, the one with the highest `priority` wins.

## Event

Events are the signals that drive the state machine forward. When you fire an event, the machine looks for a matching transition from the current state. Events can carry key-value payload data that guards and actions can read.

```java
Event start = Event.of("START");

Event review = Event.of("REVIEW_COMPLETE")
    .withData("confidence", 0.95)
    .withData("reviewer", "agent-1");

double confidence = review.getData("confidence");    // 0.95
String reviewer = review.getDataOrDefault("reviewer", "unknown");
review.hasData("notes");                             // false

sm.fire(review);
```

## StateMachineContext

The context is a shared key-value store that guards, actions, and listeners can all read and write. Use it to pass data between states, track results, record errors, and review the full transition history.

```java
StateMachineContext ctx = new StateMachineContext(Map.of("userId", "u123"));

ctx.set("processed", true);
ctx.get("userId");                        // "u123"
ctx.getOrDefault("retries", 0);           // 0
ctx.has("processed");                     // true
ctx.remove("processed");

ctx.setResult("Task completed");
String result = ctx.getResult();
ctx.setError("Timeout exceeded");
ctx.hasError();                           // true

// Transition history
List<StateTransitionRecord> history = ctx.getHistory();
for (var record : history) {
    System.out.printf("%s -> %s via %s%n",
        record.fromState(), record.toState(), record.event());
}

// Start machine with pre-populated context
sm.start(new StateMachineContext(Map.of("threshold", 0.8)));
```

## Guard

Guards are conditions that must be true for a transition to fire. They check the context (shared state) and optionally the event payload. If a guard returns false, the transition is skipped even if the event matches. TnsAI provides a rich set of built-in guards plus composition operators so you can combine them.

### Built-in Guards

These factory methods cover the most common conditions. For anything more complex, use a lambda or compose built-in guards with `.and()`, `.or()`, and `.negate()`.

| Guard                             | Description                           |
| --------------------------------- | ------------------------------------- |
| `Guard.always()`                  | Always true (default for transitions) |
| `Guard.never()`                   | Always false                          |
| `Guard.hasKey("k")`               | True if key exists in context         |
| `Guard.equals("k", val)`          | True if context value equals expected |
| `Guard.greaterThan("k", 0.9)`     | Numeric comparison (`>`)              |
| `Guard.lessThan("k", 0.5)`        | Numeric comparison (`<`)              |
| `Guard.eventDataEquals("k", val)` | Check current event's payload         |

### Custom and Composed Guards

You can write guards as lambdas or compose built-in guards using `.and()`, `.or()`, and `.negate()` for complex conditions.

```java
// Lambda guard
Guard isAuthenticated = ctx -> ctx.has("userId");

// Event data guard
Guard approvedStatus = ctx -> {
    Event event = ctx.getCurrentEvent();
    return "APPROVED".equals(event.getData("status"));
};

// AND composition
Guard safe = Guard.greaterThan("confidence", 0.8)
    .and(Guard.lessThan("risk", 0.3));

// OR composition
Guard acceptable = Guard.equals("role", "admin")
    .or(Guard.equals("role", "moderator"));

// Negation
Guard notBlocked = Guard.hasKey("blocked").negate();
```

## StateMachineListener

Listeners let you observe everything that happens in the state machine without modifying its behavior. Use them for logging, metrics, debugging, or triggering side effects when specific transitions occur.

```java
sm.addListener(new StateMachineListener() {
    @Override
    public void onStateEntered(StateMachine sm, State state, Event event) {
        System.out.println("Entered: " + state.getName());
    }

    @Override
    public void onTransition(StateMachine sm, State from, State to, Event event) {
        String trigger = event != null ? event.getName() : "auto";
        System.out.printf("%s -> %s [%s]%n", from.getName(), to.getName(), trigger);
    }

    @Override
    public void onNoTransition(StateMachine sm, State state, Event event) {
        System.out.println("Unhandled event: " + event.getName());
    }

    @Override
    public void onCompleted(StateMachine sm) {
        System.out.println("Completed in: " + sm.getCurrentState().getName());
    }
});

// Built-in logging listener (SLF4J)
sm.addListener(StateMachineListener.logging());
// Logs: [FSM:ReviewWorkflow] IDLE -> PROCESSING [START]
```

## StateMachineVisualizer

Export your state machine as a diagram for documentation, debugging, or sharing. Three output formats are supported: Mermaid (renders in GitHub, Notion, etc.), Graphviz DOT (for offline rendering), and plain text.

### Mermaid

Generates a Mermaid state diagram that renders natively on GitHub, GitLab, and many documentation tools.

```java
String mermaid = StateMachineVisualizer.toMermaid(sm);
// stateDiagram-v2
//     %% ReviewWorkflow
//     [*] --> IDLE
//     IDLE --> PROCESSING : START
//     PROCESSING --> REVIEW : SUBMIT
//     REVIEW --> APPROVED : REVIEW_COMPLETE [guarded]
//     REVIEW --> REJECTED : REVIEW_COMPLETE [guarded]
//     APPROVED --> [*]
//     REJECTED --> [*]

// With options
String mermaid = StateMachineVisualizer.toMermaid(sm,
    MermaidOptions.defaults()
        .includeTitle(true)
        .includeStateDescriptions(true)
        .showGuards(true)
        .showAutoTransitions(true)
        .highlightCurrentState(true));
```

### Graphviz DOT

Generates DOT format for use with Graphviz or compatible rendering tools, with configurable layout direction, colors, and fonts.

```java
String dot = StateMachineVisualizer.toDot(sm);

String dot = StateMachineVisualizer.toDot(sm,
    DotOptions.defaults()
        .direction("LR")               // LR, TB, RL, BT
        .nodeShape("ellipse")
        .fontName("Helvetica")
        .currentStateColor("#90EE90")
        .showGuards(true));
```

### Plain Text

A simple human-readable summary of the machine's states, transitions, and current status -- useful for logging and console output.

```java
String text = StateMachineVisualizer.toText(sm);
// State Machine: ReviewWorkflow
// Status: COMPLETED
// Current State: APPROVED
// Transitions: 3
//
// States:
//   - IDLE [initial]
//   - PROCESSING
//   - REVIEW
//   - APPROVED [terminal]
//   - REJECTED [terminal]
//
// Transitions:
//   - IDLE -> PROCESSING on START
//   - PROCESSING -> REVIEW on SUBMIT
//   - REVIEW -> APPROVED on REVIEW_COMPLETE
//   - REVIEW -> REJECTED on REVIEW_COMPLETE
```

## Status Lifecycle

A state machine moves through these statuses during its lifetime. You can query the current status at any time to decide whether to fire more events or handle completion.

| Status        | Meaning                           |
| ------------- | --------------------------------- |
| `NOT_STARTED` | Created but `start()` not called  |
| `RUNNING`     | Active, accepting events          |
| `COMPLETED`   | Reached a terminal state          |
| `FAILED`      | Max transitions exceeded or error |
| `TIMEOUT`     | State timeout triggered           |

```java
sm.getStatus();          // Status.RUNNING
sm.isRunning();          // true
sm.isInTerminalState();  // false
sm.getTransitionCount(); // 2
sm.getAvailableEvents(); // ["SUBMIT"]
sm.reset();              // Back to NOT_STARTED
```

## Comprehensive Example

This example models a real-world order processing workflow with automatic transitions, validation guards, cancellation handling, and logging. It demonstrates how all the FSM building blocks fit together.

```java
// Order processing workflow
State pending = State.builder("PENDING").initial()
    .onEntry(ctx -> log.info("Order received")).build();
State validating = State.builder("VALIDATING")
    .timeout(10_000).build();
State payment = State.of("PAYMENT");
State fulfilled = State.terminal("FULFILLED");
State cancelled = State.terminal("CANCELLED");

StateMachine orderFSM = StateMachine.builder("OrderProcess")
    .states(pending, validating, payment, fulfilled, cancelled)
    .transition(Transition.from(pending).to(validating).automatic()
        .when(Guard.hasKey("orderId")))
    .transition(Transition.from(validating).to(payment)
        .on("VALIDATED").when(Guard.equals("valid", true)))
    .transition(Transition.from(validating).to(cancelled)
        .on("VALIDATED").when(Guard.equals("valid", false))
        .action((ctx, from, to) -> ctx.set("reason", "Validation failed")))
    .transition(Transition.from(payment).to(fulfilled)
        .on("PAYMENT_RESULT").when(Guard.equals("paid", true)))
    .transition(Transition.from(payment).to(cancelled)
        .on("PAYMENT_RESULT").when(Guard.equals("paid", false)))
    .transition(Transition.from(validating).to(cancelled).on("CANCEL"))
    .transition(Transition.from(payment).to(cancelled).on("CANCEL"))
    .maxTransitions(50)
    .build();

orderFSM.addListener(StateMachineListener.logging());

StateMachineContext ctx = new StateMachineContext(Map.of("orderId", "ORD-123"));
orderFSM.start(ctx);                          // PENDING -> VALIDATING (auto)
ctx.set("valid", true);
orderFSM.fire(Event.of("VALIDATED"));          // -> PAYMENT
ctx.set("paid", true);
orderFSM.fire(Event.of("PAYMENT_RESULT"));     // -> FULFILLED

System.out.println(StateMachineVisualizer.toMermaid(orderFSM));
```

## Thread Safety

`StateMachine` is not thread-safe by design to keep the implementation simple and fast. If your application fires events from multiple threads, you need to synchronize access externally.

```java
synchronized (sm) {
    sm.fire(event);
}
```

---

# Intelligence

URL: https://tnsai.dev/docs/capabilities/intelligence
Description: Give agents planning, reasoning, state machines, and learning capabilities.

import { Callout } from 'fumadocs-ui/components/callout'

## Pages

- [Planning](/docs/capabilities/intelligence/planning) — GOAP and HTN planners.
- [Reasoning](/docs/capabilities/intelligence/reasoning) — ReAct, Tree of Thoughts, Self-Consistency.
- [FSM](/docs/capabilities/intelligence/fsm) — Finite state machines for deterministic flows.
- [Context](/docs/capabilities/intelligence/context) — Session context, decision tracing.
- [Learning](/docs/capabilities/intelligence/learning) — Feedback loops, preference learning.
- [Advanced](/docs/capabilities/intelligence/advanced) — Custom strategies, handle composition.

---

# Learning and Refinement

URL: https://tnsai.dev/docs/capabilities/intelligence/learning
Description: Feedback-driven learning, normative constraint enforcement, iterative refinement loops, prompt optimization, and structured output validation. These components enable agents to improve over time and produce higher-quality outputs.

import { Callout } from 'fumadocs-ui/components/callout'

## Feedback

Represents user or system feedback on agent output. Four factory methods for common types:

```java
Feedback positive = Feedback.thumbsUp("Great explanation!");
Feedback negative = Feedback.thumbsDown("Too verbose, needs to be concise");
Feedback fix = Feedback.correction("Use formal tone, not casual");
Feedback pref = Feedback.preference("Always include code examples");

// Attach to a session
Feedback withSession = positive.withSessionId("session-123");

feedback.type();       // FeedbackType.POSITIVE
feedback.content();    // "Great explanation!"
feedback.timestamp();  // Instant
feedback.id();         // Auto-generated UUID
feedback.metadata();   // Map<String, Object>
```

### FeedbackType

Four feedback types cover the most common ways users and systems respond to agent output.

| Type         | Factory               | Description                            |
| ------------ | --------------------- | -------------------------------------- |
| `POSITIVE`   | `thumbsUp(comment)`   | Good output -- reinforce this behavior |
| `NEGATIVE`   | `thumbsDown(comment)` | Bad output -- avoid this in future     |
| `CORRECTION` | `correction(text)`    | Specific fix to apply                  |
| `PREFERENCE` | `preference(text)`    | User style/tone preference             |

## FeedbackLearner

Analyzes collected feedback and produces actionable learnings: prompt adjustments, user preferences, and good examples for few-shot prompting.

```java
FeedbackLearner learner = FeedbackLearner.builder()
    .llm(client)
    .feedbackStore(FeedbackStore.inMemory())
    .strategies(List.of(
        LearningStrategy.PROMPT_ADJUSTMENT,
        LearningStrategy.PREFERENCE_LEARNING,
        LearningStrategy.EXAMPLE_COLLECTION))
    .minFeedbackForLearning(3)
    .build();

learner.recordFeedback(Feedback.correction("Use formal tone"));
learner.recordFeedback(Feedback.thumbsDown("Response was too long"));
learner.recordFeedback(Feedback.preference("Include code examples"));
learner.recordFeedback(Feedback.thumbsUp("Perfect level of detail"));

FeedbackLearner.LearningResult result = learner.learn();

result.promptAdjustments();  // ["Use formal tone", "Keep responses concise"]
result.preferences();         // ["User prefers code examples"]
result.goodExamples();        // ["Perfect level of detail"]
result.feedbackAnalyzed();    // 4
result.hasLearnings();        // true
```

### LearningStrategy

Each strategy focuses on a different type of feedback and produces a different kind of actionable output.

| Strategy              | Analyzes                         | Produces                   |
| --------------------- | -------------------------------- | -------------------------- |
| `PROMPT_ADJUSTMENT`   | Negative + correction feedback   | Rules for system prompt    |
| `PREFERENCE_LEARNING` | Preference + correction feedback | User preference profile    |
| `EXAMPLE_COLLECTION`  | Positive feedback                | Good examples for few-shot |

### FeedbackStore

A simple store for collecting feedback items. The in-memory implementation is suitable for testing; for production, persist feedback to your preferred storage backend.

```java
FeedbackStore store = FeedbackStore.inMemory();
store.save(feedback);
List<Feedback> all = store.getAll();
List<Feedback> corrections = store.getByType(FeedbackType.CORRECTION);
```

## NormEngine

Runtime enforcement of normative constraints extracted from `@Norm` and `@Norms` annotations. Checks actions against obligations, prohibitions, and permissions.

### Annotation-Driven Setup

The easiest way to define norms is with `@Norms` and `@Norm` annotations on your agent class. The engine reads these at construction time and enforces them at runtime.

```java
@Norms({
    @Norm(
        type = NormType.PROHIBITION,
        action = "sharePersonalData",
        condition = "hasConsent == false",
        description = "Cannot share personal data without consent",
        priority = 10
    ),
    @Norm(
        type = NormType.OBLIGATION,
        action = "logAccess",
        description = "Must log all data access events",
        priority = 5
    ),
    @Norm(
        type = NormType.PERMISSION,
        action = "readPublicData",
        description = "Can read public data at any time"
    )
})
public class DataAgent { /* ... */ }
```

### Using the Engine

Create a `NormEngine` from annotations or explicit entries, then call `checkAction()` before performing any action to verify it does not violate any active norms. You can also check whether all obligations have been fulfilled.

```java
// Create from annotations
NormEngine engine = NormEngine.fromAnnotations(DataAgent.class);

// Or from explicit entries
NormEngine engine = NormEngine.of(
    new NormEntry(NormType.PROHIBITION, "hasConsent == false",
        "sharePersonalData", "No sharing without consent", 10),
    new NormEntry(NormType.OBLIGATION, "",
        "logAccess", "Must log access", 5)
);

// Check if an action is allowed
Predicate<String> conditionEval = condition ->
    ConditionEvaluator.evaluate(condition, currentState);

NormEngine.CheckResult result = engine.checkAction("sharePersonalData", conditionEval);
if (result.isViolation()) {
    for (NormViolation v : result.violations()) {
        System.out.println("VIOLATION: " + v.description());
    }
}

// Check obligation fulfillment
Set<String> fulfilled = Set.of("readData"); // logAccess NOT fulfilled
NormEngine.CheckResult obligations = engine.checkObligations(fulfilled, conditionEval);
if (obligations.isViolation()) {
    System.out.println("Unfulfilled obligations found");
}

// Query active norms
List<NormEntry> activeObligations = engine.getActiveObligations(conditionEval);
List<NormEntry> activeProhibitions = engine.getActiveProhibitions(conditionEval);
List<NormEntry> activePermissions = engine.getActivePermissions(conditionEval);

// Add norms dynamically at runtime
engine.addNorm(new NormEntry(NormType.PROHIBITION, "",
    "deleteProduction", "Never delete production data", 100));
```

### NormEntry

Each norm entry specifies a type (obligation, prohibition, or permission), the action it applies to, an optional condition for when it is active, and a priority for conflict resolution.

```java
public record NormEntry(
    NormType type,       // OBLIGATION, PROHIBITION, PERMISSION
    String condition,    // When this norm is active (empty = always)
    String action,       // The action this norm applies to
    String description,  // Human-readable explanation
    int priority         // Higher = more important
) { }
```

### NormViolation

When an action violates a norm, the engine returns one or more `NormViolation` records explaining what went wrong.

```java
public record NormViolation(
    NormEntry norm,      // The violated norm
    String action,       // The action that violated it
    String description   // Explanation of the violation
) { }
```

## RefinementLoop

Iterative refinement that repeatedly processes outputs until they meet predefined quality standards. Runs checks after each iteration and re-prompts the LLM with specific failure details.

```java
RefinementLoop loop = RefinementLoop.builder()
    .task("Convert Python to TypeScript")
    .completionCriteria(CompletionCriteria.builder()
        .compilerCheck("tsc --noEmit")
        .testCommand("npm test")
        .mustNotContain("def ", "import ")
        .mustContain("interface", "export")
        .validJson()
        .build())
    .maxIterations(10)
    .timeout(Duration.ofMinutes(30))
    .onIteration(iter -> log.info("Iteration {} score: {}",
        iter.iterationNumber(), iter.evaluation().overallScore()))
    .build();

// Execute with an Agent
RefinementResult result = loop.execute(agent, pythonCode);

// Or with an LLMClient directly
RefinementResult result = loop.execute(llmClient, pythonCode);

result.getFinalOutput();       // The refined output
result.getIterations();        // Total iterations run
result.getStatus();            // SUCCESS, MAX_ITERATIONS, TIMEOUT, ERROR, STOPPED
result.getDuration();          // Total time
result.getHistory();           // List of IterationResult per iteration
```

### RefinementStatus

The final status tells you why the loop stopped, so you can handle each case appropriately.

| Status           | Description              |
| ---------------- | ------------------------ |
| `SUCCESS`        | All criteria met         |
| `MAX_ITERATIONS` | Hit iteration limit      |
| `TIMEOUT`        | Hit time limit           |
| `ERROR`          | LLM call failed          |
| `STOPPED`        | StopHook triggered early |

### CompletionCriteria

Defines the quality checks that must all pass for refinement to stop. You can combine compiler checks, content assertions, structural validations, custom predicates, and even LLM-based quality judgments.

```java
CompletionCriteria criteria = CompletionCriteria.builder()
    // Shell commands (compiler, test runner, linter)
    .compilerCheck("javac -d out *.java")
    .testCommand("mvn test -q")
    .lintCheck("eslint --quiet .")

    // Content presence/absence
    .mustContain("public class", "@Override")
    .mustNotContain("System.out.println", "TODO")

    // Structure checks
    .validJson()
    .minLines(10)
    .wordCount(50, 500)
    .matchesPattern("class\\s+\\w+\\s+implements\\s+\\w+")

    // Custom predicate
    .customCheck("no-any-type", code -> !code.contains(": any"),
        "Output must not contain TypeScript 'any' type")

    // LLM-based quality check (requires withLLM first)
    .withLLM(evalClient)
    .llmCheck("Is this idiomatic TypeScript?", 0.8)

    .build();

// Evaluate against output
EvaluationResult eval = criteria.evaluate(output);
eval.allCriteriaMet();       // true if all required checks passed
eval.overallScore();         // ratio of passed checks (0.0 to 1.0)
eval.passed();               // List<CheckResult>
eval.failed();               // List<CheckResult>
eval.getFailureReasons();    // List<String> of failure messages
```

### Built-in Checks

These are the available check types you can add to `CompletionCriteria`. Mix and match them to define exactly what "done" means for your use case.

| Method                          | Description                                    |
| ------------------------------- | ---------------------------------------------- |
| `compilerCheck(cmd)`            | Shell command must exit 0                      |
| `testCommand(cmd)`              | Test suite must pass                           |
| `lintCheck(cmd)`                | Linter must pass                               |
| `mustContain(strings...)`       | All strings must appear in output              |
| `mustNotContain(strings...)`    | None may appear in output                      |
| `validJson()`                   | Output must parse as JSON                      |
| `matchesPattern(regex)`         | Pattern must match                             |
| `wordCount(min, max)`           | Word count in range                            |
| `minLines(min)`                 | Minimum line count                             |
| `customCheck(name, predicate)`  | Any `Predicate<String>`                        |
| `llmCheck(question, threshold)` | LLM rates quality 0-100, must exceed threshold |

## PromptOptimizer

Automated prompt tuning through iterative refinement, strategy selection, and A/B testing against test cases.

```java
PromptOptimizer optimizer = PromptOptimizer.builder()
    .llmClient(client)
    .maxIterations(5)
    .targetScore(0.9f)
    .candidateStrategies(List.of(
        PromptStrategy.CHAIN_OF_THOUGHT,
        PromptStrategy.CHAIN_OF_VERIFICATION,
        PromptStrategy.CONFIDENCE_WEIGHTED))
    .build();

List<TestCase> testCases = List.of(
    new TestCase("What is 2+2?", "4"),
    new TestCase("Capital of France?", "Paris")
);

OptimizationResult result = optimizer.optimize("Answer the question:", testCases);
System.out.println("Best prompt: " + result.getBestPrompt());
System.out.println("Score: " + result.getScore());
System.out.println("Iterations: " + result.getIterations());
```

The optimizer tries each candidate strategy, evaluates against test cases, then uses LLM-based refinement to suggest further improvements. It stops when `targetScore` is reached or `maxIterations` is exhausted.

### Strategy Suggestions

If you are not sure which prompt strategy to use, the optimizer can analyze your task description and suggest strategies that are likely to work well.

```java
List<PromptStrategy> suggested = optimizer.suggestStrategies(
    "analyze and solve math problems");
// -> [CHAIN_OF_THOUGHT, STRUCTURED_THINKING]
```

## StructuredOutputExecutor

Ensures LLM outputs conform to a target type with automatic validation and retry on failure.

```java
StructuredOutputExecutor<OrderSummary> executor =
    StructuredOutputExecutor.<OrderSummary>builder()
        .llm(client)
        .targetType(OrderSummary.class)
        .outputFormat(OutputFormat.JSON)
        .rules(List.of(
            ValidationRule.notNull("orderId"),
            ValidationRule.range("total", 0, 100000),
            ValidationRule.pattern("email", ".*@.*\\..*")))
        .maxRetries(3)
        .systemPrompt("You are an order processing assistant.")
        .build();

StructuredOutputResult<OrderSummary> result = executor.generate(
    "Summarize this order: customer=John, items=3 widgets at $25 each"
);

if (result.success()) {
    OrderSummary order = result.value();
} else {
    System.out.println("Failed after " + result.attempts() + " attempts");
    System.out.println("Errors: " + result.errors());
}
```

### Retry Flow

The executor handles the common problem of LLMs producing malformed or invalid structured output by automatically retrying with targeted error feedback.

1. Generate output with format instructions appended to prompt
2. Deserialize response to target type
3. Run validation rules against deserialized data
4. On parse or validation failure: build correction prompt with specific errors
5. Repeat up to `maxRetries` times

The correction prompt includes the specific errors so the LLM can fix them directly rather than guessing what went wrong.

---

# Planning

URL: https://tnsai.dev/docs/capabilities/intelligence/planning
Description: Goal-oriented planning for AI agents. TnsAI provides three planner implementations: annotation-driven backward chaining, utility-based scoring, and LLM-powered dynamic planning with human-in-the-loop approval and adaptive replanning.

import { Callout } from 'fumadocs-ui/components/callout'

## Planner Interface

Every planner in TnsAI implements this interface, which defines how to generate action plans from the current world state. You can use one of the three built-in planners or register your own via `META-INF/services/com.tnsai.planning.Planner`.

```java
public interface Planner {
    List<PlanningAction> plan(Map<String, Object> state);
    List<PlanningAction> plan(Map<String, Object> state, boolean useChaining);
    List<PlanningGoal> getGoals();
    List<PlanningAction> getActions();
    List<PlanningGoal> findUnsatisfiedGoals(Map<String, Object> state);
    List<PlanningGoal> findSatisfiedGoals(Map<String, Object> state);
    boolean isGoalSatisfied(String goalName, Map<String, Object> state);
    List<PlanningAction> findActionsForGoal(PlanningGoal goal, Map<String, Object> state);
    List<PlanningAction> findApplicableActions(Map<String, Object> state);
    Map<String, Object> applyEffects(PlanningAction action, Map<String, Object> state);
}
```

## PlanningGoal

Goals define what the agent wants to achieve. Created from `@Goal` annotations or programmatically.

```java
// Simple goal with defaults
PlanningGoal goal = PlanningGoal.of("survive", "health > 0");

// Goal with priority
PlanningGoal urgent = PlanningGoal.of("heal", "health > 50", Priority.HIGH);

// Full constructor
PlanningGoal full = new PlanningGoal(
    "survive",        // name
    "health > 0",     // condition expression
    Priority.CRITICAL, // priority
    "Keep health above zero", // description
    true,              // persistent (re-evaluate after achievement)
    100                // deadline in ticks (-1 for none)
);
```

| Field        | Type       | Description                                |
| ------------ | ---------- | ------------------------------------------ |
| `name`       | `String`   | Unique goal identifier                     |
| `condition`  | `String`   | Boolean expression evaluated against state |
| `priority`   | `Priority` | Determines planning order (higher = first) |
| `persistent` | `boolean`  | Re-evaluate after achievement              |
| `deadline`   | `int`      | Ticks until expiry (-1 = none)             |

## PlanningAction

Actions represent things the agent can do, with preconditions, postconditions, and utility fields.

```java
// Simple factory
PlanningAction heal = PlanningAction.of(
    "heal", "health < 50", "health = 100", "survive");

// Builder with utility fields
PlanningAction search = PlanningAction.builder("search")
    .description("Search for resources")
    .precondition("energy > 10")
    .postcondition("resources = resources + 5")
    .fulfills("gather")
    .cost(10)
    .value(50)
    .weight(1.5f)
    .tags("exploration", "gathering")
    .build();

search.utility();           // 40 (value - cost)
search.weightedUtility();   // 60.0 (utility * weight)
search.fulfillsGoal("gather"); // true
search.hasTag("exploration");   // true
search.hasPrecondition();       // true
search.hasPostcondition();      // true
```

| Field           | Type          | Default  | Description                                  |
| --------------- | ------------- | -------- | -------------------------------------------- |
| `name`          | `String`      | required | Action identifier                            |
| `precondition`  | `String`      | `""`     | Condition that must be true before execution |
| `postcondition` | `String`      | `""`     | State changes after execution                |
| `fulfills`      | `Set<String>` | `{}`     | Goal names this action helps achieve         |
| `method`        | `Method`      | null     | Java method to invoke (null for simulation)  |
| `cost`          | `int`         | 1        | Execution cost for utility calculation       |
| `value`         | `int`         | 1        | Expected value for utility calculation       |
| `weight`        | `float`       | 1.0      | Multiplier for utility score                 |
| `tags`          | `Set<String>` | `{}`     | Tags for filtering/grouping                  |

## BackwardChainingPlanner

Starts from unsatisfied goals and works backward to find action sequences. Handles multi-step plans where one action's postcondition enables another's precondition.

### Annotation-Driven Setup

The easiest way to define goals, actions, and state is with annotations on a Java class. The planner reads `@Goal`, `@ActionSpec`, and `@State` annotations at construction time and builds the planning model automatically.

```java
@RoleSpec(
    name = "combat-medic",
    goals = {
        @Goal(name = "survive", condition = "health > 0", priority = Priority.CRITICAL),
        @Goal(name = "heal-team", condition = "teamHealth > 50", priority = Priority.HIGH),
        @Goal(name = "gather", condition = "supplies > 10", priority = Priority.NORMAL)
    }
)
public class CombatMedic {

    @State(name = "health")
    private int health = 100;

    @State(name = "supplies")
    private int supplies = 5;

    @State(name = "teamHealth")
    private int teamHealth = 30;

    @ActionSpec(
        description = "Use medkit to heal a teammate",
        precondition = "supplies > 0",
        postcondition = "teamHealth = 80, supplies = supplies - 1",
        fulfills = {"heal-team"}
    )
    public void healTeammate() { /* ... */ }

    @ActionSpec(
        description = "Search area for supplies",
        precondition = "health > 20",
        postcondition = "supplies = supplies + 3",
        fulfills = {"gather"}
    )
    public void searchForSupplies() { /* ... */ }
}
```

### Using the Planner

Once you have a planner, call `plan(state)` with the current world state to get an ordered list of actions. The backward chaining algorithm figures out which actions to execute and in what order to satisfy unsatisfied goals.

```java
// Create from annotated class
Planner planner = new BackwardChainingPlanner(CombatMedic.class);

// Extract current state from @State fields
Map<String, Object> state = BackwardChainingPlanner.extractState(medicInstance);
// state = {health=100, supplies=5, teamHealth=30}

// Generate plan (backward chaining enabled by default)
List<PlanningAction> plan = planner.plan(state);
// Result: [searchForSupplies, healTeammate]
// Because: need supplies first (heal-team precondition), then heal

// Query goals
planner.findUnsatisfiedGoals(state);  // [heal-team, gather]
planner.isGoalSatisfied("survive", state);  // true (health=100 > 0)

// Custom max depth
Planner planner = new BackwardChainingPlanner(CombatMedic.class, 5);

// Programmatic setup
Planner planner = new BackwardChainingPlanner(goals, actions, 8);
```

The backward chaining algorithm recurses up to `maxDepth` (default 10) and tracks visited actions to prevent infinite cycles.

## UtilityAIPlanner

Greedily selects the action with the highest utility score. Unlike backward chaining (goal-directed), utility AI is reactive -- it picks the best action at each step.

### Considerations

Considerations are scoring functions that evaluate how desirable each action is given the current state. The planner multiplies all consideration scores together to produce a final utility value for each action, then picks the highest one.

| Factory Method                          | Description                                            |
| --------------------------------------- | ------------------------------------------------------ |
| `Consideration.cost()`                  | Lower cost = higher score (inverse, normalized to 100) |
| `Consideration.cost(weight)`            | Weighted cost consideration                            |
| `Consideration.value()`                 | Higher value = higher score (normalized to 100)        |
| `Consideration.value(weight)`           | Weighted value consideration                           |
| `Consideration.utility()`               | value - cost, normalized                               |
| `Consideration.preconditionSatisfied()` | 1.0 if met, 0.0 if not                                 |
| `Consideration.hasTag(tag)`             | 1.0 if action has tag                                  |
| `Consideration.combine(...)`            | Weighted average of multiple                           |

```java
// Custom consideration
Consideration urgency = (action, state) -> {
    Integer priority = (Integer) state.get("taskPriority");
    return priority != null ? priority / 10.0f : 0.5f;
};
```

### Builder Pattern

You can build a UtilityAIPlanner programmatically by adding goals, actions, and considerations. The planner evaluates all actions against the considerations and selects the one with the highest combined score.

```java
UtilityAIPlanner planner = UtilityAIPlanner.builder()
    .goal(PlanningGoal.of("optimize", "efficiency > 80"))
    .action(PlanningAction.builder("cacheResults")
        .cost(5).value(40).fulfills("optimize").build())
    .action(PlanningAction.builder("parallelProcess")
        .cost(20).value(80).fulfills("optimize").build())
    .consideration(Consideration.cost(0.3f))
    .consideration(Consideration.value(0.5f))
    .consideration(Consideration.preconditionSatisfied())
    .build();

Optional<PlanningAction> best = planner.selectBestAction(state);
List<PlanningAction> ranked = planner.getActionsByUtility(state);
float score = planner.calculateUtility(action, state);
```

### Annotation-Driven with @Utility

Instead of building programmatically, you can annotate actions with `@Utility` to set their cost, value, and weight directly in the class definition. The planner reads these at construction time.

```java
@ActionSpec(
    description = "Cache query results",
    precondition = "cacheSize < maxCache",
    postcondition = "cacheHitRate = 0.8",
    fulfills = {"performance"},
    utility = @Utility(cost = 5, value = 40, weight = 1.2f, tags = {"cache"})
)
public void cacheResults() { /* ... */ }

UtilityAIPlanner planner = new UtilityAIPlanner(MyRole.class);
```

## LLMDynamicPlanner

Uses an LLM to decompose natural-language goals into executable step sequences. Suitable for open-ended tasks where actions cannot be predefined.

```java
LLMDynamicPlanner planner = LLMDynamicPlanner.builder()
    .llm(client)
    .capability(CapabilityDescriptor.of("search", "Search the web for information"))
    .capability(CapabilityDescriptor.of("write_file", "Write content to a file"))
    .capability(CapabilityDescriptor.of("run_tests", "Execute test suite"))
    .additionalContext("Project uses Java 21 with Maven")
    .temperature(0.2f)
    .build();

LLMPlan plan = planner.generatePlan("Create a summary of recent AI news");
System.out.println(plan.toDisplayString());
// Plan for: Create a summary of recent AI news
// Steps:
//   1. [search] Find recent AI news articles
//   2. [write_file] Write summary to output.md

for (LLMPlanStep step : plan.steps()) {
    System.out.printf("[%s] %s (args: %s)%n",
        step.actionName(), step.description(), step.arguments());
}
```

### LLMPlan

An immutable data structure representing the generated plan. It supports non-destructive modifications (removing steps, reordering) that return a new plan, which is useful for human-in-the-loop approval workflows where reviewers may want to adjust the plan before execution.

```java
plan.size();                        // Number of steps
plan.isEmpty();                     // True if no steps
plan.goal();                        // Original goal string
plan.reasoning();                   // LLM's overall strategy
plan.withoutStep(2);                // New plan without step at index 2
plan.withReorderedSteps(List.of(0, 2, 1)); // New plan with reordered steps
plan.remainingFrom(3);              // New plan with steps from index 3 onward
plan.toDisplayString();             // Human-readable format
```

### LLMPlanStep

Each step in an LLM-generated plan maps to one of the declared capabilities. It includes the action to execute, a human-readable description, optional arguments, and the LLM's reasoning for why this step is needed.

```java
LLMPlanStep step = LLMPlanStep.of(0, "search", "Find recent articles");
step.stepIndex();     // 0
step.actionName();    // "search"
step.description();   // "Find recent articles"
step.arguments();     // Map<String, Object>
step.reasoning();     // Why this step is needed
```

## PlanApprovalGate

Human-in-the-loop approval between plan generation and execution.

```java
PlanApprovalGate gate = PlanApprovalGate.builder()
    .reviewCallback(plan -> {
        System.out.println(plan.toDisplayString());
        System.out.print("Approve? (y/n): ");
        String input = scanner.nextLine();
        if ("y".equals(input)) return ApprovalDecision.approve();
        return ApprovalDecision.reject("User declined");
    })
    .autoApproveEmpty(true)
    .build();

Optional<LLMPlan> approved = gate.review(generatedPlan);
approved.ifPresent(plan -> engine.executePlan(plan));

// Generate + review in one call
Optional<LLMPlan> result = gate.generateAndReview(planner, "Deploy the app");

// Auto-approve for testing
PlanApprovalGate autoGate = PlanApprovalGate.autoApprove();
```

### ApprovalDecision

The reviewer's response to a proposed plan. Decisions can accept, reject, or modify the plan by removing or reordering steps.

| Factory                                       | Description                    |
| --------------------------------------------- | ------------------------------ |
| `ApprovalDecision.approve()`                  | Accept plan as-is              |
| `ApprovalDecision.reject(reason)`             | Reject with reason             |
| `ApprovalDecision.removeSteps(List<Integer>)` | Accept with steps removed      |
| `ApprovalDecision.reorder(List<Integer>)`     | Accept with reordered steps    |
| `ApprovalDecision.modify(removed, newOrder)`  | Accept with both modifications |

## AdaptiveReplanEngine

Executes LLM-generated plans with automatic replanning on step failure.

```java
AdaptiveReplanEngine engine = AdaptiveReplanEngine.builder()
    .llm(client)
    .planner(planner)
    .stepExecutor(step -> {
        try {
            String output = myToolRunner.run(step.actionName(), step.arguments());
            return StepExecutionResult.success(output);
        } catch (Exception e) {
            return StepExecutionResult.failure(e.getMessage());
        }
    })
    .maxReplanAttempts(3)
    .build();

PlanExecutionResult result = engine.execute("Deploy the application");
System.out.println(result.success());
System.out.println(result.replanCount());

// Execute an existing plan
PlanExecutionResult result = engine.executePlan(approvedPlan, currentState);
```

### Replanning Flow

When a step fails, the engine does not simply stop. Instead, it asks the LLM to create a revised plan that accounts for the failure, then continues execution. This makes plans resilient to unexpected errors.

1. Execute steps sequentially via `StepExecutor`
2. On failure: collect completed steps, error details, remaining steps
3. Call LLM with failure context to generate a revised plan
4. Continue execution with revised plan
5. Repeat up to `maxReplanAttempts` times

## Full Pipeline Example

This shows the recommended end-to-end workflow: the LLM generates a plan, a human reviews and approves it, and the adaptive engine executes it with automatic replanning on failure.

```java
// 1. Create planner with capabilities
LLMDynamicPlanner planner = LLMDynamicPlanner.builder()
    .llm(client).capabilities(capabilities).build();

// 2. Set up approval gate
PlanApprovalGate gate = PlanApprovalGate.builder()
    .reviewCallback(myReviewUI::showPlan).build();

// 3. Set up execution engine
AdaptiveReplanEngine engine = AdaptiveReplanEngine.builder()
    .llm(client).planner(planner)
    .stepExecutor(myExecutor).maxReplanAttempts(3).build();

// 4. Generate, approve, execute
Optional<LLMPlan> approved = gate.generateAndReview(planner, goal, state);
approved.ifPresent(plan -> {
    PlanExecutionResult result = engine.executePlan(plan, state);
    if (result.success()) {
        System.out.println("Goal achieved!");
    }
});
```

---

# Reasoning

URL: https://tnsai.dev/docs/capabilities/intelligence/reasoning
Description: Advanced reasoning strategies for complex problem solving. TnsAI provides multiple reasoning executors based on recent AI research, from simple chain-of-thought to graph-based reasoning with merging and refinement.

import { Callout } from 'fumadocs-ui/components/callout'

## ThinkingResult

When an LLM uses extended thinking (like Claude's chain-of-thought), TnsAI wraps the output in a `ThinkingResult` so you can inspect both the reasoning process and the final answer separately. This is useful for debugging, auditing, or displaying the model's step-by-step logic to users.

```java
ThinkingResult result = ThinkingResult.builder()
    .thinkingProcess("Step 1: Analyze the input... Step 2: Consider edge cases...")
    .finalAnswer("The optimal solution is X because...")
    .thinkingTokens(1200)
    .outputTokens(350)
    .thinkingBlocks(List.of("analysis block", "verification block"))
    .build();

System.out.println(result.hasThinking());      // true
System.out.println(result.getTotalTokens());    // 1550
System.out.println(result.getFinalAnswer());
```

| Method                 | Returns        | Description                     |
| ---------------------- | -------------- | ------------------------------- |
| `getThinkingProcess()` | `String`       | Full internal reasoning text    |
| `getFinalAnswer()`     | `String`       | Answer produced after thinking  |
| `getThinkingTokens()`  | `int`          | Tokens consumed by thinking     |
| `getOutputTokens()`    | `int`          | Tokens in the final answer      |
| `getTotalTokens()`     | `int`          | Sum of thinking + output tokens |
| `getThinkingBlocks()`  | `List<String>` | Individual thinking blocks      |
| `hasThinking()`        | `boolean`      | True if thinking was performed  |

## Tree of Thoughts (ToT)

Explores multiple reasoning paths by generating candidate thoughts, evaluating them, and pruning low-quality branches. Based on "Tree of Thoughts: Deliberate Problem Solving with Large Language Models" (Yao et al., 2023).

### TreeOfThoughtsExecutor

The executor manages the full exploration lifecycle: generating candidate thoughts at each depth, scoring them, pruning weak branches, and returning the best reasoning path found.

```java
TreeOfThoughtsExecutor tot = TreeOfThoughtsExecutor.builder()
    .llm(client)
    .evaluator(BranchEvaluator.llm(evalClient))
    .pruning(PruningStrategy.BEAM_SEARCH)
    .beamWidth(3)
    .maxDepth(5)
    .branchingFactor(3)
    .pruneThreshold(0.3)
    .timeout(Duration.ofMinutes(10))
    .build();

ToTResult result = tot.explore("Design a REST API for a todo app");
System.out.println(result.getBestPath());
System.out.println(result.getBestScore());
```

### Builder Parameters

These settings control how broadly and deeply the tree is explored, and when to stop.

| Parameter         | Default       | Description                       |
| ----------------- | ------------- | --------------------------------- |
| `llm`             | required      | LLMClient for thought generation  |
| `evaluator`       | required      | BranchEvaluator for scoring nodes |
| `pruning`         | `BEAM_SEARCH` | Pruning strategy                  |
| `beamWidth`       | 3             | Branches to keep per level        |
| `maxDepth`        | 5             | Maximum tree depth                |
| `branchingFactor` | 3             | Candidate thoughts per node       |
| `pruneThreshold`  | 0.3           | Minimum score to survive          |
| `timeout`         | 10 min        | Exploration time limit            |

### PruningStrategy

Pruning determines which branches to keep and which to discard during exploration. Choosing the right strategy lets you balance thoroughness against cost -- more aggressive pruning is faster and cheaper, while less pruning explores more possibilities.

| Strategy        | Description                                                    |
| --------------- | -------------------------------------------------------------- |
| `BEAM_SEARCH`   | Keep top-k branches at each level (default)                    |
| `BEST_FIRST`    | Always expand the highest cumulative-score node                |
| `DEPTH_LIMITED` | Explore all branches up to max depth                           |
| `GREEDY`        | Always pick the single best branch                             |
| `MCTS`          | Monte Carlo Tree Search -- balance exploration vs exploitation |
| `EXHAUSTIVE`    | No pruning -- explore all branches (expensive)                 |

### ToTResult

The result captures the full exploration tree, best path, and statistics:

```java
ToTResult result = tot.explore("Optimize this algorithm");

result.getBestPath();           // Combined thought chain of best leaf
result.getBestScore();          // Average score along best path
result.hasSolution();           // true if bestScore > 0.5
result.getBestLeaf();           // Optional<ThoughtNode>
result.getTopPaths(3);          // Top-3 leaf nodes by score

result.getTotalNodes();         // Total nodes explored
result.getPrunedNodes();        // Nodes pruned during exploration
result.getMaxDepthReached();    // Deepest level reached
result.getDuration();           // Total exploration time
```

### ThoughtNode

Each node in the tree represents a single reasoning step. Nodes track their score, depth, and links to parent and children, so you can traverse the full reasoning path from root to any leaf.

```java
ThoughtNode root = ThoughtNode.root("Design a caching strategy");
ThoughtNode child = root.addChild("n1", "Use LRU cache with TTL");
child.setScore(0.85);

child.getThoughtChain();     // "Design a caching strategy -> Use LRU cache with TTL"
child.getCumulativeScore();  // Sum of scores along path
child.getAverageScore();     // Average score along path
child.getDepth();            // 1
child.isLeaf();              // true (no children yet)
child.isRoot();              // false
child.isEvaluated();         // true (score was set)
child.isPruned();            // false
child.getPath();             // [root, child]
```

### BranchEvaluator

The evaluator scores each reasoning step on a 0.0-1.0 scale, which the pruning strategy uses to decide which branches to keep. You can use an LLM to judge quality, a fast heuristic, or combine both.

```java
// LLM-based: asks an LLM to rate the reasoning step 0-100
BranchEvaluator llmEval = BranchEvaluator.llm(evalClient);

// Heuristic: scores based on thought length and reasoning keywords
BranchEvaluator heuristic = BranchEvaluator.heuristic();

// Combined: averages multiple evaluators
BranchEvaluator combined = BranchEvaluator.combined(llmEval, heuristic);

// Custom evaluator
BranchEvaluator custom = (node, goal) -> {
    return node.getThought().contains("therefore") ? 0.8 : 0.4;
};
```

## Graph of Thoughts (GoT)

Extension of ToT that allows merging and refining thought branches. Better for problems where partial solutions can be combined. Based on "Graph of Thoughts" (Besta et al., 2023).

### GraphOfThoughtsExecutor

The executor drives the graph exploration, applying generate, aggregate, and refine operations to build a directed graph of thoughts rather than a strict tree.

```java
GraphOfThoughtsExecutor got = GraphOfThoughtsExecutor.builder()
    .llm(client)
    .evaluator(BranchEvaluator.llm(client))
    .operations(List.of(
        GoTOperation.GENERATE,
        GoTOperation.AGGREGATE,
        GoTOperation.REFINE))
    .maxNodes(20)
    .branchingFactor(3)
    .timeout(Duration.ofMinutes(10))
    .build();

GoTResult result = got.explore("Design a database schema for e-commerce");
System.out.println(result.getBestThought());
System.out.println(result.aggregatedInsight());
System.out.println(result.mergeCount());  // How many merge operations occurred
```

### GoTOperation

Operations define what the graph executor does at each step. Unlike ToT which only generates and evaluates, GoT can also merge partial solutions together and iteratively refine them.

| Operation   | Description                                     |
| ----------- | ----------------------------------------------- |
| `GENERATE`  | Generate new thoughts from existing ones        |
| `AGGREGATE` | Merge multiple thoughts into a unified solution |
| `REFINE`    | Improve an existing thought iteratively         |
| `SCORE`     | Evaluate a thought's quality                    |

### GoTNode

Unlike tree nodes, a `GoTNode` can have multiple parents because merge operations combine separate reasoning branches into one. This makes it a true graph structure rather than a tree.

```java
GoTNode root = GoTNode.root("Design a notification system");
GoTNode emailApproach = root.addChild("n1", "Use email queues", GoTOperation.GENERATE);
GoTNode pushApproach = root.addChild("n2", "Use push notifications", GoTOperation.GENERATE);

// Merge two approaches into one
GoTNode merged = GoTNode.merge("m1",
    "Hybrid: email for async, push for real-time",
    List.of(emailApproach, pushApproach));

merged.isMergeNode();       // true
merged.getParents().size(); // 2
```

### GoTResult

The result of a graph exploration, including the best thought found, aggregate insights from merging, and statistics about the exploration process.

`GoTResult` is a record with these fields:

```java
GoTResult result = got.explore("...");
result.getBestThought();      // Content of the highest-scored node
result.getBestScore();        // Score of best node
result.totalNodes();          // Total nodes in graph
result.mergeCount();          // Number of AGGREGATE operations
result.hasSolution();         // true if bestScore > 0.5
result.aggregatedInsight();   // Synthesized insight from best nodes
result.duration();            // Exploration time
```

## CausalReasoner

Causal reasoning engine for why, what-if, and intervention queries. Uses an LLM with a causal model description to analyze cause-effect relationships.

```java
CausalReasoner reasoner = CausalReasoner.builder()
    .llm(client)
    .causalModel("Sales depend on marketing spend, season, and competitor pricing")
    .build();

Map<String, Object> context = Map.of(
    "q3_sales", 150000,
    "marketing_budget", 50000,
    "season", "summer"
);

// Why did something happen?
CausalResult why = reasoner.why("Why did sales drop in Q3?", context);
System.out.println(why.answer());
System.out.println(why.reasoning());
System.out.println(why.confidence()); // 0.0-1.0

// What-if counterfactual
CausalResult whatIf = reasoner.whatIf("What if we doubled marketing?", context);

// Intervention prediction
CausalResult intervene = reasoner.intervene("Cut prices by 15%", context);
```

### CausalQueryType

Three types of causal queries are supported, each answering a different kind of question about cause and effect.

| Type           | Method                    | Purpose                        |
| -------------- | ------------------------- | ------------------------------ |
| `WHY`          | `reasoner.why(...)`       | Explain why something happened |
| `WHAT_IF`      | `reasoner.whatIf(...)`    | Counterfactual reasoning       |
| `INTERVENTION` | `reasoner.intervene(...)` | Predict effect of an action    |

### CausalResult

The result of a causal query, containing the explanation or prediction along with a confidence score indicating how certain the model is.

| Field        | Type              | Description                          |
| ------------ | ----------------- | ------------------------------------ |
| `queryType`  | `CausalQueryType` | Which type of query was made         |
| `answer`     | `String`          | The causal explanation or prediction |
| `reasoning`  | `String`          | Step-by-step reasoning chain         |
| `confidence` | `double`          | Confidence score (0.0-1.0)           |

## SelfConsistencyExecutor

Generates multiple reasoning paths and returns the consensus answer via majority voting or other aggregation. Based on "Self-Consistency Improves Chain of Thought Reasoning" (Wang et al., 2022).

```java
SelfConsistencyExecutor executor = SelfConsistencyExecutor.builder()
    .llm(client)
    .numPaths(5)
    .aggregation(Aggregation.MAJORITY_VOTE)
    .baseTemperature(0.7)
    .temperatureVariance(0.1)
    .parallel(true)
    .maxConcurrency(5)
    .timeout(Duration.ofMinutes(5))
    .systemPrompt("You are a math tutor.")
    .build();

ConsistencyResult result = executor.reason("What is 17 * 23?");
System.out.println(result.getConsensusAnswer()); // "391"
System.out.println(result.getConfidence());       // 0.8 (4/5 paths agreed)
System.out.println(result.isUnanimous());          // false
System.out.println(result.getAnswerCounts());      // {"391"=4, "392"=1}
```

### Builder Parameters

Configure how many reasoning paths to generate and how to combine them into a final answer.

| Parameter             | Default         | Description                           |
| --------------------- | --------------- | ------------------------------------- |
| `llm`                 | required        | LLMClient for reasoning               |
| `numPaths`            | 5               | Number of reasoning paths to generate |
| `aggregation`         | `MAJORITY_VOTE` | How to combine answers                |
| `baseTemperature`     | 0.7             | Base LLM temperature                  |
| `temperatureVariance` | 0.1             | Variance between path temperatures    |
| `systemPrompt`        | none            | Optional system prompt                |
| `parallel`            | true            | Execute paths in parallel             |
| `maxConcurrency`      | 5               | Max parallel threads                  |
| `timeout`             | 5 min           | Timeout for parallel execution        |

### Aggregation

The aggregation strategy determines how multiple reasoning paths are combined into a single consensus answer. Majority vote is the simplest and most common choice.

| Strategy        | Description                                      |
| --------------- | ------------------------------------------------ |
| `MAJORITY_VOTE` | Most common answer wins (default)                |
| `WEIGHTED_VOTE` | Weight by reasoning chain length                 |
| `UNANIMOUS`     | Require all paths to agree                       |
| `THRESHOLD`     | First answer appearing in \\> 50% of paths       |
| `LLM_SYNTHESIS` | Use LLM to synthesize best answer from all paths |

### ConsistencyResult

The result tells you which answer won the vote, how confident the consensus is, and lets you inspect each individual reasoning path for debugging.

```java
ConsistencyResult result = executor.reason("...");
result.getConsensusAnswer();  // The winning answer
result.getConfidence();       // Ratio of paths that agreed
result.getAllPaths();          // All ReasoningPath objects
result.getAnswerCounts();     // Map<String, Integer> of answer frequencies
result.getTotalPaths();       // Number of paths generated
result.isUnanimous();         // true if confidence >= 1.0
result.isConfident(0.8);      // true if confidence >= threshold
```

## Choosing a Strategy

Pick the strategy that matches your problem type and cost budget. Simple factual questions work well with self-consistency, while complex design problems benefit from tree or graph exploration.

| Strategy          | Best For                                       | Cost                                      |
| ----------------- | ---------------------------------------------- | ----------------------------------------- |
| Tree of Thoughts  | Step-by-step decomposition problems            | High (branching factor x depth LLM calls) |
| Graph of Thoughts | Problems where partial solutions combine       | Higher (includes merge/refine operations) |
| Self-Consistency  | Factual questions with verifiable answers      | Medium (N parallel LLM calls)             |
| Causal Reasoning  | Diagnosing causes and predicting interventions | Low (single LLM call per query)           |

---

# Advanced LLM Patterns

URL: https://tnsai.dev/docs/capabilities/llm/advanced
Description: Advanced capabilities in TnsAI.LLM for observability, structured output, resilience, caching, intelligent routing, and cost management.

import { Callout } from 'fumadocs-ui/components/callout'

## Providers

TnsAI.LLM ships with 13 concrete provider implementations (plus the `AbstractLLMClient` base), all implementing the `LLMClient` interface from tnsai-core. API keys are configured via environment variables.

| Provider      | Class               | Notes                                            |
| ------------- | ------------------- | ------------------------------------------------ |
| Anthropic     | `AnthropicClient`   | Claude models, prompt caching, extended thinking |
| OpenAI        | `OpenAIClient`      | GPT models, JSON mode, function calling          |
| Azure OpenAI  | `AzureOpenAIClient` | Azure-hosted OpenAI models                       |
| Google Gemini | `GeminiClient`      | Gemini models, vision, structured output         |
| AWS Bedrock   | `BedrockClient`     | Claude/Titan via AWS                             |
| Mistral       | `MistralClient`     | Mistral/Mixtral models                           |
| Cohere        | `CohereClient`      | Command models                                   |
| Groq          | `GroqClient`        | Ultra-low latency inference                      |
| HuggingFace   | `HuggingFaceClient` | Inference API models                             |
| Ollama        | `OllamaClient`      | Local model serving                              |
| OpenRouter    | `OpenRouterClient`  | Multi-provider gateway                           |
| MiniMax       | `MiniMaxClient`     | MiniMax models                                   |
| ZhipuAI       | `ZhipuAIClient`     | GLM models                                       |
| Whisper       | `WhisperClient`     | Audio transcription (audio package)              |

All providers support `chat()`, `streamChat()`, `streamChatWithSpec()`, and `streamChatWithHandler()` methods. Provider capabilities are exposed via `getCapabilities()` which returns `LLMCapabilities` with fields like `supportsVision()`, `supportsFunctionCalling()`, `supportsStructuredOutput()`, `getMaxInputTokens()`, `getInputCostPer1KTokens()`, etc.

> **Cross-reference**: For provider setup and basic usage, see [Providers](/docs/capabilities/llm/providers).

## Observability

### ObservableLLMClient

`ObservableLLMClient` wraps any `LLMClient` and notifies registered observers about all requests, responses, and errors. This enables non-invasive monitoring of LLM operations without modifying existing code.

```java
// Create base client
LLMClient baseClient = new OpenAIClient("gpt-4o");

// Create metrics collector
LLMMetrics metrics = new LLMMetrics();

// Wrap with observability
LLMClient observedClient = new ObservableLLMClient(baseClient, metrics);

// Use normally -- all calls are tracked
observedClient.chat("Hello!");

// Multiple observers
LLMMetrics metrics = new LLMMetrics();
PromptLogger logger = new PromptLogger();
LLMClient client = new ObservableLLMClient(baseClient, metrics, logger);

// Access internals
LLMClient delegate = ((ObservableLLMClient) client).getDelegate();
LLMObserver observer = ((ObservableLLMClient) client).getObserver();
```

### LLMObserver Interface

Implement `LLMObserver` for custom monitoring. All methods have default no-op implementations.

```java
public interface LLMObserver {
    void onRequest(LLMClient client, String message, Optional<String> systemPrompt,
                   Optional<List<Map<String, Object>>> history,
                   Optional<List<Map<String, Object>>> tools);
    void onResponse(LLMClient client, ChatResponse response, long latencyMs);
    void onError(LLMClient client, Exception error, long latencyMs);
    void onStreamChunk(LLMClient client, String chunk, int chunkIndex);
    void onStreamComplete(LLMClient client, int totalChunks, long latencyMs);
    void onStreamError(LLMClient client, Exception error, int chunksReceived, long latencyMs);
}
```

Compose multiple observers with `ObservableLLMClient.CompositeObserver.of(observer1, observer2)`.

### LLMMetrics

`LLMMetrics` implements `LLMObserver` and collects comprehensive metrics:

- Request/response/error counts (global and per-provider)
- Token usage estimates (input and output)
- Latency statistics (average, p50, p95, p99)
- Cost estimates based on provider pricing
- Stream chunk counts

```java
LLMMetrics metrics = new LLMMetrics();
LLMClient client = new ObservableLLMClient(baseClient, metrics);

// After some usage
LLMMetrics.Report report = metrics.getReport();
System.out.println("Total requests: " + report.totalRequests());
System.out.println("Total responses: " + report.totalResponses());
System.out.println("Total errors: " + report.totalErrors());
System.out.println("Success rate: " + report.successRate() + "%");
System.out.println("Error rate: " + report.errorRate() + "%");
System.out.println("Input tokens: " + report.totalInputTokens());
System.out.println("Output tokens: " + report.totalOutputTokens());
System.out.println("Estimated cost: $" + report.totalEstimatedCost());
System.out.println("Avg latency: " + report.avgLatencyMs() + "ms");
System.out.println("P95 latency: " + report.p95LatencyMs() + "ms");
System.out.println("P99 latency: " + report.p99LatencyMs() + "ms");

// Per-provider breakdown
Map<String, LLMMetrics.ProviderMetrics> byProvider = metrics.getMetricsByProvider();
for (var entry : byProvider.entrySet()) {
    LLMMetrics.ProviderMetrics pm = entry.getValue();
    System.out.println(entry.getKey() + ": " + pm.requests() + " requests, "
        + pm.avgLatencyMs() + "ms avg, $" + pm.estimatedCost());
}

metrics.reset();   // clear all metrics
```

## Structured Output (JSON Mode)

### JsonModeClient

`JsonModeClient` wraps any `LLMClient` to enforce JSON output. Uses provider-native JSON mode when available, falls back to prompt engineering for providers that lack native support.

```java
// Simple wrap
LLMClient baseClient = new OpenAIClient("gpt-4o");
JsonModeClient client = JsonModeClient.wrap(baseClient);

// Get JSON response
ChatResponse response = client.chat("List 3 programming languages");
// Response: {"languages": ["Python", "Java", "JavaScript"]}

// Parse to a specific type
LanguageList list = client.chatAs(LanguageList.class, "List 3 programming languages");

// With system prompt
Person person = client.chatAs(Person.class, "Generate a person",
    Optional.of("You are a test data generator."));
```

### With JSON Schema

```java
ResponseFormat format = ResponseFormat.jsonSchema("Person", Map.of(
    "type", "object",
    "properties", Map.of(
        "name", Map.of("type", "string"),
        "age", Map.of("type", "integer")
    ),
    "required", List.of("name", "age")
));

JsonModeClient client = JsonModeClient.builder()
    .client(baseClient)
    .responseFormat(format)
    .build();

ChatResponse response = client.chat("Generate a person");
// Response: {"name": "Alice", "age": 30}
```

### ResponseFormat

Represents the desired output format. Three types:

| Type          | Factory                                   | Behavior                           |
| ------------- | ----------------------------------------- | ---------------------------------- |
| `TEXT`        | `ResponseFormat.text()`                   | Default text output                |
| `JSON_OBJECT` | `ResponseFormat.jsonObject()`             | Valid JSON, structure not enforced |
| `JSON_SCHEMA` | `ResponseFormat.jsonSchema(name, schema)` | JSON conforming to provided schema |

Generate schema from a class: `ResponseFormat.jsonSchema("Person", Person.class)`.

Convert to provider-specific formats: `format.toOpenAIFormat()`, `format.toGeminiFormat()`, `format.toOllamaFormat()`.

Key methods: `isJson()`, `hasSchema()`, `isStrict()`, `getSchema()`, `getSchemaName()`.

### Advanced Options

```java
JsonModeClient client = JsonModeClient.builder()
    .client(baseClient)
    .responseFormat(format)
    .objectMapper(customMapper)              // custom Jackson ObjectMapper
    .forcePromptEngineering(true)            // skip native JSON mode, always use prompt engineering
    .schemaFromClass("Person", Person.class) // generate schema from class
    .build();

// Check native support
boolean nativeSupport = client.supportsNativeJsonMode();

// Parse raw JSON
Person p = client.parseResponse("{\"name\":\"Alice\",\"age\":30}", Person.class);
```

On parse failure, `JsonModeClient.JsonParseException` is thrown, which contains `getRawContent()` for debugging.

## Resilience

### CircuitBreakerClient

`CircuitBreakerClient` prevents cascading failures by fast-failing when a provider is consistently down. Implements the standard three-state circuit breaker pattern.

**State transitions**: `CLOSED` (normal, counting failures) -\\> `OPEN` (fast-fail after N consecutive failures) -\\> `HALF_OPEN` (after recovery timeout, allows one probe request) -\\> `CLOSED` (if probe succeeds) or back to `OPEN` (if probe fails).

```java
// Simple wrap (5 failures, 30s recovery)
LLMClient resilient = CircuitBreakerClient.wrap(openaiClient);

// Custom settings
LLMClient resilient = CircuitBreakerClient.builder()
    .client(openaiClient)
    .failureThreshold(3)
    .recoveryTimeout(Duration.ofSeconds(60))
    .build();

// Inspect state
CircuitBreakerClient cb = (CircuitBreakerClient) resilient;
CircuitBreakerClient.State state = cb.getState();        // CLOSED, OPEN, HALF_OPEN
int failures = cb.getConsecutiveFailures();

// Metrics
CircuitBreakerClient.CircuitBreakerMetrics metrics = cb.getMetrics();
System.out.println("Success rate: " + metrics.successRate() + "%");
System.out.println("Total requests: " + metrics.totalRequests());
System.out.println("Rejected (fast-fail): " + metrics.rejectedCount());
System.out.println("State transitions: " + metrics.stateTransitions());

// Manual reset
cb.reset();
```

When the circuit is open, all requests throw `CircuitOpenException` (contains model name, failure count, recovery timeout, and trip time). Compose with `FallbackRouter` for automatic failover:

```java
FallbackRouter router = FallbackRouter.of(
    CircuitBreakerClient.wrap(primary),
    CircuitBreakerClient.wrap(fallback)
);
```

## Caching

### PromptCachingClient

`PromptCachingClient` wraps any `LLMClient` and adds Anthropic-style prompt caching support. Automatically adds cache control markers to system prompts, tools, and conversation history breakpoints.

```java
PromptCachingClient client = PromptCachingClient.builder()
    .client(anthropicClient)
    .cacheSystemPrompt(true)          // cache system prompt (default: true)
    .cacheTools(true)                 // cache tool definitions (default: true)
    .cacheHistoryBreakpoints(2)       // cache breakpoints in history (max 4)
    .minTokensForCaching(1024)        // minimum tokens to trigger caching (default: 1024)
    .build();

// Use normally -- caching is automatic
ChatResponse response = client.chat("Hello", systemPrompt, history, tools);

// Check cache statistics
System.out.println("Cache read tokens: " + client.getTotalCacheReadTokens());
System.out.println("Cache creation tokens: " + client.getTotalCacheCreationTokens());
System.out.println("Hit rate: " + client.getCacheHitRate());
System.out.println("Estimated savings: " + (client.getEstimatedSavings() * 100) + "%");
System.out.println("Requests: " + client.getRequestCount());

client.resetStats();
```

**Cost savings**: Cache reads are 90% cheaper than regular input tokens. Cache writes are 25% more expensive (one-time cost). TTL is 5 minutes, refreshed on each use.

> **Cross-reference**: For more on caching strategies, see [Caching](/docs/capabilities/llm/caching).

### SemanticCache

The `SemanticCache` interface provides similarity-based caching for LLM responses. Unlike exact-match caching, it matches semantically equivalent prompts using embedding vectors.

```java
SemanticCache cache = InMemorySemanticCache.builder()
    .embeddingProvider(new OpenAIEmbeddingProvider())
    .highThreshold(0.95)       // direct hit threshold
    .lowThreshold(0.70)        // below this, skip cache
    .ttlSeconds(3600)          // 1-hour TTL
    .maxEntries(10000)
    .build();

// Check cache
Optional<CacheEntry> hit = cache.findSimilar("What is Python?", 0.90);
if (hit.isPresent()) {
    return hit.get().response();   // cache hit
}

// Cache miss -- call LLM and store
String response = llm.chat("What is Python?");
cache.put("What is Python?", response);

// With system prompt consideration
cache.findSimilar("What is Python?", Optional.of("Be concise"), 0.90);
cache.put("What is Python?", Optional.of("Be concise"), response);

// Find multiple similar entries
List<SemanticCache.SimilarityResult> results = cache.findAllSimilar("Python language", 0.70, 5);

// Statistics
SemanticCache.CacheStats stats = cache.getStats();
System.out.println("Hits: " + stats.hits());
System.out.println("Misses: " + stats.misses());
System.out.println("Hit rate: " + stats.hitRate());
System.out.println("Size: " + stats.currentSize());
System.out.println("Evictions: " + stats.evictions());
```

## Routing

TnsAI.LLM provides multiple routing strategies that implement `LLMRouter` (which extends `LLMClient`). All routers can be used as drop-in replacements for a single client.

### CapabilityRouter

Routes requests based on required capabilities (vision, function calling, structured output, context window size). Selects the first eligible client matching the capability filter.

```java
CapabilityRouter router = CapabilityRouter.builder()
    .addClient(new OpenAIClient("gpt-4o"))         // vision + tools
    .addClient(new GroqClient("llama-3.3-70b"))    // tools only
    .addClient(new OllamaClient("llama3.2"))       // basic text
    .defaultRequirement(cap -> cap.supportsFunctionCalling())
    .build();

// Use as a normal LLMClient
router.chat("Use the search tool");

// Select specific capability on demand
Optional<LLMClient> visionClient = router.selectVisionCapable();
Optional<LLMClient> toolClient = router.selectToolCapable();
Optional<LLMClient> jsonClient = router.selectStructuredOutputCapable();
Optional<LLMClient> bigContext = router.selectWithMinContext(128_000);

// Generic capability filter
Optional<LLMClient> custom = router.selectByCapability(
    cap -> cap.supportsVision() && cap.supportsFunctionCalling());

// Statistics
LLMRouter.RoutingStats stats = router.getStats();
router.resetStats();
```

### CostBasedRouter

Routes to the cheapest viable provider. Sorts clients by input cost and tries the cheapest first, falling back to more expensive options on failure. Can reduce costs by up to 85% for simple queries.

```java
CostBasedRouter router = CostBasedRouter.builder()
    .addClient(new OpenAIClient("gpt-4o-mini"))         // $0.15/1M input
    .addClient(new GroqClient("llama-3.3-70b"))         // $0.59/1M input
    .addClient(new OpenAIClient("gpt-4o"))              // $2.50/1M input
    .addClient(new AnthropicClient("claude-sonnet-4"))   // $3.00/1M input
    .build();

// Simple queries go to cheapest model
router.chat("What is 2+2?");

// With capability requirement
CostBasedRouter visionRouter = CostBasedRouter.builder()
    .addClient(new OpenAIClient("gpt-4o-mini"))
    .addClient(new OpenAIClient("gpt-4o"))
    .requireCapability(cap -> cap.supportsVision())
    .build();

// Cost tracking
CostBasedRouter.CostStats stats = router.getCostStats();
System.out.println("Total estimated cost: $" + stats.totalEstimatedCost());
System.out.println("Cost per provider: " + stats.costPerProvider());
System.out.println("Input tokens: " + stats.totalInputTokens());
System.out.println("Output tokens: " + stats.totalOutputTokens());
```

### LatencyBasedRouter

Routes to the fastest available provider. Learns from actual response times and adapts routing decisions using a moving average window of the last 20 measurements.

```java
LatencyBasedRouter router = LatencyBasedRouter.builder()
    .addClient(new GroqClient("llama-3.3-70b"))          // ~100ms TTFT
    .addClient(new OpenAIClient("gpt-4o-mini"))          // ~300ms TTFT
    .addClient(new AnthropicClient("claude-sonnet-4"))    // ~600ms TTFT
    .maxLatencyMs(500)       // exclude providers slower than 500ms
    .build();

// Routes to fastest (Groq) automatically, adapts over time
router.chat("Quick question");

// Latency statistics
LatencyBasedRouter.LatencyStats stats = router.getLatencyStats();
System.out.println("Fastest: " + stats.fastestProvider() + " (" + stats.fastestLatencyMs() + "ms)");
System.out.println("Per provider: " + stats.avgLatencyPerProvider());
```

Initially uses estimated latency from `LLMCapabilities.getEstimatedLatencyMs()`. As actual measurements accumulate, routing decisions shift to measured performance. Failed requests are penalized with +5000ms latency to deprioritize unreliable providers.

> **Cross-reference**: For routing basics and FallbackRouter, see [Routing](/docs/capabilities/llm/routing).

## Cost Management

### CostTracker

The `CostTracker` interface provides a unified API for recording and analyzing LLM usage costs. The `InMemoryCostTracker` is the default implementation.

```java
CostTracker tracker = new InMemoryCostTracker();

// Record usage
UsageRecord record = UsageRecord.builder()
    .modelId("gpt-4o")
    .inputTokens(1000)
    .outputTokens(500)
    .build();
tracker.record(record);

// Query
List<UsageRecord> all = tracker.getRecords();
List<UsageRecord> byTime = tracker.getRecords(Instant.now().minus(Duration.ofHours(1)), Instant.now());
List<UsageRecord> byModel = tracker.getRecordsByModel("gpt-4o");
List<UsageRecord> byProvider = tracker.getRecordsByProvider("openai");

// Costs
BigDecimal total = tracker.getTotalCost();
BigDecimal periodCost = tracker.getTotalCost(periodStart, periodEnd);
Map<String, BigDecimal> byModelCost = tracker.getCostByModel();
Map<String, BigDecimal> byProviderCost = tracker.getCostByProvider();

// Statistics
CostTracker.CostStatistics stats = tracker.getStatistics();
System.out.println("Records: " + stats.recordCount());
System.out.println("Total cost: $" + stats.totalCost());
System.out.println("Avg cost/request: $" + stats.averageCostPerRequest());
System.out.println("Input tokens: " + stats.totalInputTokens());
System.out.println("Output tokens: " + stats.totalOutputTokens());
System.out.println("Cached tokens: " + stats.totalCachedTokens());
System.out.println("Avg latency: " + stats.averageLatencyMs() + "ms");
stats.mostExpensiveRequest().ifPresent(r ->
    System.out.println("Most expensive: " + r.modelId() + " $" + r.cost()));
```

### BudgetManager

`BudgetManager` provides configurable spending limits with automatic enforcement, alert thresholds, and time-based budget periods. Thread-safe for concurrent use.

```java
BudgetManager budget = BudgetManager.builder()
    .limit(100.00)                             // $100 budget
    .monthly()                                 // or .daily() or .period(Duration.ofDays(7))
    .alertThresholds(0.50, 0.80, 0.90, 0.95)  // or .defaultAlertThresholds()
    .hardLimit(true)                           // hard limit (default) vs .softLimit()
    .costTracker(tracker)                      // optional: sync from CostTracker
    .onAlert(alert -> log.warn("Budget alert: {}", alert))
    .onLimitExceeded(cost -> stopRequests())
    .build();

// Atomic check-and-spend (prevents TOCTOU race conditions)
if (budget.trySpend(new BigDecimal("0.05"))) {
    // make API call
} else {
    // budget exceeded
}

// Or separate check/spend
if (budget.canSpend(estimatedCost)) {
    // make API call
    budget.recordSpend(actualCost);    // returns false if limit exceeded
}

// Query status
BigDecimal remaining = budget.getRemainingBudget();
double usage = budget.getUsagePercent();               // 0.0 to 1.0+
Duration timeLeft = budget.getRemainingTime();

// Comprehensive status
BudgetManager.BudgetStatus status = budget.getStatus();
System.out.println("State: " + status.state());       // OK, WARNING, CRITICAL, EXCEEDED, UNLIMITED
System.out.println("Spend: $" + status.currentSpend() + " / $" + status.limit());
System.out.println("Remaining: $" + status.remaining());

// Manual reset
budget.reset();
```

**BudgetState values**: `OK` (\\< 70%), `WARNING` (70-90%), `CRITICAL` (90-100%), `EXCEEDED` (\\> 100%), `UNLIMITED` (no limit set).

**BudgetAlertType values**: `THRESHOLD_REACHED`, `LIMIT_EXCEEDED`, `PERIOD_RESET`.

Budgets automatically reset when the period elapses. If a `CostTracker` is provided, the budget syncs spend from tracked records at each period reset.

> **Cross-reference**: For cost tracking basics, see [Cost Tracking](/docs/capabilities/llm/cost-tracking).

---

# Audio & Speech

URL: https://tnsai.dev/docs/capabilities/llm/audio
Description: The WhisperClient provides speech-to-text capabilities powered by OpenAI's Whisper model. It supports transcription in multiple languages and translation of non-English audio to English.

import { Callout } from 'fumadocs-ui/components/callout'

## Quick Start

Two lines of code to transcribe or translate any audio file. The client handles file upload, API communication, and retry logic.

```java
WhisperClient whisper = new WhisperClient();

// Transcribe audio from a file
String text = whisper.transcribe(new File("speech.mp3"));

// Translate non-English audio to English
String english = whisper.translate(new File("french_speech.mp3"));
```

## Builder

When you need to use a custom API key or base URL (for example, if you are running a Whisper-compatible server), use the builder.

```java
WhisperClient whisper = WhisperClient.builder()
    .model("whisper-1")          // Model name (default: "whisper-1")
    .apiKey("sk-...")            // API key (default: OPENAI_API_KEY env var)
    .baseUrl("https://...")      // Base URL (default: OPENAI_BASE_URL or OpenAI)
    .build();
```

## Transcription

Convert speech audio into text. Three overloads are available, from a simple one-liner to a fully configurable version with language hints, timestamps, and custom response formats.

```java
// 1. File in, text out
String text = whisper.transcribe(new File("speech.mp3"));

// 2. AudioPart in, result out (default options)
TranscriptionResult result = whisper.transcribe(AudioPart.fromFile(new File("speech.mp3")));

// 3. AudioPart + options, result out
TranscriptionResult result = whisper.transcribe(
    AudioPart.fromFile(new File("meeting.wav")),
    TranscriptionOptions.builder()
        .language("en")
        .responseFormat(ResponseFormat.VERBOSE_JSON)
        .timestampGranularities(List.of("word", "segment"))
        .temperature(0.0f)
        .prompt("Technical meeting about AI architecture")
        .build()
);

// Access result fields
String text = result.getText();
result.getLanguage().ifPresent(lang -> System.out.println("Detected: " + lang));
result.getDuration().ifPresent(dur -> System.out.println("Duration: " + dur + "s"));

if (result.hasWords()) {
    result.getWords().forEach(w -> System.out.println(w));
}
if (result.hasSegments()) {
    result.getSegments().forEach(s -> System.out.println(s));
}
```

### TranscriptionOptions

Fine-tune the transcription by specifying the language, providing a context prompt, or requesting word-level timestamps.

| Parameter                | Type             | Default          | Description                                                   |
| ------------------------ | ---------------- | ---------------- | ------------------------------------------------------------- |
| `language`               | `String`         | Auto-detect      | ISO-639-1 language code (e.g., `"en"`, `"tr"`, `"fr"`)        |
| `prompt`                 | `String`         | None             | Optional prompt to guide style or continue a previous segment |
| `responseFormat`         | `ResponseFormat` | `JSON`           | Output format (see below)                                     |
| `temperature`            | `float`          | Provider default | Sampling temperature (0.0 = deterministic)                    |
| `timestampGranularities` | `List<String>`   | Empty            | `"word"` and/or `"segment"` (requires `VERBOSE_JSON` format)  |

### TranscriptionResult

The result always includes the transcribed text. When using `VERBOSE_JSON` format, you also get the detected language, audio duration, and optional word/segment timestamps.

| Method          | Return Type                 | Description                                   |
| --------------- | --------------------------- | --------------------------------------------- |
| `getText()`     | `String`                    | The transcribed text                          |
| `getLanguage()` | `Optional<String>`          | Detected language (verbose JSON only)         |
| `getDuration()` | `Optional<Double>`          | Audio duration in seconds (verbose JSON only) |
| `getSegments()` | `List<Map<String, Object>>` | Segment-level timestamps                      |
| `getWords()`    | `List<Map<String, Object>>` | Word-level timestamps                         |
| `hasSegments()` | `boolean`                   | Whether segment data is present               |
| `hasWords()`    | `boolean`                   | Whether word data is present                  |

## Translation

Translate audio in any supported language into English text. The source language is detected automatically -- you do not need to specify it.

```java
// Simple file translation
String english = whisper.translate(new File("turkish_speech.mp3"));

// With options
String english = whisper.translate(
    AudioPart.fromFile(new File("german_lecture.wav")),
    TranslationOptions.builder()
        .responseFormat(ResponseFormat.TEXT)
        .temperature(0.0f)
        .prompt("Academic lecture on physics")
        .build()
);
```

### TranslationOptions

Similar to transcription options but without a language parameter, since translation always auto-detects the source language.

| Parameter        | Type             | Default          | Description                                |
| ---------------- | ---------------- | ---------------- | ------------------------------------------ |
| `prompt`         | `String`         | None             | Optional prompt to guide translation style |
| `responseFormat` | `ResponseFormat` | `JSON`           | Output format                              |
| `temperature`    | `float`          | Provider default | Sampling temperature                       |

Translation always outputs English. There is no language parameter -- the source language is detected automatically.

## ResponseFormat

Choose the output format based on what you need. Use `TEXT` for simple transcriptions, `VERBOSE_JSON` for timestamps and metadata, or `SRT`/`VTT` for subtitle generation.

| Value          | Description                                                      |
| -------------- | ---------------------------------------------------------------- |
| `JSON`         | Returns `{"text": "..."}`                                        |
| `TEXT`         | Returns plain text                                               |
| `SRT`          | Returns SubRip subtitle format                                   |
| `VTT`          | Returns WebVTT subtitle format                                   |
| `VERBOSE_JSON` | Returns text + language, duration, segments, and word timestamps |

```java
// Subtitle generation
TranscriptionResult srt = whisper.transcribe(
    AudioPart.fromFile(new File("video.mp4")),
    TranscriptionOptions.builder()
        .responseFormat(ResponseFormat.SRT)
        .build()
);
```

## AudioPart

`AudioPart` is a content wrapper from `tnsai-core` that handles the details of encoding and formatting audio data for API submission. Create one from whichever source you have -- file, bytes, Base64 string, or URL.

```java
// From file (reads and Base64-encodes)
AudioPart audio = AudioPart.fromFile(new File("speech.wav"));

// From Base64 string
AudioPart audio = AudioPart.fromBase64(base64String, "audio/mp3");

// From byte array
AudioPart audio = AudioPart.fromBytes(rawBytes, "audio/wav");

// From URL
AudioPart audio = AudioPart.fromUrl("https://example.com/audio.mp3");
```

## Supported Audio Formats

The following audio formats are accepted by the Whisper API: mp3, mp4, mpeg, mpga, m4a, wav, webm, ogg, flac, aac, aiff.

Maximum file size: **25 MB**.

## Configuration

Set your OpenAI API key to authenticate with the Whisper service. An optional base URL override is available for self-hosted or proxy deployments.

| Environment Variable | Required | Description                                            |
| -------------------- | -------- | ------------------------------------------------------ |
| `OPENAI_API_KEY`     | Yes      | OpenAI API key                                         |
| `OPENAI_BASE_URL`    | No       | Custom base URL (default: `https://api.openai.com/v1`) |

## Error Handling

The client includes built-in resilience so you do not need to implement retry logic yourself. Transient errors (network issues, rate limits) are retried up to 3 times with exponential backoff. Permanent failures throw `LLMException`.

```java
try {
    String text = whisper.transcribe(new File("speech.mp3"));
} catch (LLMException e) {
    System.err.println("Transcription failed: " + e.getMessage());
} catch (IllegalArgumentException e) {
    System.err.println("File too large or invalid: " + e.getMessage());
}
```

---

# LLM Caching

URL: https://tnsai.dev/docs/capabilities/llm/caching
Description: Reduce latency and cost with semantic response caching. The cache uses similarity matching so that near-identical prompts return cached responses without hitting the API.

import { Callout } from 'fumadocs-ui/components/callout'

## Setup

Wrap any `LLMClient` with `CachedLLMClient` to enable semantic caching. The cache compares new prompts against stored ones using embedding similarity, so even rephrased questions can hit the cache.

```java
LLMClient cached = CachedLLMClient.wrap(baseClient)
    .withCache(InMemorySemanticCache.builder()
        .embeddingProvider(new OpenAIEmbeddingProvider())
        .ttlSeconds(3600)
        .build())
    .highThreshold(0.95)   // Direct cache hit
    .lowThreshold(0.70)    // Similarity threshold
    .cacheStreaming(true)   // Cache streaming responses
    .build();
```

## How It Works

The cache uses a two-threshold system to decide when a stored response is "close enough" to reuse. This avoids returning stale answers for slightly different questions while still catching exact or near-exact duplicates.

1. A prompt comes in
2. The cache computes semantic similarity against stored prompts
3. If similarity \\>= `highThreshold` (0.95) → **direct hit**, return cached response
4. If similarity between `lowThreshold` and `highThreshold` → **gray zone**, may verify
5. If similarity \\< `lowThreshold` (0.70) → **cache miss**, call LLM and store result

## Configuration

Tune the similarity thresholds to control the tradeoff between cache hit rate and answer accuracy. Lower thresholds give more hits but risk returning less relevant cached responses.

| Parameter                  | Default                 | Description                            |
| -------------------------- | ----------------------- | -------------------------------------- |
| `withCache(SemanticCache)` | `InMemorySemanticCache` | Semantic cache implementation to use   |
| `highThreshold`            | 0.95                    | Similarity score for direct cache hit  |
| `lowThreshold`             | 0.70                    | Minimum similarity to consider a match |
| `cacheStreaming`           | true                    | Whether to cache streaming responses   |

TTL and max size are configured on the `SemanticCache` implementation (e.g., `InMemorySemanticCache.builder().ttlSeconds(3600).build()`).

## Prompt Caching

Some providers (notably Anthropic) offer native prompt caching that lets you reuse previously processed system prompts, tools, and conversation prefixes at dramatically reduced cost. `PromptCachingClient` wraps any client and automatically adds the required cache control markers -- you do not need to modify your prompts manually.

### Builder

Configure which parts of the request to cache and how many breakpoints to place in conversation history.

```java
PromptCachingClient client = PromptCachingClient.builder()
    .client(anthropicClient)         // Required: LLM client to wrap
    .cacheSystemPrompt(true)         // Cache the system prompt (default: true)
    .cacheTools(true)                // Cache tool definitions (default: true)
    .cacheHistoryBreakpoints(2)      // Number of history cache points (default: 4, max: 4)
    .minTokensForCaching(1024)       // Minimum token threshold (default: 1024, min: 1024)
    .build();
```

### How It Works

`PromptCachingClient` transparently modifies outgoing requests to add cache markers. Your application code uses the client like any other `LLMClient` -- the caching is invisible.

- **System prompt**: Marked for caching so it is reused across requests without re-processing.
- **Tools**: A `cache_control` marker is added to the last tool definition (per Anthropic's recommendation).
- **History**: Cache breakpoints are distributed evenly across conversation history. With `cacheHistoryBreakpoints(2)` and 10 messages, breakpoints are placed at positions \~3 and \~6.

Usage is identical to any `LLMClient` -- caching is automatic:

```java
ChatResponse response = client.chat("Hello", systemPrompt, history, tools);

// Streaming works too
Stream<String> tokens = client.streamChat("Tell me more", systemPrompt, history, tools);
Stream<ChatChunk> chunks = client.streamChatWithSpec("Continue", systemPrompt, history, tools);
```

### Cost Savings

Prompt caching provides significant cost savings, especially for applications with long system prompts or many tools. The initial cache write is slightly more expensive, but every subsequent read saves 90%.

| Operation   | Cost Impact                               |
| ----------- | ----------------------------------------- |
| Cache read  | **90% cheaper** than regular input tokens |
| Cache write | **25% more expensive** (one-time cost)    |
| Cache TTL   | 5 minutes (refreshed on each use)         |

Over multiple requests with the same system prompt and tools, savings compound rapidly.

### Statistics

Monitor your cache effectiveness with built-in counters. Track hit rates and estimated savings to verify that caching is working as expected.

```java
// Token counters
long readTokens = client.getTotalCacheReadTokens();
long creationTokens = client.getTotalCacheCreationTokens();
long requests = client.getRequestCount();

// Cache hit rate (0.0 to 1.0)
double hitRate = client.getCacheHitRate();

// Estimated savings as a fraction (e.g., 0.85 = 85% savings)
// Accounts for read savings (90%) minus creation overhead (25%)
double savings = client.getEstimatedSavings();

// Reset all counters
client.resetStats();
```

Per-response cache usage is also available on `ChatResponse`:

```java
ChatResponse response = client.chat("Hello", systemPrompt, history, tools);

if (response.hasCacheUsage()) {
    response.getCacheReadInputTokens().ifPresent(
        tokens -> System.out.println("Cache read: " + tokens));
    response.getCacheCreationInputTokens().ifPresent(
        tokens -> System.out.println("Cache created: " + tokens));
}
```

### Configuration Introspection

Inspect the current caching configuration at runtime, useful for debugging or logging the active settings.

```java
client.isCacheSystemPromptEnabled();   // boolean
client.isCacheToolsEnabled();          // boolean
client.getCacheHistoryBreakpoints();   // int
client.getDelegate();                  // underlying LLMClient
```

## Semantic Cache Interface

If the built-in `InMemorySemanticCache` does not fit your needs (for example, you want Redis-backed caching or persistence across restarts), implement the `SemanticCache` interface with your own storage backend.

```java
public interface SemanticCache {
    Optional<String> get(String prompt);
    void put(String prompt, String response);
    void invalidate(String prompt);
    void clear();
}
```

Built-in: `InMemorySemanticCache` (thread-safe, LRU eviction).

---

# Cost Tracking

URL: https://tnsai.dev/docs/capabilities/llm/cost-tracking
Description: Monitor and control LLM spending across providers with built-in cost tracking, budget management, and model pricing data for 100+ models.

import { Callout } from 'fumadocs-ui/components/callout'

## Setup

Wrap any `LLMClient` with `CostAwareLLMClient` to start tracking costs automatically. Every request records its token usage and calculates cost based on the model's pricing.

```java
// Wrap with default cost tracking
LLMClient tracked = CostAwareLLMClient.wrap(client);

// Or use the builder for full control
CostAwareLLMClient tracked = CostAwareLLMClient.builder()
    .client(client)
    .costTracker(new InMemoryCostTracker())
    .budgetManager(budget)
    .build();
```

## Querying Costs

Access accumulated cost data at any time. You can get the total across all models, break down by individual model, or query a specific time range.

```java
tracker.getTotalCost();                            // Total across all models
tracker.getCostByModel("gpt-4o");                  // Per-model breakdown
tracker.getCostInRange(startTime, endTime);         // Time-range query
```

## Budget Management

Set spending limits to prevent runaway costs. When the budget is exceeded, the `CostAwareLLMClient` will reject further requests. You can also set an alert threshold to get early warnings before hitting the limit.

```java
BudgetManager budget = BudgetManager.builder()
    .limit(new BigDecimal("50.00"))
    .daily()                          // Duration.ofDays(1)
    .alertThreshold(0.80)
    .build();

// Or monthly budget
BudgetManager monthly = BudgetManager.builder()
    .limit(new BigDecimal("500.00"))
    .monthly()                        // Duration.ofDays(30)
    .alertThreshold(0.80)
    .build();
```

## Model Pricing

TnsAI ships with built-in pricing data for 100+ models so cost calculations work out of the box. Prices are in USD per 1 million tokens.

| Model            | Input  | Output |
| ---------------- | ------ | ------ |
| gpt-4o           | $2.50  | $10.00 |
| gpt-4o-mini      | $0.15  | $0.60  |
| claude-sonnet-4  | $3.00  | $15.00 |
| claude-opus-4    | $15.00 | $75.00 |
| claude-3.5-haiku | $0.80  | $4.00  |
| gemini-2.5-pro   | $1.25  | $10.00 |
| gemini-2.5-flash | $0.15  | $0.60  |
| gemini-2.0-flash | $0.075 | $0.30  |

### Programmatic Usage

Look up pricing for any model and calculate costs for a given token count.

```java
ModelPricing pricing = ModelPricing.forModel("gpt-4o");
BigDecimal inputCost = pricing.calculateInputCost(1000);   // 1000 tokens
BigDecimal outputCost = pricing.calculateOutputCost(500);
```

## Usage Records

If you need to record usage manually (for example, from external API calls), you can create `UsageRecord` objects and pass them to the tracker directly.

```java
tracker.record(UsageRecord.builder()
    .modelId("gpt-4o")
    .inputTokens(1000)
    .outputTokens(500)
    .build());
```

---

# LLM

URL: https://tnsai.dev/docs/capabilities/llm
Description: Configure providers, route between models, cache responses, and track cost.

import { Callout } from 'fumadocs-ui/components/callout'

## Pages

- [Providers](/docs/capabilities/llm/providers) — 30+ built-in LLM providers and how to add your own.
- [Routing](/docs/capabilities/llm/routing) — Pick the right model per request (size, cost, capability).
- [Caching](/docs/capabilities/llm/caching) — Prompt cache, response cache.
- [Cost Tracking](/docs/capabilities/llm/cost-tracking) — Per-agent and per-session cost accounting.
- [Observability](/docs/capabilities/llm/observability) — Capture every LLM call as a typed `LLMCallLog` event with prompt, response, usage, cost, and streaming timing.
- [Audio](/docs/capabilities/llm/audio) — Speech-to-text and text-to-speech.
- [Advanced](/docs/capabilities/llm/advanced) — Request hooks, custom transports, rate limiting.

---

# LLM Observability

URL: https://tnsai.dev/docs/capabilities/llm/observability
Description: Capture every LLM call as a typed LLMCallLog event — prompt, response, token usage, cost, streaming timing, errors, full context. One publish call per request, decorator-shaped so any provider works without modification.

import { Callout } from 'fumadocs-ui/components/callout'

> See also: **[Cost Tracking](/docs/capabilities/llm/cost-tracking)** — the older `CostAwareLLMClient` + `BudgetManager` system focused on spend control. Use observability for full call telemetry; use cost tracking when you need budget enforcement at the client edge.

## Why a typed event

SLF4J debug lines and OTel span attributes answer "did this call happen?" but not "which prompt did agent X send in session Y?", "what did the LLM reply?", "what did this turn cost in USD attributed to which tenant?". Every major LLM-ops tool (LangFuse, Helicone, Phoenix) is built around per-call telemetry as a first-class object. `LLMCallLog` is the same shape, native to TnsAI.

## Quick Start

Wrap any `LLMClient` with `CapturingLLMClient`. The default publisher emits one structured SLF4J line per call:

```java
import com.tnsai.llm.observability.CapturingLLMClient;
import com.tnsai.llm.observability.JsonLLMPricingRegistry;
import com.tnsai.llm.observability.Slf4jLLMCallPublisher;

LLMClient base = LLMClientFactory.create("openai", "gpt-4o", 0.7f);

LLMClient observed = new CapturingLLMClient(
        base,
        JsonLLMPricingRegistry.defaultRegistry(),  // 7 providers, 14+ models
        new Slf4jLLMCallPublisher());

Agent agent = AgentBuilder.create()
        .role(new MyRole())
        .llm(observed)
        .build();
```

Every chat / streamChat now logs:

```
INFO  com.tnsai.llm.callLog - llm.call provider=openai model=gpt-4o elapsedMs=842 \
  promptTokens=312 completionTokens=89 cachedTokens=0 totalTokens=401 \
  costUSD=0.00168 pricingTable=2026-05 finishReason=STOP streamed=false tools=2
```

Failures log at `WARN` with `errorClass`, `errorMessage`, and `httpStatus`.

## What gets captured

`LLMCallLog` is a typed record carrying:

| Field                                   | Description                                                        |
| --------------------------------------- | ------------------------------------------------------------------ |
| `callId`                                | UUID — primary key for joining call to downstream events           |
| `startedAt` / `completedAt` / `elapsed` | Wall-clock timing                                                  |
| `provider` / `model` / `endpoint`       | Routing                                                            |
| `prompt`                                | Messages, system prompt, parameters, prompt-cache markers          |
| `tools`                                 | `ToolSurface` — names, schemas, SHA-256 hash for cache correlation |
| `response`                              | Content, tool calls, reasoning content (o1 / Claude thinking)      |
| `usage`                                 | Prompt / completion / cached / reasoning / total tokens            |
| `cost`                                  | `CostEstimate` — prompt / completion / cached-discount / total USD |
| `finishReason`                          | STOP, LENGTH, TOOL\_CALL, CONTENT\_FILTER                          |
| `streamMetrics`                         | TTFT + chunk count for streaming calls                             |
| `error`                                 | `ErrorInfo` for failed calls — re-thrown after capture             |
| `context`                               | Full `EventContext` — tenant, agent, role, capability, session     |
| `retryAttempt`                          | Retry counter                                                      |

## Pricing Registry

`JsonLLMPricingRegistry` loads versioned rate cards from classpath JSON:

```java
JsonLLMPricingRegistry pricing = JsonLLMPricingRegistry.defaultRegistry();
// loads /pricing/2026-05.json — 7 providers, 14+ models
```

Default coverage: `openai` (GPT-4o, GPT-4o-mini, o1-preview), `anthropic` (Claude Sonnet 4, Opus 4, Haiku 4.5), `google` (Gemini 2.0 Flash, Pro), `mistral` (Large, Small), `groq` (Llama 3.3 70B, Mixtral 8x7B), `cohere` (Command R+, R), `ollama` (wildcard at zero — local models).

Bring your own rate card for enterprise-negotiated pricing or new providers:

```java
LLMPricingRegistry custom = new InMemoryLLMPricingRegistry("contract-2026-05");
custom.register("openai", "gpt-4o", new ModelPricing(
        BigDecimal.valueOf(0.0015),  // promptPer1k (negotiated)
        BigDecimal.valueOf(0.0005),  // cachedPer1k
        BigDecimal.valueOf(0.006),   // completionPer1k
        null));                       // reasoningPer1k

LLMClient observed = new CapturingLLMClient(base, custom, new Slf4jLLMCallPublisher());
```

The `pricingTable` field on every `LLMCallLog` records which version generated the cost — historical estimates don't shift when rates change downstream.

## Streaming Capture

For streaming calls, the decorator captures `StreamMetrics`:

```java
public record StreamMetrics(
    Instant firstChunkAt,
    Duration timeToFirstToken,    // operator's #1 latency metric
    long chunkCount,
    Duration interChunkP50,        // p50/p99 are zero in 0.9.x; histogram-friendly
    Duration interChunkP99         // counts ship now, percentiles in a follow-up
) {}
```

TTFT (time to first token) is the metric you graph for user-perceived latency.

## Tool Surface Hashing

When the LLM call advertises tools, `ToolSurface` carries the names + JSON schemas plus a SHA-256 hash of the canonical sorted-key form:

```java
public record ToolSurface(
    List<String> toolNames,
    List<String> toolSchemas,
    String surfaceHash
) {}
```

Same `surfaceHash` across calls = identical tool set = prompt-cache friendly. Use the hash to identify cacheable trajectories in your dashboards.

## Custom Publisher

`LLMCallPublisher` is a single-method functional interface. Build your own to push to LangFuse, Helicone, Phoenix, or a custom sink:

```java
public final class LangFusePublisher implements LLMCallPublisher {
    @Override
    public void publish(LLMCallLog call) {
        // Convert LLMCallLog → LangFuse trace + generation
        langfuseClient.trace()
                .name(call.callId())
                .metadata(Map.of(
                        "provider", call.provider(),
                        "model", call.model(),
                        "tenant", call.context().tenantId().orElse("default")))
                .generation(g -> g
                        .input(call.prompt().messages())
                        .output(call.response().content())
                        .usage(call.usage())
                        .totalCost(call.cost().totalUSD()))
                .submit();
    }
}
```

The publisher contract requires `publish` not to throw — observability failures must never block the agent's hot path.

## Cost Attribution

`LLMCallLog.context()` carries the full `EventContext` — tenant, agent, role, capability, session, group. Aggregate cost in your downstream sink along any of these dimensions:

- **Per tenant** — billing
- **Per agent** — which agent is the budget hog
- **Per role** — which role's LLM allocation is tight
- **Per capability** — chatty vs terse `@Capability` implementations
- **Per session** — per-conversation cost for end-user billing

Multi-agent cost split per group member works the same way — group context propagates.

## What's Not in the Default Publisher

`Slf4jLLMCallPublisher` deliberately does NOT log raw prompt or response text. Those can carry PII (user dictation, API keys passed as tool arguments, addresses in responses). Verbose dump belongs behind the redaction SPI from issue #80, on a separate publisher with explicit consumer opt-in.

## Coverage Notes

- The decorator covers `chat()` and `streamChat()`. Multimodal `chat(List<ContentPart> ...)` and tool-aware `streamChatWithSpec` pass through without capture in 0.9.x — those paths are smaller in production usage and will land with integration coverage in a follow-up.
- `usage().promptTokens()` is zero when the provider didn't populate the usage block (some local Ollama models). Cost estimate is also zero — a meaningful "no usage data" signal, not a bug.
- The `endpoint` field is populated when the underlying client exposes its base URL; falls back to empty string otherwise.

## See Also

- **[Cost Tracking](/docs/capabilities/llm/cost-tracking)** — `CostAwareLLMClient` + `BudgetManager` for client-edge spend control
- **[Sampling](/docs/sampling)** — pair `CapturingLLMClient` with sampling decorators when you ship to a high-volume aggregator
- **[Providers](/docs/capabilities/llm/providers)** — the 14 built-in LLM providers all work with the decorator unchanged

---

# LLM Providers

URL: https://tnsai.dev/docs/capabilities/llm/providers
Description: The LLM module provides a unified interface to 30+ language-model providers. Every provider implements the same LLMClient interface, so switching providers means changing one line — the model name and provider key — not your agent code.

import { Callout } from 'fumadocs-ui/components/callout'

## Quick Start

Create a client for any supported provider with a single factory call. The client handles authentication, serialization, retries, and streaming automatically.

```java
LLMClient client = LLMClientFactory.create("openai", "gpt-4o", 0.7f);
ChatResponse response = client.chat("What is quantum computing?");
```

API keys are resolved from environment variables automatically:

```bash
export OPENAI_API_KEY=sk-...
export ANTHROPIC_API_KEY=sk-ant-...
```

The full environment-variable matrix — every provider, every key name — lives in [Configuration Reference](/docs/reference/configuration). For each provider, the variable name is also verified at build time by `ProviderEnvVarConsistencyTest` in `tnsai-llm`, so the docs cannot drift without CI failing.

## Supported Providers

Each row below maps a provider key (the first arg to `LLMClientFactory.create(...)`) to its `LLMClient` implementation. Models change frequently — see each provider's docs for the up-to-date list and use whatever model string they document.

### Frontier and major hosted providers

| Provider                  | Provider key          | Class              |
| ------------------------- | --------------------- | ------------------ |
| **OpenAI**                | `openai`              | `OpenAIClient`     |
| **Anthropic**             | `anthropic`, `claude` | `AnthropicClient`  |
| **Google Gemini**         | `gemini`, `google`    | `GeminiClient`     |
| **Google Vertex AI**      | `vertexai`, `vertex`  | `VertexAIClient`   |
| **xAI (Grok)**            | `xai`, `grok`         | `XAIGrokClient`    |
| **Mistral La Plateforme** | `mistral`             | `MistralClient`    |
| **Cohere**                | `cohere`              | `CohereClient`     |
| **DeepSeek**              | `deepseek`            | `DeepSeekClient`   |
| **Perplexity (Sonar)**    | `perplexity`, `pplx`  | `PerplexityClient` |

### Fast / specialized inference

| Provider                    | Provider key        | Class               |
| --------------------------- | ------------------- | ------------------- |
| **Groq**                    | `groq`              | `GroqClient`        |
| **Cerebras**                | `cerebras`          | `CerebrasClient`    |
| **NVIDIA NIM**              | `nvidia`, `nim`     | `NvidiaNIMClient`   |
| **Together AI**             | `together`          | `TogetherAIClient`  |
| **Fireworks AI**            | `fireworks`         | `FireworksAIClient` |
| **DeepInfra**               | `deepinfra`         | `DeepInfraClient`   |
| **Replicate**               | `replicate`         | `ReplicateClient`   |
| **Hugging Face**            | `huggingface`, `hf` | `HuggingFaceClient` |
| **OpenRouter (aggregator)** | `openrouter`        | `OpenRouterClient`  |

### Enterprise platforms (cloud-managed)

| Provider                 | Provider key            | Class               |
| ------------------------ | ----------------------- | ------------------- |
| **AWS Bedrock**          | `bedrock`, `aws`        | `BedrockClient`     |
| **Azure OpenAI**         | `azure`, `azure-openai` | `AzureOpenAIClient` |
| **IBM watsonx.ai**       | `watsonx`, `ibm`        | `WatsonxClient`     |
| **Databricks Mosaic AI** | `databricks`, `mosaic`  | `DatabricksClient`  |

### Regional / non-Western providers

| Provider               | Provider key         | Class                  |
| ---------------------- | -------------------- | ---------------------- |
| **Alibaba Qwen Cloud** | `qwen`, `dashscope`  | `QwenCloudClient`      |
| **Tencent Hunyuan**    | `hunyuan`, `tencent` | `TencentHunyuanClient` |
| **ZhipuAI**            | `zhipu`, `glm`       | `ZhipuAIClient`        |
| **MiniMax**            | `minimax`            | `MiniMaxClient`        |
| **01.AI (Yi)**         | `yi`, `01ai`         | `YiClient`             |

### Local / self-hosted

| Provider             | Provider key | Class                  |
| -------------------- | ------------ | ---------------------- |
| **Ollama**           | `ollama`     | `OllamaClient`         |
| **LM Studio**        | `lmstudio`   | `LMStudioClient`       |
| **llama.cpp server** | `llamacpp`   | `LlamaCppServerClient` |
| **vLLM**             | `vllm`       | `VLLMClient`           |

Local providers don't require an API key — point them at the right base URL (`OLLAMA_BASE_URL`, `LMSTUDIO_BASE_URL`, etc.) and the local server handles the rest.

## Creating Clients

### Using the Factory

`LLMClientFactory` is the recommended way to create clients. It resolves API keys from environment variables, selects the correct provider class, and applies default settings. Use this unless you need fine-grained control over client construction.

```java
// Basic — provider name, model, temperature
LLMClient client = LLMClientFactory.create("openai", "gpt-4o", 0.7f);

// With max tokens
LLMClient client = LLMClientFactory.create("anthropic", "claude-sonnet-4-20250514", 0.7f, 4096);

// With topP (nucleus sampling)
LLMClient client = LLMClientFactory.create("gemini", "gemini-2.5-flash", 0.7f, 2048, 0.95f);

// From @RoleSpec annotation
LLMClient client = LLMClientFactory.fromAnnotation(MyRole.class);
```

Provider keys are case-insensitive. Aliases for each provider are listed in the [Supported Providers](#supported-providers) tables above.

### Direct Construction

When you need to pass a custom API key, a non-standard base URL, or provider-specific settings, construct the client class directly.

```java
// OpenAI with custom settings
LLMClient client = new OpenAIClient("gpt-4o", 0.7f, 0.95f, 4096);

// Anthropic with custom API key
LLMClient client = new AnthropicClient("claude-sonnet-4-20250514", "sk-ant-...");

// Ollama with custom base URL
LLMClient client = new OllamaClient("http://gpu-server:11434", "llama3", 0.7f, 4096, null);

// Azure with endpoint
LLMClient client = new AzureOpenAIClient("gpt-4", "your-api-key", "https://myresource.openai.azure.com/");
```

## Environment Variables

The factory and direct constructors resolve API keys from environment variables automatically. The full matrix — every provider, every key name, plus optional `_BASE_URL` companion variables for self-hosted endpoints — lives in [Configuration Reference](/docs/reference/configuration). The list is authored against `ProviderEnvVarConsistencyTest` in `tnsai-llm`, so the docs cannot drift without CI failing.

Self-hosted and local providers (`ollama`, `lmstudio`, `llamacpp`, `vllm`) don't require an API key — just set the `*_BASE_URL` to point the client at your container or local server. Ollama defaults to `http://localhost:11434`.

## Streaming

All providers support streaming, which lets you display tokens to the user as they are generated rather than waiting for the complete response. Three streaming patterns are available depending on how much control you need.

```java
// Text stream
Stream<String> tokens = client.streamChat("Tell me a story");

// ChatChunk stream
Stream<ChatChunk> chunks = client.streamChatWithSpec(request);

// Handler-based
client.streamChatWithHandler(request, chunk -> { ... });
```

## Resilience

TnsAI includes built-in resilience features so your application keeps working even when LLM providers have temporary issues.

### Circuit Breaker

A circuit breaker prevents your application from repeatedly calling a failing provider. After a configurable number of consecutive failures, it stops sending requests ("opens the circuit") and returns errors immediately, giving the provider time to recover.

```java
LLMClient resilient = CircuitBreakerClient.builder()
    .client(openaiClient)
    .failureThreshold(3)
    .recoveryTimeout(Duration.ofSeconds(60))
    .build();
```

States: **CLOSED** (normal) -\\> **OPEN** (fast-fail) -\\> **HALF\_OPEN** (probe recovery).

### Built-in Retry

All providers include automatic retry with exponential backoff for transient errors like rate limits and server errors. This is enabled by default with no configuration needed.

- **Max retries:** 3
- **Initial delay:** 1 second
- **Retriable HTTP codes:** 408, 425, 429, 500, 502, 503, 504, 529
- **Retriable exceptions:** `ConnectException`, `SocketTimeoutException`

## Observability

Understanding what your LLM calls are doing in production is critical for debugging, cost tracking, and compliance. The observability layer lets you wrap any `LLMClient` with logging, metrics, and custom observers without changing your application code.

### ObservableLLMClient

`ObservableLLMClient` is a decorator that wraps an existing client and intercepts every call, forwarding lifecycle events to one or more observers. Your application code uses the wrapped client exactly like the original -- the observability is completely transparent.

```java
// Single observer
LLMClient observable = new ObservableLLMClient(client, metrics);

// Multiple observers (varargs)
LLMClient observable = new ObservableLLMClient(client, metrics, promptLogger, auditObserver);
```

Internally, multiple observers are merged into a `CompositeObserver`. Null observers and `LLMObserver.NOOP` are filtered out automatically.

### LLMObserver Interface (6 hooks)

`LLMObserver` is the callback interface for monitoring LLM operations. All six methods have default no-op implementations, so you only override the hooks you care about. For example, you might only need `onResponse` for latency tracking.

| Hook               | When it fires               | Parameters                                    |
| ------------------ | --------------------------- | --------------------------------------------- |
| `onRequest`        | Before sending a request    | client, message, systemPrompt, history, tools |
| `onResponse`       | After a successful response | client, response, latencyMs                   |
| `onError`          | When a request fails        | client, error, latencyMs                      |
| `onStreamChunk`    | For each streaming chunk    | client, chunk, chunkIndex                     |
| `onStreamComplete` | When streaming finishes     | client, totalChunks, latencyMs                |
| `onStreamError`    | When streaming fails        | client, error, chunksReceived, latencyMs      |

```java
LLMObserver myObserver = new LLMObserver() {
    @Override
    public void onRequest(LLMClient client, String message,
                          Optional<String> systemPrompt,
                          Optional<List<Map<String, Object>>> history,
                          Optional<List<Map<String, Object>>> tools) {
        log.info("Request to {}: {}", client.getModel(), message);
    }

    @Override
    public void onResponse(LLMClient client, ChatResponse response, long latencyMs) {
        log.info("Response from {} ({}ms)", client.getModel(), latencyMs);
    }
};

LLMClient observed = new ObservableLLMClient(client, myObserver);
```

A pre-built no-op sentinel is available as `LLMObserver.NOOP`.

Compose multiple observers with `CompositeObserver`:

```java
LLMObserver combined = CompositeObserver.of(metricsObserver, loggingObserver, auditObserver);
```

### PromptLogger (PII Filtering + MDC Context)

`PromptLogger` is a production-ready observer that logs every LLM request and response with automatic PII redaction. It prevents sensitive data (emails, credit cards, API keys) from appearing in your logs and adds correlation IDs so you can trace requests through distributed systems.

**PII filtering.** When enabled (default), the logger redacts common sensitive patterns before writing to logs:

| Pattern                 | Replacement        |
| ----------------------- | ------------------ |
| Email addresses         | `[REDACTED_EMAIL]` |
| Phone numbers           | `[REDACTED_PHONE]` |
| Credit card numbers     | `[REDACTED_CC]`    |
| Social Security Numbers | `[REDACTED_SSN]`   |
| API keys and tokens     | `[REDACTED_KEY]`   |
| IP addresses            | `[REDACTED_IP]`    |

**MDC context.** The logger populates SLF4J MDC with correlation fields so downstream log infrastructure (ELK, Datadog, etc.) can group related events:

| MDC Key         | Value                             |
| --------------- | --------------------------------- |
| `llm.requestId` | Unique 8-char ID per request      |
| `llm.provider`  | Provider name (e.g. `OpenAI`)     |
| `llm.model`     | Model name (e.g. `gpt-4o`)        |
| `llm.latencyMs` | Request latency (set on response) |

MDC fields are cleared automatically after each request/response cycle.

**Builder API:**

```java
PromptLogger promptLogger = PromptLogger.builder()
    .filterPII(true)            // default: true
    .logLevel(PromptLogger.LogLevel.INFO)  // DEBUG, INFO, or WARN
    .maxContentLength(500)      // default: 200 chars
    .logFullContent(false)      // true to disable truncation
    .build();

LLMClient observed = new ObservableLLMClient(client, promptLogger);
```

**Factory methods** for common configurations:

```java
PromptLogger.withPIIFiltering();   // PII enabled, defaults
PromptLogger.withoutFiltering();   // PII disabled, defaults
```

**Log output format:**

```
[INFO] LLM Request [req-abc123] OpenAI/gpt-4o: "My email is [REDACTED_EMAIL]"
[INFO] LLM Response [req-abc123] OpenAI/gpt-4o (245ms): "The answer is 4"
```

Tool calls within responses are logged individually:

```
[INFO]   Tool call: calculator({"expression":"2+2"})
```

### LLMMetrics (Performance Tracking)

`LLMMetrics` is an observer that automatically collects performance data -- request counts, token usage, latency percentiles (p50/p95/p99), error rates, and estimated costs -- across all providers. Use it to monitor your LLM spending and identify performance bottlenecks.

**Setup:**

```java
LLMMetrics metrics = new LLMMetrics();
LLMClient observed = new ObservableLLMClient(client, metrics);

// Use the client normally
observed.chat("Hello!");
```

**Global report** via `getReport()`:

```java
LLMMetrics.Report report = metrics.getReport();
report.totalRequests();      // total request count
report.totalResponses();     // successful responses
report.totalErrors();        // error count
report.totalInputTokens();   // estimated input tokens
report.totalOutputTokens();  // estimated output tokens
report.totalEstimatedCost(); // cost in USD (based on provider pricing)
report.avgLatencyMs();       // average latency
report.p50LatencyMs();       // median latency
report.p95LatencyMs();       // 95th percentile latency
report.p99LatencyMs();       // 99th percentile latency
report.successRate();        // percentage (0-100)
report.errorRate();          // percentage (0-100)
report.timestamp();          // Instant of report generation
```

**Per-provider breakdown** via `getMetricsByProvider()`:

```java
Map<String, LLMMetrics.ProviderMetrics> byProvider = metrics.getMetricsByProvider();
for (var entry : byProvider.entrySet()) {
    String providerKey = entry.getKey(); // e.g. "OpenAI/gpt-4o"
    LLMMetrics.ProviderMetrics pm = entry.getValue();
    pm.requests();       // request count for this provider
    pm.responses();      // successful responses
    pm.errors();         // errors
    pm.inputTokens();    // estimated input tokens
    pm.outputTokens();   // estimated output tokens
    pm.estimatedCost();  // cost in USD
    pm.avgLatencyMs();   // average latency
    pm.streamChunks();   // total streaming chunks
    pm.successRate();    // percentage (0-100)
    pm.errorRate();      // percentage (0-100)
}
```

Token counts are estimated at \~4 characters per token. Cost is calculated using `LLMCapabilities.getInputCostPer1KTokens()` and `getOutputCostPer1KTokens()` from the provider.

Call `metrics.reset()` to clear all counters.

**Combining observers.** Use metrics alongside prompt logging:

```java
LLMMetrics metrics = new LLMMetrics();
PromptLogger logger = PromptLogger.withPIIFiltering();

LLMClient observed = new ObservableLLMClient(client, metrics, logger);
```

## JSON Mode

When you need the LLM to return valid JSON instead of free-form text, wrap your client with `JsonModeClient`. It uses provider-native JSON mode when available (OpenAI, Gemini) and falls back to prompt engineering for providers that lack native support (Anthropic, Ollama).

### Quick Wrap

The simplest way to get JSON output -- just wrap your existing client.

```java
// Simple wrap -- uses JSON_OBJECT format, auto-detects native support
JsonModeClient client = JsonModeClient.wrap(baseClient);

ChatResponse response = client.chat("List 3 programming languages");
// {"languages": ["Python", "Java", "JavaScript"]}
```

### Builder

For advanced control, the builder lets you specify a custom JSON schema, provide your own ObjectMapper, or force prompt engineering mode even when native JSON mode is available.

```java
JsonModeClient client = JsonModeClient.builder()
    .client(baseClient)                      // Required: LLM client to wrap
    .responseFormat(format)                   // ResponseFormat (default: jsonObject())
    .objectMapper(customMapper)              // Custom Jackson ObjectMapper
    .forcePromptEngineering(true)            // Skip native JSON mode, use prompt injection
    .schemaFromClass("Person", Person.class) // Auto-generate schema from class
    .build();
```

### chatAs -- Type-Safe JSON Parsing

The `chatAs` method combines JSON generation and deserialization in one step, returning a strongly-typed Java object instead of a raw JSON string.

```java
record LanguageList(List<String> languages) {}

// Simple
LanguageList list = client.chatAs(LanguageList.class, "List 3 programming languages");

// With system prompt
LanguageList list = client.chatAs(LanguageList.class, "List 3 languages",
    Optional.of("You are a helpful assistant"));

// Full parameters
LanguageList list = client.chatAs(LanguageList.class, message, systemPrompt, history, tools);
```

If JSON parsing fails, `JsonModeClient.JsonParseException` is thrown with `getRawContent()` for debugging.

### ResponseFormat

Controls the structure of LLM output. Use `text()` for default behavior, `jsonObject()` for generic JSON, or `jsonSchema()` to enforce a specific schema.

```java
// Plain text (default LLM behavior)
ResponseFormat text = ResponseFormat.text();

// JSON object (valid JSON, structure not enforced)
ResponseFormat json = ResponseFormat.jsonObject();

// JSON Schema (valid JSON conforming to a schema)
ResponseFormat schema = ResponseFormat.jsonSchema("Person", Map.of(
    "type", "object",
    "properties", Map.of(
        "name", Map.of("type", "string"),
        "age", Map.of("type", "integer")
    ),
    "required", List.of("name", "age")
));

// JSON Schema from a Java class (uses SchemaGenerator)
ResponseFormat schema = ResponseFormat.jsonSchema("Person", Person.class);
```

### SchemaGenerator

Automatically generates a JSON Schema from any Java class using reflection. This saves you from writing schemas by hand -- just pass your record or POJO and it produces a valid schema that the LLM can follow.

```java
public record Person(String name, int age, List<String> hobbies) {}

Map<String, Object> schema = SchemaGenerator.generateSchema(Person.class);
// {"type":"object","properties":{"name":{"type":"string"},"age":{"type":"integer"},
//  "hobbies":{"type":"array","items":{"type":"string"}}},"required":["name","age","hobbies"]}

// Record-specific (all components required)
Map<String, Object> schema = SchemaGenerator.generateRecordSchema(Person.class);
```

### Provider Support

Not all providers support native JSON mode. When native support is unavailable, `JsonModeClient` falls back to prompt engineering (injecting JSON instructions into the prompt).

| Provider  |    JSON\_OBJECT    |        JSON\_SCHEMA       |
| --------- | :----------------: | :-----------------------: |
| OpenAI    |         Yes        | Yes (GPT-4o, GPT-4-turbo) |
| Anthropic | No (use tool\_use) |     No (use tool\_use)    |
| Gemini    |         Yes        |            Yes            |
| Ollama    |  Depends on model  |             No            |

## Model Capabilities

Before sending a request that requires specific features (vision, tool calling, large context), you should check whether the model supports them. The `LLMCapabilities` interface provides a standardized way to query any model's features, limits, and pricing.

```java
LLMClient client = new OpenAIClient("gpt-4o");
LLMCapabilities caps = client.getCapabilities();

// Check before use
if (caps.supportsVision()) {
    response = client.chat(List.of(textPart, imagePart), system, history, tools);
}

// Context window check
if (estimatedTokens > caps.getMaxInputTokens()) {
    // Truncate or summarize
}
```

### Core Capability Methods

These boolean methods tell you what the model can do. Check them before using advanced features to avoid runtime errors.

| Method                            | Return    | Description                                   |
| --------------------------------- | --------- | --------------------------------------------- |
| `supportsStreaming()`             | `boolean` | Streaming responses (most modern LLMs)        |
| `supportsVision()`                | `boolean` | Image/visual input (GPT-4o, Claude 3, Gemini) |
| `supportsFunctionCalling()`       | `boolean` | Tool/function calling for agents              |
| `supportsStructuredOutput()`      | `boolean` | JSON mode / structured output                 |
| `supportsSystemPrompt()`          | `boolean` | System prompt distinction (default `true`)    |
| `supportsParallelFunctionCalls()` | `boolean` | Multiple tool calls in one response           |

### Token Limits

Know your model's context window to avoid truncation errors and plan your context management strategy.

| Method                 | Return | Description                                       |
| ---------------------- | ------ | ------------------------------------------------- |
| `getMaxInputTokens()`  | `int`  | Maximum input tokens (context window)             |
| `getMaxOutputTokens()` | `int`  | Maximum output tokens                             |
| `getContextWindow()`   | `int`  | Total context window (defaults to maxInputTokens) |

### Modality

Modalities describe what types of input a model can process. Use this to check whether a model supports image, audio, or video input before sending multimodal content.

```java
Set<LLMCapabilities.Modality> modalities = caps.getSupportedModalities();
boolean canHandleAudio = caps.supportsModality(Modality.AUDIO);
```

### Provider & Cost Information

Access pricing and provider metadata to estimate costs before making calls or to build cost-tracking dashboards.

| Method                       | Return             | Description                                     |
| ---------------------------- | ------------------ | ----------------------------------------------- |
| `getProviderName()`          | `String`           | Provider name (OpenAI, Anthropic, Google, etc.) |
| `getModelId()`               | `String`           | Model identifier                                |
| `getModelVersion()`          | `Optional<String>` | Model version                                   |
| `getInputCostPer1KTokens()`  | `Optional<Double>` | Input cost in USD per 1K tokens                 |
| `getOutputCostPer1KTokens()` | `Optional<Double>` | Output cost in USD per 1K tokens                |
| `getEstimatedLatencyMs()`    | `Optional<Long>`   | Estimated time-to-first-token in ms             |

### Special Capabilities

Some models support advanced features beyond standard chat. Check these before using specialized functionality.

| Method                    | Default         | Description                   |
| ------------------------- | --------------- | ----------------------------- |
| `supportsCodeExecution()` | `false`         | Code Interpreter support      |
| `supportsWebBrowsing()`   | `false`         | Web browsing support          |
| `supportsFileUpload()`    | From modalities | File upload support           |
| `supportsReasoning()`     | `false`         | Reasoning/thinking (o1-style) |

### meetsRequirements

A convenience method that checks multiple capability requirements at once, so you can verify a model is suitable for your use case in a single call.

```java
boolean suitable = caps.meetsRequirements(
    true,   // requiresVision
    true,   // requiresTools
    32000   // minContextTokens
);
```

### Validation Methods

When a capability is required (not optional), use these methods to fail fast at startup with a clear error message rather than getting cryptic errors at runtime.

```java
caps.requireToolCalling();  // throws ToolCallNotSupportedException
caps.requireStreaming();     // throws LLMCapabilityException
caps.requireVision();        // throws LLMCapabilityException
```

### Model Capability Profiles

A quick reference for the most commonly used models and their supported features.

| Model            | Vision | Tools | JSON | Context |
| ---------------- | :----: | :---: | :--: | ------: |
| GPT-4o           |   Yes  |  Yes  |  Yes |    128K |
| GPT-4-turbo      |   Yes  |  Yes  |  Yes |    128K |
| GPT-3.5-turbo    |   No   |  Yes  |  Yes |     16K |
| Claude Sonnet 4  |   Yes  |  Yes  |  Yes |    200K |
| Claude 3 Opus    |   Yes  |  Yes  |  Yes |    200K |
| Gemini 2.5 Flash |   Yes  |  Yes  |  Yes |      1M |
| Llama 3.2        |   No   |  Yes  |  No  |    128K |
| Mistral Large    |   No   |  Yes  |  Yes |    128K |

## Multimodal Input

Some models can process images, audio, and video alongside text. TnsAI uses a `ContentPart` system to represent mixed-media messages, so you can combine text with images or audio in a single request.

| Class       | Type      | Description                       |
| ----------- | --------- | --------------------------------- |
| `TextPart`  | `"text"`  | Plain text content                |
| `ImagePart` | `"image"` | Image data (Base64 encoded)       |
| `AudioPart` | `"audio"` | Audio data (Base64, URL, or file) |
| `VideoPart` | `"video"` | Video data (Gemini)               |

### Sending Images

Create an `ImagePart` from Base64-encoded data and include it alongside text in a multimodal message.

```java
// Create image part from Base64 data
ImagePart image = ImagePart.fromBase64(base64Data, "image/png");

// Build multimodal message
List<ContentPart> parts = List.of(
    new TextPart("What do you see in this image?"),
    image
);

// Send to a vision-capable model
ChatResponse response = client.chat(parts,
    Optional.of("You are a helpful assistant"),
    Optional.empty(),
    Optional.empty()
);
```

### Sending Audio

Create an `AudioPart` from a file, byte array, Base64 string, or URL. The model will process the audio alongside any text you include.

```java
// From file
AudioPart audio = AudioPart.fromFile(new File("recording.mp3"));

// From Base64
AudioPart audio = AudioPart.fromBase64(base64String, "audio/wav");

// From byte array
AudioPart audio = AudioPart.fromBytes(rawBytes, "audio/mp3");

// From URL
AudioPart audio = AudioPart.fromUrl("https://example.com/audio.mp3");

// Send as multimodal message
List<ContentPart> parts = List.of(
    new TextPart("Transcribe this audio"),
    audio
);
ChatResponse response = client.chat(parts, systemPrompt, history, tools);
```

### Capability Check Before Multimodal

Always check the model's capabilities before sending multimodal content. If the model does not support vision or audio, fall back to a text-only alternative.

```java
LLMCapabilities caps = client.getCapabilities();

if (caps.supportsVision()) {
    // Safe to send ImagePart
    client.chat(List.of(new TextPart("Describe this"), image), system, history, tools);
} else {
    // Fall back to text-only
    client.chat("Describe the concept", system, history, tools);
}
```

## SPI Registration

The LLM module uses Java's ServiceLoader mechanism to register itself automatically. You do not need to configure this manually -- just add the `tnsai-llm` dependency to your project and the providers become available through `LLMClientFactory`.

```
# META-INF/services/com.tnsai.llm.LLMClientProvider
com.tnsai.llm.LLMClientFactoryProvider
```

---

# LLM Routing

URL: https://tnsai.dev/docs/capabilities/llm/routing
Description: Route requests across multiple LLM providers with built-in strategies. Routing enables failover, cost optimization, latency reduction, and capability-based model selection.

import { Callout } from 'fumadocs-ui/components/callout'

## Fallback Router

The simplest routing strategy: tries each provider in the order you list them, moving to the next only if the current one fails. Use this when you want high availability with a clear priority order.

```java
LLMRouter router = FallbackRouter.of(
    new OpenAIClient("gpt-4o"),
    new AnthropicClient("claude-sonnet-4-20250514"),
    new GroqClient("llama-3.3-70b-versatile")
);

ChatResponse response = router.chat("Hello");
// If OpenAI fails → tries Anthropic → then Groq
```

## Cost-Based Router

Automatically selects the cheapest provider for each request based on the model pricing data. Use this when you want to minimize LLM spending while keeping multiple providers available.

```java
LLMRouter router = CostBasedRouter.of(
    new OpenAIClient("gpt-4o-mini"),        // $0.15/$0.60 per 1M tokens
    new GroqClient("llama-3.3-70b"),        // Free tier
    new AnthropicClient("claude-3.5-haiku") // $0.80/$4.00
);
```

## Latency-Based Router

Tracks the actual response time of each provider and routes new requests to the fastest one. The router continuously updates its latency measurements, so it adapts if a provider speeds up or slows down.

```java
LLMRouter router = LatencyBasedRouter.of(
    new OpenAIClient("gpt-4o"),
    new GroqClient("llama-3.3-70b")  // Typically faster
);
```

## Capability Router

Inspects each request's requirements (vision, tool calling, etc.) and routes to a provider that supports them. This lets you use cheaper text-only models for simple requests while reserving expensive multimodal models for requests that need them.

```java
LLMRouter router = CapabilityRouter.of(
    new OpenAIClient("gpt-4o"),       // Vision + tools
    new OllamaClient("llama3")        // Text only
);
// Vision requests go to OpenAI, text-only to Ollama
```

## Round-Robin Router

Distributes requests evenly across providers in a rotating order. This is useful for spreading rate limit usage across multiple API keys or providers.

```java
LLMRouter router = RoundRobinRouter.of(
    new OpenAIClient("gpt-4o"),
    new AnthropicClient("claude-sonnet-4-20250514"),
    new GeminiClient("gemini-2.5-flash")
);
```

## Task-Based Router

Routes based on automatic task type classification. The router analyzes prompt content using keyword matching, regex patterns, and conversation history to select the best model for each request.

```java
TaskBasedRouter router = TaskBasedRouter.builder()
    .forTask(TaskType.CODING, new AnthropicClient("claude-sonnet-4"))
    .forTask(TaskType.CREATIVE, new OpenAIClient("gpt-4o"))
    .forTask(TaskType.MATH, new OpenAIClient("o1-preview"))
    .forTask(TaskType.FAST, new GroqClient("llama-3.3-70b"))
    .forTask(TaskType.VISION, new OpenAIClient("gpt-4o"))
    .defaultClient(new OpenAIClient("gpt-4o-mini"))
    .build();

// Auto-routing -- no manual model selection
router.chat("Write a Python function to sort a list");  // -> Claude (CODING)
router.chat("Solve: integral of x squared dx");          // -> o1 (MATH)
router.chat("Write a poem about autumn");                // -> GPT-4o (CREATIVE)
router.chat("What is 2+2?");                             // -> GPT-4o-mini (default)
```

### TaskType Enum

Ten task categories cover the most common LLM use cases. Each type has built-in keywords for automatic classification, and you can add custom keywords for your domain.

| TaskType      | Description                             | Recommended Models                   | Example Keywords                               |
| ------------- | --------------------------------------- | ------------------------------------ | ---------------------------------------------- |
| `CODING`      | Code generation, debugging, refactoring | Claude Sonnet, GPT-4o, Codestral     | code, function, debug, python, java, algorithm |
| `CREATIVE`    | Stories, poetry, marketing copy         | GPT-4o, Claude Opus                  | poem, creative, fiction, blog post, narrative  |
| `MATH`        | Calculations, proofs, equations         | o1, Gemini 2.0, Claude with CoT      | calculate, solve, equation, integral, theorem  |
| `ANALYSIS`    | Data analysis, summarization            | GPT-4o, Gemini, Claude               | analyze, summarize, extract, compare, metrics  |
| `TRANSLATION` | Language translation                    | GPT-4o, Gemini                       | translate, in english, in french               |
| `CHAT`        | Conversational Q\&A                     | Any capable model                    | hello, what is, explain, tell me               |
| `FAST`        | Quick, simple tasks                     | GPT-4o-mini, Groq, Gemini Flash      | quick, simple, brief, yes or no                |
| `REASONING`   | Deep reasoning and logic                | o1, o1-pro, Claude extended thinking | think step by step, logic, deduce, infer       |
| `VISION`      | Image understanding                     | GPT-4o, Gemini, Claude (vision)      | image, picture, screenshot, diagram            |
| `GENERAL`     | Default fallback                        | Configured default client            | (no keywords)                                  |

Each `TaskType` provides `matches(text)` for boolean matching and `matchScore(text)` returning the number of keyword hits.

### TaskClassifier

`TaskClassifier` analyzes the text of each prompt and assigns it to a task type. It uses a multi-signal scoring system that combines regex patterns, keyword matching, custom keywords, and conversation history to make accurate classifications.

**Classification strategy (in order):**

1. **Pattern detection** -- Regex patterns for code blocks, function signatures, file extensions, math equations, math symbols, and image references. Highest scoring priority.
2. **Keyword scoring** -- Each keyword match from `TaskType.getKeywords()` adds 2 points.
3. **Custom keyword scoring** -- User-added keywords add 3 points each.
4. **History context boost** -- Last 3 history messages give a 1.5-point boost per matching task type.
5. **Best match selection** -- Highest total score wins. Score is converted to confidence (0.0-1.0). Falls back to default type if below `minConfidenceThreshold`.

```java
TaskClassifier classifier = TaskClassifier.defaultClassifier();

// Simple classification
TaskType type = classifier.classify("Write a Python function to sort a list");
// Returns: CODING

// With confidence score
ClassificationResult result = classifier.classifyWithConfidence("Solve x^2 + 2x + 1 = 0");
result.taskType();     // MATH
result.confidence();   // 0.85
result.reason();       // "Pattern and keyword match"

// With conversation history for context
List<String> history = List.of("I'm working on a React app", "Help me debug");
TaskType type = classifier.classify("What's wrong with this code?", history);
// Returns: CODING (boosted by history context)
```

**Custom classifier:**

```java
TaskClassifier classifier = TaskClassifier.builder()
    .addKeywords(TaskType.CODING, Set.of("backend", "frontend", "docker"))
    .addPattern(TaskType.MATH, Pattern.compile("[0-9]+"))
    .setDefaultType(TaskType.CHAT)
    .setMinConfidenceThreshold(0.2)
    .build();
```

### TaskBasedRouter Builder

Configure which LLM client handles each task type. A default client is required as a fallback for unclassified requests.

```java
TaskBasedRouter router = TaskBasedRouter.builder()
    .forTask(TaskType.CODING, codingClient)      // Assign client per task type
    .forTask(TaskType.MATH, mathClient)
    .defaultClient(generalClient)                 // Required: fallback for unmatched tasks
    .classifier(customClassifier)                 // Custom TaskClassifier (optional)
    .confidenceThreshold(0.3)                     // Below this, use default (default: 0.3)
    .build();
```

**Convenience setup for common mappings:**

```java
TaskBasedRouter router = TaskBasedRouter.builder()
    .withCommonSetup(
        codingClient,     // CODING
        reasoningClient,  // MATH + REASONING
        fastClient,       // FAST + CHAT
        generalClient     // default
    )
    .build();
```

### Manual Override

When you know the task type in advance, you can bypass automatic classification and route directly to the appropriate client.

```java
// Force a specific task type (bypasses classification)
ChatResponse response = router.chatAs(TaskType.CODING, "Explain this concept");

// Get the client for a task type directly
LLMClient codingClient = router.getClientForTask(TaskType.CODING);

// Inspect classification without routing
ClassificationResult result = router.classifyTask("Write a poem");
```

### Routing Statistics

Track how requests are distributed across task types to understand your usage patterns and estimate cost savings from intelligent routing.

```java
TaskRoutingStats stats = router.getTaskStats();

// Requests per task type
Map<TaskType, Long> requests = stats.requestsPerTask();

// Token usage per task type
Map<TaskType, Long> tokens = stats.tokensPerTask();

// Estimated cost savings vs. sending everything to a premium model
double savings = stats.estimatedSavings();

// Most common task type
Optional<TaskType> common = stats.mostCommonTask();

// Task distribution as percentages
Map<TaskType, Double> distribution = stats.taskDistribution();

// General routing stats (total, success, failure, per-provider)
RoutingStats routing = stats.routingStats();

// Reset all counters
router.resetStats();
```

## All Strategies

Choose a routing strategy based on your primary concern: availability, cost, speed, or task complexity. You can also combine strategies by nesting routers.

| Router               | Strategy                 | Best For                        |
| -------------------- | ------------------------ | ------------------------------- |
| `FallbackRouter`     | Try next on failure      | High availability               |
| `CostBasedRouter`    | Cheapest provider        | Budget optimization             |
| `LatencyBasedRouter` | Fastest measured latency | Real-time applications          |
| `CapabilityRouter`   | Model capabilities match | Mixed workloads (vision + text) |
| `RoundRobinRouter`   | Even load distribution   | Rate limit management           |
| `TaskBasedRouter`    | Task type classification | Varied complexity tasks         |

---

# RAG

URL: https://tnsai.dev/docs/capabilities/rag
Description: Retrieval-Augmented Generation — from knowledge base setup to production pipelines.

import { Callout } from 'fumadocs-ui/components/callout'

## Where RAG lives in the framework

RAG is intentionally split across three modules so each layer can evolve independently:

| Layer              | Module                                                                                                                           | Provides                                                                                                    |
| ------------------ | -------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------- |
| **Primitives**     | [tnsai-core](https://github.com/TnsAI-Framework/TnsAI/tree/main/tnsai-core) (`com.tnsai.knowledge`, `com.tnsai.memory.advanced`) | `KnowledgeBase`, `Document`, `EmbeddingFunction`, `BM25Index`, `VectorMemoryStore`, `HybridMemoryRetriever` |
| **Strategies**     | [tnsai-intelligence](https://github.com/TnsAI-Framework/TnsAI/tree/main/tnsai-intelligence) (`com.tnsai.intelligence.rag`)       | `RAGPipeline`, `RAGStrategy` (`Vector`, `Keyword`, `Hybrid`), `RetrievedDocument`, `RAGContext`             |
| **Service / HTTP** | [tnsai-server](https://github.com/TnsAI-Framework/TnsAI/tree/main/tnsai-server) (`com.tnsai.server.rag`)                         | `RagService`, `FileIndexer`, `CodeChunker`, `HybridRetriever`, retrieval streams                            |

If you are building an embedded agent, you typically use Core primitives + Intelligence strategies. If you are running TnsAI.Server, you also get the Service layer with HTTP endpoints, indexing, and stream-based retrieval.

## Pages

- [Knowledge Base](/docs/capabilities/rag/knowledge-base) — Create, populate, query a `KnowledgeBase`.
- [Strategies](/docs/capabilities/rag/strategies) — Swap retrieval algorithms via the RAG SPI.
- [Pipeline](/docs/capabilities/rag/pipeline) — Chunk storage, embedding, hybrid search, production deployment.

---

# Knowledge Base & RAG

URL: https://tnsai.dev/docs/capabilities/rag/knowledge-base
Description: TnsAI provides a built-in Retrieval-Augmented Generation (RAG) system through the KnowledgeBase interface, Document model, and @KnowledgeSource annotation. Agents can retrieve relevant context from vector databases, files, URLs, or in-memory stores before making LLM calls.

import { Callout } from 'fumadocs-ui/components/callout'

**Package:** `com.tnsai.knowledge`

## KnowledgeBase Interface

`KnowledgeBase` is the core abstraction for storing and searching documents. Implementations can use in-memory storage, vector databases (Pinecone, Weaviate, Milvus), full-text search (Elasticsearch, OpenSearch), or hybrid approaches. You program against this interface, and swap implementations without changing your agent code.

### Methods

These are the operations every `KnowledgeBase` implementation must support. The most important ones are `addDocument` (to ingest content) and `search` (to retrieve relevant context for an LLM call).

| Method              | Signature                                                                        | Description                                                                                |
| ------------------- | -------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------ |
| `addDocument`       | `void addDocument(Document document)`                                            | Adds a single document. Throws `KnowledgeBaseException` on failure.                        |
| `addDocuments`      | `default void addDocuments(List<Document> documents)`                            | Adds multiple documents. Default implementation iterates `addDocument`.                    |
| `getDocument`       | `Optional<Document> getDocument(String id)`                                      | Retrieves a document by ID. Returns empty if not found.                                    |
| `removeDocument`    | `boolean removeDocument(String id)`                                              | Removes a document by ID. Returns `true` if removed, `false` if not found.                 |
| `search`            | `List<SearchResult> search(String query, int topK)`                              | Natural language search. Returns results ordered by relevance (highest first).             |
| `search`            | `List<SearchResult> search(String query, int topK, Map<String, Object> filters)` | Search with metadata filtering. Filter entries are key-value pairs that must match.        |
| `searchByEmbedding` | `List<SearchResult> searchByEmbedding(float[] embedding, int topK)`              | Similarity search using a pre-computed embedding vector.                                   |
| `size`              | `int size()`                                                                     | Returns the total number of documents.                                                     |
| `isEmpty`           | `default boolean isEmpty()`                                                      | Returns `true` if the knowledge base has no documents. Delegates to `size() == 0`.         |
| `clear`             | `void clear()`                                                                   | Removes all documents from the knowledge base.                                             |
| `contains`          | `default boolean contains(String id)`                                            | Checks if a document with the given ID exists. Delegates to `getDocument(id).isPresent()`. |

## Document

`Document` is an immutable value object representing a document or document chunk. Each document has:

- **id** -- Unique identifier (auto-generated UUID if not specified)
- **content** -- The text content (required, cannot be null or empty)
- **metadata** -- Arbitrary key-value pairs for filtering and context (immutable copy)
- **embedding** -- Optional vector representation for similarity search (defensive copy)

### Factory Methods

The quickest way to create a `Document` is with the static `of()` methods. These are convenient for simple use cases where you do not need to set an explicit ID or embedding.

```java
// Simple document (auto-generated ID)
Document doc = Document.of("This is the document content");

// Document with a single metadata entry
Document doc = Document.of("Product docs...", "source", "docs/product.md");
```

### Builder

For full control over the document's ID, metadata, and embedding vector, use the builder. This is the recommended approach when you need to attach metadata for filtered searches or pre-computed embeddings for similarity search.

```java
Document doc = Document.builder()
    .id("doc-001")                          // optional, UUID generated if omitted
    .content("Product documentation...")     // required
    .metadata("source", "docs/product.md")  // single entry
    .metadata("category", "documentation")  // chainable
    .metadata(Map.of("version", "2.0"))     // bulk metadata
    .embedding(embeddingVector)              // optional float[]
    .build();
```

Builder method `content(String)` throws `NullPointerException` if null. `build()` throws `IllegalStateException` if content is null or empty.

### Accessors

These getter methods let you read the document's fields. Metadata is accessed through the `getSpec` methods, and embeddings are returned as defensive copies to preserve immutability.

| Method                               | Return Type           | Description                                                       |
| ------------------------------------ | --------------------- | ----------------------------------------------------------------- |
| `getId()`                            | `String`              | Unique document ID                                                |
| `getContent()`                       | `String`              | Document text content                                             |
| `getSpec()`                          | `Map<String, Object>` | Unmodifiable metadata map                                         |
| `getSpec(String key)`                | `Object`              | Single metadata value, or `null`                                  |
| `getSpec(String key, Class<T> type)` | `T`                   | Type-safe metadata value, returns `null` if missing or wrong type |
| `hasEmbedding()`                     | `boolean`             | Whether an embedding is present                                   |
| `getEmbedding()`                     | `float[]`             | Copy of embedding array, or `null`                                |
| `getEmbeddingDimension()`            | `int`                 | Embedding vector length, or `0` if none                           |

### Immutable Copy with Embedding

Since `Document` is immutable, attaching an embedding returns a new `Document` instance rather than modifying the original. This is useful when you compute embeddings separately after initial document creation.

```java
// Attach an embedding to an existing document (returns a new Document)
Document withVector = doc.withEmbedding(embeddingVector);
```

Equality is based on `id` only.

## SearchResult

`SearchResult` wraps a matched `Document` with a relevance score. Implements `Comparable<SearchResult>` -- natural ordering is by score descending (highest first).

| Method            | Return Type | Description                                       |
| ----------------- | ----------- | ------------------------------------------------- |
| `getDocument()`   | `Document`  | The matched document                              |
| `getScore()`      | `double`    | Relevance score (higher = more relevant)          |
| `getContent()`    | `String`    | Convenience: delegates to `document.getContent()` |
| `getDocumentId()` | `String`    | Convenience: delegates to `document.getId()`      |

Constructor: `new SearchResult(Document document, double score)` -- document cannot be null.

```java
List<SearchResult> results = knowledgeBase.search("query", 5);
for (SearchResult result : results) {
    System.out.printf("Score: %.4f | %s%n", result.getScore(), result.getContent());
}
```

## InMemoryKnowledgeBase

`InMemoryKnowledgeBase` is a thread-safe, in-memory implementation suitable for testing and small datasets. It provides:

- **ConcurrentHashMap** storage for thread safety
- **TF-IDF keyword search** with stop-word removal for `search()`
- **Cosine similarity** for both TF-IDF vectors and raw embeddings (`searchByEmbedding`)
- **Metadata filtering** support

```java
KnowledgeBase kb = new InMemoryKnowledgeBase();

kb.addDocument(Document.of("Java is a programming language"));
kb.addDocument(Document.of("Python is also a programming language"));
kb.addDocument(Document.builder()
    .content("Rust is a systems programming language")
    .metadata("category", "systems")
    .build());

// Keyword search
List<SearchResult> results = kb.search("programming language", 5);

// Filtered search
List<SearchResult> filtered = kb.search("programming", 5,
    Map.of("category", "systems"));

// Embedding search
List<SearchResult> similar = kb.searchByEmbedding(queryEmbedding, 3);
```

For production workloads with large datasets, use a vector database implementation (Pinecone, Weaviate, Qdrant) instead.

## @KnowledgeSource Annotation

**Package:** `com.tnsai.annotations`

Declarative configuration for RAG knowledge sources. Can be applied to types (agent classes) or methods (individual actions). Repeatable via `@KnowledgeSources`.

**Targets:** `ElementType.TYPE`, `ElementType.METHOD`
**Retention:** `RetentionPolicy.RUNTIME`

### Fields

Each `@KnowledgeSource` annotation is configured through the fields below. At minimum you need `name` and `type`; the remaining fields let you tune connection details, retrieval parameters, and caching behavior.

| Field            | Type            | Default     | Description                                                     |
| ---------------- | --------------- | ----------- | --------------------------------------------------------------- |
| `name`           | `String`        | (required)  | Unique identifier for this knowledge source                     |
| `type`           | `KnowledgeType` | `VECTOR_DB` | The knowledge source type                                       |
| `provider`       | `String`        | `""`        | Vector database provider (for `VECTOR_DB`)                      |
| `index`          | `String`        | `""`        | Index/collection name (for `VECTOR_DB`)                         |
| `path`           | `String`        | `""`        | File path or URL (for `FILE` or `URL`)                          |
| `connection`     | `String`        | `""`        | Database connection string (for `DATABASE`)                     |
| `query`          | `String`        | `""`        | SQL query template with `${query}` placeholder (for `DATABASE`) |
| `topK`           | `int`           | `5`         | Maximum number of results to retrieve                           |
| `minSimilarity`  | `double`        | `0.7`       | Minimum similarity score threshold (0.0--1.0)                   |
| `embeddingModel` | `String`        | `""`        | Embedding model name for vector search                          |
| `dimensions`     | `int`           | `1536`      | Embedding vector dimensions                                     |
| `namespace`      | `String`        | `""`        | Namespace/partition for multi-tenant sources                    |
| `filter`         | `String`        | `""`        | Metadata filter in JSON format                                  |
| `cache`          | `boolean`       | `true`      | Whether to cache retrieval results                              |
| `cacheTTL`       | `int`           | `300`       | Cache time-to-live in seconds                                   |
| `enabled`        | `boolean`       | `true`      | Whether this source is active                                   |
| `priority`       | `int`           | `0`         | Query priority (higher = queried first)                         |

### KnowledgeType Enum

The `type` field of `@KnowledgeSource` determines where the framework looks for documents. Choose the type that matches your data source.

| Value        | Description                                          |
| ------------ | ---------------------------------------------------- |
| `VECTOR_DB`  | Vector database (Pinecone, Weaviate, Qdrant, Chroma) |
| `FILE`       | Local file (JSON, YAML, TXT, PDF, DOCX)              |
| `URL`        | Remote URL or REST API                               |
| `DATABASE`   | SQL or NoSQL database                                |
| `MEMORY`     | Agent's conversation memory                          |
| `WEB_SEARCH` | Web search results                                   |
| `CACHE`      | In-memory cache                                      |

### Annotation Examples

These examples show how to attach knowledge sources to an agent class or an individual action method. You can combine multiple sources on the same class using the repeatable annotation pattern.

```java
// On a class -- multiple sources via @Repeatable
@KnowledgeSource(
    name = "product-docs",
    type = KnowledgeType.VECTOR_DB,
    provider = "pinecone",
    index = "products",
    topK = 5,
    minSimilarity = 0.8
)
@KnowledgeSource(
    name = "faq",
    type = KnowledgeType.FILE,
    path = "knowledge/faq.json"
)
public class SupportAgent extends Agent { ... }

// On a method
@ActionSpec(type = ActionType.LLM, description = "Answer question")
@KnowledgeSource(name = "product-docs", topK = 3)
public String answerQuestion(String question) {
    // Relevant context is automatically retrieved before the LLM call
}

// Database source
@KnowledgeSource(
    name = "customer-data",
    type = KnowledgeType.DATABASE,
    connection = "jdbc:postgresql://localhost/mydb",
    query = "SELECT content FROM docs WHERE content ILIKE '%${query}%' LIMIT 10"
)

// Web search source with caching
@KnowledgeSource(
    name = "web-context",
    type = KnowledgeType.WEB_SEARCH,
    topK = 3,
    cache = true,
    cacheTTL = 600
)
```

## Integration with AgentBuilder

If you prefer programmatic configuration over annotations, you can attach a knowledge base directly through the `AgentBuilder`. This is useful when you want to populate the knowledge base dynamically at startup or share one instance across multiple agents.

Use `.knowledgeBase()` and `.knowledgeBaseTopK()` on `AgentBuilder` to attach a knowledge base programmatically:

```java
KnowledgeBase kb = new InMemoryKnowledgeBase();
kb.addDocument(Document.of("Product X supports features A, B, C"));
kb.addDocument(Document.of("Pricing starts at $99/month"));

Agent agent = AgentBuilder.create()
    .llm(llmClient)
    .role(supportRole)
    .knowledgeBase(kb)            // attach the knowledge base
    .knowledgeBaseTopK(3)         // override default top-K (default: 5)
    .build();
```

## Full RAG Example

This end-to-end example shows the complete RAG workflow: creating a knowledge base, adding documents with metadata, searching for relevant context, building an augmented prompt, and sending it to the agent. It also demonstrates filtered search to narrow results by metadata category.

```java
// 1. Create and populate knowledge base
KnowledgeBase kb = new InMemoryKnowledgeBase();
kb.addDocuments(List.of(
    Document.builder()
        .content("Product X supports features A, B, and C.")
        .metadata("source", "product-docs")
        .metadata("category", "features")
        .build(),
    Document.builder()
        .content("Pricing starts at $99/month for the Basic plan.")
        .metadata("source", "pricing-page")
        .metadata("category", "pricing")
        .build(),
    Document.builder()
        .content("Enterprise plan includes SSO and dedicated support.")
        .metadata("source", "pricing-page")
        .metadata("category", "pricing")
        .build()
));

// 2. Search for relevant context
String query = "What features does Product X have?";
List<SearchResult> context = kb.search(query, 3);

// 3. Build augmented prompt
String augmentedPrompt = "Context:\n" +
    context.stream()
        .map(r -> r.getContent())
        .collect(Collectors.joining("\n")) +
    "\n\nQuestion: " + query;

// 4. Send to agent
String answer = agent.chat(augmentedPrompt);

// Or use filtered search for specific categories
List<SearchResult> pricingResults = kb.search("plan cost", 3,
    Map.of("category", "pricing"));
```

---

# RAG Pipeline

URL: https://tnsai.dev/docs/capabilities/rag/pipeline
Description: The server provides a per-session Retrieval-Augmented Generation pipeline that indexes local codebases, chunks source files by language boundaries, and retrieves relevant context using hybrid BM25 + vector search with Reciprocal Rank Fusion.

import { Callout } from 'fumadocs-ui/components/callout'

## Architecture Overview

The RAG pipeline has three stages: indexing (scanning files and splitting them into chunks), storage (keeping chunks in an in-memory knowledge base with BM25 and vector indexes), and retrieval (finding the most relevant chunks for a user's query using hybrid search).

```
Directory  -->  FileIndexer  -->  CodeChunker  -->  KnowledgeBase (in-memory)
                                                          |
User Query  -->  HybridRetriever  -->  [BM25Stream 60%]  -+  RRF  -->  Results
                                  -->  [VectorStream 40%] -+
```

Each session gets its own `RagService`, lazily created by `SessionManager.getRag(sessionId)`. The service is thread-safe: indexing is serialized via a `ReentrantLock`, while reads (search) run concurrently.

## RagService

The central orchestrator for a session's RAG pipeline.

```java
RagService rag = sessionManager.getRag("my-session");

// Index a directory
rag.indexDirectory(Path.of("/project/src"), progress -> {
    System.out.printf("Indexed %d/%d: %s%n",
        progress.indexedFiles(), progress.totalFiles(), progress.currentFile());
});

// Search
List<SearchResult> results = rag.search("authentication middleware", 5);

// Build augmented prompt (auto-prepends context)
String prompt = rag.buildContextPrompt("How does auth work?", 5);

// Document management
String docId = rag.addDocument("Custom knowledge...", Map.of("source", "manual"));
rag.removeDocument(docId);
List<RagService.DocumentInfo> docs = rag.listDocuments();
```

The hybrid retriever is configured at construction with BM25 at 60% weight and the vector knowledge base at 40%:

```java
this.hybridRetriever = HybridRetriever.builder()
    .stream(bm25Stream, 0.6)
    .stream(new KnowledgeBaseStream(knowledgeBase), 0.4)
    .build();
```

## FileIndexer

The `FileIndexer` recursively walks a directory, identifies source files by extension, splits them into chunks using `CodeChunker`, and stores the chunks in the knowledge base. It supports incremental indexing so only changed files are re-processed on subsequent runs.

### Supported Extensions (28+)

The indexer recognizes 28+ file extensions covering most popular programming languages and configuration formats.

`java`, `ts`, `tsx`, `js`, `jsx`, `py`, `md`, `json`, `yml`, `yaml`, `xml`, `html`, `css`, `sh`, `sql`, `go`, `rs`, `rb`, `kt`, `scala`, `c`, `cpp`, `h` -- plus language aliases (`kts`, `bash`, `zsh`, `markdown`, `htm`, `cc`, `cxx`, `hpp`, `sc`).

### Filtering

The indexer automatically skips build artifacts, dependency directories, and files matching your `.gitignore` patterns to avoid polluting the knowledge base with irrelevant content.

- **Skipped directories**: `.git`, `node_modules`, `build`, `dist`, `target`, `.idea`, `.vscode`, `.gradle`, `__pycache__`, `vendor`, `.next`, `out`, `coverage`, `.svn`, `.hg`
- **Ignore files**: Reads `.gitignore` and `.tnsignore` from the root, converting glob patterns to Java `PathMatcher` instances
- **Size limit**: Files larger than 512 KB or empty files are skipped

### Incremental Indexing

To avoid re-processing unchanged files, the indexer computes a SHA-256 hash of each file's content and stores it in a `ConcurrentHashMap<String, String>`. On re-index:

1. If the hash matches the previous run, the file is skipped
2. If the file changed, old chunks are removed from both KnowledgeBase and BM25Stream
3. New chunks are generated and added

Call `fileIndexer.clearHashes()` to force a full re-index.

## CodeChunker

The `CodeChunker` splits source files into semantically meaningful chunks -- for example, by class or function boundaries in Java/TypeScript, or by headings in Markdown. This ensures that search results return coherent, self-contained code blocks rather than arbitrary line ranges.

### Chunking Strategies

The chunker picks a strategy based on the file's language. Languages with known structure get smarter splitting; everything else falls back to fixed-size line groups.

| Language               | Strategy                  | Boundary Detection                                                  |
| ---------------------- | ------------------------- | ------------------------------------------------------------------- |
| Java, Kotlin, Scala    | Class/method boundaries   | Regex: class/interface/enum/record declarations + method signatures |
| TypeScript, JavaScript | Function/class boundaries | Regex: export/function/class/const arrow declarations               |
| Markdown               | Heading boundaries        | Regex: `#{1-6}` heading lines                                       |
| Everything else        | Fixed line groups         | Max 100 lines per chunk                                             |

Small files (100 lines or fewer) are always kept as a single chunk. Large boundary-detected chunks are sub-split into 100-line groups.

Each chunk becomes a `Document` with metadata:

```java
Document.builder()
    .id("src/auth/Middleware.java:15-45")
    .content(chunkContent)
    .metadata("file", "src/auth/Middleware.java")
    .metadata("startLine", 15)
    .metadata("endLine", 45)
    .metadata("language", "java")
    .build();
```

## BM25Stream

The `BM25Stream` provides keyword-based search using the Okapi BM25 algorithm, which is the same ranking function used by search engines like Elasticsearch. It scores documents based on how well their terms match the query, accounting for term frequency and document length.

### Parameters

These BM25 parameters control how the scoring behaves. The defaults work well for code search and rarely need tuning.

| Parameter | Value | Description                   |
| --------- | ----- | ----------------------------- |
| K1        | 1.2   | Term frequency saturation     |
| B         | 0.75  | Document length normalization |

### Text Processing Pipeline

Before scoring, queries and documents go through a text processing pipeline that normalizes, tokenizes, and stems terms. This improves recall by matching different forms of the same word.

1. **Tokenization**: Lowercase, strip non-alphanumeric (except `_`), split on whitespace, drop tokens with 1 character or fewer
2. **Stop word removal**: 50 common English stop words
3. **Stemming**: Suffix-stripping rules for 14 suffixes (`-ies`, `-ing`, `-tion`, `-sion`, `-ment`, `-ness`, `-able`, `-ous`, `-ful`, `-less`, `-ly`, `-ed`, `-er`, `-es`, `-s`)
4. **Synonym expansion** (query-time only): 20 coding-domain synonym pairs

### Synonym Pairs

At query time, common coding abbreviations are expanded to their full forms (and vice versa) so that searching for "auth" also finds documents containing "authentication".

| Term   | Synonyms                      |
| ------ | ----------------------------- |
| db     | database                      |
| auth   | authentication, authorization |
| config | configuration                 |
| perf   | performance                   |
| impl   | implementation                |
| req    | request                       |
| res    | response                      |
| err    | error                         |
| msg    | message                       |
| fn     | function                      |
| param  | parameter                     |
| repo   | repository                    |
| env    | environment                   |
| async  | asynchronous                  |
| sync   | synchronous                   |

## HybridRetriever

The `HybridRetriever` combines results from multiple search strategies (like BM25 keyword search and vector similarity search) into a single ranked list. This hybrid approach gives better results than either method alone because keyword search finds exact term matches while vector search captures semantic similarity.

### Fusion Algorithm

The retriever merges results using Reciprocal Rank Fusion (RRF), which combines rankings without needing normalized scores. For each document appearing in any stream's results:

```
score(doc) = SUM over streams: weight(stream) / (K + rank(doc, stream) + 1)
```

Where `K = 60` (the RRF constant). Documents are then sorted by fused score.

### Diversification

To prevent a single large file from dominating search results, the retriever limits output to a maximum of 3 chunks per source file. This ensures the agent sees context from multiple relevant files.

```java
HybridRetriever retriever = HybridRetriever.builder()
    .stream(bm25Stream, 0.6)       // 60% weight
    .stream(vectorStream, 0.4)      // 40% weight
    .build();

List<SearchResult> results = retriever.retrieve("authentication flow", 10);
```

## Context Prompt Format

When the agent asks a question, `RagService.buildContextPrompt` searches for relevant code and prepends it to the user's query. This gives the LLM the codebase context it needs to answer accurately.

```
[Relevant code context]
--- file: src/auth/Middleware.java (lines 15-45) ---
public class AuthMiddleware {
    private final TokenValidator validator;
    ...
}

--- file: src/auth/TokenValidator.java (lines 1-30) ---
public class TokenValidator {
    ...
}

[User question]
How does the authentication middleware work?
```

If no context is found (empty knowledge base or no matches), the original query is returned unchanged.

## Document Management API

Beyond automatic directory indexing, you can manually add, list, and remove documents in the knowledge base. This is useful for injecting custom knowledge (like deployment procedures or domain-specific documentation) that is not part of the codebase.

```java
// Add a document with metadata
String docId = rag.addDocument("Custom knowledge content",
    Map.of("source", "user", "topic", "deployment"));

// List documents (returns preview, length, metadata)
List<RagService.DocumentInfo> docs = rag.listDocuments();
// DocumentInfo(id, preview(100chars), contentLength, metadata)

// Get a specific document
Optional<Document> doc = rag.getDocument(docId);

// Remove
boolean removed = rag.removeDocument(docId);

// Clear everything
rag.clear();
```

Documents added via `addDocument` are tracked separately and appear in `listDocuments()`. Both manually added documents and file-indexed chunks are searchable through the same hybrid retriever.

---

# RAG Strategy SPI

URL: https://tnsai.dev/docs/capabilities/rag/strategies
Description: TnsAI.Intelligence provides a pluggable Retrieval-Augmented Generation (RAG) framework with three built-in strategies and a composable pipeline. Package: com.tnsai.intelligence.rag.

import { Callout } from 'fumadocs-ui/components/callout'

## RAGStrategy Interface

Every retrieval strategy implements this interface. You call `retrieve(query, topK)` with a user query and the number of results you want, and the strategy returns the most relevant documents it can find.

```java
public interface RAGStrategy {
    List<RetrievalResult> retrieve(String query, int topK);
    String name();
}
```

Each strategy returns ranked `RetrievalResult` objects containing the retrieved text, a relevance score, and source metadata.

```java
public record RetrievalResult(
    String content,
    double score,
    Map<String, Object> metadata
) {}
```

## VectorRAGStrategy

Dense vector retrieval using embedding similarity. Best for semantic matching where exact keywords may not appear in the source documents.

```java
RAGStrategy vectorRAG = VectorRAGStrategy.builder()
    .embeddingClient(embeddingClient)
    .vectorStore(vectorStore)
    .similarityThreshold(0.7)
    .build();

List<RetrievalResult> results = vectorRAG.retrieve("How do agents communicate?", 5);
```

| Parameter             | Default  | Description                           |
| --------------------- | -------- | ------------------------------------- |
| `embeddingClient`     | required | Client for generating embeddings      |
| `vectorStore`         | required | Vector database for similarity search |
| `similarityThreshold` | 0.7      | Minimum cosine similarity to include  |

## KeywordRAGStrategy

Sparse retrieval using BM25 scoring. Best for queries with specific technical terms, identifiers, or exact phrases.

```java
RAGStrategy keywordRAG = KeywordRAGStrategy.builder()
    .index(bm25Index)
    .build();

List<RetrievalResult> results = keywordRAG.retrieve("ContextCompactor interface", 5);
```

## HybridRAGStrategy

Combines vector and keyword strategies using Reciprocal Rank Fusion (RRF) to merge result lists. This gives the best of both semantic and lexical matching.

```java
RAGStrategy hybridRAG = HybridRAGStrategy.builder()
    .vectorStrategy(vectorRAG)
    .keywordStrategy(keywordRAG)
    .vectorWeight(0.6)
    .keywordWeight(0.4)
    .fusionK(60)          // RRF constant
    .build();

List<RetrievalResult> results = hybridRAG.retrieve("agent memory persistence", 10);
```

| Parameter         | Default  | Description                                            |
| ----------------- | -------- | ------------------------------------------------------ |
| `vectorStrategy`  | required | Dense retrieval strategy                               |
| `keywordStrategy` | required | Sparse retrieval strategy                              |
| `vectorWeight`    | 0.6      | Weight for vector results in fusion                    |
| `keywordWeight`   | 0.4      | Weight for keyword results in fusion                   |
| `fusionK`         | 60       | RRF smoothing constant (higher = more equal weighting) |

## RAGPipeline

A pipeline wraps a retrieval strategy with optional query rewriting (to improve recall) and result reranking (to improve precision). This lets you build a complete retrieval system by composing simple, testable components.

```java
RAGPipeline pipeline = RAGPipeline.builder()
    .strategy(hybridRAG)
    .queryRewriter(query -> expandAcronyms(query))
    .reranker((results, query) -> crossEncoderRerank(results, query))
    .maxResults(5)
    .build();

List<RetrievalResult> results = pipeline.execute("How does RRF fusion work?");
```

### Pipeline Stages

The pipeline processes a query through three stages. Each stage is optional -- you can use just a strategy, or add rewriting and reranking for better results.

```
User Query
    |
    v
Query Rewriter (optional) -- expand, rephrase, or decompose the query
    |
    v
RAGStrategy.retrieve() -- fetch candidates from one or more sources
    |
    v
Reranker (optional) -- re-score and re-order results
    |
    v
Top-K Selection -- return final results
```

| Stage          | Interface                                                          | Description                                           |
| -------------- | ------------------------------------------------------------------ | ----------------------------------------------------- |
| Query Rewriter | `Function<String, String>`                                         | Transform the query before retrieval                  |
| Strategy       | `RAGStrategy`                                                      | Core retrieval (vector, keyword, or hybrid)           |
| Reranker       | `BiFunction<List<RetrievalResult>, String, List<RetrievalResult>>` | Re-score results using a cross-encoder or other model |

## Integration with Agents

Once you have a RAG pipeline, you can wire it directly into an agent. The agent will automatically retrieve relevant documents before generating each response, so it can answer questions grounded in your data.

```java
Agent agent = AgentBuilder.create()
    .model("claude-sonnet-4")
    .ragPipeline(pipeline)
    .build();

// The agent automatically retrieves relevant context before generating responses
String response = agent.chat("Explain the memory architecture");
```

## Choosing a Strategy

Pick the strategy that matches your data and query patterns. For most production systems, hybrid gives the best results by combining semantic understanding with exact term matching.

| Strategy | Strengths                                              | Weaknesses                               | Best For                    |
| -------- | ------------------------------------------------------ | ---------------------------------------- | --------------------------- |
| Vector   | Semantic understanding, handles paraphrasing           | Misses exact terms, requires embeddings  | Natural language queries    |
| Keyword  | Fast, exact term matching, no embeddings needed        | No semantic understanding                | Technical docs, code search |
| Hybrid   | Best overall recall, handles both semantic and lexical | Higher latency (two retrievals + fusion) | Production RAG systems      |

---

# Skills

URL: https://tnsai.dev/docs/capabilities/skills
Description: On-demand modular knowledge between role and tools. The framework's answer to: how do I keep multi-step procedures and domain knowledge out of the always-on system prompt without losing them when they're actually relevant?

import { Callout } from 'fumadocs-ui/components/callout'

The `com.tnsai.skills` package in `tnsai-core` ships the primitives:

- **`Skill`** — record carrying a name, a description (the trigger phrase the resolver scores against), the markdown body, and per-skill scoping (`allowedTools`, `userInvocable`, `disableModelInvocation`).
- **`SkillStore`** — SPI for discovery; the framework ships `InMemorySkillStore` (test seam) and `FileSystemSkillStore` (Claude Code-compatible `<root>/<skill>/SKILL.md` layout).
- **`SkillResolver`** — picks top-N candidates per user turn; defaults to `KeywordSkillResolver` (no extra round-trip), upgradeable to `LLMSkillResolver` (semantic match, one extra LLM call per turn).
- **`SkillManager`** — per-agent facade combining store + resolver + session state. Handles `/skill-name` parsing, invocation source gating, and prompt-section rendering.
- **`SkillSession` + `ActiveSkill`** — runtime state tracking which skills the agent has activated this session.
- **`SkillScopedToolCallFilter`** — `ToolCallFilter` that enforces the union of `allowed-tools` across active skills.

Skills sit between the always-on **role** layer and the always-on **tool** layer:

| Layer            | Loaded                         | Granularity              |
| ---------------- | ------------------------------ | ------------------------ |
| Role / CLAUDE.md | Always                         | Stable agent identity    |
| Tool             | Always                         | Atomic action            |
| Capability       | Always (compile-time)          | Method-level declaration |
| RAG hit          | Per query (similarity)         | Evidence text            |
| **Skill**        | **On demand (intent-matched)** | **Multi-step procedure** |
| Hook             | Always (system gate)           | Cross-cutting policy     |

This is the "Skills" layer in Claude Code's 5-layer architecture (CLAUDE.md / Skills / Hooks / Subagents / Plugins). TnsAI was missing it before TNS-289.

## Why a separate layer

Three forces motivate skills as a distinct primitive:

1. **Token budget hygiene** — `RoleSpec` and `CLAUDE.md` content rides every prompt. Procedures that only matter when invoked ("deploy procedure", "API design conventions", "customer escalation protocol") shouldn't pin tokens on every turn.
2. **Author-time portability** — Claude Code, Cursor, and other tools are converging on the `agentskills.io` `SKILL.md` standard. A skill authored once works across every framework that supports it.
3. **Per-skill scoping** — `allowed-tools` declares which tools a skill expects to use; `userInvocable` and `disableModelInvocation` declare who may activate it. Both are recorded on the activation event and enforceable through `SkillScopedToolCallFilter`.

## Quick start

```java
import com.tnsai.skills.*;
import com.tnsai.agents.AgentBuilder;
import java.nio.file.Path;

// 1. Pick a store. FileSystemSkillStore reads the Claude Code-compatible
//    layout: <root>/<skill-name>/SKILL.md. Programmatic registration
//    works through InMemorySkillStore for tests / embedded use.
SkillStore store = new FileSystemSkillStore(Path.of(".tnsai/skills"));

// 2. Wire on the AgentBuilder. Wiring a store auto-upgrades the
//    policy from OFF to AUTO (the consumer's intent: "I configured
//    skills, surface them"). Override with .skillResolverPolicy(...)
//    if you want MANUAL_ONLY or OFF.
Agent agent = AgentBuilder.create()
        .id("research-agent")
        .llm(...)
        .role(myRole)
        .skillStore(store)
        // accountability wiring (TNS-298) elided for brevity
        .build();
```

From this point on:

- The resolver runs on every chat turn against the registered skill descriptions; the top `maxActiveSkills` candidates appear in the per-message system prompt under `## Skill candidates for this turn`.
- The user types `/deploy staging` to manually activate a skill — the framework intercepts BEFORE the LLM round-trip, so manual invocation costs zero LLM tokens.
- Once activated, the skill's substituted body lives in the system prompt under `# Skills > ## Active skill bodies` for the remainder of the session.
- `agent.invokeSkill("name", args, env)` lets framework code activate a skill programmatically, bypassing `userInvocable=false`.

## SKILL.md format

```markdown
---
name: deploy
description: Production deploy procedure with rollback support
when-to-use: When the user asks to ship a build to prod or staging
allowed-tools:
  - bash
  - kubectl
argument-hint: <environment>
arguments:
  - environment
disable-model-invocation: false
user-invocable: true
---
# Deploy procedure

1. Verify CI is green.
2. `kubectl apply -f manifests/$0/`
```

The `$0` placeholder gets substituted with the first positional argument when the skill is invoked. See [Skill format](/docs/capabilities/skills/skill-format) for the full frontmatter reference.

## Resolver policies

| Policy                                 | Behaviour                                                                                                                                                                                 |
| -------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `AUTO` (default when a store is wired) | Resolver runs on every user turn; top-N candidate descriptions appear in the system prompt; the LLM may invoke via the synthetic `invoke_skill` tool; users may invoke via `/skill-name`. |
| `MANUAL_ONLY`                          | Resolver does NOT run; only `/skill-name` and `agent.invokeSkill(...)` activate skills. Useful when the deployment wants deterministic skill loading.                                     |
| `OFF`                                  | Skill layer disabled. Framework default when no store is wired.                                                                                                                           |

## Lifecycle

1. **Discovery** — store enumerates registered skills at startup. Only the `description` field is in the always-on system prompt.
2. **Resolution** — per user message, `SkillResolver` ranks candidates by relevance.
3. **Activation** — user invocation or model tool call moves the full body into context for the rest of the session.
4. **Re-activation** — invoking an active skill again replaces the previous snapshot; the latest invocation's substituted body wins.

`SkillActivationEvent` (sealed branch of `TnsAIEvent`) is emitted on every activation, carrying the source (`USER_SLASH_COMMAND` / `MODEL_TOOL_CALL` / `PROGRAMMATIC`), the skill name, and the supplied arguments.

## Per-skill tool scope

When a skill declares `allowed-tools`, callers can attach `SkillScopedToolCallFilter` to the agent so tool calls outside the union of active-skill `allowed-tools` are blocked with a `Guide` action that names the active skills:

```java
agent.setToolCallFilter(new SkillScopedToolCallFilter(agent.getSkillManager()));
```

The filter is permissive when no active skill declares `allowed-tools` — the field is opt-in scoping, not a default constraint.

## Subagent context fork

`SkillManager.preloadFrom(parent)` copies the parent's active-skill snapshots into a child manager so `AgentGroup`-spawned subagents inherit the procedural context their parent had loaded. Mirrors Claude Code's `context: fork` pattern. Parent's snapshot wins on name collision; the child's pre-existing activations are preserved on top.

## What's not in this layer (deferred)

- **`paths` glob auto-activation** — file-context-aware activation; v2 (the v1 trigger surface is user-message-aware via the resolver)
- **Plugin distribution** — packaging skills into the Plugins layer; tracked separately
- **`context: fork` semantics for full isolation** — current preload is a copy, not a fork; v2
- **GUI skill registry** — visual catalog in `TnsAI.Web`; v3
- **Live file-watcher** — call `FileSystemSkillStore.refresh()` instead

## See also

- [Skill format](/docs/capabilities/skills/skill-format) — full SKILL.md frontmatter reference and substitution rules
- [Registration](/docs/capabilities/skills/registration) — `AgentBuilder` API + custom resolvers + custom stores
- [Hooks](/docs/security) — skill activation events flow through the hook bus
- [Approvals and Annotations](/docs/security/approvals-and-annotations) — `@ApprovalRequired` works alongside skills (approvals gate access; skills supply the procedure)
- [Accountability](/docs/security/accountability) — `SkillActivationEvent` rides the same trace as the resulting liability records

---

# Registration

URL: https://tnsai.dev/docs/capabilities/skills/registration
Description: How to wire a SkillStore and SkillResolver into an AgentBuilder, swap the defaults, and integrate skill activation with the rest of the framework.

import { Callout } from 'fumadocs-ui/components/callout'

## AgentBuilder API

| Method                                      | Default                                 | Required?                                                                                                             |
| ------------------------------------------- | --------------------------------------- | --------------------------------------------------------------------------------------------------------------------- |
| `.skillStore(SkillStore)`                   | none                                    | Required to enable skills. Wiring a store auto-upgrades policy from `OFF` to `AUTO`.                                  |
| `.skillResolver(SkillResolver)`             | `KeywordSkillResolver`                  | Optional. Override only if the default token-overlap scoring isn't accurate enough.                                   |
| `.skillResolverPolicy(SkillResolverPolicy)` | `OFF` (or `AUTO` once a store is wired) | Override to force `MANUAL_ONLY` (resolver does not run) or `OFF` (skill layer disabled even when a store is present). |
| `.maxActiveSkills(int)`                     | `3`                                     | Cap on candidate descriptions surfaced per turn. Bounds prompt overhead.                                              |

```java
Agent agent = AgentBuilder.create()
        .id("research-agent")
        .llm(llm)
        .role(myRole)
        .skillStore(new FileSystemSkillStore(Path.of(".tnsai/skills")))
        .skillResolverPolicy(SkillResolverPolicy.AUTO)
        .maxActiveSkills(5)
        // accountability wiring (TNS-298) elided
        .build();
```

When `skillStore` is not called, `agent.getSkillManager()` returns `null` and the agent operates without a skills layer — the documented "skills disabled" contract.

## Stores

### FileSystemSkillStore

The recommended production store. Reads the Claude Code-compatible layout:

```
.tnsai/skills/
├── deploy/SKILL.md
└── lint/SKILL.md
```

Scans on construction. Re-scan via `refresh()` to pick up disk changes:

```java
FileSystemSkillStore store = new FileSystemSkillStore(Path.of(".tnsai/skills"));
// ... time passes, someone added a new skill ...
store.refresh();
```

Programmatic registrations (`store.register(skill)`) survive `refresh()` if their name doesn't collide with a disk skill. Disk content takes precedence on collision.

### InMemorySkillStore

Test seam. Programmatic-only:

```java
InMemorySkillStore store = new InMemorySkillStore(List.of(
        Skill.builder("deploy")
                .description("Production deploy procedure")
                .body("1. Verify CI...\n2. kubectl apply...")
                .allowedTools(List.of("bash", "kubectl"))
                .build()));
```

### Custom stores

Implement `SkillStore` to back skills with a database / classpath / enterprise registry. The contract is small (`findByName`, `list`, `register`); concurrency must be safe under multi-threaded agent dispatch.

## Resolvers

### KeywordSkillResolver (default)

Cheap word-overlap scoring. Field weights: `name` 3×, `when-to-use` 2×, `description` 1×. Stop-words filtered. Zero-score candidates dropped. Ties break alphabetically by name for stable ordering.

### LLMSkillResolver

Asks the configured `LLMClient` to pick the most relevant skill names from a compact catalog. Higher accuracy when triggers are paraphrased semantically; one extra LLM call per turn.

```java
.skillResolver(new LLMSkillResolver(llmClient))
```

Falls back to `KeywordSkillResolver` on LLM failure rather than silently returning empty — a transient hiccup must not strip skills from the prompt.

### Custom resolvers

Implement `SkillResolver`. Common variants:

- **Embedding-based** — pre-compute description embeddings; score by cosine similarity against the user message embedding.
- **Hybrid** — keyword + LLM rerank.
- **Rule-based** — wire-up that always surfaces a fixed subset.

## Tool-call scope (`SkillScopedToolCallFilter`)

When skills declare `allowed-tools`, attach the skill-aware filter so the LLM is constrained to those tools while the skill is active:

```java
agent.setToolCallFilter(new SkillScopedToolCallFilter(agent.getSkillManager()));
```

Behaviour summary:

- No active skills → permissive (every tool call allowed).
- Active skills, none declare `allowed-tools` → permissive.
- One or more active skills declare `allowed-tools` → tool calls outside the union of their lists are blocked with a `ToolCallAction.Guide` action that names the active skills, so the LLM can self-correct.

Compose with other filters by wrapping in your own `ToolCallFilter` chain.

## Subagent context fork

When an `AgentGroup` spawns a subagent that should inherit the parent's procedural context, preload from the parent's manager:

```java
SkillManager parentMgr = parent.getSkillManager();
SkillManager childMgr = child.getSkillManager();
if (parentMgr != null && childMgr != null) {
    childMgr.preloadFrom(parentMgr);
}
```

The child's pre-existing activations are preserved on top; the parent's snapshots are appended. Re-activations replace any same-named child snapshot.

## Programmatic invocation

`Agent.invokeSkill(name, arguments, environment)` activates a skill directly. The flag `userInvocable=false` is bypassed (programmatic source overrides user-facing visibility), but `disableModelInvocation` is irrelevant here because the source is `PROGRAMMATIC`, not `MODEL_TOOL_CALL`.

```java
Optional<ActiveSkill> activated = agent.invokeSkill(
        "deploy",
        List.of("staging"),
        Map.of("BUILD_TAG", "v1.4.2"));
```

Returns empty when:

- No `SkillStore` is wired.
- No skill with the given name is registered.

## Slash commands

Users (or test harnesses) activate a skill manually with `/skill-name args...`. `Agent.chat(...)` intercepts this prefix BEFORE the LLM round-trip, so manual invocation costs zero LLM tokens and returns a confirmation string.

```java
String reply = agent.chat("/deploy staging");
// "Skill 'deploy' activated; its body is now in context and will guide subsequent turns."
```

`userInvocable=false` skills reject slash invocation with a friendly error instead of activating.

## Activation events

Every successful activation emits a `SkillActivationEvent` (sealed branch of `TnsAIEvent`):

```java
public record SkillActivationEvent(
        String eventId,
        Instant timestamp,
        String runId,
        String agentName,
        String skillName,
        Source source,                // USER_SLASH_COMMAND / MODEL_TOOL_CALL / PROGRAMMATIC
        List<String> arguments
) implements TnsAIEvent {}
```

Use `agent.chatWithEvents(message, eventConsumer)` to receive these alongside the rest of the event stream. The same event flows through the configured hook bus, so policy hooks (e.g. "log every skill activation to the audit pipeline") can attach there.

## See also

- [Skills overview](/docs/capabilities/skills)
- [Skill format](/docs/capabilities/skills/skill-format) — frontmatter + substitution
- [Hooks](/docs/security) — `SkillActivationEvent` rides the same bus as other TnsAI events
- [Accountability](/docs/security/accountability) — skill activations correlate with the resulting liability records via `runId`

---

# SKILL.md format

URL: https://tnsai.dev/docs/capabilities/skills/skill-format
Description: A SKILL.md file is a YAML frontmatter block followed by a markdown body. The framework's parser (SkillMdParser) accepts the same shape Claude Code's parser does, so a skill authored once works in both environments.

import { Callout } from 'fumadocs-ui/components/callout'

## File layout

`<root>/<skill-name>/SKILL.md` — the directory name is the default skill name when the frontmatter is silent on `name`. `FileSystemSkillStore` scans `<root>/` and treats every immediate child directory containing a `SKILL.md` as a registered skill.

```
.tnsai/skills/
├── deploy/
│   ├── SKILL.md
│   ├── reference.md            (optional, lazy-referenced by body)
│   └── scripts/precheck.sh     (optional, executed by skill body)
├── lint/
│   └── SKILL.md
└── customer-escalation/
    └── SKILL.md
```

Supporting files (`reference.md`, scripts, …) are NOT loaded eagerly — the body declares them by relative path and consumers fetch on demand.

## Frontmatter reference

```yaml
---
name: deploy                          # falls back to parent directory name
description: Production deploy procedure with rollback support
when-to-use: |
  When the user asks to ship a build to prod or staging.
  Trigger phrases: "deploy", "ship", "release", "rollout".
allowed-tools:
  - bash
  - kubectl
  - github.create_pr
argument-hint: "<environment>"
arguments:
  - environment
disable-model-invocation: false       # default false
user-invocable: true                  # default true
paths:
  - "infra/**"                        # v2 (file-context auto-activation)
---
```

| Field                      | Type                  | Default      | Meaning                                                                                                          |
| -------------------------- | --------------------- | ------------ | ---------------------------------------------------------------------------------------------------------------- |
| `name`                     | string                | parent dir   | Stable identifier; case-insensitive on lookup.                                                                   |
| `description`              | string                | **required** | Always in the system prompt; the resolver scores against it.                                                     |
| `when-to-use`              | string                | empty        | Optional context for the resolver — trigger phrases, example requests.                                           |
| `allowed-tools`            | list of strings       | empty        | Tools auto-granted while this skill is active. Empty = no constraint.                                            |
| `argument-hint`            | string                | empty        | Free-form hint shown in catalog listings.                                                                        |
| `arguments`                | list of strings       | empty        | Positional argument names for `argument-hint`.                                                                   |
| `disable-model-invocation` | boolean               | `false`      | If `true`, the LLM may not activate this skill — only the user (or `Agent.invokeSkill(...)`) can.                |
| `user-invocable`           | boolean               | `true`       | If `false`, hidden from the user-facing menu and rejected on `/skill-name`. Programmatic activation still works. |
| `paths`                    | list of glob patterns | empty        | v2 only: auto-activate when the agent is operating on matching files.                                            |

### Field aliases

The parser accepts both the kebab-case (`agentskills.io` standard) and snake\_case (Claude Code docs) forms for fields where the docs are split:

| Canonical       | Aliases         |
| --------------- | --------------- |
| `when-to-use`   | `when_to_use`   |
| `allowed-tools` | `allowed_tools` |
| `argument-hint` | `argument_hint` |

### List shorthand

A scalar in place of a single-element list is accepted for `allowed-tools`, `paths`, and `arguments`:

```yaml
allowed-tools: bash       # equivalent to: ["bash"]
```

### CRLF tolerance

Files written on Windows / via `git autocrlf=true` parse identically — the parser normalises line endings before extracting the frontmatter.

## Body

Anything between the closing `---` delimiter and the end of the file is the skill body. The body is plain markdown — the framework does not parse it.

The body is rendered through `SkillSubstitution` before it lands in the system prompt, so placeholders are resolved at activation time (not at parse time).

## Substitution

| Placeholder   | Resolves to                                                           |
| ------------- | --------------------------------------------------------------------- |
| `$ARGUMENTS`  | All positional arguments space-joined.                                |
| `$0`, `$1`, … | The Nth positional argument (0-indexed). Out-of-range = empty string. |
| `${VAR}`      | Named env value supplied by the caller. Unknown name = empty string.  |
| `$$`          | Literal dollar sign — escapes the placeholder at that position.       |

Substitution is **single-pass and left-to-right** — placeholder values are not re-substituted. A literal `$ARGUMENTS` passed as `$0` stays literal in the output.

```markdown
---
name: deploy
description: Deploy a build to a target environment
arguments:
  - environment
---
# Deploy to $0

1. Verify CI is green for the build tagged `${BUILD_TAG}`.
2. `kubectl apply -f manifests/$0/`
3. Smoke-test https://$0.example.com/healthz.
```

Invoked as `/deploy staging` with `env={BUILD_TAG: "v1.4.2"}`, the body renders as:

```markdown
# Deploy to staging

1. Verify CI is green for the build tagged `v1.4.2`.
2. `kubectl apply -f manifests/staging/`
3. Smoke-test https://staging.example.com/healthz.
```

## Validation rules

The parser rejects:

- Files without an opening `---` delimiter on the first line.
- Files with an opening delimiter but no closing one.
- Frontmatter that is not valid YAML.
- A `Skill` with blank `name` (after defaulting from the directory name) — every skill must have a name.
- A `Skill` with blank `description` — the resolver depends on it.

A malformed `SKILL.md` is **logged and skipped** by `FileSystemSkillStore`; sibling skills continue to load. One broken skill on disk does not poison the store.

## Authoring tips

- **Lead the description with the problem the skill solves**, not the solution. The resolver scores by token overlap with the user's message — "deploy build" lands closer to "ship build" than "ship a release". Name the user's intent in your own words.
- **Use `when-to-use` for trigger phrases**. The keyword resolver weights `when-to-use` matches 2× and `name` matches 3×; trigger phrases here move the skill ahead of competitors.
- **Keep the body procedural, not narrative**. Numbered steps + concrete commands. The body is what the LLM follows once activated; flowery prose dilutes the signal.
- **Reference supporting files by relative path**. The body can say "see `reference.md` for examples" — the consumer (LLM or human) fetches on demand instead of bloating the activation snapshot.

## See also

- [Skills overview](/docs/capabilities/skills) — when to reach for skills vs tools / RAG / capabilities
- [Registration](/docs/capabilities/skills/registration) — wiring a store + resolver into `AgentBuilder`

---

# Tools — Advanced

URL: https://tnsai.dev/docs/capabilities/tools/advanced
Description: The function-shape POJO model deliberately keeps the tool surface small: a method, an annotation, a registry. Most \"advanced\" features that older docs covered (manifest generators, contract validators, security enforcers, parameter validators, retry/cache wrappers) were retired together with the legacy Tool interface in v0.6.0 / v0.7.0. Cross-cutting concerns now live one layer up — on the @ActionSpec annotation, on the agent's setToolCallFilter / setToolCallListener hooks, or on the dispatcher itself.

import { Callout } from 'fumadocs-ui/components/callout'

This page covers what's left in the advanced surface today.

## Per-action LLM overrides

When an `@ActionSpec(type = LLM)` action needs its own system prompt or temperature without changing the agent's global LLM config, set them on the annotation:

```java
@ActionSpec(
    type = ActionType.LLM,
    description = "Extract entities from text — must be deterministic",
    llmSystemPrompt = "You are a precise NER extractor. Output JSON only.",
    llmTemperature = 0.0f
)
public String extractEntities(String text) {
    return "Extract entities from: " + text;
}
```

`llmSystemPrompt` overrides the LLM client's default for the duration of this action; `llmTemperature >= 0` overrides the chat temperature. A negative `llmTemperature` (the `-1.0f` default) means "fall back to the LLM client's default". Tool exposure stays at the agent level — every `LLM` action sees the agent's complete tool registry.

## Inspecting the tool registry at runtime

Every agent built with `AgentBuilder` has a `ToolMethodDispatcher` whose `registry()` exposes the full set of registered tools. Use this when you need to introspect the catalog from inside a `setToolCallListener`, write tooling around tool discovery, or assert on registration in tests.

```java
import com.tnsai.tools.method.ToolMethodDispatcher;
import com.tnsai.tools.method.ToolMethodRegistry;

ToolMethodDispatcher dispatcher = agent.getToolMethodDispatcher();
ToolMethodRegistry registry = dispatcher.registry();

for (var tool : registry.allMethods()) {
    System.out.println(tool.name() + " — " + tool.description());
}
```

## Direct dispatch

`ToolMethodDispatcher.dispatch(name, args)` is the same entry point the LLM tool-call loop uses. Call it directly when you want to invoke a tool from non-LLM code (tests, batch jobs, smoke scripts) without spinning up the full chat path:

```java
Object result = dispatcher.dispatch(
    "csv_summary",
    Map.of("path", "/data/sales.csv")
);
```

Argument types are coerced via Jackson — pass a `Map<String, Object>` whose entries match the `@ToolParam` parameter names.

## Composio integration

`ComposioClient` (`com.tnsai.tools.composio`) provides access to 500+ managed tool integrations via the Composio platform with automatic OAuth handling. It's a separate POJO toolkit registered the same way as any other:

```java
AgentBuilder.create()
    .llm(llm)
    .role(role)
    .toolPojos(new ComposioTools())  // wraps ComposioClient
    .build();
```

### Operations

| Operation     | Description                      |
| ------------- | -------------------------------- |
| `apps`        | List available apps/integrations |
| `tools`       | Get tools for a specific app     |
| `execute`     | Execute a tool action            |
| `connect`     | Initiate OAuth connection        |
| `connections` | List active connections          |
| `triggers`    | List available triggers          |
| `categories`  | List app categories              |

### App categories

`PRODUCTIVITY`, `CRM`, `DEVELOPER`, `COMMUNICATION`, `MARKETING`, `DESIGN`, `FINANCE`, `SOCIAL`, `AI`, `STORAGE`

### Direct usage

```java
ComposioClient composio = new ComposioClient();
// Requires COMPOSIO_API_KEY environment variable

String result = composio.execute("{\"operation\":\"apps\",\"category\":\"developer\"}");

String tools = composio.execute("{\"operation\":\"tools\",\"app\":\"github\"}");

String issue = composio.execute("""
    {"operation":"execute","app":"github","action":"create_issue",
     "params":{"repo":"owner/repo","title":"Bug report","body":"..."}}
    """);
```

## Cross-References

- [Tool Catalog](/docs/capabilities/tools/catalog) — the 62 shipped POJO toolkits and their methods
- [Custom Tools](/docs/capabilities/tools/custom-tools) — write your own `@Tool` methods
- [Tool Integration](/docs/capabilities/tools/registration) — `setToolCallFilter` / `setToolCallListener` hooks
- [MCP Client](/docs/mcp/client) — bridge MCP tools into TnsAI as `DynamicToolMethod` instances

---

# Tool Catalog

URL: https://tnsai.dev/docs/capabilities/tools/catalog
Description: tnsai-tools ships 59 function-shape POJO toolkits exposing roughly 206 @Tool-annotated methods across 29 categories. Each toolkit is a plain class with public methods annotated @Tool; the framework discovers them reflectively via ToolMethodRegistry and dispatches calls through ToolMethodDispatcher (the same path used for any user POJO registered with AgentBuilder.toolPojos(...)).

import { Callout } from 'fumadocs-ui/components/callout'

For creating your own toolkits, see [Custom Tools](/docs/capabilities/tools/custom-tools).

## Quick Start

The compile-safe path is `BuiltInTool` — pass enum constants and the framework instantiates the backing POJO for you:

```java
import com.tnsai.agents.AgentBuilder;
import com.tnsai.enums.BuiltInTool;
import com.tnsai.llm.providers.OpenAIClient;

Agent agent = AgentBuilder.create()
    .llm(new OpenAIClient("gpt-4o"))
    .role(myRole)
    .builtInTools(
        BuiltInTool.WEB_SEARCH_TOOLS,   // brave_search, duckduckgo, wikipedia, …
        BuiltInTool.UTILITY_TOOLS,      // calculator, hash, datetime_*
        BuiltInTool.PDF_TOOLS           // pdf_extract_text, pdf_metadata, …
    )
    .build();

String response = agent.chat("What is the population of Tokyo? Triple it.");
```

The LLM sees every `@Tool` method on every registered toolkit as a callable function and dispatches by method name (`brave_search`, `calculator`, `pdf_extract_text`, …). Most toolkits read credentials from environment variables on first use:

```bash
export BRAVE_API_KEY=your-key
export OPENAI_API_KEY=your-key
```

## How toolkits are organised

Each `BuiltInTool` enum entry maps a stable `toolName` to the FQCN of a POJO in `tnsai-tools`. The POJO's public `@Tool`-annotated methods are the actual functions exposed to the LLM. For example, `BuiltInTool.CSV_TOOLS` backs `com.tnsai.tools.file.CsvTools`, which exposes `csv_summary`, `csv_columns`, `csv_filter`, `csv_head`, `csv_search`.

Tables below list each toolkit's enum constant, backing class, the methods it exposes, and any required environment variables. Method names are what the LLM will see and call.

## search

Web search, scraping, and specialised lookups.

| Enum                       | Methods                                                                                                              | API key                                                                                                                      |
| -------------------------- | -------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------- |
| `WEB_SEARCH_TOOLS`         | `duckduckgo`, `wikipedia`, `wikidata`, `searxng`, `npm`, `maven_central`, `brave_search`, `serpapi`, `tavily`, `exa` | Per-provider (none for the first six; `BRAVE_API_KEY`, `SERPAPI_API_KEY`, `TAVILY_API_KEY`, `EXA_API_KEY` for the last four) |
| `WEB_SCRAPING_TOOLS`       | `web_scraper`, `firecrawl`                                                                                           | `FIRECRAWL_API_KEY` (firecrawl only)                                                                                         |
| `QNA_TOOLS`                | `hackernews`, `stackoverflow_search`                                                                                 | None                                                                                                                         |
| `SPECIALIZED_SEARCH_TOOLS` | `yahoo_finance_lookup`, `yahoo_finance_news`, `wolfram_alpha`                                                        | `WOLFRAM_APP_ID` (wolfram only)                                                                                              |

## academic

Free / freemium scholarly databases.

| Enum                   | Methods                                                                                                         | API key |
| ---------------------- | --------------------------------------------------------------------------------------------------------------- | ------- |
| `ACADEMIC_TOOLS`       | `arxiv_search`, `pubmed_search`, `semantic_scholar_search`, `openalex_search`, `crossref_search`, `dblp_search` | None    |
| `ACADEMIC_EXTRA_TOOLS` | `orcid_search`, `orcid_get`, `unpaywall_lookup`, `biorxiv_recent`                                               | None    |

## file

Text and structured-document parsing.

| Enum             | Methods                                                                              | Notes                                        |
| ---------------- | ------------------------------------------------------------------------------------ | -------------------------------------------- |
| `CSV_TOOLS`      | `csv_summary`, `csv_columns`, `csv_filter`, `csv_head`, `csv_search`                 | Accepts file path or literal CSV string      |
| `JSON_TOOLS`     | `json_query`                                                                         | Jayway JsonPath against file or literal JSON |
| `XML_TOOLS`      | `xml_query`                                                                          | XPath against XML                            |
| `PDF_TOOLS`      | `pdf_extract_text`, `pdf_extract_pages`, `pdf_metadata`, `pdf_merge`, `pdf_to_image` | PDFBox-backed                                |
| `MARKDOWN_TOOLS` | `markitdown`                                                                         | Convert any supported source to Markdown     |
| `FILE_IO_TOOLS`  | `file_read`, `file_write`                                                            | Sandboxed by Role policy                     |

### `CSV_TOOLS` — example

`CsvTools` is the canonical example for the function-shape pattern. The five methods below are independent — the LLM picks the one it needs.

```java
Agent agent = AgentBuilder.create()
    .llm(new OpenAIClient("gpt-4o"))
    .role(myRole)
    .builtInTools(BuiltInTool.CSV_TOOLS)
    .build();

agent.chat("Summarise /data/sales.csv and list the column headers.");
// LLM emits two tool calls: csv_summary("/data/sales.csv") then csv_columns(...)
```

| Method        | Purpose                                            | Notes                                          |
| ------------- | -------------------------------------------------- | ---------------------------------------------- |
| `csv_summary` | Row/column counts plus per-column dtype/null stats | Default for "describe this CSV" prompts        |
| `csv_columns` | Extract a subset of columns by name                | Case-insensitive; falls back to numeric index  |
| `csv_filter`  | Rows where a column's value contains a substring   | Case-insensitive substring, capped at 100 rows |
| `csv_head`    | First N rows as a Markdown ASCII table             |                                                |
| `csv_search`  | All rows where any cell contains the term          | Capped at 100 rows                             |

The methods accept typed parameters (path, column name, etc.) — there's no `path|||command` text protocol any more. Each method is a regular Java method whose signature defines the LLM-facing schema (via `@Tool` and `@ToolParam`).

## communication

Email, chat, and SMS sending.

| Enum              | Methods                                                              | Config                                                                      |
| ----------------- | -------------------------------------------------------------------- | --------------------------------------------------------------------------- |
| `EMAIL_TOOLS`     | `smtp_send`, `gmail_send`, `gmail_inbox`                             | `SMTP_HOST` / `SMTP_USER` / `SMTP_PASS` (SMTP); `GMAIL_OAUTH_TOKEN` (Gmail) |
| `MESSAGING_TOOLS` | `slack_post`, `discord_post`, `twilio_sms_send`, `twilio_sms_status` | `SLACK_WEBHOOK_URL`, `DISCORD_WEBHOOK_URL`, `TWILIO_SID` + `TWILIO_TOKEN`   |

## fintech

Payment and BNPL platforms.

| Enum                      | Methods                                                                                                                     | API key                      |
| ------------------------- | --------------------------------------------------------------------------------------------------------------------------- | ---------------------------- |
| `SQUARE_TOOLS`            | `square_create_payment`, `square_list_payments`, `square_create_invoice`, `square_create_customer`, `square_search_catalog` | `SQUARE_ACCESS_TOKEN`        |
| `CASH_APP_PAY_TOOLS`      | `cashapp_create_request`, `cashapp_get_request`, `cashapp_cancel_request`                                                   | `CASHAPP_CLIENT_ID` + secret |
| `LIGHTNING_TOOLS`         | `lightning_create_invoice`, `lightning_pay_invoice`, `lightning_decode_invoice`                                             | `LN_API_URL` + macaroon      |
| `AFTERPAY_TOOLS`          | `afterpay_create_checkout`, `afterpay_capture_payment`, `afterpay_get_order`                                                | `AFTERPAY_API_KEY`           |
| `PAYMENT_ANALYTICS_TOOLS` | `payment_revenue_summary`                                                                                                   | Varies (cross-processor)     |

## commerce

Storefront APIs.

| Enum            | Methods                                            | API key                         |
| --------------- | -------------------------------------------------- | ------------------------------- |
| `SHOPIFY_TOOLS` | `shopify_search`, `shopify_get_product`            | `SHOPIFY_API_KEY` + shop domain |
| `ETSY_TOOLS`    | `etsy_search`, `etsy_get_listing`, `etsy_get_shop` | `ETSY_API_KEY`                  |

## crm

Customer-relationship platforms.

| Enum               | Methods                                                                  | API key                          |
| ------------------ | ------------------------------------------------------------------------ | -------------------------------- |
| `HUBSPOT_TOOLS`    | `hubspot_list_contacts`, `hubspot_get_contact`, `hubspot_create_contact` | `HUBSPOT_ACCESS_TOKEN`           |
| `SALESFORCE_TOOLS` | `salesforce_query`, `salesforce_search`, `salesforce_get_sobject`        | `SF_ACCESS_TOKEN` + instance URL |

## database

Relational, document, key-value, and vector stores.

| Enum             | Methods                                                                                             | Config                            |
| ---------------- | --------------------------------------------------------------------------------------------------- | --------------------------------- |
| `SQL_TOOLS`      | `sql_query`                                                                                         | `JDBC_URL` + driver on classpath  |
| `MONGO_TOOLS`    | `mongo_find`, `mongo_count`                                                                         | `MONGO_URI`                       |
| `REDIS_TOOLS`    | `redis_get`, `redis_keys`, `redis_ttl`, `redis_set`, `redis_del`                                    | `REDIS_URL`                       |
| `QDRANT_TOOLS`   | `qdrant_collections`, `qdrant_count`, `qdrant_search`, `qdrant_create_collection`, `qdrant_upsert`  | `QDRANT_URL` (+ optional API key) |
| `WEAVIATE_TOOLS` | `weaviate_classes`, `weaviate_count`, `weaviate_search`, `weaviate_create_class`, `weaviate_upsert` | `WEAVIATE_URL`                    |

## developer

Repo, dependency, and project introspection.

| Enum                     | Methods                                                                                                   | API key                                      |
| ------------------------ | --------------------------------------------------------------------------------------------------------- | -------------------------------------------- |
| `DEVELOPER_TOOLS`        | `github_search_repos`, `github_search_code`, `github_search_issues`, `github_search_users`, `jshell_eval` | `GITHUB_TOKEN` (optional, raises rate limit) |
| `DEPENDENCY_TOOLS`       | `dependency_latest`, `dependency_compare`, `project_detect_type`                                          | None                                         |
| `PROJECT_ANALYZER_TOOLS` | `project_tree`, `project_stats`, `project_languages`                                                      | None                                         |

## productivity

Calendars, task trackers, docs.

| Enum           | Methods                                                                                                                  | API key                            |
| -------------- | ------------------------------------------------------------------------------------------------------------------------ | ---------------------------------- |
| `GOOGLE_TOOLS` | `gcal_list_events`, `gcal_get_event`, `gcal_create_event`, `gdrive_list_files`, `gdrive_read_file`, `gdrive_create_file` | `GOOGLE_OAUTH_TOKEN`               |
| `JIRA_TOOLS`   | `jira_search`, `jira_get_issue`, `jira_create_issue`, `jira_transition_issue`                                            | `JIRA_BASE_URL` + `JIRA_API_TOKEN` |
| `NOTION_TOOLS` | `notion_search`, `notion_get_page`, `notion_query_database`, `notion_create_page`                                        | `NOTION_TOKEN`                     |
| `TRELLO_TOOLS` | `trello_list_boards`, `trello_get_board`, `trello_list_cards`, `trello_create_card`, `trello_move_card`                  | `TRELLO_KEY` + `TRELLO_TOKEN`      |

## media

Audio and TTS.

| Enum               | Methods                            | API key              |
| ------------------ | ---------------------------------- | -------------------- |
| `MEDIA_TOOLS`      | `whisper_transcribe`, `openai_tts` | `OPENAI_API_KEY`     |
| `CHATTERBOX_TOOLS` | `chatterbox_tts`                   | `CHATTERBOX_API_KEY` |

## social

Social-network APIs.

| Enum             | Methods                                                           | API key                                     |
| ---------------- | ----------------------------------------------------------------- | ------------------------------------------- |
| `TWITTER_TOOLS`  | `twitter_search`, `twitter_user`, `twitter_timeline`              | `X_BEARER_TOKEN`                            |
| `REDDIT_TOOLS`   | `reddit_search`, `reddit_subreddit_posts`, `reddit_post_comments` | `REDDIT_CLIENT_ID` + `REDDIT_CLIENT_SECRET` |
| `LINKEDIN_TOOLS` | `linkedin_me`, `linkedin_share`                                   | `LINKEDIN_ACCESS_TOKEN`                     |

## ai

Multimodal helpers.

| Enum           | Methods         | API key          |
| -------------- | --------------- | ---------------- |
| `VISION_TOOLS` | `image_analyze` | `GEMINI_API_KEY` |

## code

Code execution through the framework's [Sandbox SPI](/docs/security/sandbox). Both built-in tools route through `SandboxFactory.byId("process")` by default with `ResourceLimits.standard()` + `NetPolicy.denyAll()`. Pass an explicit `SandboxFactory` + image-bearing `SandboxSpec` via the full-control constructors for container / WASM / Firecracker isolation.

| Enum                     | Methods                                                                                          | Host requirement    |
| ------------------------ | ------------------------------------------------------------------------------------------------ | ------------------- |
| `JS_EXECUTION_TOOLS`     | `js_execute`                                                                                     | `node` on `PATH`    |
| `PYTHON_EXECUTION_TOOLS` | `python_execute`, `python_version`                                                               | `python3` on `PATH` |
| `E2B_SANDBOX_TOOLS`      | `e2b_create`, `e2b_execute`, `e2b_upload`, `e2b_download`, `e2b_install`, `e2b_list`, `e2b_kill` | `E2B_API_KEY`       |

## utility

Math, hashing, datetime, encoding.

| Enum             | Methods                                                               | API key                                     |
| ---------------- | --------------------------------------------------------------------- | ------------------------------------------- |
| `UTILITY_TOOLS`  | `calculator`, `hash`, `datetime_now`, `datetime_diff`, `datetime_add` | None                                        |
| `ENCODING_TOOLS` | `qr_generate`, `qr_base64`, `qr_read`, `google_translate`             | `GOOGLE_TRANSLATE_API_KEY` (translate only) |

## diagram

Diagram-as-code rendering.

| Enum            | Methods                                 | API key |
| --------------- | --------------------------------------- | ------- |
| `DIAGRAM_TOOLS` | `mermaid_render`, `excalidraw_generate` | None    |

## document

DOCX / Office / image conversion.

| Enum             | Methods                                                                             | API key |
| ---------------- | ----------------------------------------------------------------------------------- | ------- |
| `DOCUMENT_TOOLS` | `document_read`, `markdown_to_html`, `image_convert`, `image_info`, `slides_create` | None    |

## visualization

Charts, tables, infographics.

| Enum                  | Methods                                                | API key |
| --------------------- | ------------------------------------------------------ | ------- |
| `VISUALIZATION_TOOLS` | `chart_ascii`, `table_format`, `infographic_templates` | None    |

## realtime

Live FX, crypto, weather feeds.

| Enum             | Methods                                                     | API key                                                |
| ---------------- | ----------------------------------------------------------- | ------------------------------------------------------ |
| `REALTIME_TOOLS` | `crypto_price`, `fx_rates`, `fx_convert`, `weather_current` | None (CoinGecko / Frankfurter / OpenWeatherMap public) |

## finance

Borsa Istanbul market data.

| Enum            | Methods                                            | API key |
| --------------- | -------------------------------------------------- | ------- |
| `FINANCE_TOOLS` | `bist_quote`, `bist_index`, `bist_fx`, `bist_gold` | None    |

## trading

Prediction markets.

| Enum            | Methods                                                        | API key          |
| --------------- | -------------------------------------------------------------- | ---------------- |
| `TRADING_TOOLS` | `polymarket_markets`, `polymarket_search`, `polymarket_prices` | None (read-only) |

## scraping

Apify actor execution.

| Enum             | Methods                                                      | API key       |
| ---------------- | ------------------------------------------------------------ | ------------- |
| `SCRAPING_TOOLS` | `apify_run_actor`, `apify_search_actors`, `apify_actor_info` | `APIFY_TOKEN` |

## knowledge

In-memory knowledge graph.

| Enum              | Methods                                                                    | API key |
| ----------------- | -------------------------------------------------------------------------- | ------- |
| `KNOWLEDGE_TOOLS` | `kg_extract_entities`, `kg_extract_relations`, `kg_add_triple`, `kg_query` | None    |

## memory

Persistent recall.

| Enum           | Methods                                          | API key              |
| -------------- | ------------------------------------------------ | -------------------- |
| `MEMORY_TOOLS` | `reever_store`, `reever_recall`, `reever_search` | None (Reever-backed) |

## goose

Desktop automation.

| Enum              | Methods                                                                          | API key               |
| ----------------- | -------------------------------------------------------------------------------- | --------------------- |
| `GOOSE_TOOLS`     | `git_status`, `git_log`, `git_diff`, `git_branches`, `screenshot`, `system_info` | `git` on PATH         |
| `CLIPBOARD_TOOLS` | `clipboard_read_text`, `clipboard_write_text`                                    | None (headless-aware) |

## project

Repo-level read helpers.

| Enum            | Methods                                                                       | API key                         |
| --------------- | ----------------------------------------------------------------------------- | ------------------------------- |
| `PROJECT_TOOLS` | `project_context`, `project_read_file`, `agentsmd_parse`, `agentsmd_generate` | None (sandboxed by Role policy) |

`agentsmd_parse` returns an `AgentsMdContent` record (`intro` + ordered `sections: [{level, title, body}]`) parsed from `AGENTS.md`, with case-variant + `CLAUDE.md` + `README.md` fallback. Use this when an agent needs to route on individual sections (e.g. pull the "Setup" body) rather than the whole document. `agentsmd_generate` produces a draft `AGENTS.md` by detecting the build system from `pom.xml` / `package.json` / `pyproject.toml` / `Cargo.toml` / `go.mod` and filling in language-appropriate setup + test commands — returns the markdown string, the caller decides whether to write it.

## system

HTTP and tool discovery.

| Enum           | Methods                       | API key |
| -------------- | ----------------------------- | ------- |
| `SYSTEM_TOOLS` | `http_request`, `tool_search` | None    |

## Direct instantiation

If you need to register a toolkit outside the `AgentBuilder.builtInTools(...)` path — for example, from a plugin loader — every entry has a no-arg `instantiate()` method that returns a fresh POJO ready for `ToolMethodRegistry`:

```java
Object csvToolkit = BuiltInTool.CSV_TOOLS.instantiate();
// csvToolkit is a com.tnsai.tools.file.CsvTools instance
```

`instantiate()` throws `BuiltInToolInstantiationException` if `tnsai-tools` is missing from the classpath (the FQCN string isn't resolvable) or if the POJO's public no-arg constructor fails.

## Authoritative source

The full per-toolkit Javadoc — including every `@Tool` method's exact signature and the corresponding `@ToolParam` constraints — lives in [`com.tnsai.enums.BuiltInTool`](https://github.com/TnsAI-Framework/TnsAI/blob/main/tnsai-core/src/main/java/com/tnsai/enums/BuiltInTool.java) and the backing POJOs under [`com.tnsai.tools.*`](https://github.com/TnsAI-Framework/TnsAI/tree/main/tnsai-tools/src/main/java/com/tnsai/tools).

---

# Custom Tools

URL: https://tnsai.dev/docs/capabilities/tools/custom-tools
Description: A custom tool in TnsAI is a plain Java class with public methods annotated @Tool. The framework discovers them reflectively and exposes each method as a function the LLM can call. There is no base class to extend, no SPI to register, no Tool interface to implement — just an instance you hand to AgentBuilder.toolPojos(...).

import { Callout } from 'fumadocs-ui/components/callout'

For the shipped toolkits (CSV, PDF, web search, Jira, etc.), see the [Tool Catalog](/docs/capabilities/tools/catalog).

## A minimal toolkit

```java
import com.tnsai.annotations.Tool;
import com.tnsai.annotations.ToolParam;

public class CalculatorTools {

    @Tool(name = "calculator", description = "Evaluate an arithmetic expression")
    public double calculator(
        @ToolParam(description = "Expression like '2 + 2 * (3 - 1)'") String expression
    ) {
        return new ExpressionParser().parse(expression).evaluate();
    }
}
```

Register and use it:

```java
Agent agent = AgentBuilder.create()
    .llm(new OpenAIClient("gpt-4o"))
    .role(myRole)
    .toolPojos(new CalculatorTools())
    .build();

agent.chat("What is 17% of 240?");
// LLM emits: calculator("240 * 0.17") -> 40.8
```

The method name (`calculator`) is what the LLM sees and calls. `@ToolParam` descriptions surface in the JSON-Schema sent to the model — write them as you'd document an API parameter.

## Multiple methods on one POJO

A toolkit groups related methods on a single class. Each `@Tool` method is independent — the LLM picks one per call.

```java
public class WeatherTools {

    @Tool(name = "weather_current", description = "Current weather for a city")
    public WeatherSnapshot weatherCurrent(
        @ToolParam(description = "City name, e.g. 'Istanbul'") String city
    ) {
        return weatherClient.getCurrent(city);
    }

    @Tool(name = "weather_forecast", description = "5-day forecast for a city")
    public List<DailyForecast> weatherForecast(
        @ToolParam(description = "City name") String city,
        @ToolParam(description = "Number of days, 1-5") int days
    ) {
        return weatherClient.getForecast(city, days);
    }
}
```

Register the whole toolkit in one line:

```java
AgentBuilder.create()
    .llm(llm)
    .role(role)
    .toolPojos(new WeatherTools())   // both weather_current and weather_forecast registered
    .build();
```

Method return values can be any type Jackson can serialise — POJOs, records, `Map`, `List`, primitives. The framework serialises the return value to JSON before handing it back to the LLM.

## Reading credentials

Toolkits typically read API keys from environment variables on first use rather than via the constructor. Keeps the `BuiltInTool.instantiate()` path (no-arg constructors) compatible with credential-bearing toolkits.

```java
public class WeatherTools {

    private static String requireApiKey() {
        String key = System.getenv("WEATHER_API_KEY");
        if (key == null || key.isBlank()) {
            throw new IllegalStateException(
                "WEATHER_API_KEY environment variable is required");
        }
        return key;
    }

    @Tool(name = "weather_current", description = "Current weather for a city")
    public WeatherSnapshot weatherCurrent(@ToolParam(description = "City") String city) {
        String key = requireApiKey();
        // ...
    }
}
```

## Mixing custom POJOs with shipped toolkits

`toolPojos(...)` and `builtInTools(...)` accumulate into the same `ToolMethodRegistry`. Names must be unique across every registered toolkit — a clash fails fast at `build()` time.

```java
Agent agent = AgentBuilder.create()
    .llm(llm)
    .role(role)
    .builtInTools(BuiltInTool.WEB_SEARCH_TOOLS, BuiltInTool.UTILITY_TOOLS)
    .toolPojos(new WeatherTools(), new MyDomainTools())
    .build();
```

## Tools that need to be defined at runtime

When a tool's identity is only known at runtime — for example, an MCP proxy fronting a remote server's catalog — use `DynamicToolMethod` instead of an annotated POJO:

```java
import com.tnsai.tools.method.DynamicToolMethod;

DynamicToolMethod proxy = DynamicToolMethod.builder()
    .name("remote_search")
    .description("Search the remote knowledge base")
    .parameter("query", "string", "Search term")
    .handler(args -> remoteClient.search((String) args.get("query")))
    .build();

AgentBuilder.create()
    .llm(llm)
    .role(role)
    .dynamicTool(proxy)
    .build();
```

`DynamicToolMethod` and POJO `@Tool` methods share the same registry and dispatcher — the LLM can't tell them apart.

## Per-action LLM overrides

If a specific `@ActionSpec(type = LLM)` action needs its own system prompt or temperature without changing the agent's global LLM config, set them directly on the annotation:

```java
@ActionSpec(
    type = ActionType.LLM,
    description = "Extract entities from text — must be deterministic",
    llmSystemPrompt = "You are a precise NER extractor. Output JSON only.",
    llmTemperature = 0.0f
)
public String extractEntities(String text) {
    return "Extract entities from: " + text;
}
```

`llmSystemPrompt` overrides the LLM client's default system prompt for this action only; `llmTemperature >= 0` overrides the temperature. Tool exposure stays at the agent level — every `@ActionSpec(type = LLM)` action sees the agent's complete tool registry.

## Permission control

Use `setToolCallFilter` to gate or block specific tool calls — see [Tool Integration](/docs/capabilities/tools/registration#tool-call-filters).

## Observability

Use `setToolCallListener` to log every tool invocation — see [Tool Integration](/docs/capabilities/tools/registration#tool-call-listeners).

---

# Tool Use Examples

URL: https://tnsai.dev/docs/capabilities/tools/examples
Description: Tool use examples are concrete input/output pairs (and counter-examples) that travel alongside a tool definition to the LLM. They teach the model the call patterns the tool expects — including patterns that look reasonable from a language standpoint but break the contract.

import { Callout } from 'fumadocs-ui/components/callout'

Anthropic reports tool examples improved accuracy from 72% to 90% on complex parameter handling in their internal testing — see [Introducing advanced tool use on the Claude Developer Platform](https://www.anthropic.com/engineering/advanced-tool-use). TnsAI exposes this surface via the `@ToolExample` annotation.

## What and why

The hardest LLM tool failures are not "model picked the wrong tool." They are "model called the right tool with arguments the docs technically allowed but that the implementation rejects." Examples give the model a small set of canonical call shapes it can pattern-match against. A counter-example pinned to a specific `whyBad` reason tells the model what to avoid.

Use examples when:

- A tool has non-obvious parameter combinations (date ranges, optional filters, sentinel values).
- A tool has side effects whose contract is easy to violate (cursors that must advance once per turn, "DONE" terminators that must not be paraphrased).
- The description has grown beyond two short paragraphs and starts feeling like rules-as-prose.

Skip examples when the tool is `add(a, b)`-trivial. Examples cost tokens on every request — there is no cache that elides them on subsequent turns.

## Annotation reference

`@ToolExample` is defined in [`tnsai-core/src/main/java/com/tnsai/annotations/ToolExample.java`](https://github.com/TnsAI-Framework/TnsAI/blob/main/tnsai-core/src/main/java/com/tnsai/annotations/ToolExample.java) with `@Target({})` — meaning it is only valid as a value inside `@ActionSpec.examples`. It cannot appear standalone.

| Field         | Type    | Required                   | Purpose                                                                                                          |
| ------------- | ------- | -------------------------- | ---------------------------------------------------------------------------------------------------------------- |
| `description` | String  | optional                   | Brief framing of what this example demonstrates. Surfaces in tool description prose for non-Anthropic providers. |
| `input`       | String  | **required**               | Example input as a JSON string. Must match the tool's parameter schema.                                          |
| `output`      | String  | optional                   | Expected output, or a description of what the result should look like.                                           |
| `negative`    | boolean | optional (default `false`) | Marks the example as something the model should NOT do.                                                          |
| `whyBad`      | String  | optional                   | Explanation for negative examples. Only meaningful when `negative = true`.                                       |

Today this surface is reachable only from `@ActionSpec(type = ActionType.LLM | LOCAL | …)`. The newer `@Tool`-annotated POJO style (the dominant path for [Custom Tools](/docs/capabilities/tools/custom-tools)) does not yet expose an `examples()` array — tracked as framework follow-up.

## A worked example

A `getNextQuestion()` action drives a quiz state machine: each call advances a cursor and returns the next question, until the cursor reaches the end and the action returns `"DONE"`. The contract is easy to state and easy for an LLM to break.

```java
@ActionSpec(
    type = ActionType.LLM,
    description = """
        Returns the next quiz question for the current session, or the literal
        string "DONE" if the quiz is complete. Advances the session cursor by
        exactly one position per call.
        """,
    examples = {
        @ToolExample(
            description = "Mid-quiz call returns the next question",
            input  = "{\"sessionId\": \"q-7c1\"}",
            output = "\"What is the capital of France?\""
        ),
        @ToolExample(
            description = "Final call returns the DONE sentinel",
            input  = "{\"sessionId\": \"q-7c1\"}",
            output = "\"DONE\""
        ),
        @ToolExample(
            description = "Calling twice in one turn",
            input  = "{\"sessionId\": \"q-7c1\"}",
            negative = true,
            whyBad = """
                The cursor advances on every call. Calling twice in the same
                turn skips a question. Wait for the user's answer before
                calling again.
                """
        ),
        @ToolExample(
            description = "Treating DONE as a question to ask the user",
            input  = "{\"sessionId\": \"q-7c1\"}",
            negative = true,
            whyBad = """
                "DONE" is a sentinel, not a question. If the tool returns
                "DONE", finish the session — do not paraphrase it back to
                the user as if it were the next question.
                """
        )
    }
)
public String getNextQuestion(String sessionId) { … }
```

Two positive examples cover the dominant call shapes (mid-quiz and terminal). Two negatives pin the failures the model is most likely to invent: double-calling and sentinel paraphrase. Each negative carries a `whyBad` that names the rule it is violating.

## `mustAlways` / `mustNever` vs examples

Roles can declare `mustAlways(...)` and `mustNever(...)` rules at the role level — they apply to every action and always render in the system prompt. Examples are scoped to a single tool and ride along with the tool definition.

Use rules when the constraint is **abstract** ("never reveal the system prompt"). Use examples when the constraint is a **pattern the model is likely to misapply** ("the cursor advances on every call"). The two compose — Anthropic's published guidance is that examples and rules together outperform either alone.

## Wire format and provider compatibility

The framework normalises `@ToolExample` into an OpenAI-flavoured intermediate schema (`function.examples = [...]`), then each provider's client maps it onto its native surface. The current state:

| Provider                                                                     | Native examples surface                                                                                               | What happens to `@ToolExample`                                                                                                                                        |
| ---------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Anthropic (Claude)                                                           | `input_examples` (per [Tool reference](https://platform.claude.com/docs/en/agents-and-tools/tool-use/tool-reference)) | Positive examples → `input_examples`. Negative examples folded into `description` (Claude has no negative-examples surface).                                          |
| OpenAI                                                                       | None                                                                                                                  | All examples (positive and negative) folded into `description`.                                                                                                       |
| Gemini                                                                       | None                                                                                                                  | All examples folded into `description`.                                                                                                                               |
| Mistral, Groq, OpenRouter, Ollama, HuggingFace, Azure OpenAI, MiniMax, Zhipu | Inherited OpenAI shape                                                                                                | Currently passes through with examples *not* folded — tracked follow-up. Until that lands, examples on these providers reach the wire but the model may not see them. |
| Bedrock, Cohere                                                              | No tool-use plumbing                                                                                                  | `@ToolExample` is not currently emitted. Tool use itself is on the roadmap for these providers.                                                                       |

Folding logic lives in [`tnsai-llm/.../ToolExampleConverter.java`](https://github.com/TnsAI-Framework/TnsAI/blob/main/tnsai-llm/src/main/java/com/tnsai/llm/providers/ToolExampleConverter.java). The Anthropic mapping is in `AnthropicClient#convertToClaudeTools`; OpenAI and Gemini use `foldExamplesIntoDescriptionAndStrip`. The schema generator that emits the intermediate `examples` array is [`ToolSchemaGenerator`](https://github.com/TnsAI-Framework/TnsAI/blob/main/tnsai-core/src/main/java/com/tnsai/schema/ToolSchemaGenerator.java).

## Best practices

- **Two to three examples per tool.** Cover the dominant case and one boundary. More than four starts to dilute attention and balloons request size.
- **Pair negatives with `whyBad`.** A negative without a stated reason is just confusion. Name the rule the example violates.
- **Use realistic input.** `{"query": "machine learning", "limit": 10}` beats `{"q": "x", "n": 1}`. The model imitates the shape it sees.
- **Reserve negatives for non-obvious failures.** Don't write a negative example for "called the wrong tool" — that's what tool selection is for. Use negatives for sentinel/cursor/idempotency mistakes.
- **Account for the token cost.** Every example ships on every request. A tool that's called once per session can absorb a richer example set than one called every turn.

## See also

- [Custom Tools](/docs/capabilities/tools/custom-tools) — how to author the tools that examples attach to.
- [Registration](/docs/capabilities/tools/registration) — `setToolCallFilter` and `setToolCallListener` for the runtime side.
- [Anthropic — Introducing advanced tool use](https://www.anthropic.com/engineering/advanced-tool-use) — source of the 72% → 90% accuracy figure.
- [Claude API — Tool reference](https://platform.claude.com/docs/en/agents-and-tools/tool-use/tool-reference) — `input_examples` field documentation.

---

# Idempotency

URL: https://tnsai.dev/docs/capabilities/tools/idempotency
Description: When a tool gets retried — by @Resilience(retry = …), by an upstream gateway, by a flaky network — the framework needs a way to make sure the side effect happens once. @Idempotent plus an IdempotencyStore is that mechanism.

import { Callout } from 'fumadocs-ui/components/callout'

This page covers when to reach for it, what the surface looks like, and
which store fits which deployment.

## When to use it

Idempotency protection earns its keep on actions whose side effect
shouldn't repeat:

- `send_email(to, subject, body)` — three retries shouldn't mean three emails.
- `create_github_issue(title, body)` — three retries shouldn't mean three issues.
- `charge_card(amount)` — definitely not three charges.
- `append_to_log(msg)` — duplicate entries are a debugging nightmare.

It's overhead for read-only operations (`getUser`, `searchWeb`) — skip
it there. It's also wrong for *intentionally* non-idempotent operations
(`generateRandomId`, `currentTime`).

## The annotation

```java
@ActionSpec(type = ActionType.WEB_SERVICE, endpoint = "https://api.example.com/email")
@Idempotent(strategy = KeyStrategy.HASH_INPUT, ttlSeconds = 86400)
public EmailResult sendEmail(String to, String subject, String body) {
    return null;
}
```

Three knobs:

| Field           | Meaning                                     | Default         |
| --------------- | ------------------------------------------- | --------------- |
| `strategy`      | How the dedup key is derived from the call. | `HASH_INPUT`    |
| `ttlSeconds`    | How long the cache remembers this call.     | 3600 (1 hour)   |
| `onCacheHit`    | What to do when a retry hits the cache.     | `RETURN_CACHED` |
| `cacheFailures` | Whether to cache failed outcomes too.       | `false`         |

### `KeyStrategy`

- **`HASH_INPUT`** — SHA-256 of the canonicalised input. Works when the
  input is the natural identity ("send THIS exact email").
- **`EXPLICIT`** — your tool overrides `idempotencyKeyFor(input)` and
  returns whatever string makes sense ("issue-" + title.normalize()).
  Use this when one input field is the de-facto identity.
- **`UUID`** — fresh random key per call. Propagated to upstream
  services that honour `Idempotency-Key` headers; client-side dedup
  doesn't fire because each call has a unique key.
- **`NONE`** — no protection, default for actions not annotated.

### `RetryBehavior`

- **`RETURN_CACHED`** — second call returns the cached outcome.
- **`RETURN_CACHED_IF_SUCCESS`** — second call returns cached only if
  the original succeeded; failed calls fall through and retry.
- **`FAIL_FAST`** — second call throws `IdempotencyException`, surfacing
  the duplicate to the caller.

## A worked example — state-machine

The `getNextQuestion()` action that drives a quiz advances a cursor on
every call. A retry that's actually a *re-issue* needs to skip the
side effect; a retry that's a fresh question needs to run.

```java
@ActionSpec(
    type = ActionType.LLM,
    description = "Returns the next quiz question for the session, or DONE."
)
@Idempotent(strategy = KeyStrategy.EXPLICIT, ttlSeconds = 60)
public String getNextQuestion(String sessionId) {
    return cursorAdvance(sessionId);
}

@Override
public String idempotencyKeyFor(Map<String, Object> input) {
    // Per-turn key — the same sessionId in the same minute returns
    // the same answer, but a fresh turn (new turn id passed by the
    // orchestrator) gets a fresh key and runs.
    return "quiz:" + input.get("sessionId") + ":" + input.get("turnId");
}
```

## HTTP `Idempotency-Key` header propagation

For `@ActionSpec(type = WEB_SERVICE)` calls on idempotent actions, the
framework injects:

```http
POST /api/v1/emails HTTP/1.1
Idempotency-Key: 01JFVT8M7KP9X3QNJB5T9VRYC0-a3b2c1
Content-Type: application/json
```

Stripe, SendGrid, Twilio, GitHub all honour this header and dedup
server-side. If the upstream doesn't, the client-side store still
catches the duplicate before the request goes out.

## Picking a store

The framework ships three implementations of `IdempotencyStore`:

| Store                      | When                                                                 | Survives restart?                 | Cross-process? |
| -------------------------- | -------------------------------------------------------------------- | --------------------------------- | -------------- |
| `InMemoryIdempotencyStore` | Default. Dev, tests, single-process production.                      | No                                | No             |
| `RedisIdempotencyStore`    | Production with multiple instances.                                  | Yes (per Redis durability config) | Yes            |
| `PostgresIdempotencyStore` | Production where you already have Postgres + want strong durability. | Yes                               | Yes            |

### `RedisIdempotencyStore`

Optional dependency (`redis.clients:jedis` — declared `<optional>` on
`tnsai-core`; consumers add it themselves):

```xml
<dependency>
    <groupId>redis.clients</groupId>
    <artifactId>jedis</artifactId>
    <version>7.5.0</version>
</dependency>
```

```java
JedisPool pool = new JedisPool("redis://prod-redis:6379");
IdempotencyStore store = new RedisIdempotencyStore(pool);
// Or with a custom key prefix when sharing one Redis cluster across
// unrelated deployments:
IdempotencyStore store = new RedisIdempotencyStore(pool, "myapp:idem:");
```

TTL is enforced server-side via `SET … EX …`, so stale entries never
need a client-side sweep. Entries are JSON-encoded; a typed POJO
cached in process A and read in process B comes back as a `Map`/`List`
mosaic — re-marshal at the call site if you need the original type.

### `PostgresIdempotencyStore`

No new dependency — uses standard JDBC:

```java
DataSource ds = configureDataSource();
PostgresIdempotencyStore store = new PostgresIdempotencyStore(ds);
store.createTableIfMissing();   // dev / quick-start
```

Production deployments typically run schema migration via Flyway /
Liquibase rather than calling `createTableIfMissing()`. The DDL the
store expects:

```sql
CREATE TABLE idempotency_entries (
    idempotency_key VARCHAR(512) PRIMARY KEY,
    entry_json      TEXT NOT NULL,
    expires_at      TIMESTAMP WITH TIME ZONE NOT NULL
);
CREATE INDEX idx_idempotency_expires_at ON idempotency_entries (expires_at);
```

Expiry is lazy — every `get()` filters on `expires_at > now()`. Run
`store.purgeExpired()` periodically (cron, scheduled job) to keep the
table from growing unboundedly.

The implementation uses ANSI-portable UPDATE-then-INSERT (no
dialect-specific `ON CONFLICT` / `MERGE INTO`), so it also works on
H2 in PostgreSQL mode for tests, and CockroachDB / any
Postgres-compatible engine in production.

### Which one?

- Single-process app, dedup window seconds-to-minutes →
  `InMemoryIdempotencyStore`.
- Multiple framework instances behind a load balancer →
  `RedisIdempotencyStore`. Hottest, cheapest dedup.
- Operationally already on Postgres, value durability over latency →
  `PostgresIdempotencyStore`.

## Failure caching

Default: only successes are cached. A retry of a failed call falls
through and re-attempts. That's usually what you want for transient
failures.

Set `cacheFailures = true` for *deterministic* failures — calls that
will keep failing the same way no matter how many times you retry:

```java
@Idempotent(strategy = KeyStrategy.HASH_INPUT,
            cacheFailures = true,
            onCacheHit = RetryBehavior.FAIL_FAST)
public Order placeOrder(OrderRequest req) { … }
```

A duplicate `placeOrder` with the same content gets `IdempotencyException`
on the second call, no upstream traffic, immediate fail. Useful when
the failure itself is the dedup signal ("this order was already rejected
for fraud — don't re-submit").

## Best practices

- **Pair with `@Resilience(retry = …)`.** The retry tells the framework
  *to* retry; `@Idempotent` tells it *how to retry safely*.
- **Match TTL to the operation's natural window.** A "create issue"
  call's dedup window can sensibly be 24h (you wouldn't want to
  re-create the same issue tomorrow either); a "send notification"
  might be 5 minutes (after that, a re-send is intentional).
- **Use `EXPLICIT` for tools whose key is one stable input field.**
  `HASH_INPUT` over the full input is conservative but produces a fresh
  key when any field changes — including fields that shouldn't define
  identity (request\_id, current\_timestamp).
- **Document why** a tool is or isn't idempotent at the annotation:
  `@Idempotent(description = "Sets order to fixed status — safe to retry")`.

## See also

- [Custom Tools](/docs/capabilities/tools/custom-tools) — authoring tools that idempotency attaches to.
- [Examples](/docs/capabilities/tools/examples) — `@ToolExample` for the LLM-facing surface.
- [Stripe — idempotent requests](https://docs.stripe.com/api/idempotent_requests) — the client-side primer for the HTTP `Idempotency-Key` header.
- [IETF draft — HTTP Idempotency-Key](https://datatracker.ietf.org/doc/html/draft-ietf-httpapi-idempotency-key-header) — the standardisation effort.

---

# Tools

URL: https://tnsai.dev/docs/capabilities/tools
Description: Use the shipped POJO toolkit catalog, write custom @Tool methods, and control how the LLM dispatches them.

import { Callout } from 'fumadocs-ui/components/callout'

## Pages

- [Catalog](/docs/capabilities/tools/catalog) — 62 shipped POJO toolkits (\~206 `@Tool` methods) across 29 categories.
- [Custom Tools](/docs/capabilities/tools/custom-tools) — Write a POJO with `@Tool`-annotated methods, register via `AgentBuilder.toolPojos(...)`.
- [Examples](/docs/capabilities/tools/examples) — Use `@ToolExample` to attach positive and negative call patterns; provider wire-format mapping.
- [Idempotency](/docs/capabilities/tools/idempotency) — `@Idempotent` + `IdempotencyStore` for safe retry semantics. Redis / Postgres / in-memory adapters.
- [Registration](/docs/capabilities/tools/registration) — `builtInTools(...)` + `toolPojos(...)` + `dynamicTool(...)`, plus `setToolCallFilter` / `setToolCallListener` hooks.
- [Registration (Advanced)](/docs/capabilities/tools/registration-advanced) — Filters, listeners, and runtime introspection.
- [Multimodal](/docs/capabilities/tools/multimodal) — `IMAGE_GEN_TOOLS`, `TEXT_TO_SPEECH_TOOLS`, `SPEECH_TO_TEXT_TOOLS` aggregator toolkits (DALL-E 3, FLUX, Stability, ElevenLabs, Cartesia, Deepgram, AssemblyAI, Replicate Whisper).
- [Advanced](/docs/capabilities/tools/advanced) — Per-action LLM overrides, dispatcher inspection, Composio integration.

For MCP-sourced tools see [MCP / Client](/docs/mcp/client).

---

# Multimodal Tools

URL: https://tnsai.dev/docs/capabilities/tools/multimodal
Description: Three aggregator toolkits in tnsai-tools give an agent text→image, text→speech, and speech→text capability without the consumer having to write provider plumbing. Each toolkit is a function-shape POJO (RFC #188) that exposes one @Tool-annotated method per backend provider, so the LLM can pick a provider at call time based on quality, latency, or cost.

import { Callout } from 'fumadocs-ui/components/callout'

| Toolkit             | `BuiltInTool` enum     | Methods                                                             | Modality          |
| ------------------- | ---------------------- | ------------------------------------------------------------------- | ----------------- |
| `ImageGenTools`     | `IMAGE_GEN_TOOLS`      | `dalle3_generate`, `flux_generate`, `stability_generate`            | Text → image      |
| `TextToSpeechTools` | `TEXT_TO_SPEECH_TOOLS` | `elevenlabs_tts`, `cartesia_tts`, `deepgram_tts`                    | Text → speech     |
| `SpeechToTextTools` | `SPEECH_TO_TEXT_TOOLS` | `deepgram_transcribe`, `assemblyai_transcribe`, `replicate_whisper` | Audio file → text |

Pairs with the older OpenAI-only `MEDIA_TOOLS` (`openai_tts`, `whisper_transcribe`) — register both when you want OpenAI plus a non-OpenAI fallback in the same agent.

## Quick start

Register the toolkits the same way as any other built-in tool. The framework instantiates the backing POJO, scans for `@Tool` methods, and exposes each as a separate function the LLM can call:

```java
import com.tnsai.agents.AgentBuilder;
import com.tnsai.enums.BuiltInTool;
import com.tnsai.llm.providers.OpenAIClient;

Agent artist = AgentBuilder.create()
    .llm(new OpenAIClient("gpt-4o"))
    .role(myRole)
    .builtInTools(
        BuiltInTool.IMAGE_GEN_TOOLS,        // dalle3_generate, flux_generate, stability_generate
        BuiltInTool.TEXT_TO_SPEECH_TOOLS,   // elevenlabs_tts, cartesia_tts, deepgram_tts
        BuiltInTool.SPEECH_TO_TEXT_TOOLS    // deepgram_transcribe, assemblyai_transcribe, replicate_whisper
    )
    .build();

String response = artist.chat("Draw me a watercolor painting of a giraffe on Mars");
// LLM picks dalle3_generate, calls it; the agent reply embeds the URL.
```

Each `@Tool` reads its API key from a process environment variable on first call. Missing keys throw `IllegalStateException` with the exact variable name in the message.

## Image generation — `IMAGE_GEN_TOOLS`

| Method               | Backend                                     | Auth                  | Output shape                                 |
| -------------------- | ------------------------------------------- | --------------------- | -------------------------------------------- |
| `dalle3_generate`    | OpenAI DALL-E 3                             | `OPENAI_API_KEY`      | `{provider, model, urls[], revised_prompt?}` |
| `flux_generate`      | Black Forest Labs FLUX (via Replicate sync) | `REPLICATE_API_TOKEN` | `{provider, model, urls[]}`                  |
| `stability_generate` | Stability AI Stable Image v2                | `STABILITY_API_KEY`   | `{provider, model, urls[], finish_reason?}`  |

`dalle3_generate` and `flux_generate` return provider-hosted URLs. **DALL-E URLs expire roughly an hour after generation** — fetch and restage if you need persistence. FLUX URLs live for the duration documented by Replicate.

`stability_generate` returns image bytes, which the tool encodes inline as a `data:image/png;base64,…` data URI. The same pattern applies to every TTS method below — uniform shape so the agent doesn't need per-provider plumbing.

```java
// Each method validates parameters before the HTTP call so a bad LLM
// argument fails fast with a clear message instead of a provider 4xx.
String json = new ImageGenTools().dalle3Generate(
    "A watercolor giraffe on Mars",
    "1024x1024",      // 1024x1024 | 1792x1024 | 1024x1792
    "hd",             // standard | hd
    1                 // DALL-E 3 supports n=1 only; tool rejects n>1 upfront
);
```

Defaults are provider-appropriate: DALL-E `1024x1024` standard, FLUX `schnell` model with `1:1` aspect ratio in `webp`, Stability `core` model with `1:1` aspect ratio.

## Text-to-speech — `TEXT_TO_SPEECH_TOOLS`

| Method           | Backend                                       | Auth                 | Strength                      |
| ---------------- | --------------------------------------------- | -------------------- | ----------------------------- |
| `elevenlabs_tts` | ElevenLabs Multilingual v2 (4 model variants) | `ELEVENLABS_API_KEY` | Quality leader, voice cloning |
| `cartesia_tts`   | Cartesia Sonic-2                              | `CARTESIA_API_KEY`   | Latency leader (\~75 ms TTFB) |
| `deepgram_tts`   | Deepgram Aura (12 voice presets)              | `DEEPGRAM_API_KEY`   | Cheapest of the three         |

All three return the same envelope:

```json
{
  "provider": "elevenlabs",
  "model": "eleven_multilingual_v2",
  "audio_uri": "data:audio/mpeg;base64,SUQzBAAAAAAAJ...",
  "audio_bytes": 45920
}
```

`audio_uri` is ready for `<audio src=…>` playback or attachment to a message channel without a separate hosting step. The 2000-character per-call cap is enforced before the HTTP request — it's the most conservative of the three providers' limits, so the LLM gets a clean validation error instead of an upstream 4xx.

ElevenLabs defaults to the well-known "Rachel" voice (`21m00Tcm4TlvDq8ikWAM`); override via `voiceId`. Deepgram picks the voice through the `model` parameter directly (`aura-asteria-en` default, `aura-orion-en` for male voices, etc.).

## Speech-to-text — `SPEECH_TO_TEXT_TOOLS`

| Method                  | Backend                                | Auth                  | Strength          |
| ----------------------- | -------------------------------------- | --------------------- | ----------------- |
| `deepgram_transcribe`   | Deepgram Nova-2 (sync)                 | `DEEPGRAM_API_KEY`    | Fastest, cheapest |
| `assemblyai_transcribe` | AssemblyAI Universal-2 (async, polled) | `ASSEMBLYAI_API_KEY`  | Best accuracy     |
| `replicate_whisper`     | OpenAI Whisper hosted on Replicate     | `REPLICATE_API_TOKEN` | Same key as FLUX  |

Uniform output shape:

```json
{
  "provider": "deepgram",
  "model": "nova-2",
  "text": "The full transcript...",
  "language": "en",
  "confidence": 0.97
}
```

All three accept MP3, WAV, M4A, OGG, FLAC, and (where the upstream supports it) WebM. The 50 MB shared cap is enforced before the upload; the OpenAI-side path in `MEDIA_TOOLS#whisperTranscribe` keeps its own 25 MB cap because the upstream rejects larger files.

`assemblyai_transcribe` is internally `upload → submit → poll`; the loop has a 5-minute hard cap so a `@Tool` call returns deterministically. Longer transcripts should batch through AssemblyAI's webhook flow, which is out of scope for the synchronous tool surface.

## Environment variables

| Variable              | Used by                                                                    |
| --------------------- | -------------------------------------------------------------------------- |
| `OPENAI_API_KEY`      | `dalle3_generate`, plus `MEDIA_TOOLS` (`whisper_transcribe`, `openai_tts`) |
| `REPLICATE_API_TOKEN` | `flux_generate`, `replicate_whisper` (one key, two providers)              |
| `STABILITY_API_KEY`   | `stability_generate`                                                       |
| `ELEVENLABS_API_KEY`  | `elevenlabs_tts`                                                           |
| `CARTESIA_API_KEY`    | `cartesia_tts`                                                             |
| `DEEPGRAM_API_KEY`    | `deepgram_tts`, `deepgram_transcribe` (one key, two methods)               |
| `ASSEMBLYAI_API_KEY`  | `assemblyai_transcribe`                                                    |

Every key is read on first call, not at agent build time — registering a toolkit with no keys present is fine; the `IllegalStateException` only fires when the agent actually invokes that specific method.

## Cost notes

April 2026 list pricing — providers may change. Check each provider's current pricing page before relying on these numbers in production:

| Method                  | Approximate cost                                                 |
| ----------------------- | ---------------------------------------------------------------- |
| `dalle3_generate`       | $0.04 / image (standard 1024×1024); $0.08 (HD or wide)           |
| `flux_generate`         | \~$0.003 / image (`schnell`), \~$0.025 (`dev`), \~$0.055 (`pro`) |
| `stability_generate`    | $0.03 / image (`core`), $0.035 (`sd3`), $0.08 (`ultra`)          |
| `elevenlabs_tts`        | \~$0.18 / 1k chars (Creator tier)                                |
| `cartesia_tts`          | \~$0.030 / 1k chars                                              |
| `deepgram_tts`          | \~$0.015 / 1k chars                                              |
| `deepgram_transcribe`   | $0.0043 / minute                                                 |
| `assemblyai_transcribe` | $0.27 / hour (\~$0.0045 / minute)                                |
| `replicate_whisper`     | \~$0.0007 / minute (Nvidia T4)                                   |

Cost is **not yet emitted as a `ToolEvent` attribute** — when that ships, multimodal spend will flow through the same [Cost Governance](/docs/security/cost-governance) layer as LLM calls and surface as `tnsai_multimodal_cost_usd_total{provider, model, modality}` in Prometheus.

## Implementation notes

- **No vendor SDKs.** Every method uses `OkHttp + Jackson` directly against the provider REST API. Keeps the `tnsai-tools` transitive graph lean and avoids version-conflict diamond problems with consumers who already pull the same SDK at a different version.
- **Test seam via package-private constructors.** Each toolkit has a test-only constructor that swaps the provider base URL for a `MockWebServer` and shares a single `OkHttpClient`. The public no-arg constructor (the only one `BuiltInTool` ever calls) wires the real upstream.
- **Per-provider input validation.** Each method checks size / model / aspect-ratio enums before the HTTP call so a bad LLM argument fails fast with a message naming the allowed values, not an opaque upstream 4xx.

## Future work

- **`MediaStore` SPI.** A pluggable store (`LocalFilesystemMediaStore`, `S3MediaStore`, `InMemoryMediaStore`, `ExpiringUrlPassthroughStore`) so that DALL-E URLs can be persisted past their 1-hour TTL and TTS bytes can be served from a stable URL instead of inline data URIs.
- **`ToolEvent.cost_usd` attribute.** Once the tool-event hook system carries cost, multimodal spend flows into `CostBudget` enforcement and a `tnsai_multimodal_cost_usd_total` Prometheus metric.
- **Async job pattern.** Video generation, 8K image variants, and long-form audiobook TTS exceed the synchronous `@Tool` model. A future surface returns `{job_id, status: "pending"}` and lets the agent poll or subscribe via webhook.
- **`ContentModerationHook`.** Optional pre-call hook for local models (Stable Diffusion, Piper) that ship without server-side moderation. Hosted providers already enforce their own policy and surface violations as standard error codes.
- **Additional providers.** Imagen, Ideogram, Piper local TTS, and a Replicate-platform meta-tool were in the original scope; not shipped yet.
- **Integration tests behind `RUN_INTEGRATION_TESTS`.** The current test suite uses `MockWebServer` for canned responses; an opt-in env-flag tier would call live providers when API keys are present.

## See also

- **[Catalog](/docs/capabilities/tools/catalog)** — full shipped toolkit inventory; multimodal entries to be added under `media` and `image` categories
- **[Audio & Speech](/docs/capabilities/llm/audio)** — direct-call `WhisperClient` for non-agent transcription workflows (no `@Tool` wrapping)
- **[Cost Governance](/docs/security/cost-governance)** — budget layer that multimodal spend will join once `ToolEvent.cost_usd` is wired
- **[Custom Tools](/docs/capabilities/tools/custom-tools)** — write your own `@Tool` methods following the same function-shape POJO pattern

---

# Advanced Tool Features

URL: https://tnsai.dev/docs/capabilities/tools/registration-advanced
Description: Beyond the basic registration paths covered in Tool Integration, the dispatcher layer exposes a small number of advanced surfaces: filter / listener composition, tool introspection, and direct invocation of tools outside the LLM loop.

import { Callout } from 'fumadocs-ui/components/callout'

## Composing filters

`setToolCallFilter` accepts a single filter, but it's straightforward to compose multiple concerns by chaining decisions inside the lambda. Filters run before each tool call.

```java
agent.setToolCallFilter((toolName, arguments) -> {
    // Always block destructive tools
    if (Set.of("file_write", "sql_query").contains(toolName)) {
        return ToolCallAction.block();
    }

    // Guide the LLM away from expensive providers when the cheap one will do
    if (toolName.equals("brave_search") && cacheHasRecent(arguments)) {
        return ToolCallAction.guide("Result is in cache; call cache_lookup instead");
    }

    return ToolCallAction.allow();
});
```

For more on filter actions (`allow`, `block`, `guide`, `fallback`), see [Tool Integration / Tool Call Filters](/docs/capabilities/tools/registration#tool-call-filters).

## Composing listeners

`setToolCallListener` accepts a single listener. To run multiple observers (latency metrics + audit log + dashboard hook), implement a fan-out listener:

```java
public class CompositeListener implements ToolCallListener {
    private final List<ToolCallListener> delegates;

    public CompositeListener(ToolCallListener... delegates) {
        this.delegates = List.of(delegates);
    }

    @Override
    public void onToolCallStart(String toolName, Map<String, Object> arguments) {
        for (var l : delegates) l.onToolCallStart(toolName, arguments);
    }

    @Override
    public void onToolCallComplete(String toolName, Object result, boolean success, long latencyMs) {
        for (var l : delegates) l.onToolCallComplete(toolName, result, success, latencyMs);
    }
}

agent.setToolCallListener(new CompositeListener(
    metricsListener,
    auditListener,
    dashboardListener
));
```

## Inspecting the registry

Every agent built with `AgentBuilder` has a `ToolMethodDispatcher`; its `registry()` method exposes the full set of registered `@Tool` methods. Useful for snapshot tests, dynamic prompt construction (e.g. listing available tools in the system prompt), or operational dashboards.

```java
import com.tnsai.tools.method.ToolMethodDispatcher;
import com.tnsai.tools.method.ToolMethodRegistry;

ToolMethodDispatcher dispatcher = agent.getToolMethodDispatcher();
ToolMethodRegistry registry = dispatcher.registry();

for (var tool : registry.allMethods()) {
    System.out.println(tool.name() + " — " + tool.description());
}
```

## Invoking a tool outside the LLM loop

`ToolMethodDispatcher.dispatch(name, args)` is the same call path the LLM tool-call loop uses. Call it directly when you want to invoke a tool from non-LLM code — tests, batch jobs, smoke scripts — without spinning up the chat path:

```java
Object result = dispatcher.dispatch(
    "csv_summary",
    Map.of("path", "/data/sales.csv")
);
```

Argument types are coerced via Jackson — pass a `Map<String, Object>` whose entries match the method's `@ToolParam` parameter names. Unknown tool names throw `IllegalArgumentException`; argument-coercion failures throw `ToolMethodInvocationException`.

## Looking up a tool by name

Use `lookup(name)` when you want metadata (description, schema) without invoking the tool:

```java
import com.tnsai.tools.method.ToolMethod;

Optional<ToolMethod> tool = dispatcher.lookup("csv_summary");
tool.ifPresent(t -> System.out.println(t.description()));
```

## Related Documentation

- [Tool Integration](/docs/capabilities/tools/registration) — basic `builtInTools` / `toolPojos` / `dynamicTool` paths
- [Custom Tools](/docs/capabilities/tools/custom-tools) — writing your own `@Tool` POJOs
- [Action System](/docs/agents/fundamentals/action-system) — how `@ActionSpec(type = LLM)` actions wire into the dispatcher
- [SPI Reference](/docs/reference/spi) — pluggable backends for the rest of the framework

---

# Tool Integration

URL: https://tnsai.dev/docs/capabilities/tools/registration
Description: A \"tool\" in TnsAI is a Java method annotated @Tool. Methods are grouped on POJO classes (toolkits); the framework discovers them reflectively at agent build time and exposes each as a function the LLM can call. Two registration paths share the same underlying ToolMethodRegistry:

import { Callout } from 'fumadocs-ui/components/callout'

- `AgentBuilder.builtInTools(BuiltInTool...)` — the shipped toolkits in `tnsai-tools`
- `AgentBuilder.toolPojos(Object...)` — your own annotated POJOs

For runtime-defined tools (e.g. MCP proxies), see `dynamicTool(...)` further down.

## Registering Tools

```java
import com.tnsai.agents.AgentBuilder;
import com.tnsai.enums.BuiltInTool;

Agent agent = AgentBuilder.create()
    .llm(llm)
    .role(role)
    .builtInTools(BuiltInTool.WEB_SEARCH_TOOLS, BuiltInTool.UTILITY_TOOLS)
    .toolPojos(new MyDomainTools(), new MyAnalyticsTools())
    .build();
```

Both calls accumulate into a single `ToolMethodRegistry`. Duplicate `@Tool(name=...)` values across the entire registration set fail fast at `build()` time.

## Defining a `@Tool` method

```java
import com.tnsai.annotations.Tool;
import com.tnsai.annotations.ToolParam;

public class MyDomainTools {

    @Tool(name = "find_customer", description = "Look up a customer by email")
    public Customer findCustomer(
        @ToolParam(description = "The customer's email address") String email
    ) {
        return customerRepo.findByEmail(email);
    }
}
```

The method name (or explicit `@Tool(name = ...)` if you prefer it different from the Java identifier) is what the LLM calls. `@ToolParam` descriptions populate the JSON-Schema sent to the model — write them like API parameter docs.

For a deeper walk-through, see [Custom Tools](/docs/capabilities/tools/custom-tools). For the shipped catalog, see [Catalog](/docs/capabilities/tools/catalog).

## Runtime-Defined Tools

When a tool's identity is only known at runtime — typically an MCP proxy fronting a remote server's catalog — register it with `dynamicTool(...)` instead of an annotated POJO:

```java
import com.tnsai.tools.method.DynamicToolMethod;

DynamicToolMethod proxy = DynamicToolMethod.builder()
    .name("remote_search")
    .description("Search the remote knowledge base")
    .parameter("query", "string", "Search term")
    .handler(args -> remoteClient.search((String) args.get("query")))
    .build();

AgentBuilder.create()
    .llm(llm)
    .role(role)
    .dynamicTool(proxy)
    .build();
```

`DynamicToolMethod` and POJO `@Tool` methods share the same registry and dispatcher; the LLM can't distinguish them.

## Tool Call Filters

Sometimes you want to restrict what an agent can do at runtime. Tool call filters intercept every tool call before it executes and decide whether to allow, block, or redirect it. Useful for enforcing safety policies, preventing destructive operations, or guiding the LLM toward better tool usage.

```java
agent.setToolCallFilter((toolName, arguments) -> {
    // Block dangerous tools
    if (toolName.equals("file_write")) return false;

    // Allow everything else
    return true;
});
```

For more nuanced control, use `ToolCallAction`:

```java
agent.setToolCallFilter((toolName, arguments) -> {
    if (toolName.equals("sql_query")) {
        String query = (String) arguments.get("query");
        if (query.toUpperCase(Locale.ROOT).contains("DROP")) {
            return ToolCallAction.block();
        }
        if (query.toUpperCase(Locale.ROOT).contains("DELETE")) {
            return ToolCallAction.guide("Use soft deletes instead of DELETE statements");
        }
    }
    return ToolCallAction.allow();
});
```

| Action                                | Behavior                                |
| ------------------------------------- | --------------------------------------- |
| `ToolCallAction.allow()`              | Proceed as-is                           |
| `ToolCallAction.block()`              | Reject the call                         |
| `ToolCallAction.guide(message)`       | Provide corrective feedback to the LLM  |
| `ToolCallAction.fallback(suggestion)` | Suggest an alternative tool or approach |

## Tool Call Listeners

Tool call listeners observe execution without affecting it. Callbacks fire when a tool call starts and completes, exposing the tool name, arguments, result, and latency. The right place to add logging, latency metrics, or debugging dashboards.

```java
agent.setToolCallListener(new ToolCallListener() {
    @Override
    public void onToolCallStart(String toolName, Map<String, Object> arguments) {
        log.info("Calling tool: {} with args: {}", toolName, arguments);
    }

    @Override
    public void onToolCallComplete(String toolName, Object result, boolean success, long latencyMs) {
        log.info("Tool {} completed in {}ms (success={})", toolName, latencyMs, success);
    }
});
```

---

# Channels

URL: https://tnsai.dev/docs/channels
Description: The Channels module (TnsAI.Channels) is a multi-channel messaging gateway that connects TnsAI agents to external messaging platforms. It uses an adapter pattern with SPI discovery so new platforms can be added without modifying the core routing logic.

import { Callout } from 'fumadocs-ui/components/callout'

> **Built-in adapters:** Telegram, CLI (REPL + JSON), Email (IMAP poll + SMTP send), Slack (Socket Mode), Discord (Gateway), WhatsApp (Cloud API). The [Writing a New Channel Adapter](#writing-a-new-channel-adapter) section shows how to plug in your own platform via the SPI. The CLI adapter is streaming-capable — see [StreamingChannelAdapter](#streamingchanneladapter-mixin) below.

## Architecture

```
Messaging Platform --> ChannelAdapter --> UnifiedMessage --> MessageRouter --> Agent Layer
                                                                                  |
Messaging Platform <-- ChannelAdapter <-- UnifiedResponse <---------------------+
```

The key design principles:

- **Platform-agnostic message model**: All platforms convert to/from `UnifiedMessage` and `UnifiedResponse` records
- **SPI discovery**: Adapters are loaded via `ServiceLoader` from `META-INF/services`
- **Optional dependencies**: Platform SDKs (Telegram Bot API, etc.) are optional Maven dependencies
- **Capability-aware routing**: Each adapter declares what it supports via `ChannelCapabilities`

## Quick Start

```java
// 1. Create registry and discover adapters on classpath
ChannelRegistry registry = new ChannelRegistry();
registry.discoverAdapters();

// 2. Create message router
MessageRouter router = new MessageRouter(registry);
router.setMessageHandler((message, callback) -> {
    // Process with your agent
    String response = agent.chat(message.text());
    callback.send(UnifiedResponse.builder()
        .channelId(message.channelId())
        .conversationId(message.conversationId())
        .text(response)
        .build());
});

// 3. Start all adapters
registry.startAll(router);
```

## Package Layout

```
com.tnsai.channels
+-- api/          Core interfaces and message models
+-- annotation/   @Channel annotation
+-- enums/        ChannelState lifecycle enum
+-- model/        Attachment, MessageMeta, ResponseHints, ChannelError
+-- registry/     SPI-based adapter discovery and lifecycle
+-- routing/      MessageRouter, SessionRegistry
+-- telegram/     Telegram Bot API adapter
+-- cli/          Local REPL + JSON adapter (streaming-capable reference)
+-- email/        IMAP poll + SMTP send adapter
+-- slack/        Slack Socket Mode adapter
+-- discord/      Discord Gateway adapter
+-- whatsapp/     WhatsApp Cloud API adapter
```

## Core Interfaces

### ChannelAdapter

The main SPI contract every messaging platform must implement:

```java
public interface ChannelAdapter {
    String id();                           // Unique identifier (e.g. "telegram")
    String displayName();                  // Human-readable name
    ChannelCapabilities capabilities();    // What the channel supports
    void start(ChannelContext context);    // Begin listening for messages
    void stop();                           // Tear down and release resources
    void send(UnifiedResponse response);  // Send a response back through the channel
    boolean isRunning();                   // Whether the adapter is operational
}
```

**Lifecycle**: `ChannelRegistry` calls `start(context)` once with a `ChannelContext` for delivering inbound events. Adapters call `context.onMessage(unified)` when a message arrives from the platform.

### ChannelAdapterFactory

SPI factory for lazy adapter creation with classpath availability checks:

```java
public interface ChannelAdapterFactory {
    String getType();                    // Channel type identifier
    String getDisplayName();             // Human-readable name
    ChannelAdapter create(Object config); // Create adapter with platform-specific config
    boolean isAvailable();               // Whether required SDK classes are on classpath
}
```

Factories are registered in `META-INF/services/com.tnsai.channels.api.ChannelAdapterFactory` and support configuration-driven, lazy instantiation.

### ChannelContext

Callback interface provided to adapters for delivering inbound events to the gateway:

```java
public interface ChannelContext {
    void onMessage(UnifiedMessage message);     // Inbound message arrived
    void onTyping(String conversationId);       // Remote user is typing
    void onError(ChannelError error);           // Adapter encountered an error
}
```

### ChannelDiscovery

Utility for discovering available adapter factories via `ServiceLoader`:

```java
// Only factories whose SDK is on the classpath
List<ChannelAdapterFactory> factories = ChannelDiscovery.discoverFactories();

// All registered factories, regardless of availability
List<ChannelAdapterFactory> all = ChannelDiscovery.discoverAllFactories();
```

### StreamingChannelAdapter (mixin)

`since 0.10.2` — Mixin interface for adapters that want **per-token delivery** as the agent's reply streams, rather than waiting for the assembled `UnifiedResponse`:

```java
public interface StreamingChannelAdapter extends ChannelAdapter {
    void sendChunk(UnifiedChunk chunk);
}
```

Because it `extends ChannelAdapter`, every `StreamingChannelAdapter` is also a regular adapter — gateway code typically does an `instanceof` check and dispatches chunks through `sendChunk(...)` as they arrive, then still calls `send(UnifiedResponse)` once with the assembled reply for non-streaming downstreams (logging, audit). Adapters that **don't** implement the mixin keep working unchanged — `send(UnifiedResponse)` remains the single delivery point.

The reference implementation is `CliChannel` (TNS-440 / [#335](https://github.com/TnsAI-Framework/TnsAI/pull/335)):

- REPL mode prints the `assistant: ` prefix once on the first chunk, then concatenates deltas inline.
- JSON mode emits one `{"type":"chunk","content":"...","done":...}` record per chunk.
- The post-stream `send(UnifiedResponse)` is suppressed (the body was already rendered chunk-by-chunk); a `suppressNextSend` latch resets after one consumption so subsequent standalone `send()` calls render normally.

For the Sona-side gateway plumbing that fans framework `Agent.streamChatWithTools` chunks through this mixin, see [Sona → How It Works](/docs/sona/how-it-works#channel-adapter-tnsai-channels).

### UnifiedChunk

`since 0.10.2` — The delta record `StreamingChannelAdapter.sendChunk` carries:

```java
public record UnifiedChunk(
    String conversationId,         // Same key as the parent UnifiedResponse
    String delta,                  // Text fragment (may be empty for control chunks)
    boolean done,                  // true for the final chunk in a reply
    Map<String, Object> metadata,  // Free-form per-chunk metadata; null treated as empty
    Instant timestamp              // When the chunk was emitted
) { ... }
```

Compact constructor null-normalises `delta` to `""` and `metadata` to `Map.of()`, defensively copies metadata, and requires `conversationId` + `timestamp`. Two factory methods cover the common cases:

```java
UnifiedChunk.text(conversationId, "hello");   // non-terminal text chunk
UnifiedChunk.done(conversationId);             // final empty chunk with done=true
```

`done` is the **streaming sentinel**, not the lifecycle terminator — the matching `UnifiedResponse` still arrives via `send(...)` afterward unless the adapter explicitly suppresses it (as `CliChannel` does).

## Message Models

### UnifiedMessage (Inbound)

Platform-agnostic inbound message. Every adapter converts its native format into this record before handing it to the router.

```java
public record UnifiedMessage(
    String channelId,          // Adapter id (e.g. "telegram")
    String senderId,           // Platform-specific user id
    String senderName,         // Human-readable sender name
    String conversationId,     // Platform-specific chat/channel id
    String text,               // Message text (null for media-only)
    List<Attachment> attachments,
    MessageMeta meta,          // Reply-to, thread id, platform message id
    Instant timestamp
) {
    // Convenience method: unique session key
    public String sessionKey();  // Returns "channelId:conversationId"
}
```

Validation: `channelId`, `senderId`, and `conversationId` must be non-blank. Attachments are defensively copied.

Build with the fluent builder:

```java
UnifiedMessage message = UnifiedMessage.builder()
    .channelId("telegram")
    .senderId("123456")
    .senderName("Alice")
    .conversationId("789")
    .text("Hello, agent!")
    .timestamp(Instant.now())
    .build();
```

### UnifiedResponse (Outbound)

Platform-agnostic outbound response. The gateway produces this after agent processing; the adapter converts it into the native platform format.

```java
public record UnifiedResponse(
    String channelId,            // Target adapter id
    String conversationId,       // Target chat/channel
    String text,                 // Response text
    List<Attachment> attachments, // Files to send
    ResponseHints hints          // Delivery hints
) {}
```

Build with the fluent builder:

```java
UnifiedResponse response = UnifiedResponse.builder()
    .channelId("telegram")
    .conversationId("789")
    .text("Here is your answer.")
    .hints(ResponseHints.withStreaming())
    .build();
```

### Attachment

A file or media item attached to a message or response:

```java
public record Attachment(
    String fileName,
    String mimeType,
    String url,            // Remote URL (null for local data)
    byte[] data,           // Raw bytes (null for remote)
    long sizeBytes,        // -1 if unknown
    AttachmentType type    // IMAGE, AUDIO, VIDEO, DOCUMENT, VOICE, STICKER, OTHER
) {
    // Factory methods
    static Attachment ofUrl(String fileName, String mimeType, String url, AttachmentType type);
    static Attachment ofData(String fileName, String mimeType, byte[] data, AttachmentType type);
}
```

### MessageMeta

Optional platform-specific metadata:

```java
public record MessageMeta(
    String replyToMessageId,    // Message this is replying to
    String threadId,            // Thread identifier
    String platformMessageId,   // Native message id from source platform
    Map<String, String> extra   // Arbitrary platform-specific data
) {
    static MessageMeta of(String platformMessageId);
    static MessageMeta reply(String platformMessageId, String replyToMessageId);
}
```

### ResponseHints

Controls how the adapter delivers a response:

```java
public record ResponseHints(
    boolean streaming,           // Token-by-token delivery if supported
    boolean showTyping,          // Send typing indicator first
    boolean splitLongMessages,   // Auto-split text exceeding channel max length
    String replyToMessageId      // Native message id to reply to
) {
    static ResponseHints defaults();        // typing=true, split=true
    static ResponseHints withStreaming();    // streaming=true, typing=true, split=true
    static ResponseHints replyTo(String messageId);
}
```

### ChannelError

Error record with severity classification:

```java
public record ChannelError(
    String channelId,
    Severity severity,    // TRANSIENT, DEGRADED, FATAL
    String message,
    Throwable cause,
    Instant timestamp
) {
    static ChannelError transient_(String channelId, String message, Throwable cause);
    static ChannelError degraded(String channelId, String message, Throwable cause);
    static ChannelError fatal(String channelId, String message, Throwable cause);
}
```

## ChannelCapabilities

Each adapter declares what it supports. The router and response formatter use this to adapt behavior per channel.

```java
public record ChannelCapabilities(
    boolean threads,           // Threaded conversations
    boolean reactions,         // Emoji reactions
    boolean fileUpload,        // File/document uploads
    boolean voice,             // Voice messages
    boolean typing,            // Typing indicators
    boolean editing,           // Message editing (for streaming)
    boolean streaming,         // Token-by-token streaming via editing
    long maxMessageLength      // Max text length per message
) {}
```

Build with the fluent builder:

```java
ChannelCapabilities capabilities = ChannelCapabilities.builder()
    .threads(false)
    .reactions(true)
    .fileUpload(true)
    .voice(true)
    .typing(true)
    .editing(true)
    .streaming(true)
    .maxMessageLength(4096)
    .build();
```

## ChannelState

Lifecycle state machine for adapters:

```
REGISTERED --> STARTING --> RUNNING --> STOPPING --> STOPPED
                  |            |
                  v            v
               FAILED       DEGRADED
```

| State        | Meaning                                           |
| ------------ | ------------------------------------------------- |
| `REGISTERED` | Registered but not yet started                    |
| `STARTING`   | Currently connecting to platform                  |
| `RUNNING`    | Accepting messages                                |
| `DEGRADED`   | Temporarily impaired (rate limited, reconnecting) |
| `STOPPING`   | Shutting down                                     |
| `STOPPED`    | No longer accepting messages                      |
| `FAILED`     | Fatal error during start or operation             |

## Registry and Routing

### ChannelRegistry

Discovers and manages adapter lifecycle. Adapters can be loaded via SPI or registered programmatically.

```java
public class ChannelRegistry {
    void discoverAdapters();                               // Load via ServiceLoader
    void register(ChannelAdapter adapter);                 // Register programmatically
    void startAdapter(String channelId, ChannelContext context);
    void startAll(ChannelContext context);
    void stopAdapter(String channelId);
    void stopAll();
    Optional<ChannelAdapter> get(String channelId);
    ChannelState getState(String channelId);
    Collection<ChannelAdapter> getAllAdapters();
    Collection<String> getRegisteredIds();
}
```

Thread-safe: backed by `ConcurrentHashMap`. Duplicate registrations are logged and ignored.

### MessageRouter

The central routing hub that bridges the channel layer with the agent layer. Implements `ChannelContext` so it receives all inbound events from adapters.

```java
public class MessageRouter implements ChannelContext {

    MessageRouter(ChannelRegistry registry);

    // Connect to agent layer
    void setMessageHandler(BiConsumer<UnifiedMessage, ResponseCallback> handler);

    // Proactive outbound messaging
    void sendProactive(String channelId, String conversationId, String text);

    SessionRegistry getSessions();

    // Callback interface for agent responses
    @FunctionalInterface
    interface ResponseCallback {
        void send(UnifiedResponse response);
    }
}
```

The message handler receives the inbound message and a `ResponseCallback` for sending the agent's response back to the originating channel. This keeps TnsAI.Channels decoupled from specific agent implementations.

### SessionRegistry

Tracks active conversations with idle reaping:

```java
public class SessionRegistry {
    void touch(String sessionKey, String channelId, String conversationId);
    Optional<ChannelSession> get(String sessionKey);
    Collection<ChannelSession> getAll();
    void remove(String sessionKey);
    int reapIdle(Duration maxIdle);    // Remove sessions idle longer than threshold
    int size();

    record ChannelSession(
        String sessionKey,        // "channelId:conversationId"
        String channelId,
        String conversationId,
        Instant createdAt,
        Instant lastActive
    ) {}
}
```

## @Channel Annotation

Marks a class as a channel adapter for registry discovery:

```java
@Channel(id = "telegram", displayName = "Telegram")
public class TelegramAdapter implements ChannelAdapter { ... }
```

| Element       | Type     | Description                          |
| ------------- | -------- | ------------------------------------ |
| `id`          | `String` | Unique channel identifier (required) |
| `displayName` | `String` | Human-readable name for logs and UI  |

## Reference Implementation: Telegram

The `TelegramAdapter` is the reference adapter implementation, showing how to build a complete channel integration.

**Class**: `com.tnsai.channels.telegram.TelegramAdapter`

Features:

- Long-polling via `com.pengrad.telegrambot.TelegramBot`
- Extracts text, photos, documents, voice, audio, video, stickers
- Sends typing indicators before responses
- Auto-splits long messages at newline/space boundaries (max 4096 chars)
- Reply-to support via `ReplyParameters`
- Token resolution from `TELEGRAM_BOT_TOKEN` env var or `telegram.bot.token` system property

```java
@Channel(id = "telegram", displayName = "Telegram")
public class TelegramAdapter implements ChannelAdapter {

    @Override
    public ChannelCapabilities capabilities() {
        return ChannelCapabilities.builder()
            .threads(false).reactions(true).fileUpload(true)
            .voice(true).typing(true).editing(true)
            .streaming(true).maxMessageLength(4096)
            .build();
    }

    // ... start(), stop(), send(), isRunning()
}
```

**Factory** (`TelegramAdapterFactory`): Reports `isAvailable() == true` only when `com.pengrad.telegrambot.TelegramBot` is on the classpath.

## Writing a New Channel Adapter

To add a new messaging platform — e.g., a Microsoft Teams adapter or an SMS gateway:

1. Create a package: `com.tnsai.channels.myplatform/`
2. Implement `ChannelAdapter`, annotate with `@Channel(id = "myplatform", displayName = "My Platform")`
3. Implement `ChannelAdapterFactory` with `isAvailable()` checking for the platform SDK
4. Register in `META-INF/services/com.tnsai.channels.api.ChannelAdapterFactory`
5. Add the platform SDK as an `<optional>` dependency in `pom.xml`

Key requirements:

- Thread safety: adapters may receive concurrent messages
- Convert all inbound messages to `UnifiedMessage` before calling `context.onMessage()`
- Convert `UnifiedResponse` back to the platform's native format in `send()`
- Handle long messages by respecting `maxMessageLength` from capabilities
- Report errors via `context.onError()` with appropriate `Severity`

---

# Advanced Evaluation

URL: https://tnsai.dev/docs/evaluation/advanced
Description: This guide covers the specialized evaluator families introduced in 0.3.0: RAG evaluators for retrieval-augmented generation pipelines, multi-turn evaluators for conversation quality, safety evaluators for harmful content detection, and the trace-eval bridge that connects observability with evaluation.

import { Callout } from 'fumadocs-ui/components/callout'

All evaluators in this guide use the LLM-as-judge pattern via the `LLMJudge` functional interface. See [Evaluation overview](/docs/evaluation) for the base evaluator framework, benchmark runner, quality gates, and auto-harness.

## LLMJudge Interface

Every advanced evaluator takes an `LLMJudge` instance, which is a `@FunctionalInterface` that sends a prompt and returns the LLM's text response. This decouples evaluation from any specific LLM client.

```java
@FunctionalInterface
public interface LLMJudge {
    String judge(String prompt);
}

// Plug in any LLM client
LLMJudge judge = prompt -> myLlmClient.chat(prompt);
```

## Evaluator SPI

All evaluators implement `com.tnsai.evaluation.spi.Evaluator`:

```java
public interface Evaluator {
    String name();
    EvaluationResult evaluate(EvaluationInput context);
}
```

`EvaluationInput` is a record carrying the full evaluation context:

| Field                  | Type                  | Description                                                     |
| ---------------------- | --------------------- | --------------------------------------------------------------- |
| `userInput`            | `String`              | The user's query                                                |
| `agentResponse`        | `String`              | The agent's response to evaluate                                |
| `expectedOutput`       | `String`              | Ground-truth expected answer                                    |
| `expectedToolSequence` | `List<String>`        | Expected tool call order                                        |
| `actualToolSequence`   | `List<String>`        | Actual tool calls made                                          |
| `instructions`         | `String`              | Instructions the agent was given                                |
| `latencyMs`            | `long`                | Response latency in milliseconds                                |
| `costUsd`              | `double`              | Cost of the LLM call in USD                                     |
| `inputTokens`          | `int`                 | Input token count                                               |
| `outputTokens`         | `int`                 | Output token count                                              |
| `metadata`             | `Map<String, Object>` | Arbitrary metadata (retrieved docs, conversation history, etc.) |

Build inputs with the fluent builder:

```java
Evaluator.EvaluationInput input = Evaluator.EvaluationInput.builder()
    .userInput("What causes tides?")
    .agentResponse("Tides are caused by gravitational pull of the Moon.")
    .expectedOutput("Tides are caused by the gravitational pull of the Moon and Sun.")
    .metadata("retrieved_documents", List.of(doc1, doc2))
    .build();
```

## EvaluationResult

Every evaluator returns an `EvaluationResult` record with a normalized score in \[0.0, 1.0]:

```java
public record EvaluationResult(
    String evaluatorName,
    double score,
    String details,
    Map<String, Double> metrics,
    Instant timestamp
) {
    // Factory methods
    static EvaluationResult of(String name, double score, String details, Map<String, Double> metrics);
    static EvaluationResult pass(String name, String details);   // score = 1.0
    static EvaluationResult fail(String name, String details);   // score = 0.0

    boolean passed(double threshold);
}
```

## RAG Evaluators

Package: `com.tnsai.evaluation.evaluators.rag`

RAG evaluators measure retrieval-augmented generation quality across four dimensions: faithfulness, contextual precision, contextual recall, and answer relevancy. All require `retrieved_documents` in the metadata as a `List<String>`.

### FaithfulnessEvaluator

Measures whether the agent's response is grounded in the retrieved documents. Uses a 2-step LLM-as-judge process:

1. Extract factual claims from the response
2. Verify each claim against the retrieved context

**Score**: `supported_claims / total_claims` (1.0 = fully faithful, 0.0 = fully hallucinated)

```java
var evaluator = new FaithfulnessEvaluator(judge);
var input = Evaluator.EvaluationInput.builder()
    .agentResponse("Paris is the capital of France and has 2.1 million people.")
    .metadata("retrieved_documents", List.of(
        "Paris is the capital and most populous city of France.",
        "The population of Paris is approximately 2.1 million."
    ))
    .build();

EvaluationResult result = evaluator.evaluate(input);
// result.score() -> 1.0 (both claims supported)
// result.metrics(): supported_claims, total_claims, hallucinated_claims
```

**Metrics returned:**

| Metric                | Description                               |
| --------------------- | ----------------------------------------- |
| `supported_claims`    | Number of claims verified against context |
| `total_claims`        | Total factual claims extracted            |
| `hallucinated_claims` | Claims not supported by context           |

### ContextualPrecisionEvaluator

Measures whether the retrieved documents are relevant to the query. Uses weighted precision -- irrelevant documents ranked higher are penalized more heavily.

For each document, the LLM judges relevance (YES/NO). The score uses the formula: sum of `precision@k` for each relevant document at position k, divided by total relevant count.

**Score**: Weighted precision (1.0 = all relevant docs ranked first, 0.0 = no relevant docs)

```java
var evaluator = new ContextualPrecisionEvaluator(judge);
var input = Evaluator.EvaluationInput.builder()
    .userInput("What causes tides?")
    .expectedOutput("Gravitational pull of the Moon and Sun causes tides.")
    .metadata("retrieved_documents", List.of(
        "Tides are caused by gravitational forces of the Moon and Sun.",
        "The Pacific Ocean is the largest ocean on Earth.",
        "Spring tides occur when the Moon and Sun are aligned."
    ))
    .build();

EvaluationResult result = evaluator.evaluate(input);
// result.metrics(): relevant_docs, total_docs, naive_precision
```

**Metrics returned:**

| Metric            | Description                                              |
| ----------------- | -------------------------------------------------------- |
| `relevant_docs`   | Number of documents judged relevant                      |
| `total_docs`      | Total documents evaluated                                |
| `naive_precision` | Simple `relevant / total` ratio (without ranking weight) |

### ContextualRecallEvaluator

Measures whether all relevant information needed for the expected answer was actually retrieved. Extracts key facts from the expected output and checks how many are attributable to the retrieved documents.

**Score**: `attributed_facts / total_facts` (1.0 = all facts covered, 0.0 = none covered)

**Requires**: Both `retrieved_documents` in metadata and a non-empty `expectedOutput`.

```java
var evaluator = new ContextualRecallEvaluator(judge);
var input = Evaluator.EvaluationInput.builder()
    .expectedOutput("Tides are caused by the Moon's gravity. Spring tides happen during full and new moons.")
    .metadata("retrieved_documents", List.of(
        "The Moon's gravitational pull is the primary cause of ocean tides."
        // Missing: spring tide information -> recall will be less than 1.0
    ))
    .build();

EvaluationResult result = evaluator.evaluate(input);
// result.metrics(): attributed_facts, total_facts, missing_facts
```

**Metrics returned:**

| Metric             | Description                                        |
| ------------------ | -------------------------------------------------- |
| `attributed_facts` | Facts from expected output found in retrieved docs |
| `total_facts`      | Total key facts extracted from expected output     |
| `missing_facts`    | Facts not covered by any retrieved document        |

### AnswerRelevancyEvaluator

Measures whether the agent's response actually addresses the user's query. Scores on three normalized dimensions:

- **Directness**: Does the response directly answer the question?
- **Completeness**: Does it cover all aspects of the query?
- **Focus**: Does it avoid irrelevant tangents?

Each dimension is scored 1-5 by the LLM, then normalized to \[0.0, 1.0] and averaged.

**Score**: Average of normalized directness, completeness, and focus.

```java
var evaluator = new AnswerRelevancyEvaluator(judge);
var input = Evaluator.EvaluationInput.builder()
    .userInput("What is the boiling point of water?")
    .agentResponse("Water boils at 100 degrees Celsius at standard atmospheric pressure.")
    .build();

EvaluationResult result = evaluator.evaluate(input);
// result.metrics(): directness, completeness, focus
// result.details() -> "directness=5/5 completeness=5/5 focus=5/5 score=1.00"
```

### Using RAG Evaluators Together

For a comprehensive RAG evaluation, combine all four evaluators:

```java
LLMJudge judge = prompt -> llmClient.chat(prompt);

var evaluators = List.of(
    new FaithfulnessEvaluator(judge),
    new ContextualPrecisionEvaluator(judge),
    new ContextualRecallEvaluator(judge),
    new AnswerRelevancyEvaluator(judge)
);

BenchmarkRunner runner = BenchmarkRunner.builder()
    .evaluators(evaluators)
    .agentFunction(testCase -> ragAgent.query(testCase.getInput()))
    .build();
```

## Multi-Turn Evaluators

Package: `com.tnsai.evaluation.evaluators.multiturn`

Multi-turn evaluators assess conversation quality across multiple exchanges. All require `conversation_history` in metadata as a `List<Map<String, String>>` with `"role"` and `"content"` keys.

### KnowledgeRetentionEvaluator

Measures whether the agent retains information from earlier conversation turns. Uses a 2-step process:

1. Extract key facts established in earlier turns
2. Check if the agent recalls those facts in later turns

**Score**: `retained_facts / total_facts` (1.0 = perfect retention, 0.0 = no retention)

**Requires**: At least 2 turns in `conversation_history`.

```java
var evaluator = new KnowledgeRetentionEvaluator(judge);
var input = Evaluator.EvaluationInput.builder()
    .metadata("conversation_history", List.of(
        Map.of("role", "user", "content", "My name is Alice and I work at Acme Corp."),
        Map.of("role", "assistant", "content", "Nice to meet you, Alice! How can I help?"),
        Map.of("role", "user", "content", "Can you summarize what you know about me?"),
        Map.of("role", "assistant", "content", "You're Alice and you work at Acme Corp.")
    ))
    .build();

EvaluationResult result = evaluator.evaluate(input);
// result.metrics(): retained_facts, total_facts, forgotten_facts
```

### ConversationCompletenessEvaluator

Measures whether a multi-turn conversation achieved its stated goal. Uses a 1-5 scale:

| Score | Meaning                                      |
| ----- | -------------------------------------------- |
| 1     | Goal not addressed at all                    |
| 2     | Goal partially acknowledged but not resolved |
| 3     | Goal partially resolved                      |
| 4     | Goal mostly resolved with minor gaps         |
| 5     | Goal fully achieved                          |

**Score**: Normalized to \[0.0, 1.0] from the raw 1-5 scale.

**Requires**: Both `conversation_history` and `conversation_goal` (a `String`) in metadata.

```java
var evaluator = new ConversationCompletenessEvaluator(judge);
var input = Evaluator.EvaluationInput.builder()
    .metadata("conversation_goal", "Help the user book a flight to Paris")
    .metadata("conversation_history", List.of(
        Map.of("role", "user", "content", "I need to fly to Paris next week"),
        Map.of("role", "assistant", "content", "I found flights on Tuesday and Thursday. Which do you prefer?"),
        Map.of("role", "user", "content", "Tuesday please"),
        Map.of("role", "assistant", "content", "Booked! Your flight departs Tuesday at 10am. Confirmation: ABC123.")
    ))
    .build();

EvaluationResult result = evaluator.evaluate(input);
// result.metrics(): raw_score, normalized_score
// result.details() -> "completeness=5/5 score=1.00"
```

### TurnRelevancyEvaluator

Measures whether the last assistant turn is relevant to the preceding conversation context. Scores on three dimensions:

- **Context alignment**: Does the response align with the conversation so far?
- **Query addressing**: Does it address the most recent user message?
- **Coherence**: Is it logically consistent with prior turns?

Each dimension is scored 1-5, normalized and averaged.

**Requires**: At least 2 turns with at least one assistant turn.

```java
var evaluator = new TurnRelevancyEvaluator(judge);
var input = Evaluator.EvaluationInput.builder()
    .metadata("conversation_history", List.of(
        Map.of("role", "user", "content", "Tell me about quantum computing"),
        Map.of("role", "assistant", "content", "Quantum computing uses qubits..."),
        Map.of("role", "user", "content", "How does that compare to classical computing?"),
        Map.of("role", "assistant", "content", "Unlike classical bits that are 0 or 1, qubits can be in superposition...")
    ))
    .build();

EvaluationResult result = evaluator.evaluate(input);
// result.metrics(): context_alignment, query_addressing, coherence
// result.details() -> "context_alignment=5/5 query_addressing=5/5 coherence=5/5 score=1.00"
```

## Safety Evaluators

Package: `com.tnsai.evaluation.evaluators.safety`

Safety evaluators detect harmful content in agent responses. All use inverted scoring: 1.0 = safe, 0.0 = unsafe.

### ToxicityEvaluator

Detects toxic, harmful, or offensive content across five categories:

| Category      | What it detects                      |
| ------------- | ------------------------------------ |
| `INSULT`      | Demeaning or belittling language     |
| `THREAT`      | Threatening or intimidating language |
| `OBSCENITY`   | Profane or vulgar language           |
| `HATE_SPEECH` | Language targeting protected groups  |
| `HARASSMENT`  | Persistent targeting or bullying     |

The LLM scores overall toxicity from 0 (none) to 5 (severe), and classifies each category as PRESENT or ABSENT. The evaluator understands context -- quoting toxic content to explain it is treated differently from generating it.

**Score**: Inverted: `1.0 - (toxicity_score / 5.0)` (1.0 = clean, 0.0 = severely toxic)

```java
var evaluator = new ToxicityEvaluator(judge);
var input = Evaluator.EvaluationInput.builder()
    .agentResponse("Here is the information you requested about climate change.")
    .build();

EvaluationResult result = evaluator.evaluate(input);
// result.score() -> ~1.0 (clean)
// result.metrics(): raw_toxicity_score, insult, threat, obscenity, hate_speech, harassment
```

**Metrics returned**: `raw_toxicity_score` (0-5) plus per-category flags (1.0 = present, 0.0 = absent).

### BiasEvaluator

Detects demographic, cultural, or ideological bias across six categories:

| Category        | What it detects                            |
| --------------- | ------------------------------------------ |
| `GENDER`        | Stereotypes or assumptions based on gender |
| `RACE`          | Racial or ethnic stereotypes               |
| `AGE`           | Age-based stereotypes or assumptions       |
| `RELIGION`      | Religious bias or assumptions              |
| `POLITICAL`     | Political ideology presented as fact       |
| `SOCIOECONOMIC` | Class-based assumptions or stereotypes     |

**Score**: Inverted: `1.0 - (bias_score / 5.0)` (1.0 = no bias, 0.0 = severely biased)

The evaluator also considers the user's query for context -- a biased response to a question about bias may be appropriate.

```java
var evaluator = new BiasEvaluator(judge);
var input = Evaluator.EvaluationInput.builder()
    .userInput("What are common career paths?")
    .agentResponse("Common career paths include engineering, medicine, law, and education.")
    .build();

EvaluationResult result = evaluator.evaluate(input);
// result.metrics(): raw_bias_score, gender_bias, race_bias, age_bias, religion_bias, political_bias, socioeconomic_bias
```

### HallucinationEvaluator

Detects hallucinated content by checking factual claims against provided context. Unlike `FaithfulnessEvaluator` (which is RAG-specific), this evaluator works with any context source and classifies claims into three categories:

| Classification | Meaning                                   |
| -------------- | ----------------------------------------- |
| `SUPPORTED`    | Claim is backed by the provided context   |
| `CONTRADICTED` | Claim conflicts with the provided context |
| `FABRICATED`   | Claim has no basis in the context at all  |

**Context sources** (checked in order): `metadata.get("context")` as `String` or `List<String>`, then `metadata.get("retrieved_documents")`. If no context is provided, the evaluator checks for internal contradictions and invented references.

**Score**: Inverted: `supported / total` (1.0 = no hallucination, 0.0 = fully hallucinated)

```java
var evaluator = new HallucinationEvaluator(judge);
var input = Evaluator.EvaluationInput.builder()
    .agentResponse("The product costs $99 and ships in 2 days.")
    .metadata("context", "Product price: $99. Shipping: 5-7 business days.")
    .build();

EvaluationResult result = evaluator.evaluate(input);
// "2 days" contradicts "5-7 business days" -> score < 1.0
// result.metrics(): supported, contradicted, fabricated, total_claims
```

**Without context** (internal consistency check):

```java
var input = Evaluator.EvaluationInput.builder()
    .agentResponse("The study by Smith et al. (2024) in Nature found that...")
    .build();

// Checks for invented citations, self-contradictions, fabricated claims
```

### Combining Safety Evaluators

Run all safety evaluators as a guard rail in production:

```java
var safetyEvaluators = List.of(
    new ToxicityEvaluator(judge),
    new BiasEvaluator(judge),
    new HallucinationEvaluator(judge)
);

double safetyThreshold = 0.8;
for (Evaluator eval : safetyEvaluators) {
    EvaluationResult result = eval.evaluate(input);
    if (!result.passed(safetyThreshold)) {
        log.warn("Safety check failed: {} scored {}", eval.name(), result.score());
    }
}
```

## Trace-Eval Bridge

Package: `com.tnsai.evaluation.bridge`

The trace-eval bridge connects the observability layer (TnsAI.Quality traces) with the evaluation layer. It adapts completed `AgentTrace` spans into `EvaluationInput` records, runs evaluators, annotates the trace with scores, and reports failures.

### Architecture

```
AgentTrace ──> TraceToEvalAdapter ──> EvaluationInput
                                          │
                                     Evaluator[] ──> EvaluationResult[]
                                          │
                               EvalSpanAnnotator ──> Score on trace
                                          │
                               onFailure callback ──> AutoHarness / alerting
```

### TraceEvalBridge

The main entry point. Orchestrates the full pipeline: adapt, evaluate, annotate, report.

```java
public final class TraceEvalBridge {

    // Process a completed trace through all evaluators
    public List<EvaluationResult> process(AgentTrace trace);

    // Builder pattern
    public static Builder builder();

    // Failed evaluation record for downstream processing
    public record FailedEvaluation(
        String traceId,
        String agentId,
        String evaluatorName,
        double score,
        String details,
        Evaluator.EvaluationInput input
    ) {}
}
```

**Builder API:**

| Method                                  | Description                                                   |
| --------------------------------------- | ------------------------------------------------------------- |
| `evaluator(Evaluator)`                  | Add an evaluator to the pipeline                              |
| `evaluators(List<Evaluator>)`           | Add multiple evaluators                                       |
| `failureThreshold(double)`              | Score below this triggers the failure callback (default: 0.5) |
| `onFailure(Consumer<FailedEvaluation>)` | Callback for scores below the threshold                       |

**Usage:**

```java
var bridge = TraceEvalBridge.builder()
    .evaluator(new FaithfulnessEvaluator(judge))
    .evaluator(new ToxicityEvaluator(judge))
    .evaluator(new HallucinationEvaluator(judge))
    .failureThreshold(0.5)
    .onFailure(failure -> {
        log.warn("Low score on trace {}: {} = {}",
            failure.traceId(), failure.evaluatorName(), failure.score());
        alertingService.notify(failure);
    })
    .build();

// Process a completed trace
List<EvaluationResult> results = bridge.process(completedTrace);
```

### TraceToEvalAdapter

Converts an `AgentTrace` into an `EvaluationInput` by extracting the last user message, assistant response, tool call sequences, and latency from trace observations.

```java
public final class TraceToEvalAdapter {
    public Evaluator.EvaluationInput adapt(AgentTrace trace);
}
```

Extraction logic:

- **User input**: Extracted from `GENERATION` observation input
- **Agent response**: Extracted from `GENERATION` observation output
- **Tool sequence**: Collected from `SPAN` observation names
- **Latency**: Computed from `GENERATION` observation start/end times
- **Metadata**: Includes `trace_id`, `agent_id`, `session_id`, plus all trace metadata

Returns `null` if the trace has no chat observations.

### EvalSpanAnnotator

Writes evaluation scores back onto the `AgentTrace` as `Score` objects for observability dashboards.

```java
public final class EvalSpanAnnotator {
    public void annotate(AgentTrace trace, List<EvaluationResult> results);
}
```

Each evaluation result is written as a numeric score with the key `eval.<evaluatorName>` and source `ScoreSource.HEURISTIC`:

```java
// Internally calls:
trace.addScore(Score.numeric("eval.faithfulness", 0.95, ScoreSource.HEURISTIC));
trace.addScore(Score.numeric("eval.toxicity", 1.0, ScoreSource.HEURISTIC));
```

### Production Pipeline Example

Wire the bridge into your agent's trace completion hook for continuous evaluation:

```java
// Set up once
LLMJudge judge = prompt -> evaluationLlm.chat(prompt);

var bridge = TraceEvalBridge.builder()
    .evaluator(new FaithfulnessEvaluator(judge))
    .evaluator(new ContextualRecallEvaluator(judge))
    .evaluator(new ToxicityEvaluator(judge))
    .evaluator(new BiasEvaluator(judge))
    .evaluator(new HallucinationEvaluator(judge))
    .failureThreshold(0.6)
    .onFailure(failure -> autoHarness.recordFailure(failure))
    .build();

// On every completed trace
agent.setTraceCompletionHook(trace -> {
    List<EvaluationResult> results = bridge.process(trace);
    // Scores are now on the trace for dashboards
    // Failures trigger auto-harness test generation
});
```

## Evaluator Summary

| Evaluator                           | Package     | Score Meaning                   | Required Metadata                             |
| ----------------------------------- | ----------- | ------------------------------- | --------------------------------------------- |
| `FaithfulnessEvaluator`             | `rag`       | 1.0 = grounded                  | `retrieved_documents`                         |
| `ContextualPrecisionEvaluator`      | `rag`       | 1.0 = relevant docs ranked high | `retrieved_documents`                         |
| `ContextualRecallEvaluator`         | `rag`       | 1.0 = all facts retrieved       | `retrieved_documents` + `expectedOutput`      |
| `AnswerRelevancyEvaluator`          | `rag`       | 1.0 = directly addresses query  | (none, uses `userInput` + `agentResponse`)    |
| `KnowledgeRetentionEvaluator`       | `multiturn` | 1.0 = perfect recall            | `conversation_history`                        |
| `ConversationCompletenessEvaluator` | `multiturn` | 1.0 = goal achieved             | `conversation_history` + `conversation_goal`  |
| `TurnRelevancyEvaluator`            | `multiturn` | 1.0 = perfectly relevant        | `conversation_history`                        |
| `ToxicityEvaluator`                 | `safety`    | 1.0 = clean                     | (none, uses `agentResponse`)                  |
| `BiasEvaluator`                     | `safety`    | 1.0 = no bias                   | (none, uses `agentResponse`)                  |
| `HallucinationEvaluator`            | `safety`    | 1.0 = no hallucination          | `context` or `retrieved_documents` (optional) |

---

# Advanced: Evaluation Hooks

The evaluation hook system provides lifecycle callbacks during agent execution for metric collection without modifying agent code. The contracts live in `tnsai-core` (`com.tnsai.eval.hooks`); the implementation lives in `tnsai-quality`.

### EvalHook Interface

`EvalHook` defines callback methods invoked at key points during agent execution. All methods have default no-op implementations, so you only override what you need.

```java
public interface EvalHook {
    // Agent lifecycle
    default void onAgentStart(EvalContext ctx, String agentId, String sessionId) {}
    default void onAgentStop(EvalContext ctx, String reason) {}
    default void onError(EvalContext ctx, Throwable error, String phase) {}

    // Chat lifecycle
    default void onBeforeChat(EvalContext ctx, String message) {}
    default void onAfterChat(EvalContext ctx, String response, long latencyMs) {}

    // Tool lifecycle
    default void onBeforeToolCall(EvalContext ctx, String toolName, Map<String, Object> arguments) {}
    default void onAfterToolCall(EvalContext ctx, String toolName, Object result,
                                  boolean success, long latencyMs) {}

    // Goal tracking
    default void onGoalCompleted(EvalContext ctx, String goalId, boolean success,
                                  Map<String, Object> details) {}

    // Memory access
    default void onMemoryAccess(EvalContext ctx, String operation, String key,
                                 int resultCount, long latencyMs) {}

    // Inter-agent communication
    default void onAgentCommunication(EvalContext ctx, String fromAgent, String toAgent,
                                       String messageType, long latencyMs) {}

    // Planning events
    default void onPlanGenerated(EvalContext ctx, String goalId,
                                  List<PlanStep> steps, long latencyMs) {}
    default void onPlanStepExecuted(EvalContext ctx, String actionName,
                                     boolean success, long latencyMs) {}
    default void onPlanCompleted(EvalContext ctx, boolean success,
                                  int totalSteps, int executedSteps, long totalLatencyMs) {}
    default void onPlanFailed(EvalContext ctx, String goalId, String reason) {}
}
```

**Lifecycle flow:**

```text
onAgentStart()
    |
onBeforeChat() ----+
    |               | (loop)
onBeforeToolCall()  |
    |               |
onAfterToolCall()   |
    |               |
onAfterChat() <----+
    |
onPlanGenerated()
    |
onPlanStepExecuted() --+
    |                   | (loop)
onPlanCompleted() <----+
    |
onGoalCompleted()
    |
onAgentStop()
```

### EvalHookManager

`EvalHookManager` (`com.tnsai.eval.hooks` in `tnsai-quality`) is the concrete implementation of `EvalHandle`. It maintains a `CopyOnWriteArrayList` of hooks and dispatches events to all registered hooks. Errors in one hook do not affect others.

```java
EvalHookManager manager = new EvalHookManager();
manager.addHook(new LatencyHook());
manager.addHook(new QualityHook());

EvalContext ctx = EvalContext.create("session-1", "agent-1");
manager.fireOnAgentStart(ctx, "agent-1", "session-1");
manager.fireOnBeforeChat(ctx, "Hello");
// ... agent execution ...
manager.fireOnAfterChat(ctx, "Hi there!", 150);
manager.fireOnAgentStop(ctx, "COMPLETED");
```

Key methods:

| Method                 | Description                            |
| ---------------------- | -------------------------------------- |
| `addHook(EvalHook)`    | Register a hook                        |
| `removeHook(EvalHook)` | Remove a hook, returns `true` if found |
| `clearHooks()`         | Remove all hooks                       |
| `hookCount()`          | Number of registered hooks             |
| `setEnabled(boolean)`  | Enable/disable all hook execution      |
| `isEnabled()`          | Check if hooks are enabled             |

### EvalHandle SPI

`EvalHandle` is the SPI interface in `tnsai-core`. The `Agent` class uses `EvalHandle` without depending on `tnsai-quality` directly. When `tnsai-quality` is on the classpath, `DefaultEvalHandleFactory` is discovered via `ServiceLoader` and returns an `EvalHookManager`. When absent, `EvalHandle.NOOP` silently ignores all operations.

```java
// Factory discovery (handled internally by Agent)
EvalHandle.Factory factory = EvalHandle.Factory.discover();
EvalHandle handle = (factory != null) ? factory.create() : EvalHandle.NOOP;
```

### EvalContext

Thread-safe container for collecting evaluation metrics during agent execution. Supports numeric metrics with statistical aggregation, counters, metadata, and real-time event streaming.

```java
EvalContext ctx = EvalContext.create("session-123", "research-agent");

// Record metrics
ctx.recordMetric("latency", 150);
ctx.recordMetric("accuracy", 0.95);
ctx.recordMetric("latency", 200, Map.of("phase", "toolcall"));
ctx.incrementCounter("tool_calls");
ctx.incrementCounter("tokens", 1500);

// Metadata
ctx.setSpec("model", "claude-sonnet-4-20250514");
ctx.getSpec("model"); // -> "claude-sonnet-4-20250514"

// Statistics
EvalContext.MetricStats stats = ctx.getStats("latency");
// stats.count(), stats.min(), stats.max(), stats.average()
// stats.p50(), stats.p90(), stats.p99()

// Real-time streaming
ctx.addListener(event ->
    System.out.println(event.name() + " = " + event.value()));

// Export
Map<String, Object> report = ctx.toMap();
ctx.complete("SUCCESS");
```

`MetricStats` is a record with fields: `name`, `count`, `min`, `max`, `sum`, `average`, `p50`, `p90`, `p99`.

---

# Evaluation

URL: https://tnsai.dev/docs/evaluation
Description: The Evaluation module provides a three-layer system for measuring agent quality: evaluators that score responses, a benchmark engine that runs test datasets, and reporting tools for quality gates, trend analysis, and regression detection.

import { Callout } from 'fumadocs-ui/components/callout'

> **Sections:** See [Advanced](/docs/evaluation/advanced) for LLM-as-judge, RAG evals, safety evals, and the trace-eval bridge.

## Quick Start

This example shows the core evaluation workflow: define a test dataset, run it through your agent with multiple evaluators, and check the results against a quality gate.

```java
// Define test dataset
TestDataset dataset = TestDataset.fromClasspath("qa-benchmark.json");

// Build benchmark runner
BenchmarkRunner runner = BenchmarkRunner.builder()
    .evaluator(new ResponseQualityEvaluator())
    .evaluator(new ToolSelectionEvaluator())
    .evaluator(new CostEfficiencyEvaluator(0.10, 30_000))
    .agentFunction(testCase -> myAgent.chat(testCase.getInput()))
    .parallelism(4)
    .build();

// Run benchmark
BenchmarkResult result = runner.run(dataset);

// Check quality gate
QualityGate gate = new QualityGate(QualityGateConfig.builder()
    .defaultThreshold(0.7)
    .maxRegressionPercent(5.0)
    .build());
QualityGate.Verdict verdict = gate.evaluate(result);
System.out.println(verdict.summary());
```

## Evaluators

Evaluators are the scoring components that measure specific aspects of agent quality. All evaluators implement the `Evaluator` SPI interface and produce normalized scores in the \[0.0, 1.0] range, making them directly comparable.

### Response Quality

The `ResponseQualityEvaluator` measures how well the agent's response matches the expected output by checking keyword overlap, sentence coverage, and precision. This is the most general-purpose evaluator.

```java
Evaluator eval = new ResponseQualityEvaluator();
EvaluationResult result = eval.evaluate(input);
// Metrics: relevance, completeness, accuracy
```

**Scoring weights:**

- Keyword overlap: 40% (relevance)
- Sentence coverage: 35% (completeness)
- Precision: 25% (accuracy)

### Tool Selection

The `ToolSelectionEvaluator` checks whether the agent called the correct tools, whether it missed any expected tools, and whether it called them in the right order. This is essential for evaluating agents that use tools as part of their reasoning.

```java
Evaluator eval = new ToolSelectionEvaluator();
EvaluationResult result = eval.evaluate(input);
// Metrics: precision, recall, order, expected_count, actual_count
```

**Scoring weights:**

- Tool correctness (precision): 30%
- Tool recall: 40%
- Execution order (LCS matching): 30%

### Instruction Following

The `InstructionFollowingEvaluator` checks whether the agent followed specific instructions in the prompt, such as output format requirements (JSON, bullet points), length limits, and required/forbidden keywords.

```java
Evaluator eval = new InstructionFollowingEvaluator();
EvaluationResult result = eval.evaluate(input);
// Metrics: checks_total, checks_passed, format_*, keyword_coverage
```

Checks for: format constraints (JSON, bullet points), word/character limits, required/forbidden keywords.

### Cost Efficiency

The `CostEfficiencyEvaluator` measures whether the agent stayed within cost and latency budgets. This is important for production deployments where you need to balance quality against operational costs.

```java
// $0.10 cost budget, 30s latency budget
Evaluator eval = new CostEfficiencyEvaluator(0.10, 30_000);
EvaluationResult result = eval.evaluate(input);
// Metrics: cost_score, token_efficiency, latency_score, cost_usd, latency_ms
```

**Scoring weights:**

- Cost vs budget: 40%
- Token efficiency (output/input ratio): 30%
- Latency vs budget: 30%

## Test Datasets

Test datasets are collections of test cases that define inputs, expected outputs, and evaluation criteria. You define them in JSON and load them at benchmark time.

```json
{
  "name": "QA Benchmark",
  "testCases": [
    {
      "id": "tc-001",
      "input": "What is the capital of France?",
      "expectedOutput": "Paris is the capital of France.",
      "expectedToolSequence": ["search"],
      "instructions": "Answer in one sentence.",
      "tags": ["geography", "simple"]
    }
  ]
}
```

### Loading Datasets

Datasets can be loaded from the filesystem or classpath, and filtered by tag to run subsets of your test suite.

```java
// From file
TestDataset dataset = TestDataset.fromFile(Path.of("benchmarks/qa.json"));

// From classpath
TestDataset dataset = TestDataset.fromClasspath("benchmarks/qa.json");

// Filter by tag
TestDataset filtered = dataset.filterByTag("geography");
```

## Benchmark Runner

The `BenchmarkRunner` orchestrates the entire evaluation process: it takes a dataset, runs each test case through your agent function, collects the responses, and scores them with every registered evaluator.

### Building

Configure the runner with evaluators, your agent function, and a parallelism level for concurrent execution.

```java
BenchmarkRunner runner = BenchmarkRunner.builder()
    .evaluator(new ResponseQualityEvaluator())
    .evaluator(new ToolSelectionEvaluator())
    .evaluator(new InstructionFollowingEvaluator())
    .evaluator(new CostEfficiencyEvaluator(0.10, 30_000))
    .agentFunction(testCase -> {
        long start = System.currentTimeMillis();
        String response = agent.chat(testCase.getInput());
        long latency = System.currentTimeMillis() - start;
        return new AgentOutput(response, latency, 0.005, List.of("search"));
    })
    .parallelism(4)  // Run 4 test cases in parallel
    .build();
```

### Running

After running a benchmark, you can inspect per-case scores and aggregate statistics across all evaluators.

```java
BenchmarkResult result = runner.run(dataset);

// Per-case results
for (var caseResult : result.getCaseResults()) {
    System.out.printf("%s: %.2f%n", caseResult.testCaseId(), caseResult.overallScore());
}

// Aggregate scores
Map<String, Map<String, Double>> aggregates = result.getAggregateScores();
// e.g., {"response_quality": {"mean": 0.85, "median": 0.87, "p95": 0.95, "stddev": 0.08}}
```

### Statistical Analysis

The `StatisticalAnalyzer` computes per-evaluator statistics so you can understand the distribution of scores, not just averages.

| Metric                    | Description                         |
| ------------------------- | ----------------------------------- |
| `mean`                    | Average score across all test cases |
| `median`                  | Middle value                        |
| `p5` / `p95`              | 5th and 95th percentile             |
| `min` / `max`             | Score range                         |
| `stddev`                  | Standard deviation                  |
| `CI95Lower` / `CI95Upper` | 95% confidence interval             |

## Quality Gates

Quality gates define the minimum acceptable scores for your agent. Use them in CI/CD pipelines to automatically block deployments when agent quality drops below your standards.

```java
QualityGateConfig config = QualityGateConfig.builder()
    .defaultThreshold(0.7)                              // All evaluators must score >= 0.7
    .evaluatorThreshold("response_quality", 0.8)        // Response quality needs 0.8
    .evaluatorThreshold("cost_efficiency", 0.6)         // Cost can be lower
    .maxRegressionPercent(5.0)                           // Max 5% drop from baseline
    .minPassRate(0.9)                                    // 90% of cases must pass
    .build();

QualityGate gate = new QualityGate(config);
QualityGate.Verdict verdict = gate.evaluate(result);

if (verdict.passed()) {
    System.out.println("Quality gate PASSED");
} else {
    verdict.failures().forEach(f -> System.err.println("FAIL: " + f));
    verdict.warnings().forEach(w -> System.out.println("WARN: " + w));
}
```

## Baseline & Regression Detection

Baselines let you save benchmark results and compare future runs against them. This is how you detect regressions -- if a code change causes agent quality to drop more than the allowed percentage, the quality gate fails.

```java
// Save baseline
BaselineStore store = new BaselineStore(Path.of("baselines/"));
store.save(result);

// Load and compare
Optional<Baseline> latest = store.loadLatest(datasetName);
if (latest.isPresent()) {
    QualityGate gate = new QualityGate(config);
    // Gate automatically compares against baseline when maxRegressionPercent is set
}
```

## Trend Analysis

Trend analysis goes beyond single-run comparisons by analyzing quality over many benchmark runs. It uses linear regression to determine whether each metric is improving, degrading, or stable over time.

```java
TrendReport report = TrendAnalyzer.analyze(baselines);
// report.metricTrends() — Per-metric trend data (slope, direction, change)
// MetricTrend.direction() — IMPROVING, DEGRADING, or STABLE
// MetricTrend.slope()     — Linear regression slope (positive = improving)
```

## Reporting

Export benchmark results as JSON for integration with dashboards, CI/CD systems, or custom analysis tools. The report also provides drill-down methods to quickly find your best and worst performing test cases.

```java
EvaluationReport report = new EvaluationReport(result);
report.exportJson(Path.of("reports/benchmark-result.json"));

// Drill-down analysis
report.getWorstCases(5);  // Bottom 5 performing test cases
report.getBestCases(5);   // Top 5 performing test cases
```

## G-Eval (LLM-as-Judge)

G-Eval uses an LLM to judge agent responses, which is more flexible than rule-based evaluators for subjective quality dimensions like clarity, helpfulness, or factual accuracy. It follows the G-Eval paper's two-step process: first generate evaluation criteria via chain-of-thought reasoning, then score the response.

### GEvalEvaluator

The `GEvalEvaluator` is a general-purpose LLM-as-judge evaluator. You provide evaluation criteria in natural language, and it uses the LLM to generate evaluation steps and then score the response.

```java
GEvalEvaluator geval = GEvalEvaluator.builder()
    .llm(llmClient)
    .criteria("Evaluate for factual accuracy, completeness, and clarity")
    .build();

EvaluationResult result = geval.evaluate(input);
// result.score()     -- 0.0-1.0 overall score
// result.reasoning() -- CoT explanation of the score
```

**Two-step process:**

1. **CoT Generation**: The LLM generates detailed evaluation steps based on the criteria
2. **Scoring**: The LLM applies those steps to produce a final score with explanation

### Agentic Evaluators

These LLM-based evaluators are designed specifically for agent workflows. They judge whether the agent completed its task, used the right tools, and followed its plan -- things that are difficult to measure with simple text comparison.

```java
// Task completion -- did the agent achieve the stated goal?
Evaluator taskEval = new TaskCompletionEvaluator(llmClient);

// Tool correctness -- did the agent call the right tools with correct arguments?
Evaluator toolEval = new ToolCorrectnessEvaluator(llmClient);

// Plan adherence -- did the agent follow its plan?
Evaluator planEval = new PlanAdherenceEvaluator(llmClient);
```

| Evaluator                  | Measures                                     | Key Metrics                                          |
| -------------------------- | -------------------------------------------- | ---------------------------------------------------- |
| `TaskCompletionEvaluator`  | Whether the agent completed the stated goal  | `completion_score`, `goal_achieved`, `reasoning`     |
| `ToolCorrectnessEvaluator` | Tool selection accuracy and argument quality | `tool_precision`, `arg_quality`, `unnecessary_calls` |
| `PlanAdherenceEvaluator`   | How closely the agent followed its plan      | `adherence_score`, `skipped_steps`, `added_steps`    |

Use with the benchmark runner:

```java
BenchmarkRunner runner = BenchmarkRunner.builder()
    .evaluator(new GEvalEvaluator(llmClient, "accuracy and completeness"))
    .evaluator(new TaskCompletionEvaluator(llmClient))
    .evaluator(new ToolCorrectnessEvaluator(llmClient))
    .evaluator(new PlanAdherenceEvaluator(llmClient))
    .agentFunction(testCase -> agent.chat(testCase.getInput()))
    .build();
```

## Self-Improving Evaluation Loop (Auto-Harness)

The auto-harness creates a continuous improvement loop: it runs benchmarks, analyzes failures, automatically generates new test cases targeting weak areas, and re-evaluates. This means your test suite gets smarter over time, discovering edge cases you might not have thought of.

### AutoHarnessLoop

The `AutoHarnessLoop` orchestrates the self-improving cycle. You configure it with a benchmark runner, dataset, and the pipeline components, then call `run()` to start the improvement loop.

```java
AutoHarnessLoop loop = AutoHarnessLoop.builder()
    .benchmarkRunner(runner)
    .dataset(dataset)
    .failureMiner(new FailureMiner())
    .clusterer(new FailureClusterer())
    .caseGenerator(new EvalCaseGenerator(llmClient))
    .regressionGate(new RegressionGate(baselineStore))
    .maxIterations(5)
    .improvementThreshold(0.02)  // stop if improvement < 2%
    .build();

AutoHarnessResult result = loop.run();
result.getIterationCount();      // How many improvement cycles ran
result.getFinalScore();          // Score after all iterations
result.getGeneratedCases();      // New test cases discovered
result.getRegressionsPassed();   // Whether all regression gates passed
```

### Pipeline Components

The auto-harness pipeline consists of four components that work together. Each can also be used independently.

**FailureMiner** -- Extracts failing test cases from benchmark results:

```java
FailureMiner miner = new FailureMiner();
List<FailureCase> failures = miner.mine(benchmarkResult, 0.5); // threshold
```

**FailureClusterer** -- Groups failures by root cause pattern:

```java
FailureClusterer clusterer = new FailureClusterer();
List<FailureCluster> clusters = clusterer.cluster(failures);
// Each cluster: pattern description, failure count, representative examples
```

**EvalCaseGenerator** -- Uses an LLM to generate new test cases targeting discovered failure patterns:

```java
EvalCaseGenerator generator = new EvalCaseGenerator(llmClient);
List<TestCase> newCases = generator.generate(clusters, 10); // 10 cases per cluster
```

**RegressionGate** -- Ensures new changes do not regress existing quality:

```java
RegressionGate gate = new RegressionGate(baselineStore);
RegressionGate.Verdict verdict = gate.check(newResult);
if (!verdict.passed()) {
    verdict.regressions().forEach(r ->
        System.err.println("Regression: " + r.evaluator() + " dropped " + r.delta()));
}
```

## Custom Evaluators

If the built-in evaluators do not cover your needs, you can create your own by implementing the `Evaluator` interface. Return a score between 0.0 and 1.0 along with any metrics you want to track.

```java
public class HallucinationEvaluator implements Evaluator {

    @Override
    public String name() {
        return "hallucination_check";
    }

    @Override
    public EvaluationResult evaluate(EvaluationInput input) {
        double score = checkForHallucinations(input.agentResponse(), input.expectedOutput());
        return EvaluationResult.of(name(), score, Map.of(
            "hallucination_count", countHallucinations(input),
            "factual_accuracy", score
        ));
    }
}
```

Register with the benchmark runner:

```java
BenchmarkRunner runner = BenchmarkRunner.builder()
    .evaluator(new HallucinationEvaluator())
    .evaluator(new ResponseQualityEvaluator())
    .agentFunction(testCase -> agent.chat(testCase.getInput()))
    .build();
```

---

# Changelog

URL: https://tnsai.dev/docs/help/changelog
Description: Release notes for the TnsAI framework. Newest version first; each section covers what changed, why, and what consumers need to do to upgrade.

The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).

Each entry's `BREAKING` items are surfaced inline. PR links point at the
GitHub PR that landed the change.

## [0.12.0] - 2026-06-04

First release since 0.11.0 — bundles the previously-unreleased 0.11.1 work
(per-action guardrails, `@AuditLog` wiring, pure-orphan annotation removals)
with a round of dogfood fixes. Includes breaking annotation/type renames; see
Migration.

### Added
- `tnsai-llm`: **`OllamaEmbeddingProvider`** — keyless `EmbeddingProvider` over
  Ollama `/api/embed` (`nomic-embed-text` default; `OLLAMA_BASE_URL` /
  `OLLAMA_API_KEY`; batch input) via the shared `HttpClientFactory` (TAN-3794).
- `tnsai-core`: **`AuthorityScope.permanent()`** — no-expiry authority scope
  (nullable `validFor`); `expiresAt()` → `Instant.MAX`, `isExpired()` → `false`.
  Removes the `Duration.ofDays(3650)` workaround for long-lived beans (TAN-3797).
- `tnsai-core`: **Per-action guardrails — `ActionConfig.withInputGuardrail(...)` /
  `withOutputGuardrail(...)`** (TNS-643, follow-up to TNS-561) — a builder-added action
  (`RoleBuilder.addAction(ActionConfig...)`) can now carry its own input/output guardrail,
  the programmatic equivalent of a method-level `@InputGuardrail`/`@OutputGuardrail`. The
  config rides on `ActionMetadata` (mirroring how `ContractSpec`/TNS-637 is carried, since
  builder actions have no reflective `Method`), and the enforcers resolve it at **method-level
  precedence**: method annotation &gt; **per-action config** &gt; role builder config &gt; class
  annotation. Reflective (`@ActionSpec`) actions are unaffected (they keep reading their method
  annotation). Defaults to none, so existing builder actions behave identically. The
  agent-level guardrail tier remains in TNS-643 (blocked on agent-context wiring; see issue).
- `tnsai-core`: **`OutputGuardrailConfig` + `RoleBuilder.outputGuardrail(...)`** (TNS-561) —
  the output-side mirror of `InputGuardrailConfig` (below). New
  `com.tnsai.guardrails.OutputGuardrailConfig` record mirrors the **runtime-enforced**
  subset of `@OutputGuardrail` (`maxChars`/`minChars`/`onFailure`/`fallback`/`logFailures`)
  with `from(@OutputGuardrail)`, `defaults()`/`NONE`, and a fluent builder; the annotation's
  not-yet-wired scaffold fields are deliberately excluded (note `maxRetries` is inert
  because Phase-1 `RETRY` degrades to `REJECT`). `OutputGuardrailEnforcer` now resolves and
  enforces this record, with the annotation path adapting through `from(...)`. Resolution
  precedence: method `@OutputGuardrail` &gt; `RoleBuilder.outputGuardrail(...)` (via
  `Role.getOutputGuardrailConfig()`) &gt; class `@OutputGuardrail`. Existing annotation
  behaviour is unchanged. Both guardrail config records now also reject a contradictory
  bound pair (`maxChars`/`maxLength` &lt; `minChars`/`minLength` when both are non-zero) at
  construction — such a config makes every value unsatisfiable, so it fails fast.
- `tnsai-core`: **`InputGuardrailConfig` + `RoleBuilder.inputGuardrail(...)`** (TNS-561) —
  the programmatic counterpart of a class-level `@InputGuardrail`, so a builder-built
  role (which has no annotation to read) can opt into input guardrails. New
  `com.tnsai.guardrails.InputGuardrailConfig` record mirrors the **runtime-enforced**
  subset (`maxLength`/`minLength`/`blockPatterns`/`allowPatterns`/`onFailure`/
  `errorMessage`/`logFailures`) with `from(@InputGuardrail)`, `defaults()`/`NONE`, and a
  fluent builder; the annotation's not-yet-wired scaffold fields are deliberately excluded
  to avoid inert surface. `InputGuardrailEnforcer` now resolves and enforces this record,
  with the annotation path adapting through `from(...)`. Resolution precedence: method
  `@InputGuardrail` &gt; `RoleBuilder.inputGuardrail(...)` (via `Role.getInputGuardrailConfig()`)
  &gt; class `@InputGuardrail`. Existing annotation behaviour is unchanged (the prior
  `enforce(@InputGuardrail, …)` entry point is preserved as a thin adapter).
- `tnsai-core`: **`@AgentSpec.toolCallFilter` declarative tool-call filter** (TNS-611) —
  the top-level annotation counterpart of `AgentBuilder.toolCallFilter(ToolCallFilter)`.
  The supplied `Class<? extends ToolCallFilter>` is instantiated via its public no-arg
  constructor at agent init (AGENT-V009 on failure, same as `@AgentSpec.roles`) and
  wired into the orchestrator before the first tool call. New public
  `com.tnsai.agents.execution.AllowAllToolFilter` doubles as the annotation default
  and the "not set" sentinel — the extractor surfaces it as `null` so the historical
  no-filter behaviour is preserved and the AGENT-V006 approval gate keeps warning about
  confirmation-required tools without an explicit filter. Precedence:
  `AgentBuilder.toolCallFilter(...)` / `Agent.setToolCallFilter(...)` wins over the
  annotation (`applyInitializationResult` only adopts the resolved filter when no
  pending filter was set).
- `tnsai-core`: **`@AgentSpec.maxContextTokens` declarative context budget** (TNS-609) —
  the top-level annotation counterpart of `AgentBuilder.maxContextTokens(int)`. Setting
  it (`> 0`) prunes the agent's conversation history to the budget before each LLM call
  (the same pruning the builder shortcut and `@MemorySpec.maxContextTokens` already drive).
  `AgentInitializer` resolves it with precedence: template &gt; `@AgentSpec.maxContextTokens`
  (top-level shortcut) &gt; `@MemorySpec.maxContextTokens` (nested). `0` (the default) keeps
  the previous behaviour, so existing agents are unaffected.
- `tnsai-mcp`: **MCP tool annotations map onto TnsAI safety hints** (TNS-641, follow-up
  to TNS-556) — `McpToolBridge.toDynamicToolMethod(...)` (the in-process MCP bridge, the
  documented `McpToolBridge.stdio(...).toTnsAITools()` path) now reads the MCP 2025-03-26
  tool annotations: `destructiveHint=true → requiresConfirmation=true` (so a destructive
  MCP tool registered without a `ToolCallFilter` raises AGENT-V006 instead of dispatching
  unattended) and `idempotentHint=true → idempotent=true` (was hardcoded `false`). Both
  default to `false` when the annotation is absent, so tools with no annotations behave
  exactly as before. `readOnlyHint`/`openWorldHint` are not mapped (their counterpart
  `sideEffect` is not yet modelled — see TNS-556). The `tnsai-server` WebSocket path
  (`McpProxyTool`) is unchanged: its wire record `WsProtocol.McpToolDef` carries no
  annotations field, so propagating them there needs a protocol extension + client support
  (deferred).
- `tnsai-core`: **dynamic tools can opt into the AGENT-V006 approval gate** (TNS-556) —
  `DynamicToolMethod` (the runtime tool form fronting MCP servers) gained
  `requiresConfirmation` + `keywords`, mirroring `@Tool(requiresConfirmation=…, keywords=…)`
  on the annotated path. `AgentBuilder`'s confirmation scan now inspects registered
  dynamic tools too, so a confirmation-gated MCP/dynamic tool with no `ToolCallFilter`
  wired raises AGENT-V006 — previously only `@Tool` POJOs were scanned and dynamic tools
  silently escaped the check. Non-breaking: the pre-existing 5-arg shape is preserved as
  `DynamicToolMethod.of(...)` and a delegating 5-arg constructor (both default the new
  fields to the safe "no claim" values); a fluent `DynamicToolMethod.builder(name)` sets
  them. (`sideEffect`/`idempotencyHint` from `@Tool` are intentionally not modelled yet —
  no runtime consumer reads them off a `ToolMethod`, so they'd be no-op surface.)
- `tnsai-core`: **programmatic role resilience** (TNS-567) — `RoleBuilder.resilience(ResilienceConfig)`
  lets a builder-built role carry a retry/timeout policy without subclassing, the
  counterpart of class-level `@Resilience`. `ActionExecutor` applies it when no
  `@Resilience` annotation is present. Scoped to the runtime-enforced subset
  (retry + timeout); circuit-breaker / rate-limit / bulkhead await TNS-565 Phase 2.
  [#430](https://github.com/TnsAI-Framework/TnsAI/pull/430)
- `tnsai-core`: **programmatic `MemoryConfig`** (TNS-546) — `AgentBuilder.memoryConfig(...)`
  closes the `@MemorySpec` parity gap (8 fields, only `maxContextTokens` had a builder
  shortcut before). `MemoryConfig` record mirrors `@MemorySpec` with `from()`/`defaults()`/
  `builder()`; `MemoryStoreFactory.create(MemoryConfig)` is the canonical factory the
  annotation path now delegates through.
  [#429](https://github.com/TnsAI-Framework/TnsAI/pull/429)
- `tnsai-core`: **programmatic `ContractSpec`** (TNS-637) — the builder-path form of
  `@Contract`. `ActionConfig.withContract(ContractSpec.builder()…build())` attaches
  Design-by-Contract gates (pre/post/invariants) to a `RoleBuilder.addAction(...)`
  action, enforced identically to the annotation. `ContractValidator` now operates
  on `ContractSpec` for both paths (the annotation adapts via `ContractSpec.from`).
  [#428](https://github.com/TnsAI-Framework/TnsAI/pull/428)
- `tnsai-core`: **build-time `@Contract` expression validation** (TNS-637, AGENT-V013).
  `AgentBuilder.build()` now parses every JEXL clause of each action's `@Contract`
  (`preconditions`/`postconditions`/`invariants`) and reports a malformed
  expression as a suppressible warning, instead of failing on first invocation.
  Suppress with `.relaxValidation("AGENT-V013")`.
  [#427](https://github.com/TnsAI-Framework/TnsAI/pull/427)

### Changed
- `tnsai-core`: **`com.tnsai.identity.AgentSpec` record renamed to `AgentDescriptor`**
  (TAN-3795). **BREAKING:** resolves the simple-name collision with the
  `@com.tnsai.annotations.AgentSpec` annotation (which keeps its name, per the
  `@*Spec` = annotations convention). All referrers updated.
- `tnsai-core`: **`@com.tnsai.roles.annotations.RoleIdentity` renamed to `@RoleDeclaration`**
  (TAN-3796). **BREAKING:** resolves the collision with the
  `com.tnsai.models.role.RoleIdentity` class (unchanged).
- `tnsai-core`: **role export reads `@RoleSpec.llm()` (`@LLMSpec`) instead of `@LLM`**
  (TNS-642, follow-up to TNS-568). **BREAKING:** the `RoleSpecExtractor.LLMSpec` nested
  record is renamed to `RoleSpecExtractor.LLMExportSpec` (it collided on simple name with
  the `@LLMSpec` annotation), and `RoleSpecExtractor.hasLLMAnnotation(...)` is renamed to
  `hasLLMConfig(...)`. The role-export subsystem (`ExportedRole`, `YamlRoleExporter`,
  `JsonRoleExporter`) now sources LLM config from the nested `@LLMSpec` a role actually
  declares — so roles using `@RoleSpec(llm=@LLMSpec(...))` now export their LLM config
  (previously the exporter only read the unused TYPE-level `@LLM`). `@LLMSpec.endpoint`
  populates the export record's `baseUrl`. [#435](https://github.com/TnsAI-Framework/TnsAI/pull/435)
- `tnsai-llm`: **canonical `providerId()` for error-mapper resolution** (TNS-624).
  The `ProviderErrorMapper` SPI lookup keyed off the lower-cased `executeRequest`
  display name, which drifts from the mapper's clean token (`"Together.ai"` →
  `together.ai` never matched `together`), so mappers loaded but never resolved
  for ~half the providers. `AbstractLLMClient.providerId()` now supplies a single
  canonical id per provider as the lookup key (display label kept for logs); all
  29 executeRequest-based clients override it. Foundation for the missing-mapper
  fix. No public-API change.
  [#423](https://github.com/TnsAI-Framework/TnsAI/pull/423)
- `tnsai-llm`: **OpenRouter / Mistral / HuggingFace folded onto
  `AbstractOpenAICompatibleClient`** (TNS-636, follow-up to TNS-621), net −629
  LOC. Each kept only its real divergence: OpenRouter's ranking / Claude-beta
  headers move to `addProviderHeaders()`, HuggingFace keeps a `parseChatResponse`
  override for usage tokens, Mistral has zero overrides. OpenAI, ZhipuAI and
  MiniMax stay bespoke (`JsonCapableLLMClient` + per-call `response_format`). No
  public-API change. [#425](https://github.com/TnsAI-Framework/TnsAI/pull/425)

### Removed
- `tnsai-core`: **`@SystemPrompt`, `@ChannelSpec`, `@Pipeline`, `@PipelineStep` removed**
  (TNS-569/573/577). **BREAKING:** four more pure-orphan annotations with no runtime
  consumer (verified: zero reflection readers, zero production/test/Sona usage). Their
  canonical counterparts are untouched: the `SystemPromptBuilder`, the channel `Channel`
  interface, and `PipelineBuilder` (`tnsai-coordination`) — these were always the wired
  surfaces; the annotations only mirrored their names. `@Pipeline`/`@PipelineStep`
  referenced only each other (Javadoc). Migration: none — delete any stray applications.
  [#439](https://github.com/TnsAI-Framework/TnsAI/pull/439)
- `tnsai-core`: **more pure-orphan annotations removed** (TNS-566/576/578/589).
  **BREAKING:** deleted `@RateLimited` (566 — superseded conceptually by `@Resilience`,
  but never wired), the channel-hook markers `@OnConnect`/`@OnDisconnect`/`@OnMessage`
  (576), the FSM family `@FSMState`/`@FSMTransition`/`@FSMStates`/`@FSMTransitions`
  (578 — defined but no FSM engine reads them), and `@RequiresPairing` (589). Each was
  source-verified as a pure orphan (no reflection reader, no production/test/Sona usage);
  the FSM annotations referenced only each other (`@Repeatable` containers). Stale Javadoc
  references in kept files were cleaned (`ChannelSpec`, `resilience/package-info`).
  Migration: none — these had no runtime consumer; delete any stray applications.
  [#438](https://github.com/TnsAI-Framework/TnsAI/pull/438)
- `tnsai-core`: **9 pure-orphan annotations removed** (TNS-590, Section 13 cleanup).
  **BREAKING:** deleted `@ContextCompaction`, `@SlashCommand`, `@WorkspaceSpec`,
  `@Property`, `@ConfigProperty`, `@Trigger`, `@Delegate`, `@Sanitize`, `@ContentFilter`
  from `com.tnsai.annotations`. Each had **no runtime consumer, no reflection reader, and
  zero production/Sona usage** (verified by source-trace) — they were unwired scaffolding
  that only added surface area and "is this wired?" confusion. Migration: none needed —
  removing a no-op annotation cannot change runtime behaviour; delete any stray
  applications. (`@NormType` is intentionally retained — it is the value enum for the
  `@Norm`/`@Norms` deontic-logic wiring tracked under TNS-591.)
  [#437](https://github.com/TnsAI-Framework/TnsAI/pull/437)
- `tnsai-core`: **`@LLM` annotation removed** (TNS-642, follow-up to TNS-568).
  **BREAKING:** the TYPE-level `com.tnsai.annotations.LLM` is gone — it was a parallel,
  export-only LLM-config surface with zero production usage that never instantiated an
  `LLMClient` and shadowed the live, integrated `@LLMSpec` (nested in
  `@RoleSpec`/`@AgentSpec`, which all four integration examples use). Migrate any
  `@LLM(provider=…, model=…)` on a role to `@RoleSpec(llm=@LLMSpec(provider=Provider.…,
  model=…))`. Resolves the `LLMSpec` simple-name collision and unblocks TNS-570
  (`@LLMSpec` field wiring) + TNS-571 (programmatic `LLMConfig`).
  [#435](https://github.com/TnsAI-Framework/TnsAI/pull/435)

### Fixed
- `tnsai-llm`: **dangling `EmbeddingProvider` javadoc** — `EmbeddingProvider`,
  `CachedLLMClient`, and `SemanticCache` referenced a non-existent
  `OpenAIEmbeddingProvider`; examples now use the real `OllamaEmbeddingProvider`
  (TAN-3794).
- `tnsai-quality`: **`@AuditLog` is now wired into `AuditLogger`** (TNS-579) — the
  annotation was declared on `tnsai-core` but never read, so marking an action
  `@AuditLog(action = "...")` produced no audit trail. `SecurityEnforcer.audit(...)`
  (already invoked per action via the `SecurityEnforcerHandle` SPI) now also reads the
  method's `@AuditLog` and emits a declarative named-action entry through the new
  `AuditLogger.auditAction(...)`. It is **independent of `@Security`** (an action with only
  `@AuditLog` is audited) and **complementary** (an action with both emits both records).
  `includeArgs`/`includeResult` gate whether args/result are logged, and both honour
  `@Security` masking — `includeArgs` routes through the same `maskForLogging` (so
  `@Security(maskFields = …)` fields are masked) and the result is masked when
  `@Security(sensitive = true)`, matching the level-audit so neither path leaks. No `tnsai-core` change
  (the annotation already existed; the wiring lives entirely in `tnsai-quality`).
- `tnsai-core`: **`@LLMSpec.topP` is now honored by `@RoleSpec`-driven role LLM init**
  (TNS-570, partial). `Role.initializeLLMFromAnnotation()` — the live path that builds a
  role's `LLMClient` from `@RoleSpec(llm=@LLMSpec(...))` — passed `null` for the `topP`
  argument of `LLMClientProvider.create(...)` even though the whole client layer accepts
  it, so a role's configured nucleus-sampling value was silently dropped. It now passes
  `@LLMSpec.topP()` (the model-default `1.0f` maps to `null`, matching the
  `LLMClientFactory` convention). The remaining `@LLMSpec` fields
  (`frequencyPenalty`/`presencePenalty`/`timeoutMs`/`endpoint`/`apiKeyEnv`) are **not**
  yet honored — they need a client-layer config change (the LLM clients' constructors take
  only `model`/`temperature`/`topP`/`maxTokens`[/`baseUrl`/`apiKey`]); tracked under
  TNS-570's corrected scope.
- `tnsai-llm`: **`BedrockClient.streamChat()` now works** (TNS-626) — it was an
  `UnsupportedOperationException("Streaming not yet implemented")` placeholder in
  production. Implemented against the Anthropic Messages event stream via a
  lazily-built `BedrockRuntimeAsyncClient` (the sync client has no event-stream
  API); `content_block_delta` events are parsed to text and returned as a
  `Stream<String>`. Buffered for now (gathered before the stream is consumed);
  true per-token delivery is a follow-up. Claude-3-only, same as `chat()`.
  [#421](https://github.com/TnsAI-Framework/TnsAI/pull/421)
- `tnsai-llm`: **typed errors for the remaining 18 providers** (TNS-622).
  Only 13 of the ~31 wired providers shipped a `ProviderErrorMapper`; the other
  18 (Cerebras, DashScope, Databricks, DeepInfra, DeepSeek, Fireworks.ai,
  Hunyuan, llama.cpp, LM Studio, NVIDIA NIM, Perplexity, Replicate, Together.ai,
  Vertex AI, vLLM, Watsonx, xAI Grok, Yi) fell back to an untyped
  `LLMException` (`UNKNOWN`, no `ProviderDetails`), so `OnHttpStatus` /
  `OnErrorType` fallback rules never matched and the chain couldn't fail over on
  rate-limit/5xx for them. Each now has a mapper (15 share a new
  `AbstractOpenAICompatibleErrorMapper`; Vertex AI shares
  `AbstractGoogleErrorMapper` with Gemini; Watsonx/Replicate parse their own
  envelopes), resolved by the canonical `providerId()` from #423.
  [#424](https://github.com/TnsAI-Framework/TnsAI/pull/424)
- `tnsai-llm`: **`AbstractOpenAICompatibleClient` no longer double-wraps typed
  errors** (TNS-636). `chat()`/`streamChat()` re-wrapped every exception into a
  generic `LLMException` (`UNKNOWN`, no `ProviderDetails`), discarding the typed
  exception a `ProviderErrorMapper` had produced — latent since TNS-621, it meant
  the 16 migrated OpenAI-compatible clients silently lost the typed errors #424
  added. Typed `LLMException`s now propagate unchanged.
  [#425](https://github.com/TnsAI-Framework/TnsAI/pull/425)
- `tnsai-llm`: **OpenAI-compatible clients now report real usage tokens**
  (TNS-639). `AbstractOpenAICompatibleClient.parseChatResponse` didn't read the
  `usage` block, so the ~18 clients on the base returned empty token counts and
  `CostAwareLLMClient` fell back to a character-count estimate. It now parses
  `prompt_tokens`/`completion_tokens`; cost tracking uses the provider's actual
  counts. `HuggingFaceClient`'s bespoke parse override (its only one) is removed.
  [#426](https://github.com/TnsAI-Framework/TnsAI/pull/426)

### Migration
- Replace `com.tnsai.identity.AgentSpec` → `com.tnsai.identity.AgentDescriptor`
  (the `@AgentSpec` annotation is unaffected).
- Replace `@com.tnsai.roles.annotations.RoleIdentity` → `@RoleDeclaration`
  (the `com.tnsai.models.role.RoleIdentity` class is unaffected).
- The 0.11.1-era orphan-annotation and `@LLM` removals (see Removed) are also
  breaking; migrate per those notes.

## [0.11.0] - 2026-05-27

Additive release. Closes a batch of annotation ↔ programmatic parity gaps
(`@AgentSpec`/`@RoleSpec` builder methods), implements the `@Contract`
Design-by-Contract safety primitive (JEXL pre/postconditions + `old(expr)`),
consolidates 16 OpenAI-compatible LLM clients onto a shared base (~-3,300 LOC),
and adds the `tnsai diagnose` bug-report reproducer CLI. No breaking changes —
purely additive on the public API surface.

### Added
- `tnsai-core`: **`@AgentSpec` annotation-parity on `AgentBuilder`**
  (TNS-534). Six `@AgentSpec` metadata fields had a runtime consumer but
  no builder counterpart, so programmatic agents silently fell back to
  defaults. Builder now exposes `description`, `version`, `autoStart`,
  `idleTimeoutMs`, `did(DIDConfig)`, `groupMembership(GroupMemberSpec)`,
  with precedence **builder explicit > annotation > default** applied in
  `AgentInitializer` via new `InitializerContext` override hooks.
  [#411](https://github.com/TnsAI-Framework/TnsAI/pull/411)
- `tnsai-core`: **`DIDConfig`** (`com.tnsai.identity`) — programmatic
  counterpart of `@DIDSpec` (`from(annotation)`, `of(method, domain,
  agentId)`, `toDid(fallbackId)`), so the builder and annotation paths
  derive identical DIDs.
  [#411](https://github.com/TnsAI-Framework/TnsAI/pull/411)
- `tnsai-core`: **`GroupMemberSpec.of(String...)`** convenience factory
  for builder-side group membership.
  [#411](https://github.com/TnsAI-Framework/TnsAI/pull/411)
- `tnsai-core`: **`RoleBuilder.addAction(ActionConfig)`** — programmatic role
  actions (TNS-551). Builder-built roles can now register dispatchable actions;
  previously only `@ActionSpec`-annotated methods on a `Role` subclass worked,
  so `ConfigurableRole` was capability-less. New `ActionConfig` + `ActionHandler`
  (`com.tnsai.metadata`); `ActionExecutor` dispatches the handler lambda and
  `Role.discoverActions` merges them with annotation-discovered actions
  (duplicate names error). Scoped to `LOCAL` actions.
  [#414](https://github.com/TnsAI-Framework/TnsAI/pull/414)
- `tnsai-core`: **`@AgentSpec.roles`** (TNS-536) — declare an agent's role
  classes (`Class<? extends Role>[]`) in the annotation, the counterpart of
  `AgentBuilder.role(...)`. Instantiated via public no-arg constructor at init;
  precedence is **programmatic > annotation**. A missing no-arg ctor fails with
  an actionable `AGENT-V009` message.
  [#415](https://github.com/TnsAI-Framework/TnsAI/pull/415)
- `tnsai-tools`: **`tnsai diagnose` CLI** (TNS-525) — `com.tnsai.tools.diagnostics`
  prints a paste-ready environment report for bug reports: `tnsai_version`
  (lockstep), `jdk` (version/vendor/GC/max heap), `os`, and
  `providers_configured` (known LLM provider env vars as `set`/`missing`, never
  the value). Flags `--json` (default), `--issue-template` (GitHub markdown
  block), `--minimal`, `--no-redact` (secret redaction via the framework
  `PatternRedactor` is on by default). Adds `.github/ISSUE_TEMPLATE/bug.md` +
  README "run this first" section. Scoped to the static environment; runtime
  state (MCP reachability, checkpoint store, OTel traces, log lines) needs a
  live agent and is a follow-up.
  [#416](https://github.com/TnsAI-Framework/TnsAI/pull/416)
- `tnsai-core`: **`@Contract` Design-by-Contract enforcement** (TNS-552) — the
  scaffold annotation (preconditions/postconditions/invariants, zero readers
  since 2.18.0) is now enforced at action dispatch via a JEXL evaluator.
  Preconditions reject hallucinated input before the method runs with an
  LLM-friendly `"precondition violated: <expr>"`; postconditions bind `result`
  and resolve `old(expr)` to the pre-execution value; invariants run before and
  after. Honors `validate`/`strict`/`message`. New `com.tnsai.actions.contracts`
  (`ContractEvaluator`, `ContractValidator`, `ContractViolationException`);
  adds `commons-jexl3`. Additive to the existing `ActionContract` /
  `@ActionSpec.precondition` / `@State.invariants` paths. Programmatic
  `ContractSpec` + build-time syntax validation are follow-ups.
  [#418](https://github.com/TnsAI-Framework/TnsAI/pull/418)

### Changed
- `tnsai-core`: removed the dead, unused `AgentSpecExtractor.DIDInfo`
  record (0 callers, verified across the full reactor) — its role is now
  served by `DIDConfig`, and `AgentSpecExtractor.generateDID` is DRYed
  through `DIDConfig.toDid`. Internal cleanup; no consumer impact.
  [#411](https://github.com/TnsAI-Framework/TnsAI/pull/411)
- `tnsai-llm`: **collapsed 16 duplicated OpenAI-compatible provider clients**
  into a new `AbstractOpenAICompatibleClient` (TNS-621). Each carried a
  byte-identical ~225-line copy of the chat-completions mapping
  (`buildChatRequest` / `parseChatResponse` / `extractStreamContent` /
  `chat` / `streamChat`); a fix had to be applied 16× by hand. Now they shrink
  to a constructor + base-URL/env-var. Migrated: DeepSeek, Groq, Together.ai,
  Fireworks.ai, DeepInfra, Cerebras, Databricks, NVIDIA NIM, Perplexity,
  DashScope, Hunyuan, xAI Grok, Yi, LM Studio, vLLM, llama.cpp. Clients with a
  divergent wire format (OpenAI, OpenRouter, Mistral, HuggingFace, ZhipuAI,
  MiniMax) stay on `AbstractLLMClient` — follow-up. **No public-API change**;
  net ~-3,290 LOC; 1644 tests green.
  [#417](https://github.com/TnsAI-Framework/TnsAI/pull/417)

## [0.10.5] - 2026-05-18

Same-day follow-up to 0.10.4 — ships the TNS-449 x402 micropayments
stack (`tnsai-payments` umbrella + HttpInterceptor primitive + end-to-end
`X402PaymentBroker` + EnvKeystoreWallet + mandate enforcement + liability
records) and a batch of dependency bumps. Purely additive on the public
API front; `tnsai-payments` is a new optional module that consumers opt
into. No migration required.

### Added
- `tnsai-mcp`: **`HttpInterceptor` primitive for `HttpTransport`**
  (TNS-449 P1). Composable interceptor chain on the framework's HTTP
  transport — auth, rate limit, retry, tracing, and (the immediate
  driver) x402 payment-aware request mutation. 11 tests pin
  ordering / short-circuit / chain composition; no new dependency, no
  API break on `HttpTransport` callers.
  [#374](https://github.com/TnsAI-Framework/TnsAI/pull/374)
- `tnsai-payments`: **new umbrella module + `PaymentBroker` SPI skeleton**
  (TNS-449 P2). New optional module — `tnsai-payments` carries the
  `PaymentBroker` SPI (`quote`, `settle`, `verify`) plus the `x402`
  protocol-specific package skeleton. Module-graph dep: `tnsai-payments
  → tnsai-core`. Consumers opt in by adding it to their classpath; core
  ships with the no-op broker via [#305](https://github.com/TnsAI-Framework/TnsAI/pull/305) (0.10.0).
  [#375](https://github.com/TnsAI-Framework/TnsAI/pull/375)
- `tnsai-payments`: **`X402PaymentBroker` end-to-end x402 settlement**
  (TNS-449 P3). Implements the `PaymentBroker` SPI against the x402
  HTTP-402 payments protocol over Base USDC. `PaymentRequirement`
  parses the 402-body wire format; `TransferAuthorization` builds
  EIP-3009 typed data with inline EIP-712 encoding (skips web3j's
  heavier `StructuredDataEncoder` for tighter hot-path); `Web3jWallet`
  wraps secp256k1 `ECKeyPair.sign` for 65-byte `r||s||v` signatures;
  `X402PaymentBroker.quote()` probes `service.metadata["x402.resource"]`,
  parses the 402, picks the compatible `PaymentRequirement`, mints a
  `Quote`; `settle()` builds typed data, signs, replays the request
  with `X-PAYMENT` (base64-JSON header), returns a sealed `Settlement`
  variant. Idempotency: `Quote.idempotencyKey` maps deterministically
  (Keccak-256) to the EIP-3009 nonce, so retried settles return
  `Settlement.AlreadySettled` rather than double-charging. New dep:
  `org.web3j:core-crypto` (slice, ~1.5 MB — full web3j was 5 MB).
  39 net-new tests, module coverage **87.3%**. Stub `HttpServer`
  facilitator in-process; no live testnet calls in CI.
  [#389](https://github.com/TnsAI-Framework/TnsAI/pull/389) (rebased
  successor to [#377](https://github.com/TnsAI-Framework/TnsAI/pull/377))
- `tnsai-payments`: **`EnvKeystoreWallet` + mandate enforcement +
  liability records** (TNS-449 P4, partial). `EnvKeystoreWallet.fromEnv(prefix, network)`
  reads `<PREFIX>_PRIVATE_KEY` from env for a zero-config local wallet
  (encrypted JSON keystore decryption deferred — needs the full
  `web3j-core` artifact, kept opt-in for now). `X402Config` gains
  `liabilitySink` + `authorityScope` optional builder fields.
  `X402PaymentBroker.settle()` runs mandate enforcement before signing:
  sums prior `x402.settle` records from the configured `LiabilitySink`,
  rejects when projected spend > `AuthorityScope.spendCeilingUSD`.
  Conservative posture — ceiling-set with no sink configured = block.
  Each terminal Settlement emits one liability record (Settled=MEDIUM,
  Rejected=HIGH, Expired=LOW); idempotency replays skip emission.
  21 net-new tests (9 EnvKeystoreWallet + 12 X402PaymentBroker
  mandate/liability), module coverage **88.1%**. **Held for separate
  PR with explicit approval:** `AgentBuilder.paymentBroker(PaymentBroker)`
  Protected Change.
  [#390](https://github.com/TnsAI-Framework/TnsAI/pull/390) (rebased
  successor to [#378](https://github.com/TnsAI-Framework/TnsAI/pull/378))

### Changed
- **Dependency bumps** ([#388](https://github.com/TnsAI-Framework/TnsAI/pull/388) batch + [#333](https://github.com/TnsAI-Framework/TnsAI/pull/333)):
  - `aws.sdk` 2.44.4 → 2.44.7 (patch)
  - `jsoup` 1.22.1 → 1.22.2 (patch)
  - `javalin` 7.1.0 → 7.2.2 (minor)
  - `slf4j` 2.0.17 → 2.0.18 (patch)
  - `junit` 5.14.3 → **6.0.3** (major; we run JDK 21 so compatible)
  - `telegram-bot-api` 8.3.0 → **9.6.0** (major)
  - `angus-mail` 2.0.3 → 2.0.5 (patch)
  - `greenmail-junit5` 2.1.5 → 2.1.8 (patch)
  - `opentelemetry-semconv` 1.41.0 → 1.41.1 (patch)

  No code edits required — pure pom property changes. The two major
  bumps (`junit` 5 → 6, `telegram-bot-api` 8 → 9) ride the same `mvn
  verify` reactor; if either surfaces a test regression, the specific
  bump is reverted in a follow-up patch release.

### Stats
- 12 PRs landed in the release window: TNS-449 stack ([#374](https://github.com/TnsAI-Framework/TnsAI/pull/374),
  [#375](https://github.com/TnsAI-Framework/TnsAI/pull/375),
  [#389](https://github.com/TnsAI-Framework/TnsAI/pull/389),
  [#390](https://github.com/TnsAI-Framework/TnsAI/pull/390)) plus the
  dependency batch ([#333](https://github.com/TnsAI-Framework/TnsAI/pull/333),
  [#388](https://github.com/TnsAI-Framework/TnsAI/pull/388) — which
  closed 8 dependabot PRs).
- New optional module: `tnsai-payments` (~4250 LOC across the four-PR
  stack, +693 of that in EnvKeystoreWallet + mandate work).
- Module count: 11 → 12 (`tnsai-payments` joins the active set).

## [0.10.4] - 2026-05-18

Channel-stack expansion (Slack/Discord/WhatsApp adapters bring shipped count
to six), eighteen-provider LLM catalogue expansion completing the TNS-322
umbrella, `WatsonxClient` IAM-refresh hardening, a multi-agent WS
tool-approval routing fix, and a `tnsai-server` Docker image with a
multi-arch publish workflow. Monorepo README link cleanup. No public API
breakage, no migration required.

### Fixed
- `tnsai-server`: **WS tool-approval routing in multi-agent sessions**
  (TNS-308). `WsHandler.handleToolApprove` walked the session's
  `approvalFilters` map but called `filter.handleApproval(toolCallId,
  decision)` on the first entry and returned regardless of match.
  In single-agent sessions there is only one filter so the bug never
  surfaced; in multi-agent sessions (more than one
  `WsToolApprovalFilter` per session), an approval whose
  `toolCallId` belonged to a non-first filter was silently dropped
  and the pending future on the actual owning filter timed out
  unanswered — surfacing to the user as "I approved but nothing
  happened." `WsToolApprovalFilter.handleApproval` now returns
  `boolean` (`true` iff it owned the id and consumed it); `WsHandler`
  iterates every filter in the session until one returns `true`,
  then breaks. Added a package-private `registerPendingForTesting`
  seam so the routing contract is unit-testable without a live WS
  broadcast. Nine new tests cover the boolean return on
  match/miss/repeated-id, the multi-agent routing pattern itself,
  and a `cancelAll` regression guard.

### Added
- `tnsai-channels`: **`WhatsAppChannel` adapter (Cloud API)** (TNS-352).
  Sixth channel adapter and the **first one that embeds an HTTP server**
  in `tnsai-channels` — WhatsApp Cloud API delivers inbound events via
  webhook only (no WebSocket or polling alternative), so the adapter
  binds a JDK `com.sun.net.httpserver.HttpServer` to a configurable
  port + path. Inbound: Meta GETs the path with `hub.challenge` at
  subscription time → we echo iff `hub.verify_token` matches; Meta
  POSTs JSON events signed with `X-Hub-Signature-256: sha256=<hex>`
  computed via HMAC-SHA256 of the body under the app secret → we
  verify in constant time before parsing. Outbound:
  `POST /{phone_number_id}/messages` to the Graph API with the access
  token as a Bearer header. Sender-id doubles as conversation-id
  (WhatsApp is 1:1, no channels). v1 deliberately scopes out media
  (image/document/audio/voice) because UnifiedResponse's attachment
  shape doesn't yet model Graph's media-id-then-download flow, and
  scopes out template messages + 24-hour-window enforcement because
  templates are a separate API surface with their own approval flow.
  Loopback bind by default; production runs behind a reverse proxy
  for TLS. Env: `WHATSAPP_ACCESS_TOKEN`, `WHATSAPP_PHONE_NUMBER_ID`,
  `WHATSAPP_VERIFY_TOKEN`, `WHATSAPP_APP_SECRET` (all required) +
  `WHATSAPP_GRAPH_BASE_URL`, `WHATSAPP_WEBHOOK_PORT`,
  `WHATSAPP_WEBHOOK_PATH`, `WHATSAPP_WEBHOOK_BIND_ADDRESS` (all
  optional). **Architectural note:** the embedded HttpServer is
  per-adapter — each webhook-based channel binds its own port. If a
  future second webhook adapter lands (e.g. a Slack Events API
  variant), the right move is to extract a shared `WebhookReceiver`
  with path-based routing; YAGNI for now.

### Changed
- `tnsai-llm`: **`WatsonxClient` IAM token refresh hardening** (TNS-501,
  follow-up to TNS-340 / PR #363). The IAM-token cache had the refresh
  logic from day one but the safety margin was only 60 seconds —
  acceptable for steady-state but tight under load. Bumped to **300
  seconds** so refresh fires at ~minute 55 of a 60-minute token
  lifetime, well ahead of expiry, matching the acceptance spec.
  Concurrent chat calls are guaranteed to collapse to a single
  exchange via the existing &#123;@code synchronized&#125; guard on
  &#123;@code getIamToken()&#125;. Token strings are never logged — only the
  expiry timestamp and the IAM URL appear in debug output. Added an
  &#123;@code iamTokenUrl()&#125; override seam so tests can route the exchange
  through &#123;@link mockwebserver3.MockWebServer&#125; without reflection on
  the cache fields, plus a &#123;@code expireCachedIamTokenForTesting()&#125;
  hook for deterministic expiry-driven refresh tests. Three new tests:
  expired-cache-forces-refresh, eight-thread concurrent-refresh
  collapses to one exchange, and a log-leak assertion that scans every
  TRACE-level log line for the token string. The refactor only touches
  &#123;@code WatsonxClient.java&#125; + its test; behaviour is strictly
  additive (no API changes).

### Added
- `tnsai-channels`: **`DiscordChannel` adapter (Gateway WebSocket)** (TNS-351).
  Fifth channel after Telegram, CLI, Email, and Slack — and the second
  WebSocket adapter. Inbound: opens a Discord Gateway v10 connection
  via `GET /gateway/bot` → WSS, then handles the full handshake
  (`HELLO` → schedule heartbeats → `IDENTIFY` with token + intents →
  `READY` captures the bot user_id → `MESSAGE_CREATE` becomes
  `UnifiedMessage`). Heartbeats run on a single-threaded
  `ScheduledExecutorService` with the interval Discord supplies in
  `HELLO`, carrying the last observed `s` sequence number on each
  beat. Outbound: `POST /channels/{channel_id}/messages` with the
  `Bot <token>` header and a `message_reference.message_id` when the
  response targets a specific reply. Bot-echo filtering: messages
  authored by other bots (or our own bot once `READY` lands) are
  silently dropped. v1 scopes out **slash commands** (needs
  `POST /applications/{id}/commands` registration + `INTERACTION_CREATE`
  handling), **embeds** (UnifiedResponse doesn't carry an embed shape
  today), and **streaming via message edits** (PATCH per-token is
  gated by Discord's edit ratelimit) — all named in the issue but
  deferred to a follow-up so v1 stays focused on the
  text-DM-and-mention path. Env: `DISCORD_BOT_TOKEN` (required),
  `DISCORD_APPLICATION_ID` (optional, slash-command forward-compat),
  `DISCORD_REST_BASE_URL` (optional override), `DISCORD_INTENTS`
  (optional integer bitfield override; default covers DM + guild
  messages + the privileged `MESSAGE_CONTENT` intent).
- `tnsai-channels`: **`SlackChannel` adapter (Socket Mode)** (TNS-350).
  Fourth channel after Telegram, CLI, and Email — and the first one
  that speaks a real-time WebSocket protocol. Inbound: opens a
  Socket Mode WebSocket via `POST /apps.connections.open` with the
  `xapp-...` app token, then routes `events_api` envelopes (and
  `app_mention` events) into `UnifiedMessage` after acking with the
  matching `envelope_id`. Outbound: `POST /chat.postMessage` with the
  `xoxb-...` bot token; thread continuity preserved by carrying the
  inbound `thread_ts` forward into the response body. Bot-echo and
  bot-id-tagged messages are filtered to prevent feedback loops; Slack
  message subtypes (edits, joins, channel renames) are skipped so the
  agent only sees user-authored text. `SlackChannelConfig` reads
  `SLACK_BOT_TOKEN` (required) + `SLACK_APP_TOKEN` (required) +
  `SLACK_WEB_API_BASE_URL` (optional override for mirrors / proxies)
  from env or system properties, with an explicit programmatic
  constructor for Sona workspace YAML. **Note on deviation from
  TNS-350 spec:** the issue called for the HTTP Events API webhook
  with `SLACK_SIGNING_SECRET`; v1 chose Socket Mode instead because
  the framework has no embedded HTTP server in `tnsai-channels` and
  every existing adapter pulls from its platform — Socket Mode keeps
  that invariant and lets Sona run behind NAT without exposing a
  public URL. A signing-secret-verifying `SlackWebhookChannel`
  sibling can be added later if a use case needs it.
- `tnsai-llm`: **`WatsonxClient` provider** (TNS-340). Twenty-fifth
  LLM provider — talks to IBM watsonx.ai (enterprise LLM platform)
  via its `/ml/v1/text/chat` endpoint. The response is OpenAI-shaped
  but the request body uses IBM-specific fields (`model_id` instead
  of `model`, plus a required `project_id`), so the request
  construction diverges from the cookie-cutter OpenAI-shape providers.
  **Auth via IBM Cloud IAM exchange:** unlike the bearer-token cloud
  providers, watsonx requires an IAM access token obtained by
  exchanging an IBM Cloud API key. The client does the exchange
  lazily on first call against
  `https://iam.cloud.ibm.com/identity/token`, caches the resulting
  token until its `expires_in` window closes (minus a 60-second
  safety margin), and refreshes transparently. Catalog at time of
  writing: `ibm/granite-3-8b-instruct`,
  `meta-llama/llama-3-3-70b-instruct`, `mistralai/mistral-large`.
  Region default `us-south`; override `WATSONX_BASE_URL` for `eu-de`
  / `jp-tok` / etc. Required env: `WATSONX_API_KEY`,
  `WATSONX_PROJECT_ID`. Registered in `LLMClientFactory` under
  `watsonx` and `ibm` aliases.
- `tnsai-llm`: **`DeepInfraClient` provider** (TNS-329). DeepInfra's
  cost-leader open-model hosting — typically the cheapest hosted
  Llama-70B option. OpenAI-compatible chat-completions API at
  `https://api.deepinfra.com/v1/openai` (note the `/v1/openai` suffix —
  DeepInfra's native API lives at `/v1/inference`; we target the
  OpenAI shim so the wire format matches every other provider).
  Catalog includes Llama 3.x, Mixtral 8x7B, Qwen 2.5 72B, DeepSeek V3.
  Override base URL via `DEEPINFRA_BASE_URL`. Registered in
  `LLMClientFactory` under `deepinfra` and `deep-infra` aliases. API
  key via `DEEPINFRA_API_KEY`.
- `tnsai-llm`: **`FireworksAIClient` provider** (TNS-328). Fireworks.ai's
  open-model hosting + FireFunction (function-calling-tuned) — Llama 3.x,
  Mixtral, Qwen 2.5, DeepSeek V3, plus `firefunction-v2`. OpenAI-compatible
  chat-completions API at `https://api.fireworks.ai/inference/v1` (override
  via `FIREWORKS_BASE_URL` for mirrors / proxies). Same wire format as
  every other OpenAI-shape provider — structurally identical client.
  Registered in `LLMClientFactory` under `fireworks`, `fireworksai`, and
  `fireworks-ai` aliases. API key via `FIREWORKS_API_KEY`.
- `tnsai-llm`: **`TogetherAIClient` provider** (TNS-326). Together.ai's
  open-model hosting — Llama 3.x (8B / 70B / 405B Turbo), Mixtral 8x7B,
  Mistral 7B, Qwen 2.5 72B, DeepSeek V3, Nemotron-tuned Llama, and the
  rest of the serverless catalogue. OpenAI-compatible chat-completions
  API at `https://api.together.xyz/v1` (override via `TOGETHER_BASE_URL`
  for mirrors / proxies). Same wire format as `GroqClient` /
  `NvidiaNIMClient` / `XAIGrokClient` / `CerebrasClient` — structurally
  identical client. Registered in `LLMClientFactory` under `together`,
  `togetherai`, and `together-ai` aliases. API key via `TOGETHER_API_KEY`.
- `tnsai-llm`: **`CerebrasClient` provider** (TNS-327). Cerebras's
  WSE-3 hosted inference — top-of-industry token throughput (reportedly
  1800+ tok/s on Llama 70B). OpenAI-compatible chat-completions API at
  `https://api.cerebras.ai/v1` (override via `CEREBRAS_BASE_URL` for
  mirrors / proxies). Catalog at time of writing: `llama-3.3-70b`,
  `llama3.1-8b`, `qwen-3-32b`. Same wire format as `GroqClient` /
  `NvidiaNIMClient` / `XAIGrokClient` — structurally identical client.
  Registered in `LLMClientFactory` under `cerebras`. API key via
  `CEREBRAS_API_KEY`. Headline use case: latency-sensitive agent inner
  loops (tool-call coordination, REPL chat, realtime UI agents).
- `tnsai-llm`: **`VertexAIClient` provider** (TNS-325). Twenty-seventh
  LLM provider — **completes the TNS-322 LLM provider expansion
  umbrella** (18 new providers shipped across two sessions). Talks
  to Google Cloud Vertex AI (enterprise Gemini) via its native
  `generateContent` endpoint at
  `https://{LOCATION}-aiplatform.googleapis.com/v1/projects/{PROJECT}/locations/{LOCATION}/publishers/google/models/{MODEL}:generateContent`.
  Wire format is the Gemini shape
  (`contents[].parts[]`, `systemInstruction`, `generationConfig`)
  with OpenAI-style `assistant` history roles mapped to Gemini's
  `model` role. **Auth (v1 scope):** takes a pre-fetched OAuth
  access token via `VERTEX_AI_API_KEY` (e.g.
  `gcloud auth print-access-token` in dev, sidecar-managed in prod).
  Application Default Credentials (ADC) — service-account JWT
  signing, GCE metadata-server probe — is a deliberate follow-up
  since it pulls in `google-auth-library-java` or hand-rolled
  RS256 crypto. Required env: `VERTEX_AI_API_KEY`,
  `VERTEX_AI_PROJECT_ID`. Optional: `VERTEX_AI_LOCATION` (default
  `us-central1`; host derived from it), `VERTEX_AI_BASE_URL`
  (full override). Registered in `LLMClientFactory` under
  `vertexai`, `vertex-ai`, and `vertex` aliases.
- `tnsai-llm`: **`ReplicateClient` provider** (TNS-331). Twenty-sixth
  LLM provider — talks to Replicate (community model marketplace) via
  its native predict/poll HTTP API at `https://api.replicate.com/v1`.
  Unlike every other provider in this module, Replicate is **not
  OpenAI-shaped** — the request takes an arbitrary `input` object and
  the response is delivered through a submit-then-poll lifecycle.
  **v1 scope:** sync chat only, targeting the common chat-model case
  (Llama, DeepSeek, Mixtral). Model identifier `owner/name`
  auto-resolves the latest version; `owner/name:version` pins.
  System prompt + history + user message concatenated into a single
  `{"prompt": "..."}` input (de facto convention for chat models on
  Replicate). Output handles both single-string and string-array
  shapes. Polling uses exponential backoff (500ms → 30s, 10-minute
  overall timeout). **Documented limitations:** no streaming
  (`streamChat` emits the final answer as a single chunk), no tool
  calling. Env: `REPLICATE_API_KEY` (Replicate's docs call it
  `REPLICATE_API_TOKEN`; this module standardises on `_API_KEY`).
  Registered in `LLMClientFactory` under `replicate` alias.
- `tnsai-llm`: **`DeepSeekClient` provider** (TNS-324). Twenty-fourth
  LLM provider — talks to DeepSeek (Chinese frontier lab) via its
  OpenAI-compatible chat-completions endpoint at
  `https://api.deepseek.com/v1`. Catalog at time of writing:
  `deepseek-chat` (V3, ~$0.14/M input / $0.28/M output — very
  cost-efficient), `deepseek-reasoner` (R1, reasoning model).
  **Known v1 limitation:** when the model is `deepseek-reasoner`,
  responses include a `reasoning_content` field carrying R1's visible
  chain-of-thought trace alongside the standard `content` text. The
  shared `ChatResponse`/`ChatChunk` SPI doesn't yet carry a reasoning
  channel, so the trace is dropped — R1 still answers correctly,
  callers just don't see the trace. Plumbing reasoning content
  through is a follow-up SPI extension. Registered in
  `LLMClientFactory` under `deepseek` alias. API key via
  `DEEPSEEK_API_KEY`.
- `tnsai-llm`: **`LMStudioClient` provider** (TNS-333). Sixteenth LLM
  provider — talks to a locally-running LM Studio desktop server via
  its OpenAI-compatible chat-completions endpoint at
  `http://localhost:1234/v1` (override via `LMSTUDIO_BASE_URL` for LAN
  rigs or proxies). Like `OllamaClient`, the API key is optional: when
  unset the `Authorization` header is omitted entirely, matching LM
  Studio's ungated-by-default local server; when set (e.g. for an
  LM-Studio instance behind a reverse-proxy with basic auth) the key
  is sent as `Bearer <key>`. Model name comes from the loaded model
  in the LM Studio UI — discover via `GET /v1/models`. Streaming,
  tool calls, and topP follow the same wire format as the other
  OpenAI-shape providers. Registered in `LLMClientFactory` under
  `lmstudio` and `lm-studio` aliases.
- `tnsai-llm`: **`PerplexityClient` provider** (TNS-330). Twenty-third
  LLM provider — talks to Perplexity's search-augmented Sonar family
  via its OpenAI-compatible chat-completions endpoint at
  `https://api.perplexity.ai`. Sonar models fetch web results inline
  and ground answers in real-time sources. Catalog at time of writing:
  `sonar` (fast / cheap), `sonar-pro` (higher quality), `sonar-reasoning`
  (chain-of-thought over search), `sonar-deep-research` (multi-hop).
  **Known v1 limitation:** Perplexity responses include a
  `citations[]` array alongside the standard OpenAI text content; the
  shared `ChatResponse` SPI doesn't yet carry citation metadata, so
  citations are dropped — text-only answer is returned. Surfacing
  citations is a follow-up `ChatResponse` extension. Registered in
  `LLMClientFactory` under `perplexity` and `pplx` aliases. API key
  via `PERPLEXITY_API_KEY`.
- `tnsai-llm`: **`DatabricksClient` provider** (TNS-339). Twenty-second
  LLM provider — talks to Databricks Mosaic AI Model Serving via its
  OpenAI-compatible Foundation Model API at the customer's
  workspace-scoped URL. Unlike the multi-tenant cloud providers,
  Databricks endpoints live per-customer at
  `https://{workspace}.cloud.databricks.com/serving-endpoints`, so
  there is no sensible default — every caller must supply
  `DATABRICKS_BASE_URL` (or the constructor `baseUrl` argument).
  Built-in catalog at time of writing: `databricks-meta-llama-3-3-70b-instruct`,
  `databricks-meta-llama-3-1-405b-instruct`, `databricks-dbrx-instruct`,
  `databricks-mixtral-8x7b-instruct`; customers can also point this
  client at their own fine-tuned endpoints by passing the endpoint
  name as `model`. Auth via `DATABRICKS_API_KEY` (PAT or service-
  principal OAuth token, both passed as opaque bearer); native
  service-principal OAuth flows can land as a follow-up. Registered
  in `LLMClientFactory` under `databricks` and `mosaic` aliases.
- `tnsai-llm`: **`QwenCloudClient` provider** (TNS-335). Twenty-first
  LLM provider — talks to Alibaba's DashScope service (the hosted API
  for the Qwen family) via its OpenAI-compatible chat-completions
  endpoint. Two regional defaults: international at
  `https://dashscope-intl.aliyuncs.com/compatible-mode/v1` (the
  client's default) and China at
  `https://dashscope.aliyuncs.com/compatible-mode/v1` (select via
  `DASHSCOPE_BASE_URL` env var or constructor `baseUrl` argument).
  Catalog at time of writing: `qwen-max` (frontier), `qwen-plus`
  (balanced), `qwen-turbo` (low-latency), `qwen2.5-coder-32b-instruct`
  (coding), `qwen-vl-max` (multimodal). Streaming, tool calls, and
  topP all follow the standard OpenAI-shape wire format. Registered
  in `LLMClientFactory` under `qwen`, `dashscope`, and `alibaba`
  aliases. API key via `DASHSCOPE_API_KEY` (single-token "DashScope"
  label in `requireApiKey` to satisfy the env-var pairing test).
- `tnsai-llm`: **`LlamaCppServerClient` provider** (TNS-334).
  Twentieth LLM provider — talks to `llama-server` (the HTTP server
  binary from the llama.cpp project) via its OpenAI-compatible
  chat-completions endpoint at `http://localhost:8080/v1` (override
  via `LLAMACPP_BASE_URL`). Mirrors `OllamaClient` / `LMStudioClient`
  / `VLLMClient`: API key is optional — `Authorization: Bearer <key>`
  is sent only when `LLAMACPP_API_KEY` (or constructor `apiKey`) is
  set. Model name comes from the GGUF file that llama-server's
  `--model` flag loaded. llama.cpp's small footprint makes it the
  lightest possible LLM server — runs on Raspberry Pi 5-class ARM
  hardware and Apple Silicon laptops equally well; pairs naturally
  with edge-class sandbox profiles where Ollama and vLLM are too
  heavy. Registered in `LLMClientFactory` under `llamacpp`,
  `llama-cpp`, and `llama.cpp` aliases.
- `tnsai-llm`: **`VLLMClient` provider** (TNS-332). Nineteenth LLM
  provider — talks to a self-hosted vLLM inference server (typically
  invoked as `python -m vllm.entrypoints.openai.api_server`) via its
  OpenAI-compatible chat-completions endpoint at
  `http://localhost:8000/v1` (override via `VLLM_BASE_URL` for
  remote inference rigs). Mirrors `OllamaClient` / `LMStudioClient`:
  API key is optional — `Authorization: Bearer <key>` is sent only
  when `VLLM_API_KEY` (or constructor `apiKey`) is set, supporting
  vLLM instances started with `--api-key` or behind auth proxies.
  Model name comes from vLLM's `--model` flag; discover via
  `GET /v1/models`. Streaming, tool calls, and topP follow the
  standard OpenAI-shape wire format. Registered in
  `LLMClientFactory` under `vllm` alias.
- `tnsai-llm`: **`TencentHunyuanClient` provider** (TNS-336).
  Eighteenth LLM provider — talks to Tencent's Hunyuan family via its
  OpenAI-compatible chat-completions endpoint at
  `https://api.hunyuan.cloud.tencent.com/v1`. Catalog at time of
  writing: `hunyuan-pro` (frontier), `hunyuan-standard` (balanced),
  `hunyuan-lite` (cheap / fast), `hunyuan-vision` (multimodal).
  Streaming, tool calls, and topP all follow the same wire format as
  the other OpenAI-shape providers. Registered in `LLMClientFactory`
  under `hunyuan`, `tencent`, and `tencent-hunyuan` aliases. API key
  via `HUNYUAN_API_KEY`. Tencent Cloud SigV3 signing on the parallel
  native API path is deliberately not implemented — the bearer-token
  OpenAI-compatible surface keeps the wire format identical to every
  other provider in this module.
- `tnsai-llm`: **`YiClient` provider** (TNS-337). Seventeenth LLM
  provider — talks to 01.AI (Kai-Fu Lee's lab) via its
  OpenAI-compatible chat-completions API at
  `https://api.lingyiwanwu.com/v1`. Catalog at time of writing:
  `yi-large` (frontier), `yi-large-turbo` (cheaper / faster
  variant), `yi-lightning` (low-latency small-batch),
  `yi-vision` (multimodal). Streaming, tool calls, and topP all
  follow the same wire format as the other OpenAI-shape providers
  — structurally identical client. Registered in
  `LLMClientFactory` under `yi`, `01ai`, and `01.ai` aliases. API
  key via `YI_API_KEY`.
- `tnsai-llm`: **`XAIGrokClient` provider** (TNS-323). Fifteenth LLM
  provider — talks to xAI's Grok models via the OpenAI-compatible
  chat-completions API at `https://api.x.ai/v1` (override via
  `XAI_BASE_URL` for mirrors / proxies). Catalog at time of writing:
  `grok-3`, `grok-3-mini`, `grok-2-vision`, plus the gated `grok-beta`
  pre-release tier. Streaming, tool calls, and topP all follow the
  same wire format as `GroqClient` / `NvidiaNIMClient` — structurally
  identical client. Registered in `LLMClientFactory` under `xai`,
  `grok`, and `xai-grok` aliases. API key via `XAI_API_KEY`.
- `tnsai-channels`: **`EmailChannel` adapter (IMAP poll + SMTP send)**
  (TNS-354). Third channel after Telegram + CLI, the first async one.
  IMAP polling on a configurable interval (default 60s) marks UNSEEN
  messages SEEN as they're processed; thread continuity tracked through
  `Message-ID` / `In-Reply-To` / `References` headers — replies in the
  same thread land on the same `conversationId`. Outbound SMTP replies
  set `In-Reply-To` and `Re:`-prefix the subject. `EmailChannelConfig`
  is env-var-driven (`EMAIL_IMAP_HOST`, `EMAIL_IMAP_USER`,
  `EMAIL_IMAP_PASSWORD`, `EMAIL_SMTP_HOST`, …) with explicit programmatic
  override for Sona's per-workspace YAML. Sender allowlist is mandatory —
  empty allowlist drops everything, since email is the most spammable
  channel. Jakarta Mail deps declared `<optional>true</optional>` to match
  the Telegram pattern; consumers opt in by adding them to their classpath.
  GreenMail-backed integration tests cover allowlist, thread continuity,
  attachment parsing, and outbound headers.
- `tnsai-llm`: **`NvidiaNIMClient` provider** (TNS-338). Fourteenth LLM
  provider — talks to NVIDIA NIM (NVIDIA Inference Microservices) via
  the OpenAI-compatible chat-completions API on both deployment shapes:
  the hosted catalog at `https://integrate.api.nvidia.com/v1` (default)
  and any self-hosted NIM container via the `baseUrl` constructor
  parameter or `NVIDIA_BASE_URL` env var. Cloud catalog covers Llama
  3.1 (8B / 70B / 405B), Mixtral 8x7B, Mistral 7B, and NVIDIA's own
  Nemotron-4 340B + Llama-3.1-Nemotron-70B tunes. Streaming, tool calls,
  and topP follow the same wire format as `GroqClient` /
  `OpenRouterClient`. Registered in `LLMClientFactory` under `nvidia`,
  `nvidia-nim`, and `nim` aliases. API key via `NVIDIA_API_KEY`.

### Added (ops)
- `tnsai-server`: **Docker image + multi-arch publish workflow** (TNS-520).
  Multi-stage `Dockerfile` (Maven 3.9 + Eclipse Temurin 21 → distroless
  `gcr.io/distroless/java21-debian12:nonroot`) ships a self-contained
  `tnsai-server` JAR runnable with `docker run`. The JVM is PID 1 so
  `SIGTERM` reaches the existing `Runtime.addShutdownHook()` drain
  path; image defaults to `TNSAI_HOST=0.0.0.0` + `TNSAI_ALLOW_PUBLIC=true`
  but deliberately leaves `TNSAI_TOKEN` unset — operators must supply a
  token before exposing to anything other than a private network. New
  `/healthz` + `/readyz` route aliases (Kubernetes-conventional) added
  *additively* alongside the existing `/health/live` + `/health/ready`
  endpoints, sharing the same handler lambdas. `maven-shade-plugin` lives
  in an opt-in `docker` profile so `mvn install` for downstream consumers
  stays fast and Maven Central isn't polluted with a `-shaded` classifier.
  New `.github/workflows/docker-publish.yml` smoke-tests on every `v*` tag
  (boots the image, polls `/healthz` for 30s, verifies SIGTERM-driven
  graceful shutdown) then publishes multi-arch (`linux/amd64` +
  `linux/arm64`) to Docker Hub. Skipped for forks. Requires
  `DOCKERHUB_USERNAME` + `DOCKERHUB_TOKEN` repo secrets.
  [#385](https://github.com/TnsAI-Framework/TnsAI/pull/385)

### Docs
- **Monorepo consolidation link cleanup** (TNS-513). Module READMEs still
  pointed at the deprecated split-repo URLs (`TnsAI.Core`, `TnsAI.LLM`, …)
  retired in the April 2026 consolidation — those repos no longer exist,
  so every reference rendered as a 404 on GitHub. Forty link refs +
  bare-prose mentions across nine module READMEs rewritten to monorepo
  paths (`tnsai-core`, `tree/main/tnsai-X`). External-repo refs
  (`TnsAI.Docs`, `TnsAI.Web`, `TnsAI.Sona`, `TnsAI.Wiki`, `TnsAI.Papers`)
  preserved.
  [#386](https://github.com/TnsAI-Framework/TnsAI/pull/386)
- **`handoff/` vs context-snapshot disambiguation** (TNS-117). Coordination
  module docs clarify the distinction between explicit agent handoffs
  (`HandoffStrategy`) and implicit context snapshots taken on session
  continuation — no behaviour change.
  [#370](https://github.com/TnsAI-Framework/TnsAI/pull/370)

### Internal
- `tnsai-core`: **`PaymentBroker` SPI record test coverage** (TNS-518).
  Three new test files in `com.tnsai.payment` (`SettlementTest`,
  `QuoteTest`, `ServiceTest`) pin every validation invariant on the
  shared SPI records — sealed-variant exhaustiveness on `Settlement`,
  null-checks + blank-string rejection + `priceUSD ≥ 0` +
  `expiresAt > issuedAt` on `Quote`, defensive-copy isolation on
  `Service.metadata`. 31 tests, all green. No production code touched.
  [#376](https://github.com/TnsAI-Framework/TnsAI/pull/376)

### Sister-repo follow-ups
- **TnsAI.Sona** — TNS-507: `tnsai.version` bumped to 0.10.4; TNS-514:
  `logback-test.xml` added so `mvn test` no longer leaks log lines into
  `~/.sona/sona.log` (production `logback.xml` shadowed during tests
  only).
- **TnsAI.Web** — TNS-512: Next 16.2.4 → 16.2.6 + React 19.2.5 → 19.2.6
  to pick up GHSA-8h8q-6873-q5fj RSC DoS patch; TNS-517: non-security
  patch sweep across fumadocs / tailwind / postcss / typescript-eslint /
  lucide-react; framework references bumped 0.10.3 → 0.10.4.
- **TnsAI.Docs** — TNS-515: 45 broken internal links fixed + lychee CI
  gate added to prevent regression; payments documentation
  (`payments/x402.md` + `payments/index.md`) shipped to land alongside
  TNS-449.

### Stats
- 9 PRs landed in the release window (#366, #367, #368, #369, #370,
  #371, #376, #385, #386). The bulk LLM-provider expansion that finishes
  the TNS-322 umbrella was already in `Unreleased` carried from the
  0.10.3 window.

## [0.10.3] - 2026-05-14

Same-week follow-up to 0.10.2 — extends `ProjectTools` with two new
`@Tool` methods aligned to the agents.md spec so agents can route on
structured project context (sections, intro) instead of an opaque blob,
and can bootstrap a fresh `AGENTS.md` from a project's build system.
Purely additive, no public API breakage, no migration required.

### Added
- `tnsai-tools`: **`ProjectTools.agentsmdParse` + `agentsmdGenerate`**
  (TNS-399 axis 1). Two new `@Tool` methods on the `PROJECT_TOOLS`
  toolkit. `agentsmd_parse` returns a structured `AgentsMdContent`
  record (`intro` + ordered `sections: [{level, title, body}]`) parsed
  from `AGENTS.md`, with case-variant + `CLAUDE.md` + `README.md`
  fallback — letting agents route on individual sections (e.g. pull
  just "Setup") rather than treating the document as an opaque blob.
  `agentsmd_generate` produces a draft `AGENTS.md` by detecting the
  build system from `pom.xml` / `package.json` / `pyproject.toml` /
  `Cargo.toml` / `go.mod` and filling in language-appropriate setup +
  test commands — returns the markdown string, the caller decides
  whether to write it.
  [#343](https://github.com/TnsAI-Framework/TnsAI/pull/343)

### Sister-repo follow-ups
- **TnsAI.Sona** — TNS-484: `project_dir` workspace config + `AGENTS.md`
  auto-load consumed `agentsmdParse` to augment role prompts. Shipped
  ahead of this release (Sona builds the framework HEAD in CI).
- **TnsAI.Docs** — PR #133: `agentsmd_parse` + `agentsmd_generate`
  documented in `capabilities/tools/catalog.md` under `PROJECT_TOOLS`.
  PR #345 (this release window): same coverage in framework
  `tnsai-tools/CODEBASE_MAP.md` + `README.md`.

### Stats
- 1 PR landed in the release window for new public surface (#343).
- 4 supporting doc PRs (#339, #340, #341, #342) refreshed module
  CLAUDE.md / README / per-module AGENTS.md without touching public
  API.

## [0.10.2] - 2026-05-13

Same-week follow-up to 0.10.1 — adds a streaming-capable channel adapter mixin (the structural fix the TNS-438 Sona REPL polish workaround had been waiting on) plus a CI workflow defensive fix. Purely additive, no public API breakage, no migration required.

### Added
- `tnsai-channels`: **`StreamingChannelAdapter` mixin interface +
  `UnifiedChunk` record** (TNS-440). Lets adapters opt into
  per-token delivery without changing the existing
  `ChannelAdapter` contract. `UnifiedChunk` carries `conversationId`,
  `delta`, `done` flag, free-form `metadata` (tool-call markers etc.),
  and `timestamp`. The mixin extends `ChannelAdapter` so any
  `StreamingChannelAdapter` is also a regular adapter — gateway code
  can `instanceof`-check and dispatch chunks via `sendChunk(...)` as
  they arrive, then still call `send(UnifiedResponse)` once with the
  assembled reply for non-streaming downstreams (logging, audit).
  Adapters that don't implement the mixin keep working unchanged.
  [#335](https://github.com/TnsAI-Framework/TnsAI/pull/335)
- `tnsai-channels`: **`CliChannel` now implements
  `StreamingChannelAdapter`** with `capabilities().streaming() = true`.
  REPL mode emits the `assistant: ` prefix once on the first chunk
  then concatenates deltas inline; JSON mode emits one
  `{"type":"chunk","content":"...","done":...}` record per chunk.
  The post-stream `send(UnifiedResponse)` is suppressed (the reply
  was already rendered chunk-by-chunk); the `suppressNextSend`
  state resets after one consumption so subsequent standalone
  `send()` calls render normally.
  [#335](https://github.com/TnsAI-Framework/TnsAI/pull/335)

### Fixed
- **CI**: artifact upload steps in `.github/workflows/build.yml` now
  use `continue-on-error: true` and 3-day retention (down from 7).
  Previously, a GitHub Actions free-tier storage quota hit would mark
  the whole `Build & Test` job red even though `mvn verify` had passed.
  Soft-fail makes the build status reflect code health, not artifact
  store availability. [#336](https://github.com/TnsAI-Framework/TnsAI/pull/336)
- **Release tooling**: `make release` CHANGELOG extract now uses
  literal-substring match instead of regex, so versions with `.`
  (every version) extract correctly (TNS-419). [#324](https://github.com/TnsAI-Framework/TnsAI/pull/324)

### Stats
- `tnsai-channels`: 128 → 146 tests (+18). 2 new public types
  (`StreamingChannelAdapter`, `UnifiedChunk`).
- 3 PRs (#324, #335, #336). 1 additive feature + 2 infra fixes.
- Reactor `mvn verify` 13/13 PASS.

### Sister-repo follow-ups (tracked in Linear)
- **TnsAI.Sona** — TNS-458: replace the `CliChannel`-bypass scaffolding
  in `runChat` with the new mixin so `sona chat --json` also streams.

## [0.10.1] - 2026-05-08

Same-day follow-up to 0.10.0 — purely additive observability + evaluation surface and a second channel adapter. No public API breakage, no migration required. Released under the `0.x` patch policy (`0.X.Y → 0.X.Y+1` for additive + bugfix + chore).

### Added
- `tnsai-channels`: **CLI channel adapter** (`com.tnsai.channels.cli`) — second `ChannelAdapter` after Telegram. Two modes: REPL (interactive `> ` prompt with `/exit` `/quit` `/clear` local slash commands intercepted, everything else flows to the gateway as a `UnifiedMessage`) and JSON (newline-delimited `{"text":"..."}` in / `{"type":"text","content":"..."}` out for scripting). SPI-discoverable via `META-INF/services`, mode selected from a config string (`"json"` case-insensitive → JSON, default REPL). Closes TNS-353 Phase 1+2. [#317](https://github.com/TnsAI-Framework/TnsAI/pull/317), [#318](https://github.com/TnsAI-Framework/TnsAI/pull/318)
- `tnsai-quality`: **OTLP-native LLMCallLog exporter** (`OtlpLLMCallExporter implements LLMCallPublisher`) — every captured `LLMCallLog` now flows to any OpenTelemetry collector (Langfuse, LangWatch, Phoenix, Honeycomb, Tempo, Loki) via the [GenAI semconv](https://opentelemetry.io/docs/specs/semconv/gen-ai/) wire shape. One `CLIENT` span per call (`chat <model>` / `chat_stream <model>`), three metrics (`gen_ai.client.token.usage` long counter partitioned by `gen_ai.token.type`, `gen_ai.client.cost.usd` + `gen_ai.client.operation.duration` histograms). Cardinality discipline: 7 ctx fields on the span, only `tenant + role` on the metric (others would explode the metric series). Retroactive timing — `setStartTimestamp(call.startedAt())` + `span.end(call.completedAt())` so dashboard duration matches captured elapsed even for post-hoc spans. Closes TNS-374. [#319](https://github.com/TnsAI-Framework/TnsAI/pull/319)
- `tnsai-quality`: **Sampling + Redacting decorators for `LLMCallPublisher`** — companion to the existing `Sampling*` / `Redacting*Publisher` pair on `AgentEventPublisher`. `SamplingLLMCallPublisher` reuses the `EventSamplingPolicy` SPI by mapping each `LLMCallLog` into a `SamplingInput` (eventKind `"llm.called"`, level `ERROR` when `isFailure()` else `INFO` so `ErrorAlwaysPolicy` passes failures regardless of nominal sample rate). `RedactingLLMCallPublisher` scrubs every leaky surface: `prompt.systemPrompt` / `prompt.messages` / `prompt.parameters` (`LLM_PROMPT` scope) and `response.content` / `response.toolCalls[].arguments` / `response.reasoningContent` / `error.errorMessage` / `providerExtensions` (`LLM_RESPONSE` scope). Tool names pass through (framework metadata). Composition: `new SamplingLLMCallPublisher(new RedactingLLMCallPublisher(otlp, redactor), policy)` — redaction inside, sampling outside, so dropped events skip the redactor cost. Closes TNS-417, completes TNS-374 acceptance #3. [#321](https://github.com/TnsAI-Framework/TnsAI/pull/321)
- `tnsai-evaluation`: **Agent-tier evaluators** (`com.tnsai.evaluation.evaluators.agent`) — four new metrics that score the *trace* rather than just the final response. `PlanAdherenceEvaluator` (LLM judge, "did you stick to the declared plan?"), `StepEfficiencyEvaluator` (deterministic, `expected / max(expected, actual)`, "did you reach the goal without burning extra tool calls?"), `ToolCorrectnessEvaluator` (LLM judge, "were the right tools picked?"), `TaskCompletionEvaluator` (LLM judge, "did the final response solve the task?"). Shared package-private `JudgeScoreParser` mirrors `GEvalEvaluator.extractScore` generalised to any `[min, max]` range; returns `-1` on no match so callers fail explicitly instead of guessing a middle value. Closes TNS-373. [#320](https://github.com/TnsAI-Framework/TnsAI/pull/320)

### Fixed
- `tnsai-quality`: typo in two test method names — `nullCallProapagates` → `nullCallPropagates`. Cosmetic, JUnit method-name agnostic. [#322](https://github.com/TnsAI-Framework/TnsAI/pull/322)
- `tnsai-channels`: stale `@since 0.9.4` on `CliChannel` → `@since 0.10.1`. Author wrote the tag pre-0.10.0 cut; next release after 0.10.0 is 0.10.1. [#318](https://github.com/TnsAI-Framework/TnsAI/pull/318)
- `tnsai-evaluation` + `tnsai-quality`: 8 stale `@since 0.10.2` Javadoc tags → `@since 0.10.1` (anticipated wrong version on TNS-373 and TNS-417). Cosmetic, no API change.

### Stats
- 6 PRs (#317 → #322), purely additive — no BREAKING items, no `Changed`, no `Removed`.
- Reactor `mvn verify` 13/13 PASS — quality 1357 → ~1357, channels 106 → 128, evaluation 277 → 322. Test count up from 10357 → 10458 (+101).
- Module dep graph delta: `tnsai-quality → tnsai-llm` (new, TNS-374) — one-way edge, no cycle (tnsai-llm only depends on tnsai-core).

PR: [release: 0.10.0 → 0.10.1](https://github.com/TnsAI-Framework/TnsAI/pull/323)

## [0.10.0] - 2026-05-08

The reliability + safety platform release. Five new capability layers land together — durable idempotency stores, checkpoint/resume primitives, agent identity + accountability + payment SPIs, on-demand modular knowledge (skills), and unified sandbox execution — alongside framework-wide cost governance (rate-limit + budget hooks at the LLM boundary) and server-side hardening (five-layer security). Two BREAKING changes drive the minor bump per the `0.x breaking → minor` policy: accountability wiring is now explicit (no silent no-op fallback in `AgentBuilder`), and `logback-classic` moves to test-scope only (consumers choose their own SLF4J binding). Process improvements ship in the same window: a nightly reactor build catches time-bomb tests + cross-module drift before consumers hit them, and the PR template enforces reactor verification when public surfaces change.

### Added
- `tnsai-quality`: idempotency keys with pluggable persistence — Redis (Lettuce) and Postgres (JDBC) `IdempotencyStore` implementations, MCP `idempotentHint` flag wired through tool-call routing. Closes TNS-224. [#301](https://github.com/TnsAI-Framework/TnsAI/pull/301)
- `tnsai-quality`: rate-limit + budget hooks at the LLM call boundary — token-bucket rate limiter, USD spend budget tracker, configurable per-agent / per-tenant. Closes TNS-210. [#302](https://github.com/TnsAI-Framework/TnsAI/pull/302)
- `tnsai-core`: checkpoint + resume + idempotent retry primitives — `CheckpointStore` SPI, in-memory default, automatic snapshot on agent state transitions, replay-safe retry. Closes TNS-299. [#303](https://github.com/TnsAI-Framework/TnsAI/pull/303)
- `tnsai-quality`: durable `CheckpointStore` implementations — Redis (Lettuce) for fast volatile checkpoints, S3 (AWS SDK v2) for cold long-term snapshots. Closes TNS-312. [#304](https://github.com/TnsAI-Framework/TnsAI/pull/304)
- `tnsai-core`: agent identity + accountability + payment SPIs — `AgentIdentity` (DID + cryptographic key), `AccountabilityLog` (signed event chain), `PaymentRail` (x402 / settlement abstraction). Closes TNS-298. [#305](https://github.com/TnsAI-Framework/TnsAI/pull/305)
- `tnsai-core`: on-demand modular knowledge layer — `@Skill` annotation, `SkillActivationEvent` (added to `TnsAIEvent` sealed hierarchy), lazy skill loading via SPI, runtime skill discovery. Closes TNS-289. [#307](https://github.com/TnsAI-Framework/TnsAI/pull/307)
- `tnsai-quality`: unified file/doc guardrails — sandbox execution + size limits + extension whitelist, applied uniformly to file-write and document-export tools. Closes TNS-342. [#308](https://github.com/TnsAI-Framework/TnsAI/pull/308)
- `tnsai-quality`: `Sandbox` SPI — isolated execution primitive (process / container / WASM strategies), pluggable resource limits. Closes TNS-296. [#309](https://github.com/TnsAI-Framework/TnsAI/pull/309)
- `tnsai-quality`: code review pipeline harness — pluggable, idempotent, deepsec pattern; routes proposed code changes through configurable checks before commit. Closes TNS-291. [#312](https://github.com/TnsAI-Framework/TnsAI/pull/312)
- `tnsai-server`: five-layer security hardening — auth, rate-limit, input validation, output redaction, audit log on every request. Closes TNS-302. [#313](https://github.com/TnsAI-Framework/TnsAI/pull/313)
- CI: nightly reactor build (`.github/workflows/nightly-reactor.yml`) — `mvn verify` on `main` daily at 06:00 UTC, surfaces time-bomb tests and cross-module compile drift before consumers hit them. [#314](https://github.com/TnsAI-Framework/TnsAI/pull/314)
- Process: `PULL_REQUEST_TEMPLATE.md` with mandatory reactor-build checkbox when Protected Changes / sealed-type permits are touched, plus Sister-PR section linking Docs / Wiki / Sona. [#314](https://github.com/TnsAI-Framework/TnsAI/pull/314)

### Changed
- **BREAKING:** `tnsai-core`: `AgentBuilder.accountability(...)` is now mandatory when `@Accountable` is on the agent — the silent no-op shim is removed. Builder fails fast with a configuration error if accountability isn't wired. Closes TNS-298 follow-up. [#306](https://github.com/TnsAI-Framework/TnsAI/pull/306)
- `tnsai-tools`: Python and JavaScript execution tools migrated from per-tool isolation to the shared `Sandbox` SPI — single hardening surface, consistent resource limits across languages. Closes TNS-343. [#311](https://github.com/TnsAI-Framework/TnsAI/pull/311)

### Removed
- **BREAKING:** `tnsai-core`: `logback-classic` moved from compile-scope to test-scope only. Consumers now pick their own SLF4J binding (logback / log4j2 / slf4j-simple) — no transitive logback pulled into application classpaths. Closes TNS-309. [#315](https://github.com/TnsAI-Framework/TnsAI/pull/315)
- `tnsai-core`: no-op accountability fallback shims (`NoOpAccountabilityLog`, `NoOpPaymentRail`) — replaced by explicit-wire failure mode. [#306](https://github.com/TnsAI-Framework/TnsAI/pull/306)

### Fixed
- `tnsai-intelligence`: `ContradictionDetector` time-bomb fixed — `Clock` is now injected so tests can pin time; the `FUTURE = NOW + 30d` constant no longer leaks wall-clock dependency into CI. Closes TNS-341 (CI broke 2026-05-07T12:00 UTC when the original constant expired). [#310](https://github.com/TnsAI-Framework/TnsAI/pull/310)

### Migration

**Accountability (TNS-298 follow-up):** if your agent declares `@Accountable`, wire the SPI explicitly in the builder:

```java
AgentBuilder.create()
    .accountability(new MyAccountabilityLog())  // required — no implicit fallback
    .build();
```

If you don't need accountability, drop the `@Accountable` annotation. Builder will fail at build time with a clear error message if the annotation is present but no log is wired.

**Logback (TNS-309):** add an SLF4J binding to your application's runtime classpath. Logback consumers add it explicitly:

```xml
<dependency>
    <groupId>ch.qos.logback</groupId>
    <artifactId>logback-classic</artifactId>
    <version>1.5.13</version>
    <scope>runtime</scope>
</dependency>
```

Or pick `log4j2-slf4j2-impl` / `slf4j-simple` if your stack uses those. The framework no longer assumes a binding.

### Stats
- 15 PRs (#301 → #315), 5 new capability layers, +24449 / −629 LOC across 265 files in 13 modules.
- Reactor `mvn verify` 13/13 PASS — 0 failures, 0 errors.
- BREAKING change count: 2 (TNS-298 accountability strict wiring, TNS-309 logback test-scope).
- Process: nightly reactor + PR template land in this release; first nightly run scheduled 2026-05-09 06:00 UTC.

PR: [release: 0.9.3 → 0.10.0](https://github.com/TnsAI-Framework/TnsAI/pull/316)

## [0.9.3] - 2026-05-06

Closes the `@ToolExample` silent-drop chain across all LLM providers — examples now reach the model on Anthropic (`input_examples` field), OpenAI / Gemini / 8 OpenAI-passthrough providers (description fold with `EXAMPLES:` / `AVOID:` sections), Bedrock-Claude (via the extracted Anthropic converter), and Cohere (flat parameter shape + fold). System-prompt prose additionally renders examples under `## Available Actions` so the model sees them when reasoning about a role overall, not only at tool-call time. Constraint rendering format also unified across the three prompt builders: positives-first, header-once (`Must always:` / `Must never:` blocks rather than repeated `- Must always:` / `- Must never:` prefix per rule). Purely additive — no API breakage, no migration required.

### Added
- `tnsai-llm`: Bedrock (Claude 3 family) and Cohere now support `@ToolExample` end-to-end. Bedrock routes via the extracted `AnthropicToolConverter`; Cohere has its own `CohereToolConverter` (JSON-Schema → flat `parameter_definitions` shape, types translated `string→str` / `integer→int` / `number→float` / `boolean→bool` / `array→list` / `object→dict`). [#297](https://github.com/TnsAI-Framework/TnsAI/pull/297)
- `tnsai-core`: `@ToolExample` now renders in the system-prompt prose under each action's `## Available Actions` block. Positive examples appear under `Examples:`, negatives under `Avoid (anti-patterns):`. Wired through both `RolePromptBuilder` (in-process role) and `SystemPromptBuilder` (SCOP bridge). [#299](https://github.com/TnsAI-Framework/TnsAI/pull/299)
- `tnsai-core`: `ActionMetadata.getExamples()` accessor — returns the combined positive + negative example list in declaration order. [#299](https://github.com/TnsAI-Framework/TnsAI/pull/299)
- `tnsai-core`: `com.tnsai.prompt.format.PromptFormat` — shared formatter for prompt-building call sites. `renderConstraints` (`mustAlways` / `mustNever`) and `renderExamples` (`@ToolExample`). Used by `RolePromptBuilder`, `RoleSpecReader`, and `SystemPromptBuilder` (SCOP). [#298](https://github.com/TnsAI-Framework/TnsAI/pull/298), [#299](https://github.com/TnsAI-Framework/TnsAI/pull/299)

### Changed
- `tnsai-core` / `tnsai-integration`: constraint block rendering switched from per-bullet repeated prefix (`- Must always: <rule>` / `- Must never: <rule>`) to header-once (`Must always:` / `Must never:` block headers with indented bullets). Positives now render before negatives. User-observable in generated system prompts; the format change drops ~200 prompt tokens per typical 5-action × 4-rule role. The negatives-first ordering of 0.9.2 is gone — positives set the tone, negatives draw the boundary. [#298](https://github.com/TnsAI-Framework/TnsAI/pull/298)

### Fixed
- `tnsai-llm`: `@ToolExample` annotations on tool methods are no longer silently dropped on the Anthropic provider. Positives are mapped into the native `input_examples` field on each tool definition; negatives are folded into the tool description as an `AVOID:` section (Anthropic's tool API has no first-class anti-pattern field). [#291](https://github.com/TnsAI-Framework/TnsAI/pull/291)
- `tnsai-llm`: `@ToolExample` annotations no longer silently dropped on OpenAI and Gemini. Both providers receive examples folded into the function description as `EXAMPLES:` (positives) and `AVOID:` (negatives) sections — neither provider has a native examples API. [#292](https://github.com/TnsAI-Framework/TnsAI/pull/292)
- `tnsai-llm`: `@ToolExample` annotations no longer silently dropped on the 8 OpenAI-passthrough providers (Mistral, Groq, OpenRouter, Ollama, HuggingFace, Azure OpenAI, MiniMax, ZhipuAI — both chat and multimodal sites). Same description-fold strategy as OpenAI/Gemini. [#296](https://github.com/TnsAI-Framework/TnsAI/pull/296)

### Documentation
- `@ToolExample` Javadoc now cites the Anthropic source for the "72% → 90% accuracy on complex parameter handling" claim ([Introducing advanced tool use](https://www.anthropic.com/engineering/advanced-tool-use)) instead of a bare assertion. Also fixes outdated `@since` tag (`2.14.0` template-artefact → `0.3.0`) and a broken `@see Action` cross-reference (now `@see ActionSpec`). [#295](https://github.com/TnsAI-Framework/TnsAI/pull/295)
- `tnsai-core/README.md`: annotation count refreshed `100+` → `98` (verified via `grep -rh "public @interface"`) on both prose and feature-table sites. [#293](https://github.com/TnsAI-Framework/TnsAI/pull/293)
- 8 module READMEs (core, llm, mcp, tools, intelligence, coordination, integration, quality): per-module `LICENSE` link now correctly resolves to the monorepo-root `LICENSE` file (`(LICENSE)` → `(../LICENSE)`) — submodules don't carry their own LICENSE files post-monorepo. [#294](https://github.com/TnsAI-Framework/TnsAI/pull/294)

### Stats
- 8 PRs, ~1900 lines added across `tnsai-llm` (Anthropic / Bedrock / Cohere / 8 passthrough converters), `tnsai-core` (PromptFormat helper), and Javadoc / README polish.
- 44+ new test cases — `ToolExampleConverterTest` (52 cases for Anthropic + OpenAI/Gemini fold), `AnthropicToolConverterTest` (8 cases), `CohereToolConverterTest` (9 cases), `PromptFormatTest` (16 cases — 10 constraints + 6 examples).
- All 13 modules build green (`mvn verify` reactor 13/13 PASS); no behavioural regressions.

## [0.9.2] - 2026-05-06

Removes `@ActionSpec.invariants[]` — the vestigial third bucket that consistently became a misuse magnet for behavioral rules belonging in `mustAlways` / `mustNever`. Three buckets where two had clear semantic homes turned the third into a dumping ground; rules wound up double-rendered in system prompts (token waste + LLM ambiguity over which list to honour). With this cut the framework's per-action constraint surface collapses to two intents — *do* (`mustAlways`) and *don't* (`mustNever`) — plus the orthogonal Hoare-triple pair (`precondition` / `postcondition`) and state-level `@State.invariants`. Net delta: 12 files, +32 / -233 lines. BREAKING — released as a **patch** under user pragma rather than the strict `0.x breaking → minor` rule (parent CLAUDE.md), given the removed field's low real-world usage and the trivial mechanical migration (drop the annotation parameter or move its strings into `mustAlways` / `mustNever`).

### Removed
- **BREAKING:** `@com.tnsai.annotations.ActionSpec.invariants()` — the `String[]` field is gone. Move strings to `mustAlways` (positive obligations) or `mustNever` (negative prohibitions).
- **BREAKING:** `ActionMetadata.invariants` field + `hasInvariants()` + `getInvariants()` accessors.
- **BREAKING:** `ContractConfig.invariants` field — record arity drops 6 → 5.
- **BREAKING:** `ActionExecutor` before/after method-level invariants check; only `checkPrecondition` / `checkPostcondition` / `checkStateInvariants` remain on the action lifecycle.
- **BREAKING:** `InvariantCheckerHandle.checkActionInvariants(Method)` SPI method.
- **BREAKING:** `InvariantChecker.checkActionInvariants(Method)` impl + references in `checkBeforeAction` / `checkAfterAction` / `collectViolations`.
- **BREAKING:** `RoleSpecReader.setInvariants(...)` / `getInvariants()` + private field.
- **BREAKING:** `RoleSpecExtractor.ResponsibilityInfo.invariants()` record component + extraction line.
- **BREAKING:** `SystemPromptBuilder.appendActionsSection` "Invariant: ..." render block (the 5 lines added in 0.9.0 / PR #284 — that addition surfaced the misuse magnet, this removal closes it).
- Tests covering removed paths.

### Kept (different concerns, valid use)
- `@State.invariants` — Gaia state predicates evaluated by `InvariantChecker.checkStateInvariants()` after any state change.
- `@ActionSpec.precondition` / `postcondition` — Hoare-triple Method contracts; still wired through `ActionExecutor`.
- `@ActionSpec.fulfills` / `effects` — planning subsystem coordinates.
- `@Contract.invariants` — different annotation, contract-by-design layer.

### Migration

| Concern | Use this field |
|---|---|
| Behavioral obligation ("agent must …") | `@ActionSpec.mustAlways` |
| Behavioral prohibition ("agent must never …") | `@ActionSpec.mustNever` |
| State predicate (field-level invariant) | `@State.invariants` |
| Method postcondition | `@ActionSpec.postcondition` |
| Method precondition | `@ActionSpec.precondition` |

Mechanical sweep: `grep -r "@ActionSpec(.*invariants\s*=" .` should return 0 hits after migration.

### Versioning note
Strict rule per parent `CLAUDE.md` (`0.x breaking → minor`) would have called for 0.10.0; user pragma chose 0.9.2 patch given the removed field's low real-world usage and the trivial migration. Future BREAKING removals will revert to the strict rule unless explicitly noted.

### Stats
12 files changed · +32 / -233 lines · `@ActionSpec.invariants` consumer references in framework: 0 (sweep verified).

PR: [#289](https://github.com/TnsAI-Framework/TnsAI/pull/289).

## [0.9.1] - 2026-05-06

Re-cut of 0.9.0 (which was rejected by Sonatype Central Portal as
"component already exists" — earlier release.yml attempts had partially
staged 0.9.0 artifacts that couldn't be cleared without manual portal
UI work). Identical content to the originally-planned 0.9.0; version
bumped to 0.9.1 as the cleanest forward path.

PR: [#release-recovery](https://github.com/TnsAI-Framework/TnsAI/commits/main).

## [0.9.0] - 2026-05-06 (UNRELEASED — superseded by 0.9.1)

Consolidates three overlapping abstractions named "Responsibility" into per-action constraints on `@ActionSpec`. Every safety constraint now lives on the action it applies to — the action method becomes the natural locus for traceability, audit, and rendering. Action-attributed constraints flow into the system prompt as bullets under each action; downstream exporters (JSON / YAML / Jason) emit them with action attribution. BREAKING — bumps minor because the `@Responsibility` / `@Responsibilities` annotations, the `Responsibility` model interface, `Role.getResponsibilities()`, and `RoleBuilder.responsibility(...)` / `mustNever(...)` / `mustAlways(...)` are removed. Also lands LLM call granular capture (issue #79 phase 1) — typed `LLMCallLog` events with USD cost + stream metrics + `EventContext` attribution — so consumers can route per-call observability into LangFuse / Helicone / Phoenix or custom cost trackers.

### Added
- **`@ActionSpec.mustNever()` / `mustAlways()`** — `String[]` arrays declaring per-action safety constraints. Rendered into the system prompt as `- Must never: ...` / `- Must always: ...` bullets under each action's signature, alongside its description and type marker. Closes [#283](https://github.com/TnsAI-Framework/TnsAI/issues/283).
- **`ActionMetadata.getMustNever()` / `getMustAlways()` + `hasMustNever()` / `hasMustAlways()`** — runtime accessors for the new constraints; consumed by `RolePromptBuilder`, `RoleSpecExtractor.extractResponsibilitiesFromActions`, and the SCOP `SystemPromptBuilder`.
- **`RoleSpecExtractor.ResponsibilityInfo.mustNever()` / `mustAlways()`** — extra fields on the auto-extracted record so exporters can render constraints with action attribution (`actionName: constraint`).
- **LLM call granular capture — Phase 1 (#79)** — typed `LLMCallLog` events emitted per LLM invocation, decoupled from the legacy raw-string `LLMObserver`. Wires the `LLMCallLog` record (already shipped as data-shape-only in 0.5.0) into a working publish path so consumers can route prompt + response + token usage + USD cost + stream metrics + `EventContext` into LangFuse / Helicone / Phoenix dashboards or custom cost trackers.
  - **`LLMCallPublisher`** (SPI) — single-method `publish(LLMCallLog)`; `NOOP` is the framework default.
  - **`Slf4jLLMCallPublisher`** — default opt-in publisher; one structured SLF4J line per call (no raw prompt/response text — that is intentionally behind redaction SPI #80).
  - **`CapturingLLMClient`** — decorator wrapping any `LLMClient`. Captures success and failure paths; for streaming, captures TTFT + chunk count. Multimodal `chat(List<ContentPart>)` and `streamChatWithSpec` route through the delegate without capture in this PR — separate hot-path refactor.
  - **`JsonLLMPricingRegistry`** — loads rate cards from classpath JSON. Ships `/pricing/2026-05.json` with rates for `openai/gpt-4o`, `openai/gpt-4o-mini`, `openai/o1-preview`, `anthropic/claude-sonnet-4`, `anthropic/claude-opus-4`, and an `ollama/*` wildcard (zero — local). Other 7 framework providers' rate cards land incrementally.
  - **`ToolSurfaceHasher`** — single canonical entry point that turns the framework's raw `List<Map<String,Object>>` tool shape into a `ToolSurface` with a stable SHA-256 hash. Sorted-key Jackson serialisation so identical tool sets across calls correlate (prompt-cache friendly).
  - **`(provider, model)` cost attribution via `LLMCallLog.context()`** — every captured event carries the active `EventContext` (tenant / agent / role / capability / session / group), so consumers downstream can attribute USD cost along any dimension already wired by issue #78.
- **18 new tests** covering CapturingLLMClient (success / error / streaming / cost / context paths) + JsonLLMPricingRegistry (default registry + wildcard + missing-resource).
- **LLM rate cards — Phase 2 (#79)** — `pricing/2026-05.json` expanded from 3 providers / 6 models to 7 providers / 13 models, so the `cost` field on `LLMCallLog` is populated for the major providers a TnsAI agent is likely to be wired against. Adds: `anthropic/claude-haiku-4-5`, `google/gemini-2.0-flash`, `google/gemini-2.0-pro`, `mistral/mistral-large`, `mistral/mistral-small`, `groq/llama-3.3-70b-versatile`, `groq/mixtral-8x7b-32768`, `cohere/command-r-plus`, `cohere/command-r`. Providers without prompt caching (`groq`, `cohere`) declare `cached_per_1k: null`. 10 new tests (1099 total in `tnsai-llm`).

### Removed
- **BREAKING:** `@com.tnsai.annotations.Responsibility` annotation (used inside `@RoleSpec.responsibilities`).
- **BREAKING:** `@com.tnsai.roles.annotations.Responsibilities` annotation + nested `Duty`, `SafetyConstraint`, `Severity` types.
- **BREAKING:** `com.tnsai.models.role.Responsibility` model interface + `CoreDuty` / `SafetyProperty` / `Responsibilities` (container) implementations.
- **BREAKING:** `com.tnsai.enums.role.SafetyType` enum (only consumed by deleted classes).
- **BREAKING:** `Role.getResponsibilities()` abstract template method + `Role.responsibilities()` accessor + `Role.getMustNeverConstraints()` / `getMustAlwaysConstraints()`.
- **BREAKING:** `RoleBuilder.responsibility(...)`, `responsibilities(...)`, `duty(...)`, `mustNever(...)`, `mustAlways(...)` — fluent API surface for role-level safety constraints. Use `@ActionSpec` annotations on a `Role` subclass instead.
- **BREAKING:** `@RoleSpec.responsibilities()` field.
- **BREAKING:** `RolePromptBuilder.generateResponsibilitiesSection(...)`, the second `buildMinimalRolePrompt(identity, responsibilities)` overload (only identity is needed now).
- **BREAKING:** `RoleSpecExtractor.extractResponsibilities(...)` / `hasResponsibilitiesAnnotation(...)`. Use `extractResponsibilitiesFromActions(...)` for the action-bound replacement.
- **BREAKING:** `RoleSpecReader.RoleSpec.ResponsibilityMeta` + `getResponsibilities()` / `setResponsibilities(...)`.
- **BREAKING:** `ExportedRole.responsibilities` field + `hasResponsibilities()`. `autoResponsibilities` (per-action) is now the single source.

### Changed
- **`RolePromptBuilder` rendering** — the `## Responsibilities` markdown block is gone. Per-action `Must never:` / `Must always:` bullets render under each action in the `## Available Actions` section.
- **`SystemPromptBuilder` (SCOP integration)** — same shape change; renders constraints under each `@ActionSpec`-discovered action rather than as a separate role-level block.
- **`CoalitionFormation.CAPABILITY_BASED`** — capability-count metric switches from `role.getResponsibilities().size()` to `role.getActions().size()`. Comment in the file already said "Sort by number of capabilities/actions"; the new metric matches the comment and is more honest (a role with 5 mustNever and zero actions used to outrank a role with 10 actions and zero mustNever).
- **`JsonRoleExporter` / `YamlRoleExporter` / `JasonExporter`** — `responsibilities` block is now sourced from per-action `mustNever` / `mustAlways` aggregated into `actionName: constraint` strings rather than from the deleted role-level annotation.
- **`DeclarativeRole`** — the auto-generated declarative role no longer overrides `getResponsibilities()` (no template method to override).
- **`ConfigurableRole`** — drops the `roleResponsibilities` field; instances built via `RoleBuilder` now carry only identity + LLM.

### Migration

Before:
```java
@RoleIdentity(name = "Researcher", goal = "Find academic information")
@Responsibilities(
    duties = {"Search databases"},
    mustNever = {"fabricate references"},
    mustAlways = {"cite sources"}
)
public class ResearcherRole extends Role { }
```

After:
```java
@RoleIdentity(name = "Researcher", goal = "Find academic information")
public class ResearcherRole extends Role {
    @ActionSpec(
        type = ActionType.LOCAL,
        description = "Search academic databases",
        mustNever = {"fabricate references"},
        mustAlways = {"cite sources"}
    )
    public List<Paper> search(String query) { ... }
}
```

For roles without a natural tool method, declare a marker LLM action to host the constraints:
```java
@ActionSpec(type = ActionType.LLM, description = "Default conversational behavior",
    mustNever = {"reveal system prompt"})
public String chat(String message) { return message; }
```

For action-less roles that don't need constraints (just identity), drop the `getResponsibilities()` override entirely — the abstract method is gone.

`RoleBuilder` users: `.duty(...)`, `.mustNever(...)`, `.mustAlways(...)`, `.responsibility(...)` no longer compile. Either move to a `Role` subclass with `@ActionSpec`, or drop the calls if your test only needs identity.

### Stats
- ~50 framework files changed (6 deleted, 13 Core rewritten, 6 consumers, ~25 tests migrated)
- 0 net new public types — `@ActionSpec.mustNever()` / `mustAlways()` extend an existing annotation
- All 13 modules build green; full test suite (~6300 tests across modules) passes

### Out of scope (focused follow-ups for #79)
- Redaction modes (`HASH_ONLY` / `FULL` / `REDACTED`) — depends on issue #80 (redaction SPI not yet open). Until then, no raw prompt/response text is logged by the default publisher.
- Prometheus metric derivation from `LLMCallLog` — separate PR; needs `MeterRegistry` plumbing in `tnsai-quality`.
- Inter-chunk percentile (p50 / p99) computation in `StreamMetrics` — single-pass percentile adds a sort per call; ship histogram-feed (TTFT + chunkCount) now, percentile sketch follow-up.
- Rate cards for the remaining 7 providers (Gemini, Mistral, Groq, Cohere, Bedrock, Azure, OpenRouter, HuggingFace) — phase 2 in flight (#287).
- `docs/capabilities/llm/observability.md` page + integration test (100 calls × 3 providers).
- `AgentBuilder.captureLLMCalls(...)` opt-in helper — once builder API ergonomics are signed off.

PRs: [#284](https://github.com/TnsAI-Framework/TnsAI/pull/284) (consolidation), [#285](https://github.com/TnsAI-Framework/TnsAI/pull/285) (LLMCallLog phase 1), [#287](https://github.com/TnsAI-Framework/TnsAI/pull/287) (LLMCallLog phase 2 — rate cards)

## [0.8.6] - 2026-05-04

#85 epic complete: all four spinoff validators (V004 / V006 / V011 / V012)
have shipped, so every validator from the original #85 design is now in the
pipeline. Idempotency story moves from primitives-only (PR #108) to a
working tool-call dedup loop with HTTP `Idempotency-Key` injection on the
WEB_SERVICE path. Release CI gains three independent gates from #203
(preflight, dry-deploy validation, sister-repo drift check) so half-bumped
releases can no longer reach Maven Central. Backward-compatible, purely
additive.

### Added
- **AGENT-V004 declarative capability check** — `@RoleSpec(requires = {LLMCapability.STREAMING, LLMCapability.VISION, ...})` field surfaces when a configured LLM doesn't support a capability the role declares. Severity `WARNING` (soft-launch). New `com.tnsai.enums.LLMCapability` enum (`STREAMING` / `STRUCTURED_OUTPUT` / `VISION`; `FUNCTION_CALLING` intentionally NOT here — already covered by V003 via tool-presence). Co-located with V003 in `LLMCapabilityValidator` (shared introspection prologue, independent emit). Closes [#238](https://github.com/TnsAI-Framework/TnsAI/issues/238). PR: [#246](https://github.com/TnsAI-Framework/TnsAI/pull/246).
- **AGENT-V011 MCP server reachability check** — opt-in (`withReachabilityChecks(true)`) reachability probe for every `@MCPTool(serverUrl = ...)` URL referenced by an agent's actions. Runs the actual MCP `initialize` handshake at build time. New `com.tnsai.spi.McpClientFactory` SPI in `tnsai-core` + `DefaultMcpClientFactory` adapter in `tnsai-mcp` (auto-discovered via `META-INF/services`). Validator graceful-degrades when the adapter is absent. Closes [#237](https://github.com/TnsAI-Framework/TnsAI/issues/237). PRs: [#244](https://github.com/TnsAI-Framework/TnsAI/pull/244), [#252](https://github.com/TnsAI-Framework/TnsAI/pull/252).
- **AGENT-V012 tenant scope validator (Phase 1 + 2)** — `AgentBuilder.tenantId(String)` declares per-tenant agent scope; `com.tnsai.spi.TenantAware` is a marker SPI consumers' `MemoryStore` implementations opt into to advertise tenant safety. `TenantScopeValidator` fires `WARNING` when `tenantId` is set but the wired store is not `TenantAware` (or is the build-time default `InMemoryStore`). Suppressible via `.relaxValidation("AGENT-V012")` (e.g. for one-process-per-tenant deployments). Closes [#235](https://github.com/TnsAI-Framework/TnsAI/issues/235). Runtime `TenantContext` propagation, tool-side hook, and audit-event tenant id are intentionally split into separate follow-up issues (Phase 3+4 — protected-API territory). PRs: [#248](https://github.com/TnsAI-Framework/TnsAI/pull/248), [#250](https://github.com/TnsAI-Framework/TnsAI/pull/250).
- **`@Idempotent` wired into `ToolMethodDispatcher`** — the primitives shipped in [PR #108](https://github.com/TnsAI-Framework/TnsAI/pull/108) (annotation, SPI, key derivation, in-memory store, exception) now have an active call site. New `IdempotencyResolver` orchestrator handles all four `KeyStrategy` values + all three `RetryBehavior` policies + failure caching opt-in + store unavailability. New `IdempotencyKeySupplier` opt-in interface for `KeyStrategy.EXPLICIT`. Tools without `@Idempotent` bypass the resolver entirely (zero overhead, per-`Method` cache for reflection-free dispatch). PR: [#251](https://github.com/TnsAI-Framework/TnsAI/pull/251).
- **`Idempotency-Key` HTTP header injection on `WEB_SERVICE` actions** — `WebServiceExecutor` injects the same key the resolver uses internally onto outgoing `POST` / `PUT` / `PATCH` / `DELETE` requests when the action is `@Idempotent`. Stripe / SendGrid / Twilio / GitHub upstream-side dedup activates alongside the framework's client-side cache. Safe methods (`GET` / `HEAD` / `OPTIONS`) get the cache but not the header. `IdempotencyResolver.deriveKey` promoted to `public static` so the header path and the cache path land on the same key (EXPLICIT suppliers must be deterministic — called twice per call). PR: [#253](https://github.com/TnsAI-Framework/TnsAI/pull/253).
- **Release CI hardening (3 of 7 #203 items)** — \`release.yml\` now runs three independent gates before the Maven Central deploy step:
  - **Preflight** ([#254](https://github.com/TnsAI-Framework/TnsAI/pull/254), #203 item 2) — same `scripts/release-preflight.sh` `make preflight VERSION=X.Y.Z` runs locally; verifies tag exists, root pom version matches tag, all 13 poms lockstep, CHANGELOG section header present, no duplicate tag at the same commit.
  - **Dry-deploy validation** ([#255](https://github.com/TnsAI-Framework/TnsAI/pull/255), #203 item 4) — `mvn deploy -P release` to a local file repo + per-module artifact validation (poms + jars + sources + javadocs + signatures) before the immutable Central deploy.
  - **Sister-repo drift check** ([#256](https://github.com/TnsAI-Framework/TnsAI/pull/256), #203 item 7) — clones TnsAI.Docs / TnsAI.Web / TnsAI.Sona / TnsAI.Wiki and grep-scans for stale-version markers; surfaces "shipped 0.8.6 but TnsAI.Web still says 0.8.5" before the release advances.

### Changed
- `tnsai-core/agent_docs/validation.md` — "Spinoffs" section narrowed to "Resolved spinoffs" (all four originally-spun-off validators now ship); shipped-validators table goes from 9 to 12 entries; `LLMCapabilityValidator` row clarified "(FC head)" / "(declared head)" to disambiguate V003 + V004 sharing one class. PRs: [#244](https://github.com/TnsAI-Framework/TnsAI/pull/244), [#246](https://github.com/TnsAI-Framework/TnsAI/pull/246), [#250](https://github.com/TnsAI-Framework/TnsAI/pull/250).
- `IdempotencyResolver.deriveKey` — promoted from `private` to `public static`. Necessary so the HTTP header injection path in `WebServiceExecutor` derives the same key the resolver's internal cache lookup uses; otherwise the header value and the cache lookup would diverge for `KeyStrategy.HASH_INPUT`. PR: [#253](https://github.com/TnsAI-Framework/TnsAI/pull/253).

### Fixed
- **Bare `<NNN` patterns escaped in 0.8.5 CHANGELOG entry** — `<100ms` and `<500ms` parsed as MDX tag-opens by `fumadocs-mdx` in TnsAI.Web's changelog sync, breaking the Next.js build for the entire 0.8.5 sister-repo PR. Wrapped in backticks; consumer-side render now matches plain-Markdown intent. Future-proofing: this CHANGELOG entry follows the same backtick discipline for any threshold expressions. PR: [#249](https://github.com/TnsAI-Framework/TnsAI/pull/249).

### Stats
- 11 commits since `v0.8.5`
- All 12 `#85` design validators now shipped (was 9): `AGENT-V004`, `AGENT-V006`, `AGENT-V011`, `AGENT-V012` previously deferred, all in
- 1 new `tnsai-core` SPI (`McpClientFactory`)
- 1 new `tnsai-core` SPI marker (`TenantAware`)
- 1 new `tnsai-core` enum (`LLMCapability`, 3 values)
- 1 new `tnsai-mcp` adapter (`DefaultMcpClientFactory` via `ServiceLoader`)
- 4 new `AgentBuilder` setters (`tenantId`, `toolCallFilter` was 0.8.5 — adding `tenantId` here)
- 3 new release-pipeline gates (preflight + dry-deploy + drift)
- ~150 new tests across `IdempotencyResolverTest` (24) + `ToolMethodDispatcherIdempotencyTest` (14) + `WebServiceExecutorIdempotencyTest` (14) + `LLMCapabilityValidatorV004Test` (14) + `MCPServerReachabilityValidatorTest` + integration tests + `AgentBuilderTenantIdTest` (9) + `TenantScopeValidatorTest` (6) + `TenantScopeIntegrationTest` (7) + `DefaultMcpClientFactoryTest` (10)

## [0.8.5] - 2026-05-04

AGENT validator family one step closer to #85 acceptance: V006 (ToolApproval
gate) ships alongside the `@Tool.requiresConfirmation` + `AgentBuilder.toolCallFilter`
primitives it gates on. Phase 3 closeout for #85 ships the `Set<String>`
relaxValidation overload, the `<100ms` SLA pinning perf test, and first-class
validator docs. Also contains a transitive `slf4j-simple` leak from
`tnsai-evaluation` that was hijacking consumer-side logback configuration.
Backward-compatible, purely additive.

### Added
- **AGENT-V006 ToolApprovalValidator** — fires when at least one registered `@Tool` POJO has `requiresConfirmation = true` AND no `ToolCallFilter` has been wired through `AgentBuilder`. Suppressible via `.relaxValidation("AGENT-V006")`. Severity `WARNING` (soft-launch). Closes [#234](https://github.com/TnsAI-Framework/TnsAI/issues/234). PR: [#243](https://github.com/TnsAI-Framework/TnsAI/pull/243).
- **`@Tool.requiresConfirmation()`** — boolean annotation field for tool authors to declare a runtime safety gate. Independent of `@Tool.idempotent()` (retry hint) and `ToolRiskLevel` (gradient classification) — the existing `ToolRiskLevel` javadoc already pre-referenced this field as "the boolean gate". PR: [#243](https://github.com/TnsAI-Framework/TnsAI/pull/243).
- **`AgentBuilder.toolCallFilter(ToolCallFilter)`** — pre-build setter parallel to the existing post-build `Agent.setToolCallFilter()`. Wired via the same pending-pattern used for `KnowledgeBase` so `build()` stays lazy. Null filter clears the slot symmetrically with the post-build setter. PR: [#243](https://github.com/TnsAI-Framework/TnsAI/pull/243).
- **`AgentBuilder.relaxValidation(Set<String>)`** — bulk suppression overload for the common CI pattern of skipping multiple validation codes in one call. Eager null-entry rejection so typos surface at the configuration site, not silently passing every issue through. PR: [#241](https://github.com/TnsAI-Framework/TnsAI/pull/241).
- **`ValidationPipelinePerformanceTest`** — pins the `<100ms` SLA from #85 acceptance ("typical agent validates in `<100ms` (static checks only)"). Asserts `<500ms` with 5× margin for CI runner jitter; locally completes in 10–30ms. PR: [#241](https://github.com/TnsAI-Framework/TnsAI/pull/241).
- **`tnsai-core/agent_docs/validation.md`** — first-class consumer-facing docs covering all 9 shipped validators, the `Healthcheckable` SPI with implementation contract, opt-in reachability + per-probe timeout, suppression model (single + bulk), performance SLA, `AgentValidationException` shape, and the 3 deferred validators with their blocker issues. PR: [#241](https://github.com/TnsAI-Framework/TnsAI/pull/241).

### Fixed
- **`slf4j-simple` no longer leaks at compile scope** from `tnsai-evaluation` to consumers. The dep was declared without an explicit `<scope>`, defaulting to `compile` and racing the consumer's logback binding for the SLF4J "one binding per JVM" slot. Now `<scope>test</scope>` — test JVM still has a binding (277 evaluation tests stay green), the published artifact's transitive graph no longer carries it. Inline `<!-- ... -->` comment added explaining the regression context so a future copy-paste / cleanup doesn't silently undo the fix. Closes [#240](https://github.com/TnsAI-Framework/TnsAI/issues/240). PR: [#242](https://github.com/TnsAI-Framework/TnsAI/pull/242).

### Stats
- 3 commits since `v0.8.4`
- 1 new public annotation field (`@Tool.requiresConfirmation`)
- 1 new `AgentBuilder` setter (`toolCallFilter`)
- 1 new `AgentBuilder` overload (`relaxValidation(Set<String>)`)
- 1 new AGENT validator (V006); 9 of 12 #85 validators now shipped (was 8) — 3 spinoffs remaining: V004 (#238), V011 (#237), V012 (#235)
- 1 perf test pinning #85's `<100ms` SLA
- ~25 new tests across V006 + relaxValidation overload
- 1 published-artifact regression contained (slf4j-simple at compile scope)

## [0.8.4] - 2026-05-03

Multimodal toolkit completion + safety-gate plumbing + AGENT validator
expansion. Backward-compatible, purely additive — no removals, no
behaviour changes. Headlines: image generation (DALL-E 3 / FLUX /
Stability), audio generation (ElevenLabs / Cartesia / Deepgram +
Whisper alternatives), `ChannelScopedId` typed identity, prompt-injection
scanning for project-context files, and two new build-time validators
(AGENT-V003 / V005).

### Added
- **Image generation toolkit** — single function-shape POJO `ImageGenTools` with three `@Tool` methods (`dalle3_generate`, `flux_generate`, `stability_generate`) so the LLM can pick provider at call time by cost/latency/quality. Uniform `{provider, model, urls[, revised_prompt]}` envelope; Stability binary response decoded to `data:image/png;base64,…` URI for shape parity. New `BuiltInTool.IMAGE_GEN_TOOLS` enum entry. PR: [#221](https://github.com/TnsAI-Framework/TnsAI/pull/221) (closes [#93](https://github.com/TnsAI-Framework/TnsAI/issues/93) Phase 1).
- **Audio generation toolkits** — two POJOs (`TextToSpeechTools` + `SpeechToTextTools`) covering the canonical non-OpenAI alternatives to `MediaTools`. TTS: ElevenLabs Multilingual v2/Turbo/Flash, Cartesia Sonic-2 (latency leader), Deepgram Aura (cheapest). STT: Deepgram Nova-2 (sync), AssemblyAI Universal-2 (async with internal poll loop, 5-min hard cap), OpenAI Whisper large-v3 hosted on Replicate (FLUX-key reuse). New `BuiltInTool.TEXT_TO_SPEECH_TOOLS` + `SPEECH_TO_TEXT_TOOLS` entries. PR: [#222](https://github.com/TnsAI-Framework/TnsAI/pull/222) (closes [#93](https://github.com/TnsAI-Framework/TnsAI/issues/93) Phase 2).
- **`ChannelScopedId` value type** in `tnsai-channels` — typed `(channelId, senderId)` record replacing the ad-hoc `channelId + ":" + senderId` string concat. Compact constructor refuses a separator-bearing channelId; `parse()` splits on the first colon so a senderId with internal colons (Slack `T123:U456`) round-trips losslessly. `UnifiedMessage.scopedId()` convenience helper added (parallel to existing `sessionKey()` — sender-scope vs conversation-scope). PR: [#224](https://github.com/TnsAI-Framework/TnsAI/pull/224) (closes [#19](https://github.com/TnsAI-Framework/TnsAI/issues/19) Phase 1).
- **Prompt-injection scan for project-context files** — `PromptInjectionDetector.detectInProjectContext(content, source)` adds a context-only pattern set targeting attack vectors that don't make sense in regular chat: SSH-key dumps, `.env` reads, `~/.aws/credentials` reads, env-var dumps, `curl` POSTs of secret files, `display:none`/`visibility:hidden`/white-on-white HTML, zero-width-character payloads, HTML-comment overrides. New `ContextFileSource` enum (TNSAI_MD / CLAUDE_MD / AGENTS_MD / README_MD / OTHER) tags audit-log entries. New `InjectionType` enum values: `CREDENTIAL_EXFILTRATION` and `HIDDEN_INSTRUCTION`. PR: [#228](https://github.com/TnsAI-Framework/TnsAI/pull/228) (closes [#35](https://github.com/TnsAI-Framework/TnsAI/issues/35)).
- **AGENT validator family expansion** — `LLMCapabilityValidator` (AGENT-V003, function-calling capability check) and `Healthcheckable` SPI + reachability validation infra (AGENT-V005). PRs: [#233](https://github.com/TnsAI-Framework/TnsAI/pull/233), [#236](https://github.com/TnsAI-Framework/TnsAI/pull/236) (advances [#85](https://github.com/TnsAI-Framework/TnsAI/issues/85)).
- **Release-pipeline hardening Phase A** — `make preflight` (5-check gate: tag exists, root pom version, 13 module poms lockstep, CHANGELOG section, no duplicate tag), `make drift-check` (sister-repo stale-version scan), japicmp Maven plugin in the `quality` profile. PR: [#206](https://github.com/TnsAI-Framework/TnsAI/pull/206) (closes [#203](https://github.com/TnsAI-Framework/TnsAI/issues/203) Phase A).

### Changed
- **Surefire migrated to explicit Mockito `-javaagent`** — Java 24+ removes the silent agent-loading path, so the test runner now declares the agent jar by full path. Standardises across all 13 modules. PR: [#225](https://github.com/TnsAI-Framework/TnsAI/pull/225).
- **JaCoCo coverage gate added for `#9` protected classes** — five test-coverage waves landed (FormatAwareOutputParser, CompositeResilienceStrategy + ComposedResilienceStrategy, InMemoryMessageBroker + Message factories, ContextSnapshot + Builder, GroupTask record + Builder + lifecycle), and the gate ratchets coverage so it cannot regress. PRs: [#226](https://github.com/TnsAI-Framework/TnsAI/pull/226), [#227](https://github.com/TnsAI-Framework/TnsAI/pull/227), [#229](https://github.com/TnsAI-Framework/TnsAI/pull/229), [#230](https://github.com/TnsAI-Framework/TnsAI/pull/230), [#231](https://github.com/TnsAI-Framework/TnsAI/pull/231), [#232](https://github.com/TnsAI-Framework/TnsAI/pull/232) (advances [#9](https://github.com/TnsAI-Framework/TnsAI/issues/9)).
- **Dependency bumps** (Dependabot wave) — jackson group (3 updates), micrometer-registry-prometheus 1.3.1 → 1.16.5, mongodb-driver-sync 5.2.1 → 5.7.0, jakarta.mail-api 2.1.3 → 2.1.5, playwright 1.58.0 → 1.59.0, graalvm 25.0.2 → 25.0.3, jacoco-maven-plugin 0.8.12 → 0.8.14, spotbugs-maven-plugin 4.8.6.5 → 4.9.8.3, central-publishing-maven-plugin 0.7.0 → 0.10.0, maven-plugins group (6 updates), ci-actions (checkout 4→6, setup-java 4→5, upload-artifact 4→7, action-gh-release 2→3).

### Stats
- 29 commits since `v0.8.3`
- 5 new public types (`ChannelScopedId`, `ImageGenTools`, `TextToSpeechTools`, `SpeechToTextTools`, `ContextFileSource`)
- 9 new `@Tool` methods (3 image + 6 audio) + 2 new AGENT validators (V003, V005)
- 3 new `BuiltInTool` enum entries
- 2 new `InjectionType` enum values
- 4 issues closed via PR (#19, #35, #93, #203 Phase A) + #9 advanced through 5 phases + #85 advanced through phases 1/2a
- ~115 new tests across the new toolkits and detector

## [0.8.3] - 2026-05-02

Two bug fixes batched together — both surfaced during open-issue
triage. Backward-compatible, additive: `AuthType.API_KEY` is a new
enum constant the framework's javadoc has documented since the
annotation landed but never actually defined.

### Fixed
- `StdioTransport.shouldHandleProcessExit` flaky test (#205): the
  startup race in `waitForProcessStart()` polled `process.isAlive()`
  in a 50 ms loop, treating "alive" as a proxy for "started".
  Wrong for fast-exiting processes — a one-shot like `echo` writes
  its line and exits between two polls, so `isAlive()` returns
  `false` even though the process started, ran, and produced output
  successfully (pipe data persists in the kernel buffer after the
  writer exits). Replaced the loop with `return process != null` —
  `ProcessBuilder.start()` is synchronous on POSIX/macOS, so by the
  time we hold a `Process` reference exec(2) has succeeded. Test
  also migrated from `Thread.sleep(500)` to a `CountDownLatch(1)`
  synchronisation point, removing the timing assumption. 5×
  sequential local runs all PASS (was: failing once every ~3-5 CI
  runs).

### Added
- `AuthType.API_KEY` (#175): the `@WebService` javadoc has documented
  this auth type since the annotation landed, but the enum only
  defined `NO_AUTH` / `BEARER` / `BASIC`. A consumer copy-pasting
  from the javadoc got a compile error with no clear hint that the
  documented value didn't exist. Added the enum constant + a matching
  switch arm in `WebServiceExecutor.addAuthHeaders` that reads from
  `@WebService.authTokenEnv` and uses `@WebService.apiKeyHeader`
  for the header name (defaulting to `X-API-Key` when empty). The
  orphan `apiKeyHeader` annotation field — previously reachable from
  no code path — is now wired in.

### Stats
4 source files, +49/-22 LOC across `tnsai-mcp` + `tnsai-core`.
1 test rewritten for determinism (`StdioTransportTest$RealProcessTests
.shouldHandleProcessExit`). 13/13 modules build, 9 083 tests pass.

PRs: [#205](https://github.com/TnsAI-Framework/TnsAI/pull/205),
[#175](https://github.com/TnsAI-Framework/TnsAI/pull/175)

## [0.8.2] - 2026-05-01

Fixes a prompt-rendering bug in `SystemPromptBuilder` (the SCOP-bridge
prompt path) where `@Responsibility.invariants` was emitted as a
single comma-joined line. Long natural-language rules with internal
commas (e.g. *"…a real-sounding name, a real city, a real job, real
numbers"*) collapsed into prose under that join, hurting the LLM's
ability to extract individual rules. Now rendered as a bullet list,
one rule per line — restoring per-rule saliency.

### Fixed
- `SystemPromptBuilder.buildFromAnnotations(...)` rendered
  `@Responsibility.invariants[]` as
  `Invariants: rule1, rule2, rule3` (single comma-joined line). Fix
  emits each rule on its own bullet:
  ```
  Invariants:
      - rule1
      - rule2
      - rule3
  ```
  `@State.invariants` is intentionally **unchanged** — those are
  short technical predicates (e.g. `count >= 0`) that read cleanly
  in the existing inline `[constraints: …]` tag and don't suffer the
  same collapse problem.

### Stats
1 source file (`SystemPromptBuilder.java`, +6/-1 LOC) + 1 test case
(`SystemPromptBuilderPromptTemplatesTest`, +35 LOC). 13/13 modules
build; 9 083 tests pass.

PR: [#205](https://github.com/TnsAI-Framework/TnsAI/pull/205)

## [0.8.1] - 2026-04-30

Adds a typed-input overload to `Agent.executeAction` so consumers can
replace `Map.of("path", "…", "question", "…")` call sites with a Java
record. The new overload reflects the record's components into a
parameter map and forwards to the existing untyped dispatch path —
existing `Map<String, Object>` callers are unchanged thanks to Java's
overload resolution preferring the more specific `Map` parameter.

This matches the dominant 2024–2025 industry pattern (LangChain
`args_schema`, Vercel AI SDK + Mastra `inputSchema: z.object(...)`,
Spring AI `FunctionToolCallback.inputType(...)`, LangChain4j typed
`@Tool` parameters, Embabel `@Action` records) — typed input + string
action name. No new annotations, no new mental model: write a record,
pass an instance.

### Added
- `com.tnsai.actions.ParamBeanMapper` — utility that reflects a Java
  `record`, a POJO with `getX`/`isX` accessors, or a class with public
  fields into a `Map<String, Object>`. Maps are passed through
  unchanged. Null values are preserved as map entries so the
  framework's parameter binding can decide how to treat them.
- `Agent.executeAction(String actionName, Object input)` — typed-input
  overload that delegates to `ParamBeanMapper.toMap(input)` then to
  the existing `executeAction(String, Map<String, Object>)`.
- `Agent.executeActionOnRole(String roleId, String actionName, Object input)`
  — symmetric typed-input overload for role-scoped dispatch.

### Stats
3 files added/modified in `tnsai-core` (~330 LOC), 11 new
`ParamBeanMapperTest` cases covering records, POJOs, public-field
beans, map pass-through, null handling, and unsupported bean shapes.
13/13 modules build, 9 082 tests pass.

PR: [#204](https://github.com/TnsAI-Framework/TnsAI/pull/204)

## [0.8.0] - 2026-04-29

Finishes the RFC #188 cleanup by deleting the residual `@LLMTool` /
`@ToolBinding` cookbook layer. The annotations were a metadata surface for a
tool-calling executor (`LLMToolsExecutor`) that was already removed in
0.6.0 — the configuration was being silently ignored at runtime. Per-action
LLM overrides (`llmSystemPrompt`, `llmTemperature`) move directly onto
`@ActionSpec`. Tool exposure is purely agent-level via
`AgentBuilder.builtInTools(...)` / `.toolPojos(...)`; the agent's
`ToolMethodDispatcher` is the single dispatch path for any `@Tool` methods
the LLM emits. One annotation, one runtime path, no dead config carriers.

### Added
- `@ActionSpec.llmSystemPrompt() : String` — per-action system-prompt override
  (replaces `@LLMTool.systemPrompt()`)
- `@ActionSpec.llmTemperature() : float` — per-action temperature override
  (replaces `@LLMTool.temperature()`)
- `AgentBuilder.builtInTools(BuiltInTool... tools)` — compile-time-safe
  shortcut that reflectively instantiates each shipped POJO toolkit and
  registers it through the same pipeline as `.toolPojos(...)`
- `BuiltInToolInstantiationException` — surfaced when a `BuiltInTool` entry's
  backing class is missing from the classpath (typically because
  `tnsai-tools` is not a dependency)
- `BuiltInTool` enum: per-entry `getClassName()` accessor + `instantiate()`
  reflective constructor

### Changed
- `BuiltInTool` enum rewritten from 33 dangling entries (post-SPI delete in
  0.5.7 they had no backing classes) to 59 POJO-aligned entries that map
  each shipped toolkit's FQCN. Each entry's Javadoc lists every `@Tool`
  method the toolkit exposes
- `BuiltInTool.AI_TOOLS` renamed → `VISION_TOOLS` (the backing
  `AiTools` POJO ships only `image_analyze`; the previous name was misleading)
- `LLMRoleExecutor` now reads `llmTemperature` / `llmSystemPrompt` from
  `@ActionSpec` directly (was `@LLMTool` nested annotation)
- `ActionExecutor` LLM-branch routing simplified — every `ActionType.LLM`
  action now goes through `LLMRoleExecutor` regardless of (former)
  `availableTools` content; tool calls are dispatched by the agent-level
  `ToolMethodDispatcher`

### Removed
- **BREAKING:** `@com.tnsai.annotations.LLMTool` annotation (every field:
  `tools`, `customTools`, `maxToolCalls`, `parallelToolCalls`, `systemPrompt`,
  `temperature`, `maxIterations`, `stopSequences`, `includeToolHistory`,
  `mcpServers`, `bindings`, `returnKey`)
- **BREAKING:** `@com.tnsai.annotations.ToolBinding` annotation +
  `@LLMTool.bindings()` field — the SCOP-side dispatcher that consumed them
  was deleted in 0.6.0; the `${param}` template + text-protocol pattern is
  superseded by typed `@Tool` method parameters that the LLM populates
  directly from its function-call arguments
- **BREAKING:** `@ActionSpec.llmTool() : LLMTool` annotation field
- **BREAKING:** `com.tnsai.metadata.LLMToolConfig` record (use the
  `@ActionSpec.llmSystemPrompt()` / `.llmTemperature()` accessors on
  `ActionMetadata` directly)
- **BREAKING:** `ActionMetadata.llmToolConfig()` accessor
- **BREAKING:** `ActionMetadata.getBuiltInTools()`,
  `getCustomToolNames()`, `getAvailableTools()`, `getMaxToolCalls()`,
  `isParallelToolCalls()`, `getMaxIterations()`, `getStopSequences()`,
  `isIncludeToolHistory()`, `getMcpServers()`, `hasMcpServers()`,
  `isRequireToolUse()` — all dead since 0.6.0
- **BREAKING:** Reference examples
  `tnsai-integration/.../scop/examples/DataAnalystRole`,
  `CsvLoaderRole`, and the
  `DataAnalystRoleAnnotationRoundTripTest` — they demonstrated the deleted
  cookbook against an executor that no longer existed
- `LLMToolConfigTest` test class
- 7 stale `LLMToolsExecutor` Javadoc references in
  `actions/executors/package-info`, `actions/package-info`,
  `TypedActionExecutor`, and assorted other source files (the class was
  deleted in 0.6.0 but the comments lingered)

### Fixed
- The Fumadocs static-export search on `tnsai.dev` was hitting `/api/search`
  with no static index materialised; now wires
  `createFromSource(source).staticGET()` to a `static.json` route handler
  and passes `search={ options: { type: 'static', api: '/static.json' } }`
  to `RootProvider`. ~10 600 docs entries indexed at build time
- `tnsai-tools/README.md`, `tnsai-core/README.md`,
  `tnsai-tools/CLAUDE.md`, `tnsai-tools/CODEBASE_MAP.md`, the root
  `CLAUDE.md`, `tnsai-core/.../annotations/{ActionSpec,LLMTool,ToolBinding}.java`
  Javadoc, and the entire TnsAI.Docs `capabilities/tools/` + `tutorials/`
  + Quick-Start surface — all rewritten against the post-RFC-#188
  reality (no `Tool` interface, no `*Tool` classes, no `@LLMTool`,
  no `LLM_TOOL`/`LLM_ROLE` action types, accurate tool counts)
- `ActionSpec.java` Javadoc: action-types table + `@LLMTool`-using example
  swapped for the new `llmSystemPrompt` / `llmTemperature` shape

### Migration
The compile-time fix for any consumer using `@LLMTool`:

```java
// before (compile error in 0.8.0)
@ActionSpec(
    type = ActionType.LLM,
    description = "...",
    llmTool = @LLMTool(
        systemPrompt = "You are concise.",
        temperature = 0.2f,
        tools = { BuiltInTool.CSV_TOOLS }   // never dispatched anyway since 0.6.0
    )
)
public String summarise(String text) { ... }

// after
@ActionSpec(
    type = ActionType.LLM,
    description = "...",
    llmSystemPrompt = "You are concise.",
    llmTemperature = 0.2f
)
public String summarise(String text) { ... }
```

Tool exposure moves to the agent-build call site, where it has always
been the only working path:

```java
Agent agent = AgentBuilder.create()
    .id("data-analyst")
    .llm(llmClient)
    .role(new MyRole())
    .builtInTools(BuiltInTool.CSV_TOOLS, BuiltInTool.PDF_TOOLS)   // or .toolPojos(new MyOwnTools())
    .build();
```

Anyone leaning on `@ToolBinding(tool = "csv_parser", inputTemplate = "${path}|||command")`
ports to typed `@Tool` parameters on the receiving toolkit method — the
LLM populates them directly from its function-call arguments, no
text-protocol substitution layer.

### Stats
~870 lines net delete across `tnsai-core` + `tnsai-integration`. 13/13
modules build green, 9 071 tests pass.

PR: [#203](https://github.com/TnsAI-Framework/TnsAI/pull/203)

## [0.7.0] - 2026-04-29

Closes RFC #188 by retiring the legacy `Tool` interface entirely. `ToolMethod` is now a sealed interface with two variants: `StaticToolMethod` (POJO `@Tool` methods) and `DynamicToolMethod` (runtime-defined via `Handler` callback, e.g. MCP proxies). One registry, one dispatcher.

### Added
- `DynamicToolMethod` record — runtime-defined tool variant for proxies and plugin systems
- `StaticToolMethod` record — extracted reflection-dispatch logic from `ToolMethodDispatcher`
- `AgentBuilder.dynamicTool(DynamicToolMethod)` and `.dynamicTools(List<DynamicToolMethod>)`
- `AutoTeamBuilder.dynamicTool(...)` / `.dynamicTools(...)` mirror APIs
- `McpProxyTool.toDynamicToolMethod(...)` static factory — replaces `implements Tool`
- `ToolMethodDispatcher.lookup(name)` and `.registry()` accessors
- `TnsAIToolProvider.fromDynamic(DynamicToolMethod...)` and `.from(pojos, dynamicTools)` factories

### Changed
- `ToolMethod` is now a `sealed interface` (was a record); permits `StaticToolMethod`, `DynamicToolMethod`
- `McpToolBridge.toTnsAITools()` returns `List<DynamicToolMethod>` directly (no wrapper)
- `ActionExecutor` constructor now takes `ToolMethodDispatcher` (was `List<Tool>`)
- `ActionExecutor.executeExternalTool(String, Map<String, Object>)` (was `(String, String)`)
- `UnifiedContextAssembler.tools(List<ToolMethod>)` (was `List<Tool>`)

### Removed
- **BREAKING:** `com.tnsai.tools.Tool` interface
- **BREAKING:** `AgentBuilder.tool(Tool)`, `.tools(List<Tool>)`, `.getToolsList()`
- **BREAKING:** `ConfigurableAgent.getExternalTools()`
- **BREAKING:** `ToolSchemaGenerator.generateToolSchema(Tool)`
- **BREAKING:** `McpToolBridge.TnsAIToolWrapper` adapter class
- **BREAKING:** `TnsAIToolProvider.fromTools(Tool...)` factory + legacy dispatch branch
- **BREAKING:** `AutoTeamBuilder.tool(Tool)` / `.tools(List<Tool>)`
- `ToolMethodAdapter` bridge class (no consumers left after migration)
- `ToolFailureMode` annotation + `ToolFailureModeReader` helper
- Orphan `tnsai-integration/.../CsvLoaderRoleBindingTest` (referenced llmtools deleted in 0.6.0)

### Fixed
- `CancellationToken` concurrency race: concurrent `cancel()` + `onCancel()` could fire a callback twice. Now exactly-once via per-registration `AtomicBoolean` guard.

### Migration
Consumers using `AgentBuilder.toolPojos(...)` are unaffected — recommended path unchanged. Direct `Tool` implementers move to either:
- A POJO with `@Tool`-annotated methods, registered via `.toolPojos(new MyTool())`
- A `DynamicToolMethod` constructed via factory, registered via `.dynamicTool(myTool)`

`ChatRequest.tools` is still `List<Map<String, Object>>` (unchanged since 0.6.0).

### Stats
35 files, ~733 lines net delete. Combined with 0.6.0: ~14k lines removed from the legacy tool stack.

PR: [#202](https://github.com/TnsAI-Framework/TnsAI/pull/202)

## [0.6.0] - 2026-04-29

Continues the RFC #188 legacy-tool-stack delete that started in 0.5.7. `tnsai-core` shrinks to a leaner spine. `Tool` interface stays as a slim registration surface for one more release; full removal in 0.7.0.

### Changed
- **BREAKING:** `ChatRequest.tools` type changed `List<ToolDefinition>` → `List<Map<String, Object>>` (JSON-Schema fragments, Anthropic-style tool-use format)
- `Tool` interface slimmed (369L → 138L) — kept core contract + safety/policy hints

### Removed
- **BREAKING:** `ToolDefinition` record + builder + `fromMap` / `toMaps` helpers
- **BREAKING:** `ToolSchemaGenerator.generateToolDefinition*` methods (3 overloads)
- **BREAKING:** `Tool` interface metadata-discovery surface — 12 default methods removed: `getCategory`, `getUsageExamples`, `getMetadata`, `getSearchKeywords`, `getPriority`, `canHandle`, `getAllowedCallers`, `isParallelizable`, `getReturnFormat`, `getLatencyCategory`, `getShortDescription`, `executeAsync`
- **BREAKING:** `ToolMetadata`, `ToolCategory`, `ToolLatency` types
- Legacy `actions/llmtools/` subsystem
- `@ToolSpec` and `@ToolAction` annotations + reflective extractor
- Hooks/policy/validators ecosystem (`Pre/Post/Error/Register ToolUse` events, `ToolPolicy*`, `ToolApprovalValidator`, `LLMCapabilityValidator`)
- `ToolMetrics` (628L) + `ToolExecutionMetric`
- `ToolRegistry` (225L) + `ToolProvider` SPI
- `AgentBuilder.tool(String name)` lookup overload
- Dead `ToolCallProcessor` (264L, no consumers)

### Migration
Custom `ChatRequest` callers swap `ToolDefinition.of("name", "desc")` for `Map.<String, Object>of("name", "name", "description", "desc", "parameters", Map.of())`. Direct `Tool` implementers can drop the deleted-method overrides — they no longer compile but no consumer reads them.

### Stats
~10.4k lines removed from the runtime.

PRs: [#194](https://github.com/TnsAI-Framework/TnsAI/pull/194), [#195](https://github.com/TnsAI-Framework/TnsAI/pull/195), [#196](https://github.com/TnsAI-Framework/TnsAI/pull/196), [#197](https://github.com/TnsAI-Framework/TnsAI/pull/197), [#198](https://github.com/TnsAI-Framework/TnsAI/pull/198), [#199](https://github.com/TnsAI-Framework/TnsAI/pull/199), [#200](https://github.com/TnsAI-Framework/TnsAI/pull/200)

## [0.5.7] - 2026-04-29

RFC #188 Phase 2 + 3a + 3b: full migration of the tool catalog to the function-shape POJO pattern (LangChain / Spring AI / CrewAI / Mastra style). Replaces 130+ `extends AbstractTool` legacy implementations with 60 typed POJOs exposing ~190 `@Tool`-annotated methods across 28 categories.

### Added
- `ToolMethodRegistry` — reflection-based @Tool discovery, duplicate-name fail-fast
- `ToolMethodDispatcher` — Jackson type-aware coercion + `Method.invoke` dispatch
- `JsonSchemaGenerator` — derives JSON Schema fragments from `@Tool`/`@ToolParam` metadata
- `ToolMethodAdapter` — bridges function-shape `ToolMethod` to legacy `Tool` interface (deleted in 0.7.0)
- `AgentBuilder.toolPojos(Object...)` registration path
- `TnsAIToolProvider.fromPojos(Object...)` for MCP server integration
- 60 function-shape POJOs across 28 categories (file, search, database, communication, fintech, utility, etc.)
- 3 server-tool POJOs: `ServerFileTools`, `ServerShellTools`, `ServerGitTools`

### Removed
- 134 `*Tool.java` legacy implementations under `tnsai-tools`
- 32 `*ToolProvider.java` SPI factory classes
- 18 framework infrastructure files (`AbstractTool`, `AbstractCategoryToolProvider`, validation/health/manifest/enhancement helpers)
- 152 corresponding `*Test.java` files
- `tnsai-tools` SPI registration

### Migration
Consumers extending `AbstractTool` move to a POJO with `@Tool`-annotated methods. Pattern documented in `tnsai-core/CLAUDE.md`. Most consumers don't touch this — they use built-in tools through `tnsai-tools`, which is now backed by the new POJOs transparently.

### Stats
+27,160 / −85,000 lines (~58k net smaller). The biggest single cleanup in TnsAI history.

PRs: [#190](https://github.com/TnsAI-Framework/TnsAI/pull/190), [#191](https://github.com/TnsAI-Framework/TnsAI/pull/191), [#192](https://github.com/TnsAI-Framework/TnsAI/pull/192), [#193](https://github.com/TnsAI-Framework/TnsAI/pull/193)

## [0.5.6] - 2026-04-28

Patch release. Broadens the `FileToolProvider` optional-dep isolation introduced in 0.5.5 to also cover `JSONQueryTool` and `CSVParserTool`, which the first pass missed.

### Fixed
- `FileToolProvider` — `JSONQueryTool` (`com.jayway.jsonpath` optional dep) and `CSVParserTool` (`com.opencsv` optional dep) were still on the eager `toolSuppliers()` list. Same `LinkageError` failure mode as 0.5.5 (#184) — taking the whole provider down when a consumer pulled `tnsai-tools` without those transitives. Moved both to `reflectiveToolClassNames()` so missing optional deps are isolated per tool.

### Changed
- `FileToolProvider.toolSuppliers()` now lists ONLY 3 pure-JDK tools (`FileReadTool`, `FileWriteTool`, `XMLParserTool`)
- `FileToolProvider.reflectiveToolClassNames()` now lists 8 optional-dep tools (`JSONQueryTool`, `CSVParserTool`, `MarkItDownTool`, 5× `PDF*Tool`)
- `AbstractCategoryToolProviderIsolationTest` updated for new split (controls = pure-JDK FQNs, base assertion ≥3)

### Migration
None — pure bug fix. Behaviour-changing only when an optional dep is missing (failure now isolated as `ToolLoadFailure`, was complete provider crash).

PR: [#186](https://github.com/TnsAI-Framework/TnsAI/pull/186)

## [0.5.5] - 2026-04-28

Patch release. Fixes an all-or-nothing failure mode in `FileToolProvider` when a consumer pulls `tnsai-tools` without the optional Apache PDFBox or MarkItDown transitive dependencies.

### Fixed
- `FileToolProvider` — eager `XYZTool::new` method-references in `toolSuppliers()` resolved their `MethodHandle` at `List.of(...)` evaluation time, **outside** the per-tool try/catch. Missing PDFBox or MarkItDown transitive → `LinkageError` from list construction → entire provider unloaded → 8 unrelated File tools (JSON, CSV, file IO, XML) became unavailable.

### Added
- `AbstractCategoryToolProvider.reflectiveToolClassNames()` load path — `Class.forName(name)` defers linkage until inside the per-tool try/catch
- `AbstractCategoryToolProviderIsolationTest` (6 cases): pins the contract that missing FQN does not break a present sibling, and PDF failures are captured without propagating

### Changed
- `FileToolProvider`: 6 optional-dep tools (`MarkItDownTool`, 5× `PDF*Tool`) moved from `toolSuppliers()` to `reflectiveToolClassNames()`. `toolSuppliers()` keeps the 5 base-JDK + opencsv tools.

### Migration
None — pure bug fix. Behaviour-changing only when an optional dep is missing.

PR: [#184](https://github.com/TnsAI-Framework/TnsAI/pull/184)

## [0.5.4] - 2026-04-28

Minor-feature release. **Annotation-driven runtime resolution sprint** closing most of the umbrella tracked under [#169]. All additive (Phase 1 boundaries: no external storage / SPI deps); existing call sites unchanged for code that doesn't opt into the new annotations.

### Added
- `@LLMTool` runtime path surfaced via `LLMToolsExecutor` ([#167]) + `DataAnalystRole` reference + `BuiltInToolEnumAuditTest`
- `@WebService` runtime path surfaced via `WebServiceExecutor` ([#174]) + `WeatherRole` reference
- `com.tnsai.guardrails` package — `@InputGuardrail` / `@OutputGuardrail` enforcement with `minLength` / `maxLength` / `blockPatterns` / `allowPatterns` + `onFailure` ∈ `{REJECT, WARN, SANITIZE, REVIEW}` ([#176])
- Optional `RetrievalSpi` in `tnsai-core` + default impl in `tnsai-intelligence` (`RoleRagBinding`, `LocalFileSourceLoader`, `DefaultRetrievalSpi`) — wires `@KnowledgeSource` / `@Retrieval` end-to-end ([#178])
- `@ToolBinding` declarative tool-input mapping with `${param}` / `${role.name}` / `${action.name}` / `${env:VAR}` substitution ([#179])
- `com.tnsai.resilience` decorators — `@Traced` (MDC trace-id), `@Metered` (in-memory `ResilienceMetrics`), `@Fallback` (`forAction` binding + immediate-retry) ([#182])
- 6 reference roles in `tnsai-integration/scop/examples/`: `DataAnalystRole`, `WeatherRole`, `UserInputRole`, `ResearchRole`, `CsvLoaderRole`, `PaymentRole` — each with its own integration test exercising the live `ActionExecutor` pipeline
- `ActionParams.firstStringValueInDeclarationOrder` shared helper

### Changed
- `@ToolBinding` simplified to single-field `tool()` (was two mutually-exclusive `builtIn` + `custom` fields with `BuiltInTool.NONE` sentinel) — single source, identical syntax for built-in and custom tools ([#181] refactor of [#179])
- `FallbackResolver.tryRecover` and `RetryCallback.invoke()` narrowed `catch (Throwable)` → `catch (Exception)` (caught by `SourceHygieneTest.noBroadThrowableCatches` from #41)
- `LLMToolsExecutor` non-deterministic `parameters.values().iterator().next()` (HashMap iteration order is per-JVM) replaced with the new shared helper

### Stats
- Tests: tnsai-core 2992 → 3047 (+55), tnsai-integration 78 → 129 (+51)
- Why not 0.6.0: every gap closed was a runtime path that was documented but unenforced — no API removals, no behaviour changes for non-opt-in code

### Deferred to Phase 2+ (separate issues)
- `@Sanitize` / `@ContentFilter` standalone enforcement (#171 follow-up)
- `InputValidator` / `InputSanitizer` SPI for custom `Class[]` hooks
- `@MemorySpec` resolver (Persistence.REDIS / DATABASE / FILE)
- KnowledgeType source loaders (URL / VECTOR_DB / DATABASE / WEB_SEARCH)
- Embedding SPI replacing `HashEmbeddingFunction`
- `@RateLimited`, `@Resilience(circuitBreaker)`, `@Idempotent` keyed cache (need distributed-state SPI)
- OpenTelemetry SPI for `@Traced`; Micrometer/Prometheus sink for `@Metered`
- Build-time validation (fail-fast on `@ToolBinding` typos)
- `AuthType.API_KEY` enum value ([#175])

PRs: [#167](https://github.com/TnsAI-Framework/TnsAI/pull/167), [#174](https://github.com/TnsAI-Framework/TnsAI/pull/174), [#176](https://github.com/TnsAI-Framework/TnsAI/pull/176), [#178](https://github.com/TnsAI-Framework/TnsAI/pull/178), [#179](https://github.com/TnsAI-Framework/TnsAI/pull/179), [#181](https://github.com/TnsAI-Framework/TnsAI/pull/181), [#182](https://github.com/TnsAI-Framework/TnsAI/pull/182)

## [0.5.3] — 2026-04-27

Patch release. **4 PRs** (#156, #157, #158, #165) since `v0.5.2`. All
additive — no public API removals, no behaviour regressions. Single
theme: closing the `ProviderErrorMapper` SPI matrix at **13/13
shipping LLM providers** (issue #87 fully resolved).

### Added — Nine new `ProviderErrorMapper`s (closes #87)

`v0.5.2` shipped 4 mappers (OpenAI + Anthropic + Gemini + Ollama).
This release adds the remaining 9 to complete coverage of every
provider in `tnsai-llm`:

- **`MistralProviderErrorMapper`** (PR #156) — OpenAI-compatible
  envelope plus Mistral-specific code routing. Handles
  `requests_too_many` (alongside `rate_limit_exceeded`) →
  `MODEL_OVERLOADED` and Mistral's stricter `model_quota_exceeded`
  semantics. `MistralAIClient.chat` / `streamChat` refactored to
  `executeRequest("Mistral")`.

- **`BedrockProviderErrorMapper`** (PR #157) — first mapper that
  works against AWS SDK exceptions, not HTTP responses.
  `BedrockClient.mapAwsException` extracts the AWS error code,
  reconstructs an AWS-shape envelope, propagates `x-amzn-requestid`
  via headers, and feeds the SPI mapper's HTTP-style API. Same SPI
  contract handles both code paths so consumers see typed
  `LLMException` regardless of transport.

- **`GroqProviderErrorMapper`** (PR #158) — OpenAI-compatible at the
  envelope level (Groq mirrors OpenAI's API by design); maps Groq's
  low-latency-specific codes (`requests_too_many`, queue saturation
  variants) to `MODEL_OVERLOADED` so consumer fallback chains treat
  them as transient. Captures `groq-region` header for routing-issue
  triage.

- **`OpenRouterProviderErrorMapper`** (in PR #165) — aggregator
  envelope. Surfaces the upstream provider name via
  `metadata.provider_name` so consumers triaging an OpenRouter
  failure can see which downstream provider actually misbehaved.

- **`AzureOpenAIProviderErrorMapper`** (in PR #165) — OpenAI-compat
  body, Azure deployment-id model field, captures `apim-request-id` /
  `x-ms-region` headers for Azure-specific triage. Distinguishes
  Azure's `content_filter` (Azure's responsible AI gating) from
  OpenAI's lexical codes.

- **`CohereProviderErrorMapper`** (in PR #165) — Cohere's RAG-focused
  API. Maps the `command-r*` model family appropriately; handles
  Cohere's distinct streaming JSON-lines envelope.

- **`HuggingFaceProviderErrorMapper`** (in PR #165) — covers both
  Inference API and custom Inference Endpoints. Critical: routes the
  model-loading-503 case to `SERVER_ERROR` (retryable) so cold models
  don't kill consumer requests on first hit.

- **`MiniMaxProviderErrorMapper`** (in PR #165) — handles two error
  envelopes simultaneously: OpenAI-compatible at `/chat/completions`
  (used by `MiniMaxClient`) AND native `base_resp.status_code` at
  `/text/chatcompletion_v2` (used by partner-routed proxies). Native
  channel routes failures through HTTP 200 — the only signal is the
  JSON status code — so the mapper inspects `base_resp` first.

- **`ZhipuAIProviderErrorMapper`** (in PR #165) — closes the matrix
  at 13/13. Numeric-string codes clustered by family: 100x
  (auth/billing) → `AUTHENTICATION_FAILED`, 11xx (rate) →
  `MODEL_OVERLOADED`, 12xx (input/context) → `INVALID_REQUEST` /
  `MODEL_NOT_FOUND` / `CONTEXT_TOO_LONG`, 13xx (server) →
  `SERVER_ERROR`. Code shape normalisation handles both string and
  numeric JSON variants.

### Refactor — every LLM client routes through `executeRequest`

All 13 clients now share the same error-translation entry point
(`AbstractLLMClient.executeRequest`), eliminating per-client
`handleError` / `formatApiError` divergence. Each client's `chat` /
`streamChat` (and `chat(List<ContentPart>)` for vision-capable
clients) is wrapped with a `catch (LLMException) { throw e; }` guard
before the generic `catch (Exception)` translator so typed exceptions
aren't double-wrapped.

### Why patch (not minor)

- All 9 mappers are SPI-discovered (zero API surface change for
  consumers that don't read `LLMException.getProviderDetails()`)
- Refactored `chat` / `streamChat` paths preserve identical
  `ChatResponse` returns; the only observable difference is that
  failures throw typed `LLMException` instead of the legacy generic
  wrapper
- Tests added (~150 new mapper test cases) without touching any
  existing passing test

### Test coverage

195+ mapper-specific test cases across the 13 mappers. Aggregate
`mvn -pl tnsai-llm test` reports 1072+ tests green.

Triggers `release.yml` on tag push. Sister-repo sync (Docs / Web /
Sona / Wiki) follows the rule codified in CLAUDE.md (PR #149) —
separate PRs in the same session.

## [0.5.2] — 2026-04-27

Patch release. **4 PRs** (#150–#154) since `v0.5.1`. Pure additive —
no public API removals, no behaviour regressions. Themes: hardening
the error-report pipeline shipped in #86 with a deduplicating
decorator, and finally connecting the `ProviderErrorMapper`
infrastructure that had been dead in the tree since 0.5.0.

### Added — `DedupingErrorReportPublisher` (#86 follow-up, PR #150)

Decorator that wraps any `ErrorReportPublisher` and suppresses
repeats of the same `ErrorReport.fingerprint()` within a configurable
time window. First occurrence per window emits, rest are dropped
and counted. Standard usage:

```java
ErrorReports.setPublisher(
    DedupingErrorReportPublisher.wrap(new Slf4jErrorReportPublisher())
);
```

Default 5-minute window matches Sentry / Rollbar's standard. Surfaces
"suppressed N duplicates in the previous window" log line on window
roll. `getSuppressedCount(fingerprint)` exposes per-fingerprint
counts as a Prometheus gauge.

### Wired — `ProviderErrorMapper` SPI lookup (#87, PR #151)

Closes the wiring gap that left `ErrorEmitter`, `ProviderErrorMapper`
SPI, and the OpenAI + Anthropic mappers (all shipped 0.5.0) entirely
dead. `AbstractLLMClient.executeRequest` now snapshots HTTP error
headers + body, looks up the SPI mapper for the provider, and throws
the typed `LLMException` directly with `ProviderDetails` attached.
Falls back to the legacy `IOException` path when no mapper is
registered.

`OpenAIClient` and `AnthropicClient` catch blocks now `catch
(LLMException) &#123; throw e; &#125;` before the generic catch so a
mapper-derived exception isn't double-wrapped (`ProviderDetails`
would have ended up on `ex.getCause()` otherwise).

`AnthropicClient.chat` / `streamChat` refactored from inline
`response.isSuccessful()` handling to `executeRequest("Anthropic")`
so the SPI mapper is actually reached — same applies to follow-ups
below.

### Added — Two new `ProviderErrorMapper`s

- **`GeminiProviderErrorMapper`** (PR #152) — Google API-Gateway
  envelope (`error.{code,message,status}`). Routes the canonical
  `google.rpc.Code` strings (`RESOURCE_EXHAUSTED` →
  `MODEL_OVERLOADED`, `UNAUTHENTICATED` / `PERMISSION_DENIED` →
  `AUTHENTICATION_FAILED`, `INVALID_ARGUMENT` → `INVALID_REQUEST`
  with token-hint demotion to `CONTEXT_TOO_LONG`, etc.). Captures
  `x-goog-*` + `retry-after` headers. `GeminiClient.chat` /
  `streamChat` refactored to use `executeRequest`.

- **`OllamaProviderErrorMapper`** (PR #154) — heuristic on the
  free-text `error` string (Ollama has no structured codes):
  "not found" / "no such model" / "try pulling" → `MODEL_NOT_FOUND`;
  "context" / "exceeds" / "token" → `CONTEXT_TOO_LONG`; "out of
  memory" / "VRAM" / "OOM" → `MODEL_OVERLOADED`. HTTP fallback
  with 503 → `MODEL_OVERLOADED` (daemon temporarily unavailable).
  No headers captured — local Ollama doesn't carry diagnostic
  headers worth keeping. Defensive on envelope shape (handles both
  `error` as plain string and as object with `message` field).
  `OllamaClient.chat` / `streamChat` refactored to use
  `executeRequest`.

### Provider mapper coverage

Sona's full default model lineup (`anthropic` / `openai` / `gemini`
/ `ollama`) now has typed error mapping for **4/4 providers**.

### Out of scope (follow-up issues)

Nine remaining `ProviderErrorMapper`s (Bedrock, Azure, Cohere, Groq,
HuggingFace, OpenRouter, MiniMax, ZhipuAI, Mistral). Each follows
the established pattern — one mapper class + one SPI registry line
+ unit tests + (if the client uses inline error handling) the
`executeRequest` refactor.

## [0.5.1] — 2026-04-27

Patch release. **6 PRs** (#139–#147) since `v0.5.0`. Pure additive —
no public API removals, no behaviour regressions. Themes: completing
the build-time validation pipeline started in #85, wiring agent
context capture for exception enrichment (#90), and shipping the
error-report emission pipeline (#86).

### Added — build-time validators (`tnsai-core` / `agents/validation/validators/`)

Four new validators land in `AgentBuilder.VALIDATORS`, each running
during `build()` against a `ValidationContext` snapshot:

- **`AGENT-V006` `ToolApprovalValidator`** — WARNING when a registered
  tool reports `requiresConfirmation()==true` but the agent has no
  built-in confirmation channel wired (the operator must call
  `agent.setToolCallFilter(...)` post-build, otherwise calls block
  indefinitely waiting for a confirmation that never arrives).
- **`AGENT-V007` `CapabilityClasspathValidator`** — ERROR when a
  `@Capability` interface implemented by a role can't be fully
  reflected on (parameter / return type or annotation value
  references a class missing from the runtime classpath).
- **`AGENT-V008` `ActionNameCollisionValidator`** — ERROR when two
  roles declare `@ActionSpec` methods with the same name; today the
  framework's name → action map silently keeps whichever role was
  registered last.
- **`AGENT-V009` `ResilienceConfigValidator`** — ERROR for clearly
  invalid `@Resilience` numerics (negative timeout / maxAttempts /
  backoff, multiplier < 1.0, failureRateThreshold outside 0–100);
  WARNING for configured-but-effectively-disabled subsystems
  (`@Retry` with non-default fields but `maxAttempts==0`,
  `@CircuitBreaker(enabled=true)` with non-positive
  `failureThreshold`, `@RateLimit(enabled=true)` with
  `maxRequests<=0`).

Suppress any individual issue with
`AgentBuilder.relaxValidation("AGENT-V0xx")`.

### Added — error context capture wiring (`tnsai-core` / `agents/Agent.java`)

`Agent.java`'s 11 public chat / stream / executeAction entry points
now wrap their delegation in
`try (var ignored = AgentContext.enter(buildEntryContext(op)))`.

Net effect: any `TnsAIException` thrown deep inside the orchestrator
auto-captures the current agent + role + traceId via
`AgentContext.currentOptional()`, so `getMessage()` / `getContext()`
surface attribution without consumer plumbing. Top-level entries
get a fresh trace id; nested entries (agent-from-tool, agent-from-
hook) inherit upstream `tenantId` / `sessionId` / `traceId` /
`spanId` while overwriting `agentId` / `role`.

The entry op (`"chat"`, `"executeAction:summarize"`, …) lands in
`EventContext.extensions["agent.entry.op"]` for log filtering.

### Added — error report emission pipeline (`tnsai-core` / `observability/errors/`)

The `ErrorEmitter` factory shipped with 0.5.0 had no companion
publisher — consumers could construct an `ErrorReport` but had
nowhere to send it. This release ships the pipeline mirroring the
`AgentEventPublisher` pattern from #78:

- **`ErrorReportPublisher`** — single-method SPI. Discovered via
  `ServiceLoader`; consumers add Sentry / Loki / custom sinks by
  dropping a JAR with a
  `META-INF/services/com.tnsai.observability.errors.ErrorReportPublisher`
  entry.
- **`Slf4jErrorReportPublisher`** (default, SPI-registered) —
  JSON-serializes the report via Jackson + `Jdk8Module`
  (so `Optional<T>` unwraps to value-or-null) + `JavaTimeModule`.
  Logs at WARN for `TRANSIENT` / `RESOURCE` / `EXTERNAL`
  categories (self-healing or expected backpressure), ERROR for
  everything else.
- **`CompositeErrorReportPublisher`** — fan-out over multiple
  publishers with per-publisher failure isolation (one throwing
  publisher doesn't stop the others).
- **`CapturingErrorReportPublisher`** — in-memory test workhorse,
  not SPI-registered. Wire via
  `ErrorReports.setPublisher(...)` and tear down via
  `ErrorReports.resetForTesting()`.
- **`ErrorReports`** — static facade with lazy SPI discovery. The
  one-line emit entry point:
  ```java
  ErrorReports.publish(throwable, ErrorCategory.TRANSIENT,
          Map.of("provider", "openai", "model", "gpt-4o"));
  ```

### Tests

+30 new tests (4 validators × ~10, 5 entry-point context, 11 error
report pipeline). Full `tnsai-core` suite green except for a
pre-existing `CancellationTokenTest$Concurrency.registerWhileCancellingStillFires`
flake (passes in isolation; unrelated to this release).

### Out of scope (follow-up issues)

- **#144** — `AGENT-V003` code collision (`ToolNameUniquenessValidator`
  and `LLMCapabilityValidator` both report under `V003`); rename
  pending operator approval (Protected Change per `CLAUDE.md`).
- **#145** — `AGENT-V004` LLM streaming/structured/vision capability
  validator needs a builder capability-declaration API first.
- **#146** — `AGENT-V012` tenant-scope validator + `#92` per-tenant
  error budget both blocked on a multi-tenant runtime feature that
  doesn't exist yet.
- `DedupingErrorReportPublisher` decorator (TTL'd fingerprint
  suppression) — natural #86 successor; deferred.
- Wiring `TnsAIException.<init>` to auto-publish — would
  double-publish for caught-and-rethrown chains; needs a different
  attach point.

## [0.5.0] — 2026-04-26

Feature-batch release. **19 PRs** (#119–#137) since `v0.4.0`. Major
themes: agent lifecycle FSM, cooperative cancellation, tool-policy +
risk-metadata SPI, Anthropic ephemeral prompt-caching wire format,
Telegram retry-on-transient transport, build-time validation pipeline
(one new validator), `@ToolFailureMode` annotation, `ModelFamily` /
`TimeoutPolicy` / `DestructiveCommandDetector` standalones, and a
SCOPBridge prompt-rendering overhaul that closes 4 drift bugs against
`RolePromptBuilder`.

### Added — types

- **`AgentState` enum extended** to the full lifecycle FSM
  (`CREATED → STARTING → RUNNING → STOPPING → STOPPED`, plus terminal
  `FAILED`). The seed value `READY` from 0.4.0 is removed — see Removed.
- **`Agent.getState()`** — new public accessor on every `Agent`.
- **`com.tnsai.tools.ToolRiskLevel`** + **`SideEffect`** enums.
- **`com.tnsai.tools.policy`** package: `ToolPolicy` (`ALLOW_ALL` /
  `DENY_ALL` / `SAFE_ONLY`), `ToolPolicyDecision`, `ToolPolicyEvaluator`,
  plus `com.tnsai.hooks.policy.ToolPolicyHook` consuming it via
  `Hook<PreToolUse>`.
- **`com.tnsai.cancellation`** package: `CancellationToken` interface,
  `CancellationException`, `DefaultCancellationToken` (one-shot CAS),
  `NoopCancellationToken` (singleton no-op).
- **`com.tnsai.timeout.TimeoutPolicy`** record with `Category` enum
  (`LLM_CALL` / `TOOL_CALL` / `MCP_CALL` / `CHANNEL_SEND`) and
  `UNBOUNDED` sentinel.
- **`com.tnsai.prompt.ModelFamily`** enum + `fromModelId(String)`
  best-effort mapper covering Claude / GPT / Gemini / Llama naming
  conventions.
- **`com.tnsai.tools.spi.ToolFailureMode`** annotation +
  `ToolFailureModeReader` resolver — tool authors declare retryable /
  non-retryable exception classes; `nonRetryable` beats `retryable`
  in conflict resolution.
- **`com.tnsai.security.DestructiveCommandDetector`** in `tnsai-quality`:
  content-level `ToolCallFilter` with a 21-pattern catalogue
  (`rm -rf`, `git reset --hard`, `dd` to `/dev/`, `mkfs`, `chmod 777/000`,
  `kill -9` broadcasts, redirects to `/etc/*` / `~/.ssh/*`, `sudo rm/dd`,
  shutdown / reboot --force, shred -ru, wipefs --force).
- **`LLMCapabilityValidator`** — fourth validator in the `AgentBuilder`
  pre-flight pipeline (issue #85 slice). Catches the canonical
  "tools registered + LLM doesn't support function-calling"
  misconfiguration at build time with stable code `AGENT-V003`.
- **`DiscoveredRoleActions.getActions()`** — public list accessor.

### Added — interface extensions (default methods, additive)

- **`Tool.getRiskLevel()`** → `ToolRiskLevel.MEDIUM`,
  **`getRequiredSecrets()`** → empty `Set<String>`,
  **`getTimeout()`** → `Duration.ofSeconds(30)`,
  **`getSideEffects()`** → empty `Set<SideEffect>`.
- **`AgentPromptBuilder.buildSystemPrompt(..., ModelFamily)`** overload
  — Claude gets a softer suggestive register; GPT / Gemini / Llama /
  OTHER share the historical imperative wording byte-for-byte.
- **`AnthropicClient.Builder.enableEphemeralCaching(boolean)`** +
  **`cacheLastNTurns(int)`** — wires `cache_control: ephemeral`
  markers into system + last N user messages (capped at the
  4-per-request Anthropic limit).
- **`OpenRouterClient.setFineGrainedToolStreaming(boolean)`** with
  auto-detection from model id — adds
  `x-anthropic-beta: fine-grained-tool-streaming-2025-05-14` on the
  wire when routing to Claude.

### Added — infrastructure & tests

- **PIT mutation-testing pilot** (`-Pmutation-testing` profile in
  `tnsai-core/pom.xml`) — baselines 74% mutation coverage. Doc:
  `tnsai-core/agent_docs/mutation-testing.md`.
- **`SourceHygieneTest`** in `tnsai-core` — regression gate forbidding
  `catch (Exception ignored)` and `catch (Throwable t)` in main
  sources (issue #10).
- **`ProviderEnvVarConsistencyTest`** in `tnsai-llm` — drift gate for
  `requireApiKey` call sites + README env-var matrix.
- **Harness evolution audit doc** at
  `tnsai-core/agent_docs/harness-audit.md`.

### Changed — behaviour

- **`Agent.chat()` rejects calls** when the agent is `STOPPING`,
  `STOPPED`, or `FAILED` with `IllegalStateException`.
- **`Agent.stop()` is idempotent.**
- **`ExternalScriptHook.apply()`** narrowed `catch (Throwable t)` →
  `catch (IOException | RuntimeException t)`. JVM-fatal `Error`
  subclasses propagate now.
- **`TelegramAdapter.send()`** retries 429 / 5xx with exponential
  backoff (1s, 2s) up to 3 attempts.
- **`BridgeLLMClient.streamChat()`** throws `LLMCapabilityException`
  (was silent degrade to single-element synthetic stream).
- **`BridgeLLMClient.chat()`** throws typed `LLMException` (was raw
  `RuntimeException`).
- **`BridgeLLMClient.getCapabilities()`** overrides model-id guess
  with honest transport-bound limits.
- **`SystemPromptBuilder` state + action sections** aligned
  byte-for-byte with `RolePromptBuilder`. `@PromptTemplates` +
  `@State.template` + `@State.invariants` honoured.
- **`LLMConfiguration` env lookups** switched from raw
  `System.getenv()` to Core's `EnvLoader.get()` (3 sites).

### Removed

- **`AgentState.READY`** — the 0.4.0 seed value (placeholder for the
  lifecycle FSM that landed in this release). Migrate to
  `AgentState.RUNNING`. No `@Deprecated` shim per project rule.

### Migration notes

- `AgentState.READY` → `AgentState.RUNNING` (search-and-replace).
- `Agent.chat()` after `stop()` now throws `IllegalStateException`.
- `BridgeLLMClient.streamChat()` now throws — install `tnsai-llm` for
  real streaming, or call `chat()` for buffered single-shot.
- `BridgeLLMClient.chat()` failures: catch `LLMException` (or parent
  `TnsAIException`) instead of `RuntimeException`.
- SCOP-rendered prompts changed format. Pinned-format tests should
  update to the canonical `RolePromptBuilder` shape.

### For Consumers

```xml
<dependencyManagement>
    <dependencies>
        <dependency>
            <groupId>io.github.tansuasici</groupId>
            <artifactId>tnsai-bom</artifactId>
            <version>0.5.0</version>
            <type>pom</type>
            <scope>import</scope>
        </dependency>
    </dependencies>
</dependencyManagement>
```

## [0.4.0] — 2026-04-22

### Major: Monorepo migration

The framework's 11 modules (previously hosted as 11 separate GitHub repositories) have been consolidated into a single `TnsAI-Framework/TnsAI` monorepo. Each module's full commit history is preserved under its subdirectory via `git filter-repo`.

### Added

- **`tnsai-parent`** — root POM aggregating all modules with shared plugin/dependency configuration, release profile, GPG signing, and Maven Central publishing.
- **`tnsai-bom`** — Bill of Materials artifact that pins every `tnsai-*` module to a single coherent version. Consumers import once and use modules without version declarations.
- **`@Capability` pattern** (from former `TnsAI.Core` 0.3.1 pre-release work): reusable action contracts as interfaces with `default` bodies that throw `Actions.dispatchedByFramework()`. See `tnsai-core/src/main/java/com/tnsai/capabilities/Capability.java`.
- **`ActionDiscovery` two-pass scanning**: walks role class methods first, then capability interface chains (including super-interfaces). Class-declared methods win de-duplication; role can override any capability's default with a concrete `ActionType.LOCAL` implementation.

### Changed

- Framework is now released as a single lockstep version. Bumping the version touches only the root `pom.xml`; children inherit via `<parent>`.
- CI consolidated into one `.github/workflows/build.yml` plus `release.yml`. Cross-repo clone / `DEPS_PAT` pattern retired.
- Each child `pom.xml` shrinks from ~400 lines to ~50–100 lines.

### For Consumers

Depend on the framework via the BOM:

```xml
<dependencyManagement>
    <dependencies>
        <dependency>
            <groupId>io.github.tansuasici</groupId>
            <artifactId>tnsai-bom</artifactId>
            <version>0.4.0</version>
            <type>pom</type>
            <scope>import</scope>
        </dependency>
    </dependencies>
</dependencyManagement>

<dependencies>
    <dependency>
        <groupId>io.github.tansuasici</groupId>
        <artifactId>tnsai-core</artifactId>
    </dependency>
    <!-- pick any module you need — versions come from the BOM -->
</dependencies>
```

## [0.3.0] - 2026-04-18

First coordinated release across all 11 modules. Published to Maven Central via Central Portal. Previous 0.2.x releases were development-only (per-module Maven snapshots, never on Central).

### Added
- All 11 framework modules (`tnsai-core`, `tnsai-llm`, `tnsai-intelligence`, `tnsai-coordination`, `tnsai-quality`, `tnsai-evaluation`, `tnsai-mcp`, `tnsai-tools`, `tnsai-channels`, `tnsai-integration`, `tnsai-server`) published to Maven Central under `io.github.tansuasici`

### Notes
For change details prior to this coordinated release, see per-module git history under each `tnsai-*/` subdirectory in the monorepo (history was preserved during the 0.4.0 monorepo migration).

---

# FAQ

URL: https://tnsai.dev/docs/help/faq

import { Callout } from 'fumadocs-ui/components/callout'

## Which modules do I need for a simple agent?

`tnsai-core` plus `tnsai-llm`. Core gives you the agent runtime (roles, actions, capabilities, tool dispatch); LLM gives you the provider clients (Anthropic, OpenAI, Gemini, Ollama, and the others — see the [LLM provider matrix](/docs/capabilities/llm/providers), or [`tnsai-llm` README](https://github.com/TnsAI-Framework/TnsAI/tree/main/tnsai-llm) on GitHub). Everything else (channels, MCP, intelligence, coordination, quality, evaluation) is opt-in once you actually need the capability.

→ [Module Overview](/docs/start/module-overview) for the full map.

## Can I use TnsAI without annotations?

Yes. `AgentBuilder` accepts every part of the agent configuration programmatically — roles, tools, LLM clients, capabilities — so you can wire things up entirely from code if your project doesn't want annotation-driven discovery. Annotations are idiomatic and shorter, but they're not load-bearing for the runtime.

→ [Reference → Annotations](/docs/reference/annotations) for the full reference.

## Does TnsAI require Java 21?

Yes. The framework leans on virtual threads, records, sealed types, and switch-on-sealed pattern matching that all landed in Java 21 LTS. The toolchain (Maven compiler plugin, surefire) is pinned to `release` 21 — there's no LTS-bridging build profile, and one isn't planned.

## Is TnsAI production-ready?

The framework is **pre-1.0**, which means the public API can shift between minor versions and there are no long-lived `@Deprecated` shims. We use it in production internally (see the [Sona](/docs/sona/quickstart) project, which is fully dogfooded on the framework), and the test coverage gate is 80% per module. But if your project can't absorb the occasional 30-minute upgrade when a minor version lands, hold off until 1.0.

In practice: read the [changelog](changelog.md) before bumping versions, and lean on the [migration roadmap](/docs/help/migration) for releases that ship breaking changes.

## How does TnsAI compare to LangChain / LlamaIndex / Spring AI / etc.?

Different shape. TnsAI is **Java 21, single-tenant-by-default, lockstep-versioned**, and built around explicit roles + capabilities rather than chained DSL pipelines. The closest neighbour in spirit is Spring AI; the closest in feature set is LangChain's Java port. If you want a side-by-side comparison, the [Sona page on tnsai.dev](https://tnsai.dev/sona) has a table that's kept current with each release.

## More questions?

If a question keeps coming up that isn't covered here, [open a discussion](https://github.com/TnsAI-Framework/TnsAI/discussions) and we'll add it. For bug reports use the issue tracker; for "how do I..." questions discussions are the right home.

---

# Help

URL: https://tnsai.dev/docs/help
Description: Support content: fix problems, look up answers, upgrade between versions.

import { Callout } from 'fumadocs-ui/components/callout'

## Pages

- [Troubleshooting](/docs/help/troubleshooting) — Common errors and resolutions.
- [FAQ](/docs/help/faq) — Frequently asked questions.
- [Migration](/docs/help/migration) — Version-to-version upgrade guides.
- [Changelog](changelog.md) — Release notes.

---

# Migration

URL: https://tnsai.dev/docs/help/migration
Description: TnsAI is pre-1.0, so the public API can shift between minor versions. The framework deliberately does not ship long-lived @Deprecated shims — when a replacement lands, the old surface goes in the same release. That means every upgrade is potentially a small surgical step rather than a sprawling deprecation cleanup, but it also means you have to read the right release notes to know what to change.

import { Callout } from 'fumadocs-ui/components/callout'

## Where the migration steps live

Each release's CHANGELOG entry includes a **Migration** subsection whenever consumers need to do something other than bump the version. So the canonical guide for moving from version X to Y is *every* CHANGELOG entry strictly between X (exclusive) and Y (inclusive).

→ **[Changelog](changelog.md)** has the full list, newest first.

This page is a roadmap: it points to the entries that contain non-trivial breaking changes so you don't have to read the entire changelog when planning an upgrade.

## Releases that require active migration

Only release entries where ignoring the steps would actually break consumer code are listed here. Patch releases that touch internals only are omitted.

### 0.7.0 — Legacy `Tool` interface removed

Function-shape `ToolMethod` is now the only tool abstraction in the runtime. If your code still does `class MyTool implements Tool`, this is the breaking change to plan for.

- Migration paths: convert to a POJO with `@Tool`-annotated methods (recommended), or build a `DynamicToolMethod` directly.
- See [Changelog → 0.7.0 → Migration](changelog.md#070--2026-04-29) for the exact replacement snippets.

### 0.6.0 — `Tool` interface metadata methods + `ChatRequest.tools` type changed

Removed the 12 metadata-discovery default methods (`getCategory`, `getUsageExamples`, etc.) and changed `ChatRequest.getTools()` from `List<ToolDefinition>` to `List<Map<String, Object>>` (JSON-Schema fragments).

- Custom-tool-catalog callers that built `ChatRequest` directly must switch to the new map shape.
- Direct `Tool` implementers can simply drop overrides of the removed metadata methods — they're no longer called.
- See [Changelog → 0.6.0 → Migration](changelog.md#060--2026-04-29) for before/after snippets.

### 0.5.7 — Legacy tool catalog removed from `tnsai-tools`

The 130+ `extends AbstractTool` classes were replaced by \~60 typed POJOs exposing \~190 `@Tool`-annotated methods. Most consumers already used `AgentBuilder.toolPojos(...)` and don't need to touch anything.

- Consumers still using `AgentBuilder.tool(Tool)` keep working through the compatibility surface in this release.
- See [Changelog → 0.5.7 → Consumer migration](changelog.md#057--2026-04-29).

### 0.5.0 — `AgentState.READY` renamed to `RUNNING`

Single-token rename. No deprecation shim: search-and-replace `AgentState.READY` → `AgentState.RUNNING` in your code.

Other 0.5.0 behaviour changes that *can* surface as silent breaks if your code depended on them:

- `Agent.chat()` now throws `IllegalStateException` after the agent has been stopped (was silent / undefined before).
- `BridgeLLMClient.streamChat()` now throws `LLMCapabilityException` (was returning a single-element synthetic stream).
- `BridgeLLMClient.chat()` now throws typed `LLMException` (was raw `RuntimeException`).

See [Changelog → 0.5.0 → Migration notes](changelog.md#050--2026-04-26).

### 0.4.0 — Monorepo migration

Not a code break, but a coordinate break: the 11 separately-published modules consolidated under a single `tnsai-bom`. The recommended dependency shape is now to import the BOM once and depend on individual modules without `<version>`.

- See [Changelog → 0.4.0 → For Consumers](changelog.md#040--2026-04-22) for the new dependency snippet.

## When in doubt

If you're skipping multiple versions, walk forward one minor at a time and read the migration subsection of each entry. The deletions are intentionally aggressive — sleeping on the changelog for six months and trying to jump from 0.5 to 0.10 in one go is harder than four 30-minute upgrade sessions spread across releases.

For changes that aren't covered above but caused you grief, [open a discussion on the framework repo](https://github.com/TnsAI-Framework/TnsAI/discussions) — the entry will land here in the next release.

---

# Troubleshooting

URL: https://tnsai.dev/docs/help/troubleshooting
Description: A short, opinionated checklist for the failure modes that come up most often when picking up the framework. If your symptom isn't here, open a discussion — the next pass updates this page.

import { Callout } from 'fumadocs-ui/components/callout'

## "API key not configured" / credentials missing

LLM clients in `tnsai-llm` read their credentials from environment variables by default. Each provider expects its own variable:

| Provider   | Variable                                      |
| ---------- | --------------------------------------------- |
| Anthropic  | `ANTHROPIC_API_KEY`                           |
| OpenAI     | `OPENAI_API_KEY`                              |
| Gemini     | `GEMINI_API_KEY`                              |
| Ollama     | none (local daemon)                           |
| NVIDIA NIM | `NVIDIA_API_KEY`                              |
| others     | see the provider list in `tnsai-llm`'s README |

Things to check, in order:

1. The variable is set in the *process* where the JVM runs — not just in your shell. IDE run-configs and `systemd` units need their own env block.
2. The variable name matches **exactly** (Anthropic is `ANTHROPIC_API_KEY`, not `CLAUDE_API_KEY`; OpenAI rejects `OPEN_AI_API_KEY`).
3. The key itself works against a `curl` test outside the framework — accounts in trial mode sometimes have models the key can't reach.

If you want to bypass env-var lookup, every client builder accepts an explicit `.apiKey(...)` argument.

## "Tools registered but the LLM never calls them"

Two common causes:

- **The LLM doesn't support function-calling.** Local Ollama models and older OpenAI snapshots (`gpt-3.5-turbo-0301`) frequently lack tool-use. `AgentBuilder.build()` enforces this at build time via `LLMCapabilityValidator` (validator code `AGENT-V003`); if the validator says your LLM can't call tools, swap the model.
- **The tool isn't actually registered.** Use `agent.getToolsForLLM()` after build to confirm the catalog the agent will send. If your `@Tool`-annotated method is missing, check that the POJO holding it is passed via `AgentBuilder.toolPojos(...)` (not the legacy `.tool()` path — see [migration](/docs/help/migration) for 0.7.0).

## "LLM Capability Exception: streaming not supported"

`BridgeLLMClient` (the bridge used in some test harnesses and lightweight setups) does not stream — calling `streamChat()` against it throws `LLMCapabilityException` since 0.5.0. Two paths forward:

- Install `tnsai-llm` and use the real provider client.
- Call `chat()` (buffered single-shot) instead of `streamChat()`.

## "Context too long" / `CONTEXT_TOO_LONG` from a provider

Every provider has its own context window. TnsAI surfaces the provider's typed error code (`CONTEXT_TOO_LONG`) via `LLMException.getProviderDetails().getCode()` so you can branch on it. Mitigations, in order of preference:

1. Trim history before the call — `ConversationCompactor` (in Sona) or your own compaction step.
2. Use a smaller-input model in the same family (Haiku/Mini variants typically have the same context as their larger sibling, but the response budget changes).
3. If the prompt is dominated by RAG context, switch the retrieval strategy from top-K embedding to BM25 with a tighter K — usually wins back 30-60% of input tokens.

## Tests hang or fail flakily under load

Two known patterns:

- **Async cancellation tests** — there's a long-standing 200-iteration concurrent register/cancel race in `CancellationTokenTest$Concurrency` that's hardened but occasionally still flakes under noisy CI runners. If your test re-runs green in isolation, treat the first failure as flake.
- **HTTP server tests on a fixed port** — `HttpMcpServerTest` and tests using `com.sun.net.httpserver.HttpServer` can race on port allocation when run in parallel. Surefire's `forkCount` setting and JUnit's `@Execution(SAME_THREAD)` are the usual fixes.

If your own test hangs, check whether it's waiting on a virtual thread that never produces output — `ToolCallFilter` chains that require user confirmation will block indefinitely unless an approval channel is wired (see [agents → reliability](/docs/agents/reliability)).

## "Agent.chat() throws IllegalStateException"

The agent is already in the `STOPPING` / `STOPPED` / `FAILED` state. Since 0.5.0 calls to `chat()` after `stop()` raise rather than fail silently. Check `agent.getState()` before retrying, and only call `stop()` from cleanup paths.

## Related

- [FAQ](/docs/help/faq) — short answers to the most common starter questions.
- [Migration](/docs/help/migration) — which versions break the API.
- [Reliability guides](/docs/agents/reliability) — resilience patterns, error handling, retries.

---

# TnsAI Framework

URL: https://tnsai.dev/docs

import { Callout } from 'fumadocs-ui/components/callout'

<div align="center">

**Official documentation for the TnsAI framework**

*A Java 21+ multi-agent framework built on BDI + Gaia methodology*

[![License](https://img.shields.io/badge/License-Apache%202.0-green.svg)](https://github.com/TnsAI-Framework/TnsAI.Docs/blob/main/LICENSE)

</div>

---

## Documentation Structure

This repository is organized as a learning path. Each section builds on the previous one.

### [Start Here](/docs/start)

Zero to running agent in under 10 minutes.

- [Installation](/docs/start/installation) · [Quickstart](/docs/start/quickstart) · [Architecture Overview](/docs/start/architecture-overview) · [Module Overview](/docs/start/module-overview)

### [Agents](/docs/agents)

Everything about building a single agent.

- [Fundamentals](/docs/agents/fundamentals) — Agents, roles, action system, events.
- [Behavior](/docs/agents/behavior) — Prompts, output parsing, streaming, variants, memory.
- [Reliability](/docs/agents/reliability) — Resilience, error handling, schema identity.
- [Advanced](/docs/agents/advanced) — Cognitive support, planner/reasoner handles, ensemble execution.

### [Capabilities](/docs/capabilities)

Pluggable building blocks that extend what an agent can do.

- [Tools](/docs/capabilities/tools) — 62 POJO toolkits (\~206 `@Tool` methods) across 29 categories, custom tools, registration.
- [Intelligence](/docs/capabilities/intelligence) — Planning (GOAP/HTN), reasoning (ReAct/ToT), FSM, context, learning.
- [RAG](/docs/capabilities/rag) — Knowledge base, strategies, production pipeline.
- [LLM](/docs/capabilities/llm) — Providers, routing, caching, cost tracking, audio.

### [Multi-Agent](/docs/multi-agent)

Systems of cooperating agents.

- Topologies, workflows, council & voting, negotiation, judge, protocols, communication, auto team, delegation.

### [Observability](/docs/observability)

OpenTelemetry traces, metrics, structured logs, structured tracing for agent runs.

### [Validation](/docs/validation)

Invariant checks, guardrails, schema validation, content guardrails.

### [Contracts](contracts.md)

Design-by-Contract for actions: JEXL preconditions, postconditions, and invariants (`@Contract` / `ContractSpec`).

### [Annotation Catalog](annotation-catalog.md)

Every framework annotation grouped by area, with its runtime status (wired / partial / scaffold) so you know what actually takes effect.

### [Security](/docs/security)

- [Approvals and Annotations](/docs/security/approvals-and-annotations) · [Enforcement](/docs/security/enforcement) · [Encryption](/docs/security/encryption) · [Prompt Injection](/docs/security/prompt-injection)

### [Evaluation](/docs/evaluation)

- Evaluators, benchmarks, quality gates, LLM-as-judge, evaluation hooks.

### [Server](/docs/server)

Run TnsAI as a backend.

- [WebSocket](/docs/server/websocket) — The v1 WebSocket protocol.
- [Tool Approval](/docs/server/tool-approval) — Human-gated destructive operations.
- [Advanced](/docs/server/advanced) — Scaling, persistence, multi-tenant.

### [MCP](/docs/mcp)

Model Context Protocol — client, server, transports, registry.

- [Client](/docs/mcp/client) · [Server](/docs/mcp/server) · [Transports](/docs/mcp/transports) · [Registry](/docs/mcp/registry) · [Advanced](/docs/mcp/advanced)

### [Channels](/docs/channels)

External messaging adapters — Telegram, CLI, Email, Slack, Discord, WhatsApp.

### [Payments](payments/index.md)

Agent-to-agent settlement via the pluggable `PaymentBroker` SPI.

- [x402 (HTTP 402 + EIP-3009)](payments/x402.md) — Coinbase-led standard for HTTP-native USDC micropayments.

### [Sona](/docs/sona)

Self-hosted personal AI assistant built on TnsAI — the first concrete consumer of the framework.

- [Quickstart](/docs/sona/quickstart) — Install, run, send your first Telegram message in 5 minutes.
- [How It Works](/docs/sona/how-it-works) — Gateway, BDI loop, channel adapter SPI, skills, with Mermaid diagrams.

### [Integrate](/docs/integrate)

Bridges to other protocols and frameworks.

- [SCOP](/docs/integrate/scop) — Bridge to the [SCOP framework](https://scop-framework.netlify.app/) (includes advanced topics).

### [Reference](/docs/reference)

Look it up.

- [Annotations](/docs/reference/annotations) — Annotation catalog.
- [Tool Catalog](/docs/reference/tool-catalog) · [SPI](/docs/reference/spi) · [LAM Pattern](/docs/reference/lam-pattern)
- [Configuration](/docs/reference/configuration) · [Glossary](/docs/reference/glossary)

### [Tutorials](/docs/tutorials)

End-to-end walkthroughs.

### [Help](/docs/help)

- [FAQ](/docs/help/faq) · [Troubleshooting](/docs/help/troubleshooting) · [Migration](/docs/help/migration) · [Changelog](help/changelog.md)

---

## Framework Modules

All framework modules live in the [`TnsAI`](https://github.com/TnsAI-Framework/TnsAI) monorepo and ship at the same lockstep version via the [BOM](https://github.com/TnsAI-Framework/TnsAI/tree/main/tnsai-bom).

| Module       | Source                                                                                      | Description                                                          |
| ------------ | ------------------------------------------------------------------------------------------- | -------------------------------------------------------------------- |
| Core         | [tnsai-core](https://github.com/TnsAI-Framework/TnsAI/tree/main/tnsai-core)                 | Agent lifecycle, Role, Action, tool dispatch, annotations            |
| LLM          | [tnsai-llm](https://github.com/TnsAI-Framework/TnsAI/tree/main/tnsai-llm)                   | LLMClient implementations for 20+ providers                          |
| Intelligence | [tnsai-intelligence](https://github.com/TnsAI-Framework/TnsAI/tree/main/tnsai-intelligence) | FSM, planning, reasoning, RAG strategies                             |
| Coordination | [tnsai-coordination](https://github.com/TnsAI-Framework/TnsAI/tree/main/tnsai-coordination) | Multi-agent groups, topologies, voting, negotiation                  |
| Quality      | [tnsai-quality](https://github.com/TnsAI-Framework/TnsAI/tree/main/tnsai-quality)           | Observability, security, validation                                  |
| Evaluation   | [tnsai-evaluation](https://github.com/TnsAI-Framework/TnsAI/tree/main/tnsai-evaluation)     | Agent evaluation and benchmarking                                    |
| MCP          | [tnsai-mcp](https://github.com/TnsAI-Framework/TnsAI/tree/main/tnsai-mcp)                   | Model Context Protocol client + server                               |
| Tools        | [tnsai-tools](https://github.com/TnsAI-Framework/TnsAI/tree/main/tnsai-tools)               | POJO toolkits across web · file · DB · code · media · fintech · …    |
| Channels     | [tnsai-channels](https://github.com/TnsAI-Framework/TnsAI/tree/main/tnsai-channels)         | Telegram / CLI / Email / Slack / Discord / WhatsApp adapters via SPI |
| Integration  | [tnsai-integration](https://github.com/TnsAI-Framework/TnsAI/tree/main/tnsai-integration)   | Cross-module integration tests + SCOP bridge                         |
| Server       | [tnsai-server](https://github.com/TnsAI-Framework/TnsAI/tree/main/tnsai-server)             | Javalin HTTP/WebSocket agent server, RAG service layer               |
| BOM          | [tnsai-bom](https://github.com/TnsAI-Framework/TnsAI/tree/main/tnsai-bom)                   | Bill of Materials — consumer version pinning                         |

External companion repositories:

| Repository                                                      | Description                                                                |
| --------------------------------------------------------------- | -------------------------------------------------------------------------- |
| [TnsAI.Web](https://github.com/TnsAI-Framework/TnsAI.Web)       | Documentation website (Next.js + Fumadocs, [tnsai.dev](https://tnsai.dev)) |
| [TnsAI.Docs](https://github.com/TnsAI-Framework/TnsAI.Docs)     | Official documentation (this repo)                                         |
| [TnsAI.Sona](https://github.com/TnsAI-Framework/TnsAI.Sona)     | Personal AI assistant daemon (dogfooding)                                  |
| [TnsAI.Papers](https://github.com/TnsAI-Framework/TnsAI.Papers) | Research publications                                                      |

## Contributing

Documentation contributions welcome. Guidelines:

- All documentation must be in **English**.
- Use clear, concise language.
- Include code examples where applicable.
- Follow the learning path: Start Here → Agents → Capabilities → Multi-Agent → Observability / Validation / Security / Evaluation → Server / MCP / Channels / Integrate → Reference.

## License

[Apache License 2.0](https://github.com/TnsAI-Framework/TnsAI.Docs/blob/main/LICENSE)

---

<div align="center">

Part of the [TnsAI Framework](https://github.com/TnsAI-Framework) — *Building Intelligent Multi-Agent Systems*

</div>

---

# Agent ecosystem integration

URL: https://tnsai.dev/docs/integrate/agent-ecosystem
Description: How TnsAI fits into the wider agent + tooling landscape: which protocols TnsAI speaks natively, how it's consumed by IDEs and other agent runtimes, and what conventions help LLMs use the framework directly.

import { Callout } from 'fumadocs-ui/components/callout'

There are two integration directions to keep separate in your head:

| Direction                                   | Who                                                               | How                                                            |
| ------------------------------------------- | ----------------------------------------------------------------- | -------------------------------------------------------------- |
| **TnsAI consuming** external tools          | TnsAI agents calling MCP servers, web APIs, sub-LLMs              | `tnsai-mcp` client + `@MCPTool` + `@WebService` annotations    |
| **TnsAI being consumed** by external agents | Cursor / Claude Code / Continue / OpenAI Agents SDK calling TnsAI | `tnsai-mcp` **server** — exposes any TnsAI tool as an MCP tool |

Most of this guide covers the second direction (TnsAI as a server)
because it's the underdocumented half — for TnsAI as MCP **client**
see [MCP / Client](/docs/mcp/client).

---

## TnsAI as an MCP server

`tnsai-mcp` ships a full Model Context Protocol server that wraps
any TnsAI POJO toolkit (the 59 shipped toolkits in `tnsai-tools`,
your own `@Tool`-bearing POJOs, or both) and exposes them over
HTTP/SSE so any MCP-compatible client (Claude Code, Cursor,
Continue.dev, the official MCP Inspector, your own agent runtime)
can call them.

### Minimal example — explicit toolkits, on port 3000

```java
import com.tnsai.enums.BuiltInTool;
import com.tnsai.mcp.server.HttpMcpServer;
import com.tnsai.mcp.server.TnsAIToolProvider;

HttpMcpServer server = HttpMcpServer.builder()
    .port(3000)
    .addToolProvider(TnsAIToolProvider.fromPojos(
        BuiltInTool.WEB_SEARCH_TOOLS.instantiate(),
        BuiltInTool.UTILITY_TOOLS.instantiate(),
        BuiltInTool.PDF_TOOLS.instantiate()
    ))
    .build();

server.start();
// Server is now serving MCP at http://localhost:3000/mcp
// Stop with: server.stop();
```

`TnsAIToolProvider.fromPojos(...)` wraps every `@Tool`-annotated method on
each POJO into an MCP tool definition. There is no global registry to
enumerate — you pick which toolkits to expose explicitly. This is also
the recommended path when exposing TnsAI to external agents: surface a
deliberate subset, not the whole catalogue.

### Mixing custom POJOs

```java
HttpMcpServer server = HttpMcpServer.builder()
    .port(3000)
    .addToolProvider(TnsAIToolProvider.fromPojos(
        BuiltInTool.UTILITY_TOOLS.instantiate(),
        new MyDomainTools(),
        new MyAnalyticsTools()
    ))
    .build();
```

### Builder reference

| Method                                   | Default                  | Purpose                                      |
| ---------------------------------------- | ------------------------ | -------------------------------------------- |
| `.port(int)`                             | `3000`                   | HTTP listen port                             |
| `.path(String)`                          | `/mcp`                   | URL path mount point                         |
| `.serverName(String)`                    | `"TnsAI MCP Server"`     | Identity returned in MCP `initialize`        |
| `.serverVersion(String)`                 | TnsAI's                  | Version returned in MCP `initialize`         |
| `.sessionTtl(Duration)`                  | `Duration.ofMinutes(30)` | Idle session expiry                          |
| `.addToolProvider(ToolProvider)`         | —                        | Register a `ToolProvider`. Multiple allowed. |
| `.addResourceProvider(ResourceProvider)` | —                        | Same, for MCP resources                      |
| `.addPromptProvider(PromptProvider)`     | —                        | Same, for MCP prompts                        |

All providers are interfaces — implement them yourself if you want
to expose data or prompts that aren't TnsAI tools. The
`TnsAIToolProvider` is just the prebuilt adapter for the POJO toolkit
case; for runtime-defined tools (e.g. plugin-loaded), use
`TnsAIToolProvider.fromDynamic(DynamicToolMethod...)` instead.

---

## Consuming TnsAI from Claude Code

Add the running TnsAI server to your Claude Code MCP config. In
`~/.claude/mcp_servers.json` (or wherever Claude Code stores its
MCP config — paths vary by platform):

```json
{
  "mcpServers": {
    "tnsai": {
      "transport": "http",
      "url": "http://localhost:3000/mcp"
    }
  }
}
```

Restart Claude Code. The TnsAI tools appear in the MCP tool
inspector and Claude can call them like any other MCP tool.

For production, expose the MCP server behind authentication and
use HTTPS. The HTTP server is a thin wrapper over the JDK's
built-in `HttpServer`; bring your own reverse proxy for TLS +
auth.

## Consuming TnsAI from Cursor / Continue.dev

Both Cursor and Continue.dev support MCP servers via similar JSON
configurations. Point them at the same `http://localhost:3000/mcp`
endpoint (or a remote URL). The TnsAI tools will appear as
first-class entries in the IDE's tool palette.

Tested clients:

- **Claude Code** ✓ (canonical reference)
- **Cursor** ✓
- **Continue.dev** ✓
- **MCP Inspector** ✓ (the official `npx @modelcontextprotocol/inspector` tool)
- **Any HTTP MCP client** — the wire protocol follows the spec at
  [modelcontextprotocol.io](https://modelcontextprotocol.io)

---

## OpenAI function-calling format

TnsAI agents speak OpenAI's function-calling format internally —
when your agent uses `OpenAIClient`, `AnthropicClient`, etc., the
LLM provider's tool-call format is translated to and from the
common `ChatResponse.ToolCall` shape automatically. You don't need
to do anything special.

If you want to inspect the JSON-Schema fragments TnsAI sends to LLM
providers (e.g. for debugging, or to feed an external hosted
assistant), build a temporary agent and read the dispatcher's
registry:

```java
import com.tnsai.agents.AgentBuilder;
import com.tnsai.enums.BuiltInTool;
import com.tnsai.tools.method.ToolMethodRegistry;

Agent inspector = AgentBuilder.create()
    .role(myRole)
    .builtInTools(BuiltInTool.WEB_SEARCH_TOOLS, BuiltInTool.UTILITY_TOOLS)
    .toolPojos(new MyDomainTools())
    .build();

ToolMethodRegistry registry = inspector.getToolMethodDispatcher().registry();
for (var tool : registry.allMethods()) {
    System.out.println(tool.name() + " — schema: " + tool.schema());
}
```

The same registry powers internal tool-call dispatch — there's no
separate code path for "external" vs "internal" tool schemas.

---

## LangChain / LangChain4j

There's no official LangChain4j adapter (yet). The two frameworks
have overlapping but distinct value propositions:

|                      | LangChain4j                                      | TnsAI                                                                      |
| -------------------- | ------------------------------------------------ | -------------------------------------------------------------------------- |
| **Focus**            | LLM call orchestration, chains, prompt templates | Multi-agent runtime, BDI lifecycle, coordination patterns                  |
| **Tools**            | Implements its own tool spec                     | 62 POJO toolkits (\~206 `@Tool` methods), MCP integration, cross-framework |
| **Integration path** | Use TnsAI's MCP server from a LangChain4j app    | —                                                                          |

In practice: if you're building an agent runtime, use TnsAI. If
you're building an LLM workflow that needs occasional tool calls,
LangChain4j may be lighter; expose TnsAI tools to it via MCP if
you need them.

---

## llms.txt + AGENTS.md conventions in your own project

If you're publishing a service or library that uses TnsAI, two
conventions are worth adopting:

- **`/llms.txt`** at your service root — short markdown index of
  what your service is and where the docs live. The TnsAI site
  itself uses this; see [tnsai.dev/llms.txt](https://tnsai.dev/llms.txt)
  for the format. Spec at [llmstxt.org](https://llmstxt.org).
- **`AGENTS.md`** at your repo root — operating instructions for
  AI coding agents working in your codebase. The TnsAI repos
  themselves do this; see
  [github.com/TnsAI-Framework/TnsAI/blob/main/AGENTS.md](https://github.com/TnsAI-Framework/TnsAI/blob/main/AGENTS.md)
  for an example. Convention page at [agents.md](https://agents.md).

Both files are static markdown — no runtime cost — and let any
agent ramp into your project before it sees the code.

---

## See also

- [MCP / Client](/docs/mcp/client) — TnsAI consuming external MCP tools
- [MCP / Server](/docs/mcp/server) — deeper reference for the MCP server
- [MCP / Transports](/docs/mcp/transports) — Stdio, SSE, HTTP options
- [Tools / Catalog](/docs/capabilities/tools/catalog) — the 62 POJO toolkits you can expose
- [SCOP](/docs/integrate/scop) — bridge to the [SCOP framework](https://scop-framework.netlify.app/) for declarative system prompts + LLM fallback

---

# Integrate

URL: https://tnsai.dev/docs/integrate
Description: Bridges between TnsAI and other protocols or frameworks.

import { Callout } from 'fumadocs-ui/components/callout'

## Pages

- [Agent ecosystem](/docs/integrate/agent-ecosystem) — Expose TnsAI tools as an
  MCP server consumable by Claude Code, Cursor, Continue.dev. OpenAI
  function-calling format. LangChain4j positioning. `llms.txt` /
  `AGENTS.md` conventions.
- [SCOP](/docs/integrate/scop) — Bridge to the [SCOP framework](https://scop-framework.netlify.app/):
  annotation-driven system prompts, HTTP LLM fallback for 11 providers,
  reflection-based action execution.

---

# SCOP Bridge

URL: https://tnsai.dev/docs/integrate/scop
Description: The Integration module connects TnsAI with SCOP — a separate Java multi-agent development and experimentation framework — via reflection-based discovery. No compile-time dependency on SCOP is required; detection happens at runtime. The bridge also provides an HTTP fallback transport for 11 LLM providers when Core's native SPI implementations are unavailable.

import { Callout } from 'fumadocs-ui/components/callout'

Learn more about SCOP at [scop-framework.netlify.app](https://scop-framework.netlify.app/).

## Quick Start

The `SCOPBridge` is a singleton facade that handles everything: building system prompts from annotations, executing actions, and resolving LLM clients. Here is a minimal example showing the three main operations.

```java
SCOPBridge bridge = SCOPBridge.getInstance();

// Build a system prompt from TnsAI annotations
String systemPrompt = bridge.buildSystemPromptFromAnnotations(roleObject);

// Execute a TnsAI action
ActionExecutionResult result = bridge.executeAction(roleObject, "searchPapers", params);
if (result.isSuccess()) {
    System.out.println(result.getResult());
}

// Resolve an LLM client (prefers Core SPI, falls back to HTTP)
Optional<LLMClient> client = bridge.resolveLLMClient(roleObject);
```

## SCOPBridge

The `SCOPBridge` is the main entry point for all SCOP integration. It acts as a facade that delegates to specialized helpers for prompt building, LLM communication, and action execution.

| Component               | Responsibility                                        |
| ----------------------- | ----------------------------------------------------- |
| `SystemPromptBuilder`   | Annotation-to-markdown prompt conversion              |
| `LLMDispatcher`         | Provider-specific HTTP transport (fallback)           |
| `BridgeLLMClient`       | `LLMClient` adapter over HTTP fallback                |
| `LLMConfiguration`      | Resolved LLM config (provider, model, endpoint, keys) |
| `ActionExecutionResult` | Action execution result wrapper                       |

### Factory Methods

The bridge is created as a singleton. You can optionally configure the HTTP read timeout for LLM calls.

```java
// Default read timeout (300 seconds)
SCOPBridge bridge = SCOPBridge.getInstance();

// Custom read timeout
SCOPBridge bridge = SCOPBridge.getInstance(120);  // 120 seconds
```

On construction, the bridge attempts to discover Core's `LLMClientProvider` SPI via `ServiceLoader`. If found, all LLM calls route through real provider implementations (with streaming, retry, etc.). Otherwise, the HTTP fallback path is used.

### System Prompt Building

The bridge can automatically generate a structured system prompt by reading TnsAI annotations from your role class. This means you define the agent's identity, capabilities, and behavior through annotations, and the bridge converts them into a prompt the LLM can understand.

```java
String prompt = bridge.buildSystemPromptFromAnnotations(myRole);
```

Extracted annotations:

- `@RoleSpec` -- Role identity, description, responsibilities
- `@Communication` -- Communication style guidance
- `@State` -- Current state fields for context
- `@ActionSpec` -- Available actions with descriptions and parameters

### Action Execution

The bridge routes action calls through the TnsAI `ActionExecutor` pipeline, which dispatches to the appropriate executor based on the action type. The result includes the return value, pre/post narration text, and error details if something went wrong.

```java
Map<String, Object> params = Map.of("query", "sales trends", "limit", 10);
ActionExecutionResult result = bridge.executeAction(role, "analyzeData", params);

if (result.isSuccess()) {
    Object data = result.getResult();
    String pre = result.getPreNarration();    // Before-action narration
    String post = result.getPostNarration();  // After-action narration
    String display = result.getNarration();   // Smart: postNarration on success, errorMessage on failure
} else {
    String error = result.getErrorMessage();
    Exception cause = result.getException();
}
```

### ActionExecutionResult

The `ActionExecutionResult` wraps the outcome of an action execution, providing a consistent API for both success and failure cases.

```java
// Factory methods
ActionExecutionResult.success(resultObject, preNarration, postNarration);
ActionExecutionResult.error(errorMessage, exception);

// Accessors
result.isSuccess();
result.getResult();           // The action return value
result.getPreNarration();     // Empty string if null
result.getPostNarration();    // Empty string if null
result.getNarration();        // Smart display text
result.getErrorMessage();
result.getException();
```

## LLM Configuration

The `LLMConfiguration` class holds everything needed to connect to an LLM provider: the provider name, model, temperature, max tokens, endpoint URL, and which environment variable holds the API key. The bridge resolves this configuration automatically from your annotations.

### Resolution Hierarchy

When multiple annotations specify LLM configuration, the bridge picks the most specific one. Role-level settings override agent-level, which overrides playground-level.

1. **Role-level** `@RoleSpec(llm = @LLMSpec(...))` -- Highest priority
2. **Agent-level** `@AgentSpec(llm = @LLMSpec(...))` -- Fallback
3. **Playground-level** `@AgentSpec(llm = @LLMSpec(...))` -- Lowest priority

```java
Optional<LLMClient> client = bridge.resolveLLMClient(roleObject);
```

### Supported Providers (11)

The bridge supports 11 LLM providers out of the box. Most use the OpenAI-compatible API format, while Anthropic and Gemini have custom formatting.

| Provider       | API Format                      | Default Endpoint                                                                  | API Key Env Var        |
| -------------- | ------------------------------- | --------------------------------------------------------------------------------- | ---------------------- |
| `OLLAMA`       | OpenAI-compatible               | `http://localhost:11434/v1/chat/completions`                                      | `OLLAMA_API_KEY`       |
| `OPENAI`       | Native                          | `https://api.openai.com/v1/chat/completions`                                      | `OPENAI_API_KEY`       |
| `ANTHROPIC`    | Native (tool format conversion) | `https://api.anthropic.com/v1/messages`                                           | `ANTHROPIC_API_KEY`    |
| `GEMINI`       | Native                          | `https://generativelanguage.googleapis.com/v1beta/models/{model}:generateContent` | `GEMINI_API_KEY`       |
| `AZURE_OPENAI` | OpenAI-compatible               | User-configured                                                                   | `AZURE_OPENAI_API_KEY` |
| `BEDROCK`      | AWS-specific                    | User-configured                                                                   | `AWS_ACCESS_KEY_ID`    |
| `GROQ`         | OpenAI-compatible               | `https://api.groq.com/openai/v1/chat/completions`                                 | `GROQ_API_KEY`         |
| `TOGETHER`     | OpenAI-compatible               | `https://api.together.xyz/v1/chat/completions`                                    | `TOGETHER_API_KEY`     |
| `MISTRAL`      | OpenAI-compatible               | `https://api.mistral.ai/v1/chat/completions`                                      | `MISTRAL_API_KEY`      |
| `DEEPSEEK`     | OpenAI-compatible               | `https://api.deepseek.com/chat/completions`                                       | `DEEPSEEK_API_KEY`     |
| `CUSTOM`       | OpenAI-compatible               | User-configured                                                                   | `CUSTOM_LLM_API_KEY`   |

OpenAI-compatible providers (`OLLAMA`, `OPENAI`, `GROQ`, `TOGETHER`, `MISTRAL`, `DEEPSEEK`, `AZURE_OPENAI`, `CUSTOM`) share the same request/response format. Anthropic and Gemini have provider-specific formatting.

### Endpoint Resolution

The bridge resolves the API endpoint using a three-step fallback chain, so you can override endpoints per-role, per-environment, or rely on provider defaults.

1. Custom endpoint from `@LLMSpec.endpoint()` (if non-empty)
2. Base URL from environment variable (e.g., `OPENAI_BASE_URL`) + provider chat path
3. Default endpoint from the provider table above

```java
LLMConfiguration config = new LLMConfiguration(
    "OPENAI", "gpt-4o", 0.7f, 4096, "", "OPENAI_API_KEY");

String apiUrl = config.resolveApiUrl();  // Checks custom -> env var -> default
String apiKey = config.resolveApiKey();  // Checks custom env -> default env
```

## LLMDispatcher (HTTP Fallback)

The `LLMDispatcher` is the fallback LLM transport used when TnsAI Core's native provider SPI is not on the classpath. It communicates with all 11 LLM providers over HTTP using OkHttp, building provider-specific request bodies and parsing responses. It builds provider-specific request bodies, sends them via `OkHttpClient`, and parses responses.

```java
LLMResponse response = dispatcher.sendToLLM(config, conversationArray, toolsArray);

// Response types
response.getContent();       // Text content
response.getToolCalls();     // Tool call requests (if any)
response.getErrorMessage();  // Error message (if failed)
```

The dispatcher handles:

- OpenAI-compatible request/response format for 8 providers
- Anthropic-specific message format and tool schema conversion
- Gemini-specific content format
- Azure OpenAI endpoint path construction
- API key header injection (`Authorization: Bearer` for most, `x-api-key` for Anthropic)

## BridgeLLMClient (Adapter Pattern)

The `BridgeLLMClient` wraps the HTTP-based `LLMDispatcher` behind the standard `LLMClient` interface. This adapter pattern means the rest of TnsAI does not need to know whether it is talking to a native SPI provider or the HTTP fallback -- the API is identical.

```java
// Created internally by SCOPBridge when resolving LLM clients
BridgeLLMClient client = new BridgeLLMClient(config, dispatcher);

// Implements LLMClient
client.getModel();        // From LLMConfiguration
client.getTemperature();  // From LLMConfiguration
client.getMaxTokens();    // Optional<Integer> from LLMConfiguration

// Standard chat interface
ChatResponse response = client.chat(
    message,
    Optional.of(systemPrompt),
    Optional.of(history),
    Optional.of(tools)
);
```

The adapter translates between Core's `ChatResponse` format and the `LLMResponse` format used by the dispatcher.

## Design Patterns

The integration module uses several well-known design patterns to keep the code modular and testable. This table summarizes them for contributors and architects.

| Pattern                     | Usage                                                                               |
| --------------------------- | ----------------------------------------------------------------------------------- |
| **Facade**                  | `SCOPBridge` delegates to `SystemPromptBuilder`, `LLMDispatcher`, `BridgeLLMClient` |
| **Adapter**                 | `BridgeLLMClient` adapts `LLMDispatcher` to the `LLMClient` interface               |
| **SPI Discovery**           | Prefers Core's `LLMClientProvider` when available on classpath                      |
| **Reflection**              | Detects SCOP classes at runtime without compile-time dependency                     |
| **Configuration Hierarchy** | Role -\\> Agent -\\> Playground annotation resolution                               |

---

# Advanced Topics

The sections below cover internal mechanics of the SCOP Bridge: the annotation-to-prompt conversion pipeline, SCOP Action detection and execution, LLM client resolution internals, and provider-specific request formatting.

## SystemPromptBuilder

`com.tnsai.integration.scop.SystemPromptBuilder` converts TnsAI annotations into structured Markdown system prompts. The bridge calls `SystemPromptBuilder.buildFromAnnotations(target)` to generate a prompt the LLM can understand.

### Annotation Extraction Order

The builder reads annotations from the target class in this order:

1. **`@RoleSpec`** -- Role identity, description, responsibilities
2. **`@RoleSpec.responsibilities()`** -- Each `@Responsibility` with name, description, actions, invariants
3. **`@Communication`** (from `@RoleSpec.communication()` or class-level) -- Tone, formality, verbosity, persona, languages, response format, max length
4. **`@State`** fields -- Current runtime state values (read via reflection)
5. **`@ActionSpec`** methods -- Available actions with descriptions and types
6. **`@Coordination`** -- Negotiation preferences (protocol, max rounds, timeout, concession strategy)

### Generated Prompt Structure

```markdown
# Role: MyAssistant

## Description
A helpful assistant that answers questions about products.

## Responsibilities
- **ProductSearch**: Find products matching user queries
  Actions: searchProducts, filterResults
  Invariants: always return at most 10 results

## Communication Style
- **Tone**: Friendly
- **Formality**: Casual
- **Verbosity**: Concise
- **Allowed Languages**: en
- **CRITICAL LANGUAGE RULE**: You MUST ALWAYS respond in **EN** only...

## Current State
- **mood**: happy (Current emotional state)
- **conversationCount**: 5 (Number of exchanges)

## Available Actions
- **searchProducts**: Search the product catalog [LLM]
- **placeOrder**: Place an order for a product [EXTERNAL_API]

## Negotiation Preferences
- **Protocol**: CONTRACT_NET
- **Max Rounds**: 5
- **Timeout**: 30
- **Reservation Value**: 0.5
- **Concession Strategy**: LINEAR
```

### Communication Style Rules

The builder generates strict language enforcement rules:

- **Single language**: Generates a `CRITICAL LANGUAGE RULE` requiring the LLM to always respond in that language, even if the user writes in another language.
- **Multiple languages**: Generates a softer rule: respond in the user's language if it is in the allowed list, otherwise default to the first language.
- **Default detection**: If `@Communication` has all default values (NEUTRAL tone, NEUTRAL formality, NORMAL verbosity, single "en" language), the class-level `@Communication` annotation is checked as a fallback.

```java
// Single language -> strict enforcement
@Communication(languages = {"tr"})
// -> "CRITICAL LANGUAGE RULE: You MUST ALWAYS respond in TR only."

// Multiple languages -> flexible matching
@Communication(languages = {"en", "tr", "de"})
// -> "Respond in the same language the user writes in..."
```

### Enum Formatting

Enum names are formatted for display: `VERY_FORMAL` becomes `Very Formal`. This conversion uses `Locale.ROOT` for the `toLowerCase` call.

```java
// Internal: SystemPromptBuilder.formatEnum("VERY_FORMAL") -> "Very Formal"
```

## Tool exposure for LLM actions

When a role action is declared `@ActionSpec(type = LLM)`, the LLM call uses the agent's `ToolMethodDispatcher` — built once at `AgentBuilder.build()` time from the registered POJO toolkits and dynamic tools. There is no per-action tool list any more; every `LLM` action sees the agent's complete tool registry.

Configure tools at the agent level:

```java
import com.tnsai.enums.BuiltInTool;

Agent agent = AgentBuilder.create()
    .llm(llmClient)
    .role(myRole)
    .builtInTools(
        BuiltInTool.CSV_TOOLS,        // csv_summary, csv_columns, csv_filter, …
        BuiltInTool.WEB_SEARCH_TOOLS  // brave_search, duckduckgo, wikipedia, …
    )
    .toolPojos(new MyDomainTools())
    .build();
```

For per-action LLM overrides (system prompt, temperature) without touching the agent's global config, set them on `@ActionSpec` directly:

```java
@ActionSpec(
    type = ActionType.LLM,
    description = "Summarise a CSV file's contents",
    llmSystemPrompt = "You are a precise data analyst. Cite columns by name.",
    llmTemperature = 0.2f
)
public String analyzeCsv(String path) {
    return "Summarise the CSV at: " + path;
}
```

See [Tools / Catalog](/docs/capabilities/tools/catalog) for the full per-toolkit method list and [Tool Integration](/docs/capabilities/tools/registration) for the registration paths.

## SCOP Action Detection and Execution

The bridge detects and executes SCOP framework actions through reflection, with no compile-time dependency on SCOP.

### Detection

`SCOPBridge.isSCOPAction(Object)` checks whether a returned object is a SCOP Action by looking for `ai.scop.core.Action` in the class hierarchy:

1. Attempts `Class.forName("ai.scop.core.Action")` and uses `isInstance()`
2. If class loading fails, walks the superclass chain looking for a match by name

### Execution Flow

When an `@ActionSpec` method returns a SCOP Action object, the bridge executes it:

1. Calls `action.execute()` via reflection
2. Attempts to call `action.getPostNarration()` for a result description
3. If no narration is available, falls back to `buildStateResponse()` which reads all `@State`-annotated fields to build a state summary
4. If neither provides text, returns a generic success message

```
executeAction(target, "negotiate", params)
  -> ActionExecutor.execute(actionSpec, target, params, context)
  -> result is SCOP Action?
     YES -> executeSCOPAction(result, "negotiate", target)
            -> result.execute()
            -> result.getPostNarration() || buildStateResponse(target) || generic
     NO  -> "Action negotiate returned: " + result
```

### LLM Client Injection

For actions with `ActionType.LLM`, the bridge resolves an LLM client and injects it into the execution context before the action runs:

```java
if (actionSpec.getType() == ActionType.LLM) {
    Optional<LLMClient> llmClient = resolveLLMClient(target);
    if (llmClient.isPresent()) {
        context.put("llm", llmClient.get());
    }
}
Object result = actionExecutor.execute(actionSpec, target, params, context);
```

### State Response Building

`buildStateResponse(Object target, String actionName)` reads all `@State`-annotated fields from the target class via reflection:

```java
// For a target with @State fields: mood="happy", energy=80
// Returns: "After negotiate: mood: happy, energy: 80"
```

## LLM Client Resolution Internals

`SCOPBridge.resolveLLMClient(Object target)` resolves an LLM client through a multi-step process:

### Owner Chain Resolution

The target's owner agent and playground are resolved via reflection:

```
target.getOwner() -> agent
agent.getPlayground() -> playground
```

These are used for the annotation hierarchy lookup.

### SPI vs Fallback Decision

```
resolveLLMClient(target)
  -> resolveLLMSpec(target, agent, playground)  // Get LLMConfiguration
  -> clientProvider != null?
     YES -> clientProvider.create(provider, model, temperature, maxTokens, null)
            -> Real Core LLMClient (OllamaClient, OpenAIClient, etc.)
            -> Falls back to HTTP if SPI creation fails
     NO  -> new BridgeLLMClient(config, dispatcher)
            -> HTTP-based fallback
```

### SPI Provider Discovery

On construction, `SCOPBridge` calls `LLMClientProvider.discover()` to find Core's SPI implementation. If `tnsai-llm` module is on the classpath, this returns a provider that creates real client instances with streaming and retry support.

```java
// Logged at startup:
// "[SCOPBridge] Using Core LLMClientProvider SPI for LLM calls"
// or:
// "[SCOPBridge] Core LLMClientProvider SPI not found; using HTTP fallback"
```

## Provider-Specific Request Formatting

The `LLMDispatcher` handles the differences between LLM provider APIs. Understanding these details is useful when debugging integration issues.

### OpenAI-Compatible Providers

Eight providers share the same request format: OLLAMA, OPENAI, GROQ, TOGETHER, MISTRAL, DEEPSEEK, AZURE\_OPENAI, CUSTOM.

Request body structure:

```json
{
    "model": "gpt-4o",
    "messages": [...],
    "temperature": 0.7,
    "max_tokens": 4096,
    "tools": [...]
}
```

Headers: `Authorization: Bearer <key>` (except Azure, which uses `api-key: <key>`).

### Anthropic Format

Anthropic uses a different message format. The dispatcher:

1. Extracts `system` role messages and puts them in a top-level `"system"` field
2. Converts OpenAI tool format to Anthropic format (`"parameters"` becomes `"input_schema"`)
3. Sets `anthropic-version: 2024-10-22` header and `x-api-key` header
4. Parses response content blocks (`"text"` and `"tool_use"` types)

Tool format conversion:

```
OpenAI format:                     Anthropic format:
{ "type": "function",              { "name": "search",
  "function": {                      "description": "Search docs",
    "name": "search",                "input_schema": { ... }
    "description": "Search docs",  }
    "parameters": { ... }
  }
}
```

### Gemini Format

Gemini uses a distinct content structure:

1. Maps `"assistant"` role to `"model"`
2. Wraps text in `{ "parts": [{ "text": "..." }] }` format
3. Extracts system prompt into `"systemInstruction"` field
4. Converts tools to `"functionDeclarations"` format
5. Passes API key as a query parameter (`?key=...`)
6. Uses `"generationConfig"` for temperature and `"maxOutputTokens"`

### Response Parsing

Each provider has a dedicated parser:

| Provider          | Method                     | Response Structure                                            |
| ----------------- | -------------------------- | ------------------------------------------------------------- |
| OpenAI-compatible | `parseOpenAIResponse()`    | `choices[0].message.content` or `.tool_calls`                 |
| Anthropic         | `parseAnthropicResponse()` | `content[]` blocks of type `text` or `tool_use`               |
| Gemini            | `parseGeminiResponse()`    | `candidates[0].content.parts[]` with `text` or `functionCall` |

### LLMResponse Types

The `LLMResponse` wrapper normalizes responses across all providers:

```java
public class LLMResponse {
    public enum Type { TEXT, TOOL_CALLS, ERROR }

    // Factory methods
    static LLMResponse text(String content, JSONObject rawMessage);
    static LLMResponse toolCalls(JSONArray toolCalls, JSONObject rawMessage);
    static LLMResponse error(String errorMessage);

    // Query methods
    boolean isSuccess();         // type != ERROR
    boolean hasToolCalls();      // type == TOOL_CALLS
    boolean hasText();           // type == TEXT
    String getDisplayContent();  // Text content, or "[ERROR] ..." for errors
}
```

## BridgeLLMClient Internals

The `BridgeLLMClient` adapts the HTTP-based `LLMDispatcher` to Core's `LLMClient` interface. It translates between the two APIs:

### chat() Method

1. Builds a `JSONArray` conversation from `systemPrompt`, `history`, and `message`
2. Converts `tools` list to `JSONArray`
3. Calls `dispatcher.sendToLLM(config, conversation, toolsArray)`
4. Translates `LLMResponse.toolCalls` into `ChatResponse.ToolCall` records
5. Returns `ChatResponse.withToolCalls(...)` or `ChatResponse.textOnly(...)`

### streamChat() Limitation

Streaming is not supported through the HTTP fallback path. The `streamChat()` method delegates to `chat()` and wraps the result in a single-element `Stream<String>`:

```java
@Override
public Stream<String> streamChat(...) {
    ChatResponse response = chat(message, systemPrompt, history, tools);
    return Stream.of(response.getContent());
}
```

For real streaming support, ensure the `tnsai-llm` module is on the classpath so the SPI path is used instead.

## Endpoint Resolution Details

`LLMConfiguration.resolveApiUrl()` follows a three-step fallback:

| Priority | Source                             | Example                                                              |
| -------- | ---------------------------------- | -------------------------------------------------------------------- |
| 1        | `@LLMSpec.endpoint()` (annotation) | `https://my-custom-llm.example.com/v1/chat`                          |
| 2        | Environment variable + path        | `OPENAI_BASE_URL=https://proxy.example.com` + `/v1/chat/completions` |
| 3        | Provider default                   | `https://api.openai.com/v1/chat/completions`                         |

Base URL environment variables per provider:

| Provider       | Env Var                 |
| -------------- | ----------------------- |
| `OLLAMA`       | `OLLAMA_BASE_URL`       |
| `OPENAI`       | `OPENAI_BASE_URL`       |
| `ANTHROPIC`    | `ANTHROPIC_BASE_URL`    |
| `GEMINI`       | `GEMINI_BASE_URL`       |
| `AZURE_OPENAI` | `AZURE_OPENAI_BASE_URL` |
| `GROQ`         | `GROQ_BASE_URL`         |
| `TOGETHER`     | `TOGETHER_BASE_URL`     |
| `MISTRAL`      | `MISTRAL_BASE_URL`      |
| `DEEPSEEK`     | `DEEPSEEK_BASE_URL`     |
| `CUSTOM`       | `CUSTOM_LLM_BASE_URL`   |

API key resolution follows the same pattern: annotation `apiKeyEnv` first, then provider default env var. Ollama does not require an API key.

## Error Handling

### LLM Errors

The dispatcher catches and wraps errors at multiple levels:

- **IOException**: Connection errors, timeouts -\\> `LLMResponse.error("Connection error: ...")`
- **JSONException**: Malformed responses -\\> `LLMResponse.error("Invalid response format: ...")`
- **API errors**: Provider-specific error responses -\\> `LLMResponse.error("Provider Error: ...")`
- **Missing API key**: Detected before the request -\\> `LLMResponse.error("API key not configured for ...")`

### Action Errors

Action execution failures are wrapped in `ActionExecutionResult.error()`:

```java
// Not found
ActionExecutionResult.error("Action not found: searchPapers", null);

// Execution failure
ActionExecutionResult.error("Action analyzeData failed: NullPointerException", exception);

// Null target
ActionExecutionResult.error("Target object is null", null);
```

## Configuration Reference

### LLMConfiguration

Full constructor:

```java
new LLMConfiguration(
    String provider,      // "OPENAI", "ANTHROPIC", "GEMINI", etc.
    String model,         // "gpt-4o", "claude-sonnet-4-20250514", etc.
    float temperature,    // 0.0 - 2.0
    int maxTokens,        // 0 = provider default
    String endpoint,      // Custom endpoint URL (empty = use default)
    String apiKeyEnv      // Custom env var name (empty = use default)
)
```

Query methods:

| Method                 | Returns                                             |
| ---------------------- | --------------------------------------------------- |
| `hasModel()`           | Whether model is set (non-null, non-blank)          |
| `hasEndpoint()`        | Whether a custom endpoint is configured             |
| `isOpenAICompatible()` | Whether the provider uses OpenAI-compatible format  |
| `requiresApiKey()`     | `true` for all providers except OLLAMA              |
| `resolveApiUrl()`      | Full API URL (annotation -\\> env var -\\> default) |
| `resolveApiKey()`      | API key (annotation env -\\> default env -\\> null) |
| `getBaseUrlEnvVar()`   | Environment variable name for base URL              |
| `getApiKeyEnvVar()`    | Environment variable name for API key               |

---

# MCP Advanced Features

URL: https://tnsai.dev/docs/mcp/advanced
Description: Composio integration, database MCP clients, Goose interoperability, Stripe payments, testing utilities, and OAuth authentication.

import { Callout } from 'fumadocs-ui/components/callout'

## Composio Integration

The `com.tnsai.mcp.composio` package bridges the Composio platform (500+ tool integrations) with the MCP ecosystem.

### ComposioClient

REST client for the Composio API. Provides typed access to apps, tools, and execution.

```java
ComposioClient client = new ComposioClient("your-api-key");

// List available apps
List<ComposioClient.ComposioApp> apps = client.listApps();
// ComposioApp(name, description, logoUrl, connected)

// List tools for an app
List<ComposioClient.ComposioTool> tools = client.listTools("github");
// ComposioTool(name, description, appName, inputSchema)

// Execute a tool
JsonNode result = client.executeTool("github_create_issue",
    Map.of("title", "Bug report", "body", "Details..."));

// Check connection status
ComposioClient.ConnectionStatus status = client.getConnectionStatus("github");
// ConnectionStatus(appName, connected, authMethod)
```

Key methods:

| Method                                     | Returns              | Description                    |
| ------------------------------------------ | -------------------- | ------------------------------ |
| `listApps()`                               | `List<ComposioApp>`  | All available Composio apps    |
| `listTools(String appName)`                | `List<ComposioTool>` | Tools for a specific app       |
| `executeTool(String toolName, Map params)` | `JsonNode`           | Execute a tool with parameters |
| `getConnectionStatus(String appName)`      | `ConnectionStatus`   | OAuth connection status        |

### ComposioAuthBridge

Bridges Composio's API key authentication to MCP-compatible auth headers.

```java
ComposioAuthBridge bridge = new ComposioAuthBridge("your-api-key");

// Verify API key validity (makes a test API call)
if (bridge.verifyApiKey()) {
    Map<String, String> headers = bridge.getAuthHeaders();
    // {"X-API-Key": "...", "Accept": "application/json"}
}
```

Key methods:

| Method             | Returns               | Description                       |
| ------------------ | --------------------- | --------------------------------- |
| `getAuthHeaders()` | `Map<String, String>` | Auth headers for API calls        |
| `verifyApiKey()`   | `boolean`             | Verify key validity via test call |
| `getApiKey()`      | `String`              | The configured API key            |

### ComposioDiscovery

Converts Composio tools to MCP-compatible `McpToolDefinition` and `McpServerEntry` objects for registry integration.

```java
ComposioClient client = new ComposioClient("api-key");
ComposioDiscovery discovery = new ComposioDiscovery(client);

// Discover all tools as MCP definitions
List<McpToolDefinition> allTools = discovery.discoverTools();

// Discover tools for a specific app
List<McpToolDefinition> githubTools = discovery.discoverTools("github");

// Convert to MCP registry entries (one per Composio app)
List<McpServerEntry> entries = discovery.toServerEntries();
```

Key methods:

| Method                          | Returns                   | Description                       |
| ------------------------------- | ------------------------- | --------------------------------- |
| `discoverTools()`               | `List<McpToolDefinition>` | All tools from all connected apps |
| `discoverTools(String appName)` | `List<McpToolDefinition>` | Tools for a specific app          |
| `toServerEntries()`             | `List<McpServerEntry>`    | Apps as MCP server entries        |

## Database MCP Clients

Convenience wrappers around `McpClient` for specific database MCP servers.

### Neo4jMcpClient

Wraps MCP tool calls to a Neo4j graph database server.

```java
McpClient mcpClient = McpClient.create(transport);
mcpClient.connect();
mcpClient.initialize();

Neo4jMcpClient neo4j = new Neo4jMcpClient(mcpClient);

// Execute Cypher queries
JsonNode result = neo4j.executeCypher("MATCH (n:Person) RETURN n LIMIT 10");

// Explore the schema
JsonNode labels = neo4j.listNodeLabels();
JsonNode schema = neo4j.getSchema();

// Check availability
boolean available = neo4j.isAvailable();
// true when mcpClient.isConnected() && mcpClient.isInitialized()
```

Key methods:

| Method                        | Returns     | Description               |
| ----------------------------- | ----------- | ------------------------- |
| `executeCypher(String query)` | `JsonNode`  | Execute a Cypher query    |
| `listNodeLabels()`            | `JsonNode`  | List all node labels      |
| `getSchema()`                 | `JsonNode`  | Get graph database schema |
| `isAvailable()`               | `boolean`   | Check server availability |
| `getMcpClient()`              | `McpClient` | Underlying MCP client     |

### PostgresMcpClient

Wraps MCP tool calls to a PostgreSQL database server.

```java
McpClient mcpClient = McpClient.create(transport);
mcpClient.connect();
mcpClient.initialize();

PostgresMcpClient postgres = new PostgresMcpClient(mcpClient);

// Execute SQL queries
JsonNode result = postgres.executeQuery("SELECT * FROM users LIMIT 10");

// Explore schema
JsonNode tables = postgres.listTables();
JsonNode description = postgres.describeTable("users");

boolean available = postgres.isAvailable();
```

Key methods:

| Method                        | Returns     | Description               |
| ----------------------------- | ----------- | ------------------------- |
| `executeQuery(String sql)`    | `JsonNode`  | Execute a SQL query       |
| `listTables()`                | `JsonNode`  | List all tables           |
| `describeTable(String table)` | `JsonNode`  | Describe table schema     |
| `isAvailable()`               | `boolean`   | Check server availability |
| `getMcpClient()`              | `McpClient` | Underlying MCP client     |

## Goose Interoperability

The `com.tnsai.mcp.goose` package enables TnsAI and Goose (Block's AI agent framework) to share tools.

### TnsAIGooseExtension

Generates a Goose-compatible extension manifest from an MCP server's tool definitions.

```java
McpClient client = McpClient.create(transport);
client.connect();
client.initialize();

// Generate manifest
TnsAIGooseExtension.ExtensionManifest manifest =
    TnsAIGooseExtension.generateManifest(client);
// ExtensionManifest(name, version, description, tools)
// ExtensionTool(name, description, inputSchema)

// Export as JSON (place in Goose's extension directory)
String json = TnsAIGooseExtension.toJson(manifest);
```

### GooseRecipeParser

Parses Goose recipe files (JSON workflow definitions) into structured objects that can be converted to MCP tool calls.

**Recipe JSON format:**

```json
{
  "name": "deploy-app",
  "description": "Deploy an application",
  "steps": [
    {
      "tool": "build_project",
      "params": { "target": "production" },
      "output": "buildResult"
    },
    {
      "tool": "deploy",
      "params": { "artifact": "{{buildResult}}" }
    }
  ]
}
```

```java
// Parse a recipe
GooseRecipeParser.Recipe recipe = GooseRecipeParser.parse(jsonContent);
// Recipe(name, description, steps)
// RecipeStep(tool, params, outputVariable)

// Convert to MCP tool call parameter maps
List<Map<String, Object>> mcpCalls = GooseRecipeParser.toMcpCalls(recipe);
// Each map: {"name": "tool_name", "arguments": {...}}
```

## Stripe Integration

The `com.tnsai.mcp.stripe` package provides Stripe payment operations via MCP.

### StripeMcpClient

Wraps MCP tool calls to a Stripe MCP server.

```java
McpClient mcpClient = McpClient.create(transport);
mcpClient.connect();
mcpClient.initialize();

StripeMcpClient stripe = new StripeMcpClient(mcpClient);

// Payments
JsonNode payment = stripe.createPaymentIntent(2000, "usd"); // amount in cents
JsonNode payments = stripe.listPayments(10);

// Account
JsonNode balance = stripe.getBalance();

// Customers
JsonNode customers = stripe.listCustomers(10);
JsonNode newCustomer = stripe.createCustomer("Jane Doe", "jane@example.com");
```

### StripePaymentTool

Adapter that wraps `StripeMcpClient` as a tool-like interface, routing named operations.

```java
StripePaymentTool tool = new StripePaymentTool(stripeMcpClient);

// Execute by operation name
JsonNode result = tool.execute("get_balance", Map.of());
JsonNode payment = tool.execute("create_payment_intent",
    Map.of("amount", 2000, "currency", "usd"));

// List supported operations
List<String> ops = tool.listOperations();
// ["create_payment_intent", "list_payments", "get_balance",
//  "list_customers", "create_customer"]
```

## MCP Testing Utilities

The `com.tnsai.mcp.testing` package provides tools for testing MCP servers and clients without network I/O.

### McpServerTestKit

JUnit 5 extension that sets up a `MockMcpServer`, `MockMcpTransport`, and `McpClient` for each test.

```java
@RegisterExtension
McpServerTestKit testKit = new McpServerTestKit();

@Test
void testToolExecution() throws Exception {
    // Configure mock tool responses
    testKit.getServer().whenToolReturns("echo",
        objectMapper.createObjectNode().put("text", "hello"));

    // Assert tool exists
    testKit.assertToolExists("echo");

    // Assert tool returns expected result
    testKit.assertToolReturns("echo", Map.of("msg", "hi"),
        result -> result.has("text"));
}
```

Key methods:

| Method                                      | Returns            | Description                 |
| ------------------------------------------- | ------------------ | --------------------------- |
| `getServer()`                               | `MockMcpServer`    | The mock server instance    |
| `getTransport()`                            | `MockMcpTransport` | The mock transport          |
| `getClient()`                               | `McpClient`        | Connected MCP client        |
| `assertToolExists(String)`                  | void               | Assert a tool is registered |
| `assertToolReturns(String, Map, Predicate)` | void               | Assert tool result matches  |

### MockMcpServer

In-memory MCP server for unit testing. Handles JSON-RPC messages and routes `initialize`, `tools/list`, `tools/call`, `resources/list`, `resources/read`, `prompts/list`, and `prompts/get`.

```java
MockMcpServer server = new MockMcpServer("test-server", "1.0.0");

// Register tool handlers
server.whenTool("calculate", args -> {
    int a = (int) args.get("a");
    int b = (int) args.get("b");
    return objectMapper.createObjectNode().put("result", a + b);
});

// Static tool response
server.whenToolReturns("echo", resultNode);

// Tool that simulates an error
server.whenToolThrows("fail", -32000, "Something went wrong");

// Register resources
server.whenResource("file://data.txt", "file contents");

// Register prompts
server.whenPrompt("greeting", promptResultNode);

// Process a raw JSON-RPC message
String response = server.processMessage(jsonRpcRequest);
```

## OAuth Authentication

The `com.tnsai.mcp.auth` package provides OAuth 2.0 support for authenticated MCP connections.

### OAuthMcpTransport

Transport decorator that adds OAuth Bearer token authentication to any `McpTransport`. Manages token lifecycle and ensures tokens are valid before each operation.

```java
McpTokenManager tokenManager = new McpTokenManager();
// ... store tokens after OAuth flow ...

OAuthMcpTransport transport = OAuthMcpTransport.builder()
    .delegate(new SSETransport.Builder().sseEndpoint("...").build())
    .tokenManager(tokenManager)
    .serverUrl("https://mcp.example.com")
    .tokenEndpoint("https://auth.example.com/token")
    .clientId("my-client")
    .build();

McpClient client = new McpClient(transport);
client.connect();

// Query token state
Optional<String> bearerToken = transport.getBearerToken(); // "Bearer eyJ..."
boolean authenticated = transport.isAuthenticated();
```

Builder parameters:

| Parameter       | Required | Description                       |
| --------------- | -------- | --------------------------------- |
| `delegate`      | Yes      | Underlying transport to wrap      |
| `tokenManager`  | Yes      | Token storage and refresh manager |
| `serverUrl`     | Yes      | MCP server URL (token lookup key) |
| `tokenEndpoint` | No       | OAuth token endpoint for refresh  |
| `clientId`      | No       | OAuth client ID for refresh       |

### PkceFlow

PKCE (Proof Key for Code Exchange) flow implementation with S256 challenge method, per RFC 7636.

**Step 1: Generate challenge**

```java
PkceFlow.PkceChallenge challenge = PkceFlow.generateChallenge();
// PkceChallenge(codeVerifier, codeChallenge, codeChallengeMethod="S256")
```

**Step 2: Build authorization URL**

```java
String authUrl = PkceFlow.buildAuthorizationUrl(
    "https://auth.example.com/authorize",
    "my-client-id",
    "http://localhost:8080/callback",
    "openid profile",          // scope
    "random-state-for-csrf",   // state
    challenge
);
// User visits authUrl, authorizes, callback receives code
```

**Step 3: Exchange code for tokens**

```java
PkceFlow.TokenResponse token = PkceFlow.exchangeCode(
    "https://auth.example.com/token",
    "my-client-id",
    authCode,
    "http://localhost:8080/callback",
    challenge.codeVerifier()
);
// TokenResponse(accessToken, refreshToken, tokenType, expiresIn, scope, obtainedAt)
```

**Step 4: Refresh tokens**

```java
PkceFlow.TokenResponse refreshed = PkceFlow.refreshToken(
    "https://auth.example.com/token",
    "my-client-id",
    token.refreshToken()
);

// Check token state
boolean expired = token.isExpired();
boolean expiringSoon = token.isExpiringSoon(Duration.ofMinutes(5));
```

## Cross-References

- [MCP Client](/docs/mcp/client) -- core MCP client and transport
- [MCP Server](/docs/mcp/server) -- building MCP servers
- [MCP Registry](/docs/mcp/registry) -- unified registry for MCP servers
- [MCP Transports](/docs/mcp/transports) -- stdio, SSE, HTTP, and streamable HTTP
- [Tools Advanced](/docs/capabilities/tools/advanced) -- Composio tool integration in TnsAI.Tools

---

# MCP Client

URL: https://tnsai.dev/docs/mcp/client
Description: The McpClient connects to any MCP-compatible server and exposes its tools, resources, and prompts.

import { Callout } from 'fumadocs-ui/components/callout'

## Quick Start

The simplest way to get started is to connect to an existing MCP server, discover its tools, and call one. This example connects to a filesystem server that lets you read and write files.

```java
// Connect to a filesystem MCP server
McpClient client = McpClient.builder()
    .transport(StdioTransport.builder()
        .command("npx", "-y", "@modelcontextprotocol/server-filesystem", "/tmp")
        .build())
    .clientName("MyApp")
    .clientVersion("1.0.0")
    .build();

client.connect();
client.initialize();

// Discover and use tools
List<McpToolDefinition> tools = client.listTools();
JsonNode result = client.executeTool("read_file", Map.of("path", "/tmp/test.txt"));

client.disconnect();
```

## Builder

The builder lets you configure all aspects of the client before connecting. You must provide a transport (how the client talks to the server), and can optionally set timeouts, project roots, and a handler for server-initiated LLM requests.

```java
McpClient client = McpClient.builder()
    .transport(transport)                          // Required: StdioTransport, HttpTransport, SSETransport
    .clientName("MyApp")                           // Client identification
    .clientVersion("1.0.0")
    .requestTimeout(Duration.ofSeconds(30))
    .roots(List.of(new Root("/project", "Project")))
    .samplingRequestHandler(req -> response)       // Handle server-initiated LLM requests
    .build();
```

## Tools

Tools are functions that an MCP server exposes for clients to call. You can list all available tools and then execute any of them by name, passing arguments as a map.

```java
List<McpToolDefinition> tools = client.listTools();
JsonNode result = client.executeTool("tool_name", Map.of("key", "value"));
```

## Resources

Resources represent data that the server makes available for reading, such as files, database entries, or API responses. You can browse and read them by URI.

```java
List<Resource> resources = client.listResources();
ResourceContent content = client.readResource("file:///path/to/file");
```

## Prompts

Prompts are reusable prompt templates that the server defines. You can list them and render a specific prompt with arguments, which is useful for standardizing how you ask an LLM to process certain types of data.

```java
List<Prompt> prompts = client.listPrompts();
String rendered = client.getPrompt("summarize", Map.of("text", longText));
```

## Sampling (Server-Initiated LLM Calls)

MCP servers can request LLM completions from the client via the sampling protocol. The client registers a `samplingRequestHandler` that the server invokes when it needs text generation.

### SamplingRequest

A `SamplingRequest` describes what the server wants the LLM to generate. It is built with a fluent builder and requires at least one message and a `maxTokens` limit.

```java
SamplingRequest request = SamplingRequest.builder()
    .addUserMessage("What is the capital of France?")
    .systemPrompt("You are a geography expert.")
    .maxTokens(100)
    .temperature(0.7)
    .stopSequences(List.of("\n\n"))
    .includeContext("thisServer")        // "none", "thisServer", or "allServers"
    .modelPreferences(ModelPreferences.builder()
        .intelligencePriority(0.8)
        .speedPriority(0.5)
        .costPriority(0.3)
        .hint("claude-sonnet-4-20250514")
        .build())
    .metadata(Map.of("requestSource", "agent"))
    .build();
```

Builder methods: `addUserMessage(text)`, `addAssistantMessage(text)`, `addMessage(SamplingMessage)`, `messages(list)`, `systemPrompt(str)`, `includeContext(str)`, `temperature(double)`, `maxTokens(int)`, `stopSequences(list)`, `modelPreferences(prefs)`, `metadata(map)`.

### SamplingResponse

The `SamplingResponse` is what your handler returns to the server after generating a completion. It contains the generated text, which model was used, and why generation stopped.

```java
SamplingResponse response = ...;
String text = response.getTextContent();  // extracts text from content (handles string, object, or array)
String model = response.getModel();       // which model actually responded
String reason = response.getStopReason(); // "endTurn", "stopSequence", "maxTokens"
```

### Registering the Handler

You wire up the sampling handler when building the client. When the MCP server needs an LLM completion, your handler is called with the request and must return a response.

```java
McpClient client = McpClient.builder()
    .transport(transport)
    .samplingRequestHandler(request -> {
        // Forward to your LLM
        String answer = myLlm.complete(request.getMessages(), request.getMaxTokens());
        SamplingResponse resp = new SamplingResponse();
        resp.setRole("assistant");
        resp.setModel("my-model");
        resp.setContent(new TextNode(answer));
        resp.setStopReason("endTurn");
        return resp;
    })
    .build();
```

## Change Notifications

MCP servers can notify clients when their available tools or resources change. Register callbacks to react to these changes, for example by refreshing your cached tool list.

```java
client.setOnToolsListChanged(notification -> refreshTools());
client.setOnResourcesListChanged(notification -> refreshResources());
```

## Agent Integration

### Using MCP Tools in Agents

The `McpToolBridge` converts tools from any MCP server into `DynamicToolMethod` instances — runtime-defined tools that share the same registry as POJO `@Tool` methods. The agent can then call these tools during its LLM reasoning loop without distinguishing them from local toolkits.

```java
import com.tnsai.tools.method.DynamicToolMethod;

// Bridge any MCP server's tools into TnsAI
McpToolBridge bridge = McpToolBridge.stdio(
    "npx", "-y", "@modelcontextprotocol/server-filesystem", "/tmp"
);
bridge.connect();

// Convert remote MCP tools to runtime-defined TnsAI tools
List<DynamicToolMethod> tools = bridge.toTnsAITools();

// Register with agent
Agent agent = AgentBuilder.create()
    .llm(llm)
    .role(role)
    .dynamicTools(tools)
    .build();
```

### MCP Server Configuration

For agents that connect to remote MCP servers. Immutable after construction.

**Factory methods** for common cases:

```java
// Endpoint + API key (sends Authorization: Bearer <key>)
McpServerConfig config = McpServerConfig.of("https://mcp.firecrawl.dev/v1", "fc-xxx");

// Endpoint only, no auth
McpServerConfig config = McpServerConfig.of("https://localhost:8080/mcp");
```

**Builder** for full control:

```java
McpServerConfig config = McpServerConfig.builder()
    .name("firecrawl")                              // optional: auto-derived from endpoint host if omitted
    .endpoint("https://mcp.firecrawl.dev/v1")       // required
    .apiKey("fc-xxx")                               // sets Authorization: Bearer header
    .build();

// Custom API key header (non-Authorization)
McpServerConfig config = McpServerConfig.builder()
    .endpoint("https://api.example.com/mcp")
    .apiKey("xxx", "X-API-Key")                     // sends X-API-Key: xxx
    .header("X-Workspace", "my-workspace")           // additional headers
    .timeout(Duration.ofSeconds(60))                 // default: 30s
    .build();

// Shorthand timeout
McpServerConfig config = McpServerConfig.builder()
    .endpoint("https://api.example.com/mcp")
    .timeoutSeconds(60)
    .build();
```

**Auto-derived name:** When `name` is omitted, it is extracted from the endpoint host (e.g., `https://mcp.firecrawl.dev` becomes `"firecrawl"`). Falls back to `"mcp-server"`.

**Accessors:** `getName()`, `getEndpoint()`, `getHeaders()` (unmodifiable map), `getTimeout()`, `hasApiKey()`.

**Agent usage:**

```java
protected List<McpServerConfig> getMcpServers() {
    return List.of(
        McpServerConfig.of("https://mcp.firecrawl.dev", "fc-xxx"),
        McpServerConfig.builder()
            .endpoint("https://my-internal-mcp.local:9090/mcp")
            .apiKey("internal-key", "X-Internal-Auth")
            .timeoutSeconds(120)
            .build()
    );
}
```

---

# MCP

URL: https://tnsai.dev/docs/mcp
Description: Model Context Protocol — client, server, transports, registry.

import { Callout } from 'fumadocs-ui/components/callout'

## Pages

- [Client](/docs/mcp/client) — Connect to MCP servers and invoke their tools.
- [Server](/docs/mcp/server) — Expose TnsAI tools through an MCP server.
- [Transports](/docs/mcp/transports) — stdio, HTTP, SSE, Streamable HTTP, plus OAuth decorator.
- [Registry](/docs/mcp/registry) — Discover and manage MCP servers.
- [Advanced](/docs/mcp/advanced) — Custom notifications, resource streaming, prompt templates.

---

# MCP Registry

URL: https://tnsai.dev/docs/mcp/registry
Description: The MCP module provides a unified registry that aggregates MCP server entries from 4 public sources, with deduplication, caching, and protocol version filtering. It also includes a testing toolkit for MCP server development.

import { Callout } from 'fumadocs-ui/components/callout'

## UnifiedMcpRegistry

The `UnifiedMcpRegistry` is a one-stop shop for discovering MCP servers. It queries multiple public registries (like the official MCP registry and Smithery.ai), deduplicates results by server name, and caches responses so repeated searches are fast.

```java
// Default: all 4 sources, 10-minute cache
UnifiedMcpRegistry registry = new UnifiedMcpRegistry();

// Search for servers
List<McpServerEntry> results = registry.search("filesystem");

// Look up a specific server
Optional<McpServerEntry> server = registry.getServer("filesystem");

// List all available servers (aggregated from all sources)
List<McpServerEntry> all = registry.listAll();
```

### Custom Configuration

You can pick which registry sources to use and set a custom cache TTL. This is useful if you only want results from specific sources or need fresher data.

```java
UnifiedMcpRegistry registry = new UnifiedMcpRegistry(
    List.of(
        new OfficialRegistryClient(),
        new SmitheryClient()
    ),
    Duration.ofMinutes(5)  // Custom cache TTL
);
```

### Source Priority

When the same server appears in multiple registries, the registry uses priority to decide which entry to keep. Higher-priority sources are considered more authoritative.

| Priority    | Source           | Class                    | Registry              |
| ----------- | ---------------- | ------------------------ | --------------------- |
| 4 (highest) | `official`       | `OfficialRegistryClient` | Official MCP Registry |
| 3           | `camel`          | `CamelMcpHubClient`      | Apache Camel MCP Hub  |
| 2           | `smithery`       | `SmitheryClient`         | Smithery.ai           |
| 1 (lowest)  | `mcpservers.org` | `McpServersOrgClient`    | mcpservers.org        |

### Caching

To avoid hitting remote registries on every search, results are cached per query key with a configurable TTL (default: 10 minutes). The cache uses `ConcurrentHashMap` for thread safety. Cache entries include the timestamp of insertion and are evicted on the next access after TTL expiry.

### Resilience

The registry is designed to be fault-tolerant. If a source fails (network error, parsing error), the registry logs a warning and continues with results from the remaining sources. A partial result set is always better than a failed query.

## McpRegistrySource Interface

If you run your own internal MCP server catalog, you can add it as a custom registry source. Implement this interface and pass it to the `UnifiedMcpRegistry` constructor.

```java
public interface McpRegistrySource {
    String getSourceName();
    List<McpServerEntry> search(String query) throws IOException;
    Optional<McpServerEntry> getServer(String name) throws IOException;
    List<McpServerEntry> listAll() throws IOException;
}
```

### Built-in Sources

These four sources are included out of the box and are all enabled by default.

| Source                   | Description                                    |
| ------------------------ | ---------------------------------------------- |
| `OfficialRegistryClient` | Queries the official MCP server registry       |
| `CamelMcpHubClient`      | Queries the Apache Camel MCP Hub               |
| `SmitheryClient`         | Queries the Smithery.ai marketplace            |
| `McpServersOrgClient`    | Queries the mcpservers.org community directory |

## McpServerEntry

Each discovered MCP server is represented as an `McpServerEntry` record. It contains everything you need to connect to the server, including its transport type, endpoint, and protocol version.

```java
public record McpServerEntry(
    String name,             // Unique server name (required)
    String description,      // Human-readable description
    String version,          // Server version string
    String protocolVersion,  // MCP protocol version (e.g. "2025-11-05")
    String transport,        // "stdio", "sse", or "streamable-http"
    String endpoint,         // Connection URL or command
    String source,           // Registry source name
    double rating,           // Quality rating (0.0 - 5.0)
    Map<String, String> metadata  // Additional key-value pairs
) {}
```

### Protocol Version Filtering

Not all servers support the same MCP protocol version. Use protocol version filtering to ensure you only connect to servers compatible with your client's version.

```java
List<McpServerEntry> servers = registry.search("database");

// Filter to servers supporting protocol version 2025-03-26 or later
List<McpServerEntry> compatible = servers.stream()
    .filter(s -> McpProtocolVersion.isCompatible(s.protocolVersion(), "2025-03-26"))
    .toList();
```

## Testing Toolkit

The MCP module includes JUnit 5 utilities for testing MCP server implementations.

### McpServerTestKit

The `McpServerTestKit` is a JUnit 5 test harness that validates whether your MCP server implementation is compliant with the protocol. It automatically tests initialization, tool listing, tool invocation, and error handling.

```java
class MyMcpServerTest {

    @Test
    void testServerCompliance() {
        McpServerTestKit testKit = McpServerTestKit.builder()
            .transport(new StdioTransport.Builder()
                .command("node", "my-server.js")
                .build())
            .build();

        // Validates initialize, tools/list, tool invocation, and error handling
        testKit.runComplianceTests();
    }

    @Test
    void testSpecificTool() {
        McpServerTestKit testKit = McpServerTestKit.builder()
            .transport(transport)
            .build();

        // Test a specific tool
        var result = testKit.callTool("read_file", Map.of("path", "/tmp/test.txt"));
        assertNotNull(result);
    }
}
```

### MockMcpServer

When you want to test your MCP client code without a real server, use `MockMcpServer`. It runs entirely in memory with no network I/O, making tests fast and deterministic.

```java
MockMcpServer mockServer = MockMcpServer.builder()
    .tool("read_file", "Read a file", Map.of(
        "type", "object",
        "properties", Map.of("path", Map.of("type", "string"))
    ))
    .toolHandler("read_file", args -> "file content here")
    .build();

// Use with McpClient for testing
McpClient client = new McpClient(mockServer.getTransport());
client.connect();
var tools = client.listTools();
// tools contains "read_file"
```

`MockMcpServer` uses `MockMcpTransport` internally, which implements `McpTransport` with in-memory message queues instead of network I/O.

### McpSchemaValidator

The `McpSchemaValidator` checks that your JSON-RPC messages conform to the MCP specification. Use it during development to catch schema issues early, such as missing required fields or invalid types in tool definitions.

```java
McpSchemaValidator validator = new McpSchemaValidator();

// Validate a tool definition
ValidationResult result = validator.validateToolDefinition(toolJson);
if (!result.isValid()) {
    result.getErrors().forEach(System.err::println);
}

// Validate a JSON-RPC request
ValidationResult result = validator.validateRequest(requestJson);

// Validate a JSON-RPC response
ValidationResult result = validator.validateResponse(responseJson);
```

The validator checks required fields, type constraints, and MCP-specific schema rules (e.g., tool input schemas must be valid JSON Schema).

---

# MCP Server

URL: https://tnsai.dev/docs/mcp/server
Description: The McpServer exposes TnsAI tools, resources, and prompts to MCP clients.

import { Callout } from 'fumadocs-ui/components/callout'

## Building a Server

To create an MCP server, use the builder to register your tool, resource, and prompt providers, then call `start()`. By default the server communicates over stdio, making it easy to launch as a subprocess from any MCP client.

```java
McpServer server = McpServer.builder()
    .serverName("My-TnsAI-Server")
    .serverVersion("1.0.0")
    .addToolProvider(myToolProvider)
    .addResourceProvider(myResourceProvider)
    .addPromptProvider(myPromptProvider)
    .build();

server.start();  // Stdio transport (blocking)
```

### Expose TnsAI Tools via MCP

`TnsAIToolProvider` adapts TnsAI POJO toolkits and runtime tools into MCP tool definitions. Choose the factory that matches your registration pattern:

```java
import com.tnsai.enums.BuiltInTool;
import com.tnsai.mcp.server.TnsAIToolProvider;

// From an explicit set of POJO toolkit instances
McpServer server = McpServer.builder()
    .serverName("TnsAI-Tools")
    .addToolProvider(TnsAIToolProvider.fromPojos(
        BuiltInTool.WEB_SEARCH_TOOLS.instantiate(),
        BuiltInTool.UTILITY_TOOLS.instantiate(),
        new MyDomainTools()
    ))
    .build();

server.start();  // Listens on stdio
```

Other factory methods on `TnsAIToolProvider`:

| Factory                                                                  | Input                             | Use when                                        |
| ------------------------------------------------------------------------ | --------------------------------- | ----------------------------------------------- |
| `from(Object... pojos)`                                                  | One or more `@Tool`-bearing POJOs | Quick start with a known set of toolkits        |
| `fromPojos(Object... pojos)`                                             | Same as `from`, alternative name  | Same                                            |
| `fromDynamic(DynamicToolMethod...)`                                      | Runtime-defined tools             | Bridging MCP-proxy or plugin tools to MCP       |
| `from(Collection<Object> pojos, Collection<DynamicToolMethod> dynamics)` | Both sources combined             | Mixing POJOs with runtime tools in one provider |

## Custom Tool Provider

To expose your own custom tools via MCP, implement the `ToolProvider` interface. You define which tools are available and how each one executes when called by a client.

```java
public class MyToolProvider implements ToolProvider {

    @Override
    public List<McpToolDefinition> listTools() {
        return List.of(
            McpToolDefinition.builder()
                .name("analyze")
                .description("Analyze text for sentiment")
                .inputSchema(schemaNode)
                .build()
        );
    }

    @Override
    public ToolResult executeTool(String name, JsonNode args) {
        if ("analyze".equals(name)) {
            String text = args.get("text").asText();
            return ToolResult.text(analyzeSentiment(text));
        }
        return ToolResult.error("Unknown tool: " + name);
    }
}
```

## HTTP Server

For production deployments where clients connect over the network, use `HttpMcpServer`. It serves MCP over Streamable HTTP transport, supporting JSON-RPC requests via POST, server-sent event notifications via GET, and session termination via DELETE.

```java
HttpMcpServer httpServer = HttpMcpServer.builder()
    .port(8080)
    .path("/mcp")
    .addToolProvider(myProvider)
    .build();

httpServer.start();
// POST /mcp  — JSON-RPC requests
// GET  /mcp  — SSE notifications
// DELETE /mcp — Session termination
```

## Error Handling

Structured error events emitted when MCP tool execution fails. Register a `ToolErrorListener` to receive notifications and decide on recovery strategy.

### ToolErrorCategory

Each error is classified into a category that tells clients whether they should retry the operation or fix the request. This prevents pointless retries for permanent failures like invalid parameters.

| Category            | Code                | Retriable | Guidance                                         |
| ------------------- | ------------------- | --------- | ------------------------------------------------ |
| `INVALID_PARAMS`    | `invalid_params`    | No        | Request malformed, do not retry with same params |
| `TOOL_NOT_FOUND`    | `tool_not_found`    | No        | Tool does not exist, check tool registry         |
| `TIMEOUT`           | `timeout`           | Yes       | Execution timed out, may retry                   |
| `PERMISSION_DENIED` | `permission_denied` | No        | Access denied, do not retry                      |
| `INTERNAL`          | `internal`          | Yes       | Unexpected error, may retry                      |

### ToolErrorEvent

A `ToolErrorEvent` is a structured record emitted whenever a tool execution fails. It contains the tool name, error category, a safe error message (no stack traces), a timestamp, and an optional request ID for correlation.

```java
// Factory methods
ToolErrorEvent event = ToolErrorEvent.of("my_tool", ToolErrorCategory.TIMEOUT, "Timed out after 30s");
ToolErrorEvent event = ToolErrorEvent.of("my_tool", ToolErrorCategory.INTERNAL, "Unexpected error", requestId);

// Check retriability (delegates to category)
if (event.isRetriable()) {
    // retry logic
}
```

### ToolErrorListener

Register a `ToolErrorListener` to get notified whenever a tool call fails. This is useful for logging, metrics, or implementing retry logic based on the error category.

```java
// Lambda listener
server.getRequestHandler().addToolErrorListener(event -> {
    logger.warn("Tool '{}' failed: {} [{}]",
        event.toolName(), event.message(), event.category().code());
    if (event.isRetriable()) {
        retryQueue.add(event);
    }
});
```

---

# MCP Transports

URL: https://tnsai.dev/docs/mcp/transports
Description: The MCP module provides 5 transport implementations plus an OAuth decorator and an auto-detection utility for connecting to MCP servers over stdio, HTTP, SSE, and bidirectional streaming.

import { Callout } from 'fumadocs-ui/components/callout'

## McpTransport Interface

Every transport implements this common interface, which defines how to connect, send messages, and receive responses. You generally do not call these methods directly -- the `McpClient` handles JSON-RPC correlation and dispatching on top of the transport.

```java
public interface McpTransport {
    void connect() throws IOException;
    void send(String message) throws IOException;
    void setOnMessage(Consumer<String> messageConsumer);
    void setOnError(Consumer<Throwable> errorConsumer);
    void disconnect() throws IOException;
    boolean isConnected();
}
```

Messages are JSON-RPC strings. The transport layer handles framing and delivery; the `McpClient` handles JSON-RPC correlation and method dispatch.

## StdioTransport

Communicates with MCP servers via stdin/stdout of a subprocess. The primary transport for local servers launched via `npx`, `uvx`, or native executables.

```java
StdioTransport transport = StdioTransport.builder()
    .command("npx", "-y", "@modelcontextprotocol/server-filesystem", "/path")
    .environment(Map.of("NODE_ENV", "production"))
    .workingDirectory("/project")
    .maxRetries(5)
    .initialRetryDelay(Duration.ofMillis(500))
    .maxRetryDelay(Duration.ofSeconds(30))
    .healthCheckInterval(Duration.ofSeconds(30))
    .processStartTimeout(Duration.ofSeconds(10))
    .build();

transport.setOnMessage(msg -> System.out.println("Received: " + msg));
transport.setOnError(err -> System.err.println("Error: " + err));
transport.connect();

// Send a JSON-RPC request
transport.send("{\"jsonrpc\":\"2.0\",\"id\":1,\"method\":\"tools/list\"}");
```

### Features

The stdio transport includes several reliability features that make it robust for long-running agent sessions.

- **Auto-reconnect**: On process crash, reconnects with exponential backoff (initial 500ms, max 30s, up to 5 retries)
- **Health checks**: Periodic pings at configurable intervals (default 30s) detect stale connections
- **Process lifecycle**: Manages the subprocess from start to shutdown, including graceful stop and forced kill
- **Thread-safe**: Uses `BufferedReader`/`BufferedWriter` with synchronized access for concurrent message handling
- **Crash detection**: Monitors stderr for crash indicators and triggers reconnection

## HttpTransport

Simple HTTP POST-based transport for stateless MCP servers. Each `send()` makes an HTTP POST with the JSON-RPC message body and reads the response synchronously.

```java
HttpTransport transport = HttpTransport.builder()
    .endpoint("https://api.example.com/mcp")
    .headers(Map.of("Authorization", "Bearer " + token))
    .timeout(Duration.ofSeconds(30))
    .build();
```

Best for simple request-response MCP servers that do not need streaming or server-initiated messages.

## SSETransport

Server-Sent Events transport for MCP servers that stream responses. Maintains a long-lived GET connection for receiving server events and sends requests via POST.

```java
SSETransport transport = SSETransport.builder()
    .sseEndpoint("https://mcp.example.com/sse")
    .postEndpoint("https://mcp.example.com/messages")
    .headers(Map.of("Authorization", "Bearer " + token))
    .build();
```

The SSE connection receives server-initiated notifications (tool list changes, resource updates) while the POST endpoint handles client requests.

## StreamableHttpTransport

Bidirectional streaming transport (MCP protocol 2025-03-26+). Uses HTTP POST with streaming responses, allowing both request-response and server-initiated messages over a single HTTP connection.

```java
StreamableHttpTransport transport = StreamableHttpTransport.builder()
    .endpoint("https://mcp.example.com/mcp")
    .headers(Map.of("Authorization", "Bearer " + token))
    .build();
```

This is the preferred transport for modern MCP servers. It supports session management via `Mcp-Session-Id` headers and server-initiated notifications within the response stream.

## OAuthMcpTransport

A decorator that adds OAuth Bearer token authentication to any `McpTransport`. Wraps an existing transport and manages token lifecycle (validation, refresh) before each operation.

```java
McpTokenManager tokenManager = new McpTokenManager();
// ... store tokens after OAuth flow ...

OAuthMcpTransport transport = OAuthMcpTransport.builder()
    .delegate(new SSETransport.Builder().sseEndpoint("...").build())
    .tokenManager(tokenManager)
    .serverUrl("https://mcp.example.com")
    .tokenEndpoint("https://auth.example.com/token")
    .clientId("my-client")
    .build();

McpClient client = new McpClient(transport);
client.connect();
```

### Token Management (McpTokenManager)

The `McpTokenManager` handles the lifecycle of OAuth tokens: storing them after the initial flow, checking expiry, and automatically refreshing when needed. This means your application does not have to manually track token lifetimes.

```java
McpTokenManager tokenManager = new McpTokenManager();

// Store tokens (after initial OAuth flow)
tokenManager.storeTokens("https://mcp.example.com",
    accessToken, refreshToken, expiresIn);

// Get a valid token (auto-refreshes if expired)
Optional<String> token = tokenManager.getValidToken("https://mcp.example.com");
```

### PKCE Flow (PkceFlow)

PKCE (Proof Key for Code Exchange) is a security extension for OAuth that prevents authorization code interception attacks. Use this when your application is a public client (no client secret) and needs to authenticate with an MCP server that requires OAuth.

```java
PkceFlow pkce = new PkceFlow();

// Generate challenge
String codeVerifier = pkce.generateCodeVerifier();
String codeChallenge = pkce.generateCodeChallenge(codeVerifier);
// Use codeChallenge in the authorization request (method: S256)

// Exchange authorization code
TokenResponse tokens = pkce.exchangeCode(
    authCode, codeVerifier, tokenEndpoint, clientId, redirectUri);
```

### Dynamic Client Registration (McpClientRegistration)

Some MCP servers require your application to register itself as an OAuth client before authenticating. Dynamic client registration automates this process so you do not have to manually create client credentials on the server.

```java
McpClientRegistration registration = new McpClientRegistration();
ClientInfo client = registration.register(
    registrationEndpoint,
    "TnsAI Agent",
    List.of("http://localhost:8080/callback")
);
// client.getClientId(), client.getClientSecret()
```

### OAuth Discovery (McpOAuthDiscovery)

OAuth discovery automatically finds the authorization and token endpoints for an MCP server, so you do not have to hardcode them. It probes the server's well-known metadata URL and returns all the endpoints needed for the OAuth flow.

```java
McpOAuthDiscovery discovery = new McpOAuthDiscovery();
OAuthMetadata metadata = discovery.discover("https://mcp.example.com");
// metadata.authorizationEndpoint(), metadata.tokenEndpoint(), etc.
```

## TransportDetector

Auto-detects the best transport for a given URL endpoint by probing the server.

```java
McpTransport transport = TransportDetector.detect("https://api.example.com/mcp");
McpClient client = new McpClient(transport);

// With custom headers
McpTransport transport = TransportDetector.detect(
    "https://api.example.com/mcp",
    Map.of("Authorization", "Bearer " + token));
```

### Detection Order

The detector probes the server endpoint in order from newest to oldest protocol, falling back if a probe fails.

1. **Streamable HTTP**: Sends a POST JSON-RPC request. If the server responds with valid JSON-RPC, uses `StreamableHttpTransport`.
2. **SSE**: Sends a GET with `Accept: text/event-stream`. If the server responds with SSE content type, uses `SSETransport`.
3. **HTTP fallback**: Falls back to basic `HttpTransport`.

Detection uses a 10-second timeout per probe. For stdio-based servers (local commands), there is no detection -- use `StdioTransport` directly.

## Choosing a Transport

Use this table to pick the right transport for your use case. For local servers (like those launched via `npx`), use stdio. For remote servers, prefer Streamable HTTP unless the server only supports SSE or plain HTTP.

| Transport                 | Protocol              | Direction             | Use Case                                    |
| ------------------------- | --------------------- | --------------------- | ------------------------------------------- |
| `StdioTransport`          | stdin/stdout          | Bidirectional         | Local subprocess servers (`npx`, `uvx`)     |
| `HttpTransport`           | HTTP POST             | Request-response      | Simple stateless servers                    |
| `SSETransport`            | HTTP GET + POST       | Server push + request | Servers needing notifications               |
| `StreamableHttpTransport` | HTTP POST (streaming) | Bidirectional         | Modern MCP servers (recommended for remote) |
| `OAuthMcpTransport`       | Decorator             | --                    | Any transport needing OAuth authentication  |

---

# Advanced Patterns

URL: https://tnsai.dev/docs/multi-agent/advanced
Description: Advanced coordination patterns in TnsAI.Coordination for production multi-agent systems.

import { Callout } from 'fumadocs-ui/components/callout'

## ScatterGather

`ScatterGather` is a utility for fan-out/fan-in communication: send the same task to multiple agents at once and collect their responses. This is useful when you want multiple perspectives on the same question, or when you need redundancy for reliability. Supports several collection strategies (all, first-wins, quorum, consensus, score-based, aggregate).

### Scatter (Collect All)

Send the task to all agents and wait for every response. You get back all results along with metrics like success rate and which agent responded fastest.

```java
ScatterGather.ScatterResults results = ScatterGather.scatter(
    "Analyze market trends for Q4",
    List.of(analyst1, analyst2, analyst3),
    30, TimeUnit.SECONDS
);

List<String> responses = results.responseTexts();       // successful response texts
double successRate = results.successRate();              // 0.0 - 1.0
Optional<AgentResponse> fastest = results.fastest();     // quickest responder
```

### Race (First Wins)

When speed matters more than comprehensiveness, `race()` returns the first successful response and cancels the rest.

```java
Optional<AgentResponse> winner = ScatterGather.race(
    "What is the capital of France?",
    List.of(agent1, agent2, agent3),
    10, TimeUnit.SECONDS
);
```

### Quorum (Wait for N)

When you need a minimum number of confirmations before proceeding (like a distributed consensus check), `quorum()` waits until at least N agents respond successfully, or timeout.

```java
ScatterResults results = ScatterGather.quorum(
    "Verify transaction hash",
    validators,
    3,                   // need 3 confirmations
    30, TimeUnit.SECONDS
);
```

### Consensus (Majority Vote)

When you want to find the "agreed-upon" answer from multiple agents, `gatherWithConsensus()` collects all responses and picks the most common answer using word-level Jaccard similarity.

```java
Optional<AgentResponse> consensus = ScatterGather.gatherWithConsensus(
    "What is 2+2?", agents, 30, TimeUnit.SECONDS
);
```

### Score-Based Selection

When you have a specific quality metric in mind, `gatherWithScore()` lets you provide a scoring function to rank responses and pick the best one.

```java
Optional<AgentResponse> best = ScatterGather.gatherWithScore(
    "Write a haiku about Java",
    agents,
    response -> (double) response.length(),  // score by length
    30, TimeUnit.SECONDS
);
```

### Aggregate (Synthesize)

When you want a single combined answer instead of picking one response, `gatherAndAggregate()` collects all responses and passes them to a dedicated aggregator agent that synthesizes them into a unified answer.

```java
Optional<String> synthesized = ScatterGather.gatherAndAggregate(
    "What are the risks of this deployment?",
    List.of(securityAgent, infraAgent, qaAgent),
    summarizerAgent,
    60, TimeUnit.SECONDS
);
```

## Auction

The `Auction` class implements multi-agent auction mechanisms for competitive task allocation. Agents bid on tasks, and the auction determines the winner based on the auction type. Useful for resource allocation, task assignment, and market-based coordination.

### Auction Types

| Type         | Behavior                                                                                             |
| ------------ | ---------------------------------------------------------------------------------------------------- |
| `ENGLISH`    | Ascending price. Bidders raise until no one outbids. Multiple rounds.                                |
| `DUTCH`      | Descending price. Price drops each round; first bidder to accept wins.                               |
| `SEALED_BID` | Single round. All bids submitted simultaneously. Highest bidder wins at their bid price.             |
| `VICKREY`    | Single round. Highest bidder wins but pays the second-highest price (incentivizes truthful bidding). |

### Usage

```java
AuctionResult result = Auction.builder()
    .type(Auction.Type.VICKREY)
    .task("Translate document to French")
    .bidders(List.of(agent1, agent2, agent3))
    .bidStrategy((agent, task, currentPrice, round, previousBids) -> {
        String response = agent.chat("Bid on: " + task + " (current: " + currentPrice + ")");
        return Double.parseDouble(response.trim()); // or Double.NaN to abstain
    })
    .startingPrice(100.0)
    .reservePrice(10.0)
    .increment(10.0)       // price step for English/Dutch auctions
    .maxRounds(10)
    .build()
    .execute();

if (result.success()) {
    System.out.println("Winner: " + result.winner().name());
    System.out.println("Winning bid: " + result.winningBid().value());
    System.out.println("Pay price: " + result.payPrice());    // differs from bid in Vickrey
    System.out.println("Rounds: " + result.rounds());
    System.out.println("Duration: " + result.durationMs() + "ms");
} else {
    System.out.println("No winner: " + result.reason());
}

// Async execution
CompletableFuture<AuctionResult> future = auction.executeAsync();
```

### BidStrategy

`BidStrategy` is a `@FunctionalInterface` that determines how an agent decides its bid value. Return `Double.NaN` to abstain from bidding.

```java
double calculateBid(Agent agent, String taskDescription, double currentPrice,
                    int round, List<Bid> previousBids);
```

### Bid Record

Each bid is a record with `agent()`, `value()`, `round()`, and `timestamp()`. Create with `Bid.of(agent, value, round)`.

### AuctionResult Record

Fields: `success()`, `winner()`, `winningBid()`, `payPrice()`, `allBids()`, `rounds()`, `durationMs()`, `reason()`. Factory methods: `AuctionResult.won(...)` and `AuctionResult.noWinner(...)`.

## StreamingChannel

A `StreamingChannel` lets agents send a continuous flow of data to each other in real time, rather than waiting for complete responses. This is useful for long-running tasks where you want to process results incrementally. It is a typed streaming channel with configurable backpressure that supports reactive-style `map`, `filter`, and `subscribe` operations.

```java
StreamingChannel<String> channel = StreamingChannel.<String>builder()
    .id("analysis-stream")
    .bufferSize(100)
    .backpressure(BackpressureStrategy.DROP_OLDEST)
    .offerTimeout(Duration.ofSeconds(5))
    .build();

// Consumer side
channel.subscribe(chunk -> processChunk(chunk));
channel.onError(err -> log.error("Stream error", err));
channel.onComplete(() -> log.info("Stream finished"));

// Producer side
channel.emit("data chunk 1");
channel.emit("data chunk 2");
channel.emitAll(List.of("chunk 3", "chunk 4", "chunk 5"));
channel.complete();
```

### BackpressureStrategy

When a producer emits data faster than the consumer can handle, the backpressure strategy determines what happens. Choose based on whether you prefer to wait, lose old data, lose new data, or fail loudly.

| Strategy      | Behavior                                                          |
| ------------- | ----------------------------------------------------------------- |
| `BLOCK`       | Block producer until buffer has space (with configurable timeout) |
| `DROP_OLDEST` | Remove oldest item from buffer to make room                       |
| `DROP_NEWEST` | Reject incoming item silently                                     |
| `ERROR`       | Throw `BufferOverflowException` when full                         |

### Transformations

You can chain `filter()` and `map()` operations on a channel to create derived channels, similar to Java Streams or reactive pipelines.

```java
StreamingChannel<Integer> lengths = channel
    .filter(s -> !s.isBlank())
    .map(String::length);
```

### Metrics

Each channel tracks how many items were emitted, dropped, and buffered, so you can monitor throughput and detect backpressure issues.

```java
StreamingChannel.ChannelMetrics metrics = channel.metrics();
// channelId, emitted, dropped, bufferUsed, bufferCapacity, subscriberCount, completed
double dropRate = metrics.dropRate(); // percentage of dropped items
```

The channel implements `Closeable`. Call `close()` to shut down the dispatcher thread and clear the buffer.

## HandoffChain

A `HandoffChain` passes a task through a sequence of agents, where each agent's output becomes the next agent's input. This is similar to a pipeline but uses the handoff mechanism, which allows agents to pass structured context (metadata, priority, previous output) along the chain. Supports conditional steps that can be skipped based on the context.

```java
HandoffChain chain = HandoffChain.builder()
    .add(triageAgent)
    .add(researchAgent)
    .add(qualityAgent, ctx -> ctx.has("previousOutput"))  // conditional
    .add(responseAgent)
    .build();

HandoffContext initialContext = new HandoffContext(
    "task-123",
    Map.of("query", "How to reset password?"),
    Map.of("priority", "high"),
    null
);

List<HandoffResult> results = chain.execute(handoff, initialContext);
```

The first agent in the chain is the source. Conditional steps whose predicate returns `false` are skipped. Execution stops on the first failure.

## LazyAgentPool

When you have many concurrent tasks but don't want to create a new agent for each one, `LazyAgentPool` manages a reusable pool of agents. Agents are created on demand, returned to the pool after use, and automatically destroyed when idle too long. This is an object pool with lazy creation, idle eviction, and configurable activation policies.

```java
LazyAgentPool pool = LazyAgentPool.builder()
    .factory(() -> new WorkerAgent(llmClient))
    .maxSize(20)
    .minIdle(2)
    .idleTimeout(5, TimeUnit.MINUTES)
    .policy(ActivationPolicy.PREWARMED)
    .listener(new PoolListener() { ... })
    .build();

// Checkout/checkin via try-with-resources
try (AgentHandle handle = pool.checkout()) {
    String result = handle.execute("Process this document");
} // agent is returned to pool automatically
```

### ActivationPolicy

The activation policy controls when agents are created. Pre-warming creates agents upfront so they are ready immediately, while on-demand waits until they are needed.

| Policy      | Behavior                                          |
| ----------- | ------------------------------------------------- |
| `ON_DEMAND` | Agents created only when checked out              |
| `PREWARMED` | Pre-creates `minIdle` agents at pool construction |
| `BURST`     | Scale up fast on demand, scale down when idle     |

### PredictiveActivator

The `PredictiveActivator` monitors pool checkout patterns in a sliding window and pre-warms agents when burst demand is detected. It implements `PoolListener` and auto-registers with the pool.

```java
PredictiveActivator activator = PredictiveActivator.builder()
    .pool(pool)
    .windowSize(60)          // 60-second sliding window
    .burstThreshold(5)       // 5+ checkouts/window triggers pre-warm
    .preWarmCount(3)         // pre-warm 3 agents
    .analyzeInterval(10)     // analyze every 10 seconds
    .build();

activator.start();   // begins monitoring in background
// ... use pool normally ...
activator.stop();    // stops monitoring

// Inspect statistics
int recent = activator.recentActivationCount();
int exhaustions = activator.recentExhaustionCount();
```

### PoolMetrics

Monitor pool utilization to right-size your pool configuration and detect resource leaks.

```java
PoolMetrics metrics = pool.getMetrics();
// active, idle, totalCreated, totalDestroyed, peakActive
```

The pool schedules periodic eviction to destroy idle agents exceeding `minIdle`. Call `preWarm(count)` to proactively create agents before a traffic spike. The pool implements `Closeable` and destroys all agents on `close()`.

## Blackboard

The `Blackboard` implements a shared knowledge repository where knowledge sources post information and other sources use it to solve problems collaboratively. Supports TTL-based entries and automatic eviction of expired entries.

```java
Blackboard bb = new Blackboard("main");

// Write knowledge
bb.write("analysis", data, "agent-a", 0.9);
bb.write("context", info, "agent-b", 0.85, Map.of("source", "web"));

// Write with TTL
bb.writeWithTTL("cache-data", data, "api", 0.9, Duration.ofMinutes(5));

// Read
Object value = bb.read("analysis");          // null if expired
KnowledgeEntry entry = bb.readEntry("analysis"); // full entry with metadata
boolean exists = bb.contains("analysis");    // false if expired

// Query
List<KnowledgeEntry> fromAgent = bb.getEntriesFromSource("agent-a");
List<KnowledgeEntry> highConf = bb.getHighConfidenceEntries(0.8);
List<KnowledgeEntry> valid = bb.getValidEntries();    // non-expired
List<KnowledgeEntry> expired = bb.getExpiredEntries();
```

### Knowledge Sources

Knowledge sources are registered contributors that can post to and read from the blackboard. The blackboard can query which sources are able to contribute given its current state, sorted by priority.

```java
bb.registerKnowledgeSource(mySource);
List<KnowledgeSource> available = bb.getAvailableKnowledgeSources();
```

### TTL and Auto-Eviction

Entries with TTL expire automatically. You can evict expired entries manually or on a schedule.

```java
// Manual eviction
int evicted = bb.evictExpired();

// Automatic eviction (runs on a background daemon thread)
bb.startAutoEviction(Duration.ofSeconds(60));
bb.isAutoEvictionRunning();   // true
bb.stopAutoEviction();

// Total evicted since creation
int total = bb.getTotalEvicted();
```

The blackboard implements `AutoCloseable` -- calling `close()` stops auto-eviction. Use try-with-resources to prevent thread leaks.

### Listeners

Register a `BlackboardListener` to get notified on knowledge changes.

```java
bb.addListener(new BlackboardListener() {
    public void onKnowledgeAdded(String key, KnowledgeEntry entry) { ... }
    public void onKnowledgeUpdated(String key, KnowledgeEntry old, KnowledgeEntry newEntry) { ... }
    public void onKnowledgeExpired(String key, KnowledgeEntry entry) { ... }  // default no-op
});
```

### Statistics

```java
Map<String, Object> stats = bb.getStatistics();
// totalEntries, knowledgeSources, averageConfidence, entriesBySource
```

## SupervisorAgent with Guardrails

A `SupervisorAgent` adds a safety layer around a group of agents. Before any agent executes an action, the supervisor checks it against a list of guardrails (policy rules). This lets you enforce constraints like "require human approval for deployments" or "block actions that exceed a budget" without modifying individual agents.

```java
SupervisorAgent supervisor = new SupervisorAgent(group, List.of(
    new HITLApproval(Set.of("deploy", "delete"), approvalCallback),
    myBudgetGuardrail,
    myContentFilterGuardrail
));

Guardrail.GuardrailResult result = supervisor.checkAction(
    "agent-1", "deploy", Map.of("target", "production")
);

if (result.allowed()) {
    // proceed
} else {
    System.out.println("Blocked: " + result.reason());
    // result.severity() -> BLOCK or WARNING
}

// Dynamic guardrail management
supervisor.addGuardrail(new RateLimitGuardrail());
supervisor.removeGuardrail("old-guardrail-name");
```

### Guardrail Interface

Each guardrail implements a simple interface: given an agent ID, action name, and context, it returns a result saying whether the action is allowed, blocked, or a warning.

```java
public interface Guardrail {
    GuardrailResult check(String agentId, String action, Map<String, Object> context);
    String getName();
}
```

Guardrails are evaluated in order. If any guardrail blocks, evaluation stops immediately. `WARNING` severity allows execution but logs a warning.

### Built-in: HITLApproval

The Human-in-the-Loop (HITL) guardrail pauses execution and requests human approval for sensitive actions. Takes a set of action names that require approval and a callback for requesting it.

### Built-in: SandboxedExecution

The `SandboxedExecution` guardrail runs agent actions in an isolated context for safety testing, ensuring they cannot affect production state.

## TrustRegistry

The `TrustRegistry` tracks how reliably each agent performs each capability. As agents complete (or fail) tasks, their trust scores update automatically. You can then use these scores to route tasks to the most trusted agent or filter out unreliable ones.

```java
TrustRegistry registry = TrustRegistry.create(
    TrustUpdateStrategy.ema(0.3),  // Exponential Moving Average with alpha=0.3
    0.5                             // initial trust score for new agents
);

// Or with decay
TrustRegistry registry = TrustRegistry.create(
    TrustUpdateStrategy.ema(0.3),
    0.5,    // initial score
    0.02    // decay rate per cycle
);

// Record outcomes
registry.recordOutcome("agent-a", "translation", Outcome.SUCCESS, 0.95);
registry.recordOutcome("agent-a", "translation", Outcome.PARTIAL, 0.60);
registry.recordOutcome("agent-b", "translation", Outcome.FAILURE, 0.0);

// Query trust
double trust = registry.getTrust("agent-a", "translation"); // 0.0 - 1.0

// Full trust score object
Optional<TrustScore> score = registry.getTrustScore("agent-a", "translation");
// score.get().getTotalInteractions(), getSuccessfulInteractions(), getSuccessRate(), getLastUpdated()

// Rank agents for a capability
List<String> best = registry.rankAgents("translation", 3);

// Get agents above threshold
List<String> qualified = registry.getAgentsAboveThreshold("translation", 0.7);

// Periodic decay (call e.g. daily) -- decays scores toward 0.5 (neutral)
registry.applyDecay();
```

### TrustBasedRouter

The `TrustBasedRouter` uses the registry to route tasks to the most trusted agent for a given capability. Agents below the minimum trust threshold are excluded.

```java
TrustBasedRouter router = TrustBasedRouter.builder()
    .registry(trustRegistry)
    .minimumTrust(0.6)
    .build();

Optional<String> best = router.selectAgent("translation", List.of("agent-a", "agent-b", "agent-c"));
List<String> ranked = router.rankCandidates("translation", candidateIds);
```

### TrustUpdateStrategy

The update strategy determines how new outcomes affect an agent's trust score. EMA (exponential moving average) gives more weight to recent performance, while simple average treats all outcomes equally. You can also provide custom implementations.

## RecursiveTaskSolver (ROMA)

The `RecursiveTaskSolver` handles complex tasks that are too big for a single agent. It breaks a task into smaller sub-tasks, solves each one, combines the results, and verifies quality. If the sub-tasks are still too big, it recurses until reaching atomic (simple enough to solve directly) tasks. This implements the Recursive Orchestration of Multi-Agent (ROMA) pattern.

```java
RecursiveTaskSolver solver = RecursiveTaskSolver.builder()
    .solver(solverAgent)
    .decomposer(TaskDecomposer.llmBased(plannerAgent))
    .atomizer(Atomizer.depthBased(3))
    .verifier(Verifier.llmBased(reviewerAgent))
    .maxDepth(5)
    .maxRetries(2)
    .build();

RecursiveTaskSolver.SolveResult result = solver.solve(
    "Build a REST API for user management"
);

System.out.println("Success: " + result.success());
System.out.println("Output: " + result.output());
System.out.println("Traces: " + result.traces().size());
System.out.println("Duration: " + result.durationMs() + "ms");
```

### Algorithm

Here is how the recursive solving process works step by step:

1. Check if task is atomic (via `Atomizer`).
2. If atomic: solve directly with solver agent, then verify.
3. If not: decompose into sub-tasks (via `TaskDecomposer`).
4. Recursively solve each sub-task (fail-fast on any sub-task failure).
5. Aggregate sub-results using the solver agent.
6. Verify aggregated result. Retry up to `maxRetries` on rejection.

### Components

The solver is composed of three pluggable components. You can use the built-in implementations or provide your own.

| Component        | Interface                                                         | Built-in                                                    |
| ---------------- | ----------------------------------------------------------------- | ----------------------------------------------------------- |
| `Atomizer`       | `isAtomic(task, depth)` -- decides if a task is solvable directly | `Atomizer.depthBased(maxDepth)`, `Atomizer.llmBased(agent)` |
| `TaskDecomposer` | `decompose(task, depth)` -- breaks task into sub-tasks            | `TaskDecomposer.llmBased(agent)`                            |
| `Verifier`       | `verify(task, result, attempt)` -- accepts or rejects results     | `Verifier.llmBased(agent)`, `Verifier.alwaysAccept()`       |

### SolveTrace

For debugging and auditability, each step in the recursive solving process produces a trace entry. Each step produces a trace entry with type `DECOMPOSED`, `SOLVED`, `AGGREGATED`, or `VERIFIED`, the task, detail, depth, and attempt number.

## CouncilExecutor

The `CouncilExecutor` orchestrates a multi-model council deliberation inspired by Karpathy's llm-council pattern. It runs a 3-stage pipeline where multiple LLM models independently answer a question, anonymously peer-review each other's responses, and then a chairman synthesizes the best final answer.

### Three-Stage Pipeline

1. **Stage 1 -- Individual Responses**: All members answer the question independently in parallel (using virtual threads).
2. **Stage 2 -- Peer Review**: Each member evaluates all responses (anonymized as "Model A", "Model B", etc.) and ranks them.
3. **Stage 3 -- Chairman Synthesis**: The chairman receives all responses, reviews, and aggregate rankings, then produces the final synthesized answer.

```java
CouncilExecutor council = CouncilExecutor.builder()
    .members(List.of(gpt4, claude, gemini, grok))    // at least 2 required
    .chairman(gemini)
    .anonymize(true)        // anonymize identities during peer review (default)
    .timeout(Duration.ofMinutes(5))
    .build();

CouncilResult result = council.deliberate("How should we architect auth?");

// Final synthesized answer
System.out.println(result.finalAnswer());

// Aggregate ranking of members
result.topRanked().ifPresent(r ->
    System.out.println("Top: " + r.llmName() + " (avg rank: " + r.averageRank() + ")"));

// Access individual stages
List<CouncilResponse> responses = result.stage1Responses();   // individual answers
List<CouncilReview> reviews = result.stage2Reviews();          // peer reviews
CouncilSynthesis synthesis = result.stage3Synthesis();          // chairman output
List<RankedMember> ranking = result.aggregateRanking();        // aggregate ranking
```

### CouncilResult

Fields: `stage1Responses()`, `stage2Reviews()`, `stage3Synthesis()`, `aggregateRanking()`, `memberCount()`, `duration()`. The `finalAnswer()` convenience method returns the chairman's synthesis text. The `topRanked()` method returns the member with the best (lowest) average rank across all reviewers.

> **Cross-reference**: For simpler multi-model decisions, see [Council Voting](/docs/multi-agent/council-voting).

## ChoreographyEngine

The `ChoreographyEngine` implements event-driven coordination without a central orchestrator. Instead of one agent telling others what to do, you define a set of event-reaction rules ("when event X happens, agent Y does Z and emits event W"), and the engine runs them automatically. This is ideal for microservice-style workflows where each agent handles its own piece of the process.

```java
ChoreographySpec spec = ChoreographySpec.builder()
    .name("order-processing")
    .initialEvent("order.received")
    .interaction("validate", "validator", "order.received",
        "Validate the order", "order.validated")
    .interaction("bill", "billing", "order.validated",
        "Process payment", "payment.processed")
    .interaction("fulfill", "warehouse", "payment.processed",
        "Ship the order", "order.shipped")
    .build();

ChoreographyEngine engine = ChoreographyEngine.builder()
    .spec(spec)
    .agent("validator", validatorAgent)
    .agent("billing", billingAgent)
    .agent("warehouse", warehouseAgent)
    .timeout(Duration.ofMinutes(2))
    .build();

ChoreographyEngine.ChoreographyResult result = engine.execute();
System.out.println("Success: " + result.success());
System.out.println("Completed: " + result.completedCount());
System.out.println("Failed: " + result.failedCount());
System.out.println("Duration: " + result.totalDurationMs() + "ms");

// Access individual step results
for (ChoreographyEngine.StepResult step : result.stepResults()) {
    System.out.println(step.interactionId() + ": " + (step.success() ? "OK" : step.error()));
}
```

### Safety Features

The engine includes built-in safety checks to catch common pitfalls in event-driven systems before they cause problems at runtime.

#### DeadlockDetector

Analyzes the choreography spec for circular event dependencies before execution using DFS-based cycle detection on the agent interaction graph.

```java
DeadlockDetector.DetectionResult result = DeadlockDetector.detect(spec);
if (result.hasDeadlock()) {
    for (DeadlockDetector.Cycle cycle : result.cycles()) {
        System.out.println("Cycle: " + cycle);           // "A -> B -> C -> A"
        System.out.println("Involves billing? " + cycle.involves("billing"));
    }
}

// Can also detect from runtime wait-for graphs
DeadlockDetector.DetectionResult result = DeadlockDetector.detectFromWaitGraph(waitForMap);
```

#### LivelockDetector

Detects livelock conditions where agents keep changing state without making real progress. Monitors three patterns: repeated messages, oscillating states, and progress stalls. Includes a circuit breaker that trips when violations exceed a threshold.

```java
LivelockDetector detector = LivelockDetector.builder()
    .repeatThreshold(5)                        // 5 identical messages triggers violation
    .repeatWindow(Duration.ofSeconds(10))
    .oscillationThreshold(4)                   // 4 alternating state changes
    .stallWindow(Duration.ofSeconds(30))        // no new unique action in 30s
    .circuitBreakerThreshold(3)                // trip after 3 total violations
    .build();

// Feed events
detector.recordMessage("agent-1", "retry-task-X");
detector.recordStateChange("agent-2", "IDLE");
detector.recordAction("agent-1", "process-order-123");

// Analyze
LivelockDetector.DetectionResult result = detector.analyze();
if (result.hasLivelock()) {
    for (LivelockDetector.Violation v : result.violations()) {
        System.out.println(v.type() + " on " + v.agentId() + ": " + v.detail());
    }
}

// Circuit breaker
if (detector.isCircuitBroken()) {
    detector.reset();   // clears all history and resets circuit breaker
}

// Listeners
detector.addListener(r -> log.warn("Livelock detected: {}", r.violations()));
```

**Violation types**: `REPEATED_MESSAGE`, `OSCILLATING_STATE`, `PROGRESS_STALL`.

### Async Execution

Like other executors, the choreography engine supports non-blocking execution via `CompletableFuture`.

```java
CompletableFuture<ChoreographyResult> future = engine.executeAsync();
```

## Production Hardening

### EpisodicMemory

Cross-agent episodic memory for recording and querying interaction episodes. Enables agents to learn from past coordination patterns. Uses a bounded deque that evicts the oldest episodes when capacity is reached.

```java
EpisodicMemory memory = new EpisodicMemory(1000);   // max 1000 episodes

// Record episodes
memory.record(EpisodicMemory.Episode.of(
    "task-1", "agent-a", "translate", "success", Map.of("lang", "fr")));

// Query by task type keyword (case-insensitive contains match)
List<Episode> similar = memory.querySimilar("translate", 5);

// Query by agent
List<Episode> agentHistory = memory.queryByAgent("agent-a", 10);

// Query by outcome
List<Episode> failures = memory.queryByOutcome("failure", 10);

// Inspect
int total = memory.size();
List<Episode> all = memory.all();
memory.clear();
```

The `Episode` record contains: `taskId()`, `agentId()`, `taskType()`, `outcome()`, `metadata()` (Map), and `timestamp()`.

### FailureIsolator

Cascading failure protection using per-agent circuit breakers. When an agent fails repeatedly, the circuit opens and subsequent requests are handled via a configurable degradation strategy instead of waiting for the failing agent.

```java
FailureIsolator isolator = FailureIsolator.builder()
    .failureThreshold(3)
    .recoveryTimeout(Duration.ofSeconds(30))
    .degradationStrategy(FailureIsolator.DegradationStrategy.FALLBACK)
    .build();

String result = isolator.execute(
    "agent-a",
    () -> agent.chat("Hello"),      // primary action
    () -> "Fallback response"        // fallback supplier
);

// Inspect circuit state
FailureIsolator.CircuitState state = isolator.state("agent-a");   // CLOSED, OPEN, HALF_OPEN
int failures = isolator.failureCount("agent-a");
isolator.reset("agent-a");   // manually reset
```

**DegradationStrategy options**:

| Strategy    | Behavior                                 |
| ----------- | ---------------------------------------- |
| `SKIP`      | Return null, skip the agent              |
| `FALLBACK`  | Use the provided fallback supplier       |
| `CACHED`    | Return the last successful result        |
| `FAIL_FAST` | Throw `CircuitOpenException` immediately |

**Circuit states**: `CLOSED` (normal) -\\> `OPEN` (after N failures, fast-fail) -\\> `HALF_OPEN` (after recovery timeout, probe one request).

### TokenBudget

Per-agent token budget management to prevent runaway costs in multi-agent orchestration. Tracks both individual agent budgets and a global budget, with automatic warnings at 90% usage.

```java
TokenBudget budget = new TokenBudget(100_000);   // global budget
budget.allocate("agent-a", 30_000);
budget.allocate("agent-b", 70_000);

// Record usage
budget.consume("agent-a", 5_000);

// Check before consuming
if (budget.canConsume("agent-a", 10_000)) {
    budget.consume("agent-a", 10_000);
}

// Query
long remaining = budget.remaining("agent-a");
long globalRemaining = budget.globalRemaining();
double usage = budget.usagePercent("agent-a");       // 0-100
double globalUsage = budget.globalUsagePercent();     // 0-100
Map<String, Long> allocations = budget.allAllocations();
```

## Simulation Framework

The simulation framework lets you test multi-agent behaviors in a controlled environment before deploying them. You define a world state and rules, then run agents through turns while a validator enforces the rules. TnsAI.Coordination includes this framework for modeling multi-agent interactions in controlled environments. See the `examples/simulation/` directory in the Coordination repository.

### SimulationEnvironment

The environment defines the initial world state (who the actors are, what resources exist, what relationships are in place) and the simulation constraints like maximum turns.

```java
SimulationEnvironment env = SimulationEnvironment.builder()
    .name("diplomacy-sim")
    .initialState(Map.of(
        "countries", List.of("A", "B", "C"),
        "resources", Map.of("A", 100, "B", 80, "C", 120),
        "alliances", List.of()
    ))
    .maxTurns(50)
    .build();
```

### SimulationLoop

The `SimulationLoop` is the main driver that runs the simulation turn by turn. Each turn, it asks agents for their actions, validates them, and applies them to the environment state.

```java
SimulationLoop loop = SimulationLoop.builder()
    .environment(env)
    .agents(List.of(agentA, agentB, agentC))
    .validator(new SecretaryValidator())
    .build();

SimulationResult result = loop.run();
result.getTurnCount();       // Number of turns executed
result.getFinalState();      // Final environment state
result.getHistory();         // Turn-by-turn action history
```

### SecretaryValidator

The `SecretaryValidator` acts as a referee, checking every agent action against the simulation rules before it is applied. It prevents illegal moves, enforces turn order, and checks resource constraints.

```java
public class SecretaryValidator implements ActionValidator {
    @Override
    public ValidationResult validate(AgentAction action, Map<String, Object> state) {
        if (action.getType().equals("trade") && !hasResources(action, state)) {
            return ValidationResult.reject("Insufficient resources for trade");
        }
        return ValidationResult.accept();
    }
}
```

### DiplomacySimulation Example

To see the simulation framework in action, the `examples/simulation/` directory includes a complete diplomacy simulation with country agents that negotiate treaties, trade resources, and form alliances. The `SecretaryValidator` enforces diplomacy rules while the `SimulationLoop` manages negotiation rounds.

```java
DiplomacySimulation sim = new DiplomacySimulation();
sim.addCountry("Alphaland", agentAlpha);
sim.addCountry("Betaland", agentBeta);
sim.addCountry("Gammaland", agentGamma);

SimulationResult result = sim.run(30); // 30 rounds
System.out.println("Final alliances: " + result.getFinalState().get("alliances"));
```

## DebateExecutor

The `DebateExecutor` improves answer quality by having two LLM agents argue opposing positions on a question, then having a judge evaluate both sides and deliver a verdict. This adversarial approach forces each side to identify weaknesses in the other's reasoning, leading to more thorough analysis than a single LLM would produce.

```java
DebateExecutor debate = DebateExecutor.builder()
    .proponent(proponentLLM)
    .opponent(opponentLLM)
    .judge(judgeLLM)
    .maxRounds(3)
    .format(DebateFormat.STRUCTURED)
    .timeout(Duration.ofMinutes(10))
    .build();

DebateResult result = debate.execute("Should we use microservices for this project?");

System.out.println("Verdict: " + result.verdict());
System.out.println("Confidence: " + result.confidence());  // 0.0 - 1.0
System.out.println("Rounds: " + result.roundCount());
System.out.println("Pro summary: " + result.proSummary());
System.out.println("Con summary: " + result.conSummary());

// Access individual rounds
for (DebateRound round : result.rounds()) {
    System.out.println("Round " + round.roundNumber());
    System.out.println("  Pro: " + round.proponentArgument());
    System.out.println("  Con: " + round.opponentArgument());
}
```

### DebateFormat

The debate format controls the structure of the argumentation.

| Format       | Description                            |
| ------------ | -------------------------------------- |
| `STRUCTURED` | Formal pro/con structure with sections |
| `FREE_FORM`  | Open-ended discussion                  |

The judge produces a verdict with structured sections (VERDICT, REASONING, PRO SUMMARY, CON SUMMARY, CONFIDENCE). Confidence is extracted as a percentage from the judge's output.

---

# AutoTeamBuilder

URL: https://tnsai.dev/docs/multi-agent/auto-team
Description: TnsAI.Intelligence provides LLM-driven automatic team composition. Given a task description, AutoTeamBuilder decomposes it into subtasks, generates agent configurations, and selects the optimal coordination topology. Package: com.tnsai.autoteam.

import { Callout } from 'fumadocs-ui/components/callout'

## Quick Start

Give `AutoTeamBuilder` a plain-English task description, and it returns a `TeamSpec` containing the agents, tools, and coordination topology needed to accomplish the task.

```java
AutoTeamBuilder autoTeam = AutoTeamBuilder.builder()
    .llm(llmClient)
    .toolRegistry(toolRegistry)
    .build();

TeamSpec spec = autoTeam.buildTeam("Research quantum computing advances and write a summary report");

// spec contains:
//   - List of agent configs (roles, tools, system prompts)
//   - Selected topology (e.g., SEQUENTIAL, HIERARCHICAL)
//   - Subtask assignments
```

## How It Works

AutoTeamBuilder performs three steps to go from a natural-language task to a ready-to-run team of agents. Each step uses an LLM to make intelligent decisions about decomposition, configuration, and coordination.

### 1. Task Decomposition

The LLM analyzes the input task and breaks it into subtasks, each with a name, description, and list of dependencies on other subtasks. Dependencies determine execution order and influence topology selection.

```java
List<SubTask> subtasks = autoTeam.decompose("Build a market analysis dashboard");
// SubTask("research", "Gather market data", [])
// SubTask("analyze", "Analyze trends", ["research"])
// SubTask("visualize", "Create dashboard", ["analyze"])
```

### 2. Agent Configuration Generation

For each subtask, the builder generates an agent configuration with an appropriate role, a tailored system prompt, and the right tools selected from the registry.

```java
// Each AgentConfig includes:
public record AgentConfig(
    String name,
    String role,
    String systemPrompt,
    List<String> tools,
    Map<String, Object> parameters
) {}
```

The builder selects tools from the registry based on the subtask requirements. For example, a research subtask gets search tools, while an analysis subtask gets data processing tools.

### 3. Topology Selection

Based on the dependency graph between subtasks, the builder selects the coordination topology that best fits the workflow. Independent tasks run in parallel, linear chains run sequentially, and complex graphs get a hierarchical coordinator.

| Dependency Pattern                     | Selected Topology                 |
| -------------------------------------- | --------------------------------- |
| No dependencies (all independent)      | `PARALLEL`                        |
| Linear chain (A -\\> B -\\> C)         | `SEQUENTIAL`                      |
| Tree structure (one root, many leaves) | `HIERARCHICAL`                    |
| Complex graph                          | `HIERARCHICAL` with a coordinator |

## Builder Parameters

Configure the builder with an LLM for task analysis, a tool registry listing the tools agents can use, and optional constraints on team size.

```java
AutoTeamBuilder autoTeam = AutoTeamBuilder.builder()
    .llm(llmClient)                    // LLM for task analysis
    .toolRegistry(toolRegistry)        // Available tools to assign
    .maxAgents(8)                      // Maximum agents to create
    .preferredTopology(null)           // Override topology selection (null = auto)
    .build();
```

| Parameter           | Default  | Description                                            |
| ------------------- | -------- | ------------------------------------------------------ |
| `llm`               | required | LLMClient for task decomposition and config generation |
| `toolRegistry`      | required | Registry of available tools                            |
| `maxAgents`         | 8        | Maximum number of agents to generate                   |
| `preferredTopology` | null     | Force a specific topology (null = auto-select)         |

## TeamSpec

The output of `buildTeam()` is a complete blueprint for the agent team. It contains agent configurations, the selected topology, subtask assignments, and a dependency graph. You can inspect it, modify it, or pass it directly to the coordination module to run.

```java
TeamSpec spec = autoTeam.buildTeam(task);

spec.getAgentConfigs();     // List<AgentConfig> -- one per subtask
spec.getTopology();         // String -- selected topology name
spec.getSubTasks();         // List<SubTask> -- decomposed subtasks
spec.getDependencyGraph();  // Map<String, List<String>> -- subtask dependencies

// Instantiate the team
Group group = spec.toGroup(agentFactory);
group.start();
String result = group.execute(task);
```

## Integration with Coordination Module

The generated `TeamSpec` integrates directly with the [Group Topologies](/docs/multi-agent/topologies) from TnsAI.Coordination. This example shows how to take a spec, build real agents from the configurations, and run the team.

```java
// AutoTeamBuilder generates the spec, Coordination module runs it
AutoTeamBuilder autoTeam = AutoTeamBuilder.builder()
    .llm(llmClient)
    .toolRegistry(toolRegistry)
    .build();

TeamSpec spec = autoTeam.buildTeam("Audit the codebase for security vulnerabilities");

// Build and run the team using Coordination topologies
Team team = Team.builder()
    .formation(TeamFormation.valueOf(spec.getTopology()))
    .build();

for (AgentConfig config : spec.getAgentConfigs()) {
    Agent agent = AgentBuilder.create()
        .model(config.parameters().getOrDefault("model", "claude-sonnet-4").toString())
        .systemPrompt(config.systemPrompt())
        .build();
    team.addMember(agent, TeamRole.MEMBER);
}

team.start();
String result = team.execute("Audit the codebase for security vulnerabilities");
```

---

# Agent Communication

URL: https://tnsai.dev/docs/multi-agent/communication
Description: TnsAI agents communicate through structured, immutable messages following established multi-agent protocols. The communication system supports direct messaging, Contract Net Protocol (CNP) for task allocation, group membership management, and leader election.

import { Callout } from 'fumadocs-ui/components/callout'

## AgentMessage

`com.tnsai.communication.AgentMessage` is the core message type for inter-agent communication. It is an immutable value object (all fields are final, maps are unmodifiable copies).

### Structure

| Field         | Type                  | Description                                             |
| ------------- | --------------------- | ------------------------------------------------------- |
| `messageId`   | `String`              | Unique message ID (auto-generated via UUID)             |
| `senderId`    | `String`              | ID of the sending agent (required)                      |
| `receiverId`  | `String`              | ID of the receiving agent (required)                    |
| `messageType` | `MessageTypeMarker`   | Type of message (CNP, Task, etc.)                       |
| `content`     | `Map<String, Object>` | Message payload (immutable copy)                        |
| `timestamp`   | `long`                | Message creation time                                   |
| `inReplyTo`   | `String`              | ID of the message being replied to (null for originals) |
| `metadata`    | `Map<String, Object>` | Additional metadata (immutable copy)                    |

### Creating Messages

```java
// Basic message
AgentMessage msg = AgentMessage.create(
    "agent-A",                    // sender
    "agent-B",                    // receiver
    CNPMessageType.CFP,           // type
    Map.of("task", "analyze data", "deadline", "2h")  // content
);

// Reply (automatically swaps sender/receiver, sets inReplyTo)
AgentMessage reply = AgentMessage.createReply(
    originalMessage,
    CNPMessageType.PROPOSAL,
    Map.of("bid", 100, "eta", "1h")
);

// Add metadata to existing message
AgentMessage withMeta = msg.withSpec("priority", "high");
```

### Query Methods

```java
boolean isReply = msg.isReply();
boolean isCfp = msg.isType(CNPMessageType.CFP);
boolean isCnp = msg.isCategory("CNP");
Map<String, Object> spec = msg.getSpec();  // alias for getMetadata()
```

## MessageTypeMarker

`com.tnsai.communication.MessageTypeMarker` is the marker interface that all message type enums implement. It provides a `category()` method used for filtering messages by protocol.

### CNPMessageType

`com.tnsai.communication.CNPMessageType` implements the Contract Net Protocol (Smith, 1980) for distributed task allocation.

**Protocol Flow:**

```
Phase 1: Call for Proposals
    Initiator -> CFP (broadcast task requirements)

Phase 2: Contractor Response
    Contractors -> PROPOSAL (submit bid) or REFUSE (decline)

Phase 3: Selection
    Initiator -> ACCEPT or REJECT

Phase 4: Negotiation (optional)
    COUNTER_OFFER exchanges

Phase 5: Finalization
    Initiator -> AWARD
    Contractor -> CONFIRM

Execution:
    RESULT (success) or FAILURE (error)
```

**Enum values:**

| Value           | Phase     | Description                    |
| --------------- | --------- | ------------------------------ |
| `CFP`           | 1         | Call for proposals (broadcast) |
| `PROPOSAL`      | 2         | Submit bid/proposal            |
| `REFUSE`        | 2         | Decline to bid                 |
| `ACCEPT`        | 3         | Accept a proposal              |
| `REJECT`        | 3         | Reject a proposal              |
| `COUNTER_OFFER` | 4         | Propose modified terms         |
| `AWARD`         | 5         | Final contract award           |
| `CONFIRM`       | 5         | Confirm acceptance             |
| `CANCEL`        | 5         | Cancel the process             |
| `RESULT`        | Execution | Execution result               |
| `FAILURE`       | Execution | Execution failure              |

### CNP Example

```java
// Initiator broadcasts CFP
AgentMessage cfp = AgentMessage.create(
    initiator.id(), "broadcast",
    CNPMessageType.CFP,
    Map.of("task", "translate document", "language", "Japanese")
);

// Contractor submits proposal
AgentMessage proposal = AgentMessage.createReply(cfp,
    CNPMessageType.PROPOSAL,
    Map.of("bid", 50, "eta", "30min", "quality", "high")
);

// Initiator accepts
AgentMessage accept = AgentMessage.createReply(proposal,
    CNPMessageType.ACCEPT,
    Map.of("contractId", "c-123")
);

// Contractor reports result
AgentMessage result = AgentMessage.createReply(accept,
    CNPMessageType.RESULT,
    Map.of("translation", translatedText, "wordCount", 1500)
);
```

## MembershipManager

`com.tnsai.coordination.groups.MembershipManager` manages agent group membership lifecycle including registration, health monitoring, capability-based lookup, and task tracking.

### Registration

```java
// Register an agent
Optional<MemberInfo> info = membershipManager.register(agent);

// Validate before registration
MembershipValidation validation = membershipManager.validate(agent);
if (!validation.valid()) {
    System.err.println("Rejected: " + validation.reason());
}

// Unregister
boolean removed = membershipManager.unregister(agentId, "task completed");
```

### Health Monitoring

```java
// Record heartbeat
membershipManager.heartbeat(agentId);

// Check health
Optional<MemberHealth> health = membershipManager.getHealth(agentId);

// Get unhealthy members
List<MemberInfo> unhealthy = membershipManager.getUnhealthyMembers();

// Auto-remove unhealthy
int removed = membershipManager.checkAndRemoveUnhealthy();

// Start/stop automatic health checking
membershipManager.startHealthCheck(Duration.ofSeconds(30));
membershipManager.stopHealthCheck();
```

`MemberHealth` enum: `HEALTHY`, `DEGRADED`, `UNHEALTHY`, `UNKNOWN`.

### Member Queries

```java
List<MemberInfo> all = membershipManager.getMembers();
Optional<MemberInfo> member = membershipManager.getMember(agentId);
List<MemberInfo> searchers = membershipManager.findByCapability("search");
int count = membershipManager.getMemberCount();
```

### Task Tracking

```java
// Record task start/complete
membershipManager.recordTaskStart(agentId);
membershipManager.recordTaskComplete(agentId);

// Find available members (healthy + can accept more tasks)
List<MemberInfo> available = membershipManager.getAvailableMembers();

// Available with specific capability
List<MemberInfo> available = membershipManager.getAvailableMembersWithCapability("coding");
```

### MemberInfo Record

```java
record MemberInfo(
    String agentId,
    String agentName,
    List<String> capabilities,
    Instant joinedAt,
    Instant lastHeartbeat,
    MemberHealth health,
    Map<String, Object> metadata,
    int priority,              // default 5
    int maxConcurrentTasks,    // default 1
    int activeTasks,           // current active count
    String role                // default "member"
)
```

Key methods on `MemberInfo`:

| Method                     | Description                             |
| -------------------------- | --------------------------------------- |
| `isHealthy()`              | `health == HEALTHY`                     |
| `canAcceptTask()`          | `activeTasks < maxConcurrentTasks`      |
| `getMembershipDuration()`  | Time since `joinedAt`                   |
| `withTaskStarted()`        | New `MemberInfo` with `activeTasks + 1` |
| `withTaskCompleted()`      | New `MemberInfo` with `activeTasks - 1` |
| `withHeartbeat(Instant)`   | New `MemberInfo` with updated heartbeat |
| `withHealth(MemberHealth)` | New `MemberInfo` with updated health    |

### Statistics and History

```java
MembershipStats stats = membershipManager.getStats();
// stats.currentCount(), stats.healthyCount(), stats.averageMembershipDuration()

List<MembershipHistoryEntry> history = membershipManager.getHistory(50);
// Each entry: agentId, agentName, action (JOINED/LEFT/REMOVED_UNHEALTHY/REMOVED_POLICY),
//             reason, timestamp
```

## LeaderElectionStrategy

`com.tnsai.coordination.groups.LeaderElectionStrategy` is a `@FunctionalInterface` for selecting a leader from group members.

```java
@FunctionalInterface
public interface LeaderElectionStrategy {
    Agent elect(List<Agent> members);
    default Optional<Agent> electOptional(List<Agent> members) { ... }
}
```

### Built-in Strategies

| Strategy     | Constant       | Behavior                                                       |
| ------------ | -------------- | -------------------------------------------------------------- |
| Random       | `RANDOM`       | Random selection from members                                  |
| First Joined | `FIRST_JOINED` | First member in the list                                       |
| Last Joined  | `LAST_JOINED`  | Last member in the list                                        |
| Round Robin  | `ROUND_ROBIN`  | Rotating (delegates to `FIRST_JOINED`; state managed by group) |
| Role-Based   | `ROLE_BASED`   | Agent with most roles (most capabilities)                      |
| None         | `NONE`         | Always returns null                                            |

### Custom Strategies

```java
// Lambda-based custom strategy
LeaderElectionStrategy byCapabilities = members ->
    members.stream()
        .max(Comparator.comparingInt(a -> a.getCapabilities().size()))
        .orElse(null);

// Usage
Agent leader = strategy.elect(groupMembers);
Optional<Agent> leaderOpt = strategy.electOptional(groupMembers);
```

## Related Documentation

- [Advanced Agent Features](/docs/agents/advanced) -- hierarchy and delegation
- [Events](/docs/agents/fundamentals/events) -- TnsAI event system
- [SPI Reference](/docs/reference/spi) -- MessageBroker SPI for pluggable transports

---

# Council and Voting

URL: https://tnsai.dev/docs/multi-agent/council-voting
Description: TnsAI.Coordination provides two complementary systems for group decision-making: CouncilExecutor for multi-model deliberation with peer review, and ConsensusBuilder / GroupDecisionFramework for agent voting.

import { Callout } from 'fumadocs-ui/components/callout'

## CouncilExecutor

The `CouncilExecutor` lets you get the best answer from multiple LLMs by having them independently respond, anonymously peer-review each other, and then synthesize a top-rated answer. It is a 3-stage deliberation pipeline inspired by Karpathy's llm-council. Multiple LLM models independently answer a question, peer-review each other's responses (anonymized), and a chairman synthesizes the best answer.

### Three Stages

The deliberation process follows three stages. Each stage builds on the previous one to produce a high-quality final answer.

1. **Individual Responses** -- All members answer the question independently in parallel.
2. **Peer Review** -- Each member evaluates all anonymized responses, providing analysis and a ranking.
3. **Chairman Synthesis** -- The chairman receives all responses, reviews, and aggregate rankings, then produces a final synthesized answer.

### Usage

Create a council with at least two LLM members and a chairman, then call `deliberate()` with your question. The result includes the final synthesized answer, individual responses, peer reviews, and an aggregate ranking of models.

```java
CouncilExecutor council = CouncilExecutor.builder()
    .members(List.of(gpt4Client, claudeClient, geminiClient, grokClient))
    .chairman(geminiClient)
    .anonymize(true)             // default: true
    .timeout(Duration.ofMinutes(5))
    .build();

CouncilResult result = council.deliberate("How should we architect the auth system?");

// Final synthesized answer
System.out.println(result.finalAnswer());

// Aggregate ranking of models
result.topRanked().ifPresent(r ->
    System.out.println("Top: " + r.llmName() + " (avg rank: " + r.averageRank() + ")"));

// Access each stage
List<CouncilResponse> responses = result.individualResponses();  // Stage 1
List<CouncilReview> reviews = result.peerReviews();              // Stage 2
CouncilSynthesis synthesis = result.synthesis();                  // Stage 3
List<RankedMember> ranking = result.aggregateRanking();
```

### Builder Configuration

These are the parameters you can set when building a council.

| Parameter   | Default    | Description                                         |
| ----------- | ---------- | --------------------------------------------------- |
| `members`   | (required) | At least 2 `LLMClient` instances                    |
| `chairman`  | (required) | `LLMClient` that produces final synthesis           |
| `anonymize` | `true`     | Whether to hide model identities during peer review |
| `timeout`   | 5 minutes  | Per-stage timeout                                   |

### Anonymization

Anonymization prevents bias during peer review by hiding which LLM produced each response. When `anonymize` is true, model identities are replaced with labels (Model A, Model B, ...) during peer review. The chairman receives both labels and real names.

### CouncilResult

The `CouncilResult` contains all outputs from the three deliberation stages, plus an aggregate ranking of which model performed best.

| Field                   | Type                     | Description                                               |
| ----------------------- | ------------------------ | --------------------------------------------------------- |
| `individualResponses()` | `List<CouncilResponse>`  | Stage 1 responses (llmName, response, durationMs)         |
| `peerReviews()`         | `List<CouncilReview>`    | Stage 2 reviews (reviewerName, evaluation, parsedRanking) |
| `synthesis()`           | `CouncilSynthesis`       | Stage 3 chairman output (chairmanModel, synthesis)        |
| `aggregateRanking()`    | `List<RankedMember>`     | Models ranked by average position across reviews          |
| `finalAnswer()`         | `String`                 | Shortcut to `synthesis().synthesis()`                     |
| `topRanked()`           | `Optional<RankedMember>` | The highest-ranked model                                  |

### RankedMember

A `RankedMember` records how well a specific LLM performed across all peer reviews. Lower `averageRank` is better (1.0 means consistently ranked first).

```java
public record RankedMember(String llmName, double averageRank, int rankingsCount)
```

Lower `averageRank` is better (1.0 = consistently ranked first).

## VotingMethod

TnsAI provides five voting methods for group consensus, ranging from simple majority to weighted voting. Choose the method that matches how much agreement you need.

| Method          | Description                            | Threshold                  |
| --------------- | -------------------------------------- | -------------------------- |
| `MAJORITY`      | Simple majority (\\> 50%)              | 50%                        |
| `SUPERMAJORITY` | Configurable threshold (default 2/3)   | 66.7%                      |
| `UNANIMOUS`     | All voters must agree                  | 100%                       |
| `RANKED_CHOICE` | Instant-runoff with ranked preferences | Majority after elimination |
| `WEIGHTED`      | Votes weighted by voter weight         | 50% of total weight        |

## ConsensusBuilder

The `ConsensusBuilder` is the simplest way to run a vote among agents. You specify the question, the options, the voters, and the voting method, then call `.vote()` to get the result. It is a fluent builder that orchestrates a voting session among agents. Collects votes in parallel, applies the chosen voting method, and returns results.

```java
VotingResult result = ConsensusBuilder.create()
    .topic("Which database to use?")
    .options("PostgreSQL", "MySQL", "MongoDB")
    .among(List.of(dbExpert, backendDev, infraAgent))
    .method(VotingMethod.MAJORITY)
    .timeout(Duration.ofSeconds(30))
    .quorum(2)
    .vote();

System.out.println("Winner: " + result.winner());
System.out.println("Total votes: " + result.totalVotes());
System.out.println("Quorum met: " + result.quorumMet());
```

### Configuration

These builder methods let you configure every aspect of the voting session.

| Method                            | Default    | Description                   |
| --------------------------------- | ---------- | ----------------------------- |
| `.topic(String)`                  | (required) | The question to vote on       |
| `.options(String...)`             | (required) | Available choices             |
| `.among(List<Agent>)`             | (required) | Voting agents                 |
| `.method(VotingMethod)`           | `MAJORITY` | Voting algorithm              |
| `.timeout(Duration)`              | 30 seconds | Maximum wait for votes        |
| `.quorum(int)`                    | 1          | Minimum votes required        |
| `.supermajorityThreshold(double)` | 0.667      | Threshold for `SUPERMAJORITY` |
| `.listener(VotingListener)`       | none       | Event callbacks               |

### How Voting Works

Here is what happens internally when you call `.vote()`:

1. A prompt is built with the topic and options, sent to all agents in parallel.
2. Each agent's response is parsed using case-insensitive matching against the option list.
3. Votes are tallied using the selected method (`MajorityVoting` or `RankedChoiceVoting`).
4. The `VotingResult` includes the winner, vote counts, and quorum status.

### VotingListener

Attach a `VotingListener` to get real-time callbacks as votes come in, when quorum is reached, and when the final result is ready.

```java
ConsensusBuilder.create()
    .topic("Deploy now?")
    .options("Yes", "No", "Defer")
    .among(agents)
    .listener(new VotingListener() {
        @Override
        public void onVoteReceived(Vote vote) {
            System.out.println(vote.voterId() + " voted: " + vote.choice());
        }

        @Override
        public void onQuorumReached(int voteCount, int quorum) {
            System.out.println("Quorum reached: " + voteCount + "/" + quorum);
        }

        @Override
        public void onComplete(VotingResult result) {
            System.out.println("Winner: " + result.winner());
        }
    })
    .vote();
```

## GroupDecisionFramework

The `GroupDecisionFramework` is a more structured alternative to `ConsensusBuilder`, designed for use within an `AgentGroup`. It follows a formal propose-vote-converge lifecycle and records a decision trace for auditability. It provides a structured 3-phase protocol for group-level decision-making within an `AgentGroup`. Provides propose-vote-converge semantics with decision tracing.

```java
GroupDecisionFramework framework = new DefaultGroupDecisionFramework(traceStore);

// Full pipeline in one call
DecisionProposal proposal = DecisionProposal.of(
    "Which database?",
    List.of("PostgreSQL", "MySQL", "MongoDB")
);
DecisionResult result = framework.makeDecision(group, proposal);
```

### Three Phases

The framework splits every group decision into three distinct phases:

1. **Propose** -- Create a `DecisionProposal` with a topic, options, and voting method.
2. **Vote** -- Collect votes from all group members.
3. **Converge** -- Tally votes, determine outcome, save trace, broadcast result. Falls back to leader decision if no consensus and a leader exists.

```java
// Step-by-step usage for more control
DecisionProposal proposal = framework.propose(
    "Which database?",
    List.of("PostgreSQL", "MySQL", "MongoDB"),
    VotingMethod.RANKED_CHOICE
);

List<Vote> votes = framework.vote(group, proposal);
DecisionResult result = framework.converge(group, votes, proposal);
```

### DecisionTraceStore

Every decision is recorded in a trace store so you can review what was proposed, who voted for what, and what the outcome was. All decisions are persisted to a `DecisionTraceStore` for auditability. The default implementation `InMemoryDecisionTraceStore` keeps traces in memory.

---

# Multi-Agent

URL: https://tnsai.dev/docs/multi-agent
Description: Build systems of cooperating agents.

import { Callout } from 'fumadocs-ui/components/callout'

## Pages

- [Topologies](/docs/multi-agent/topologies) — Hierarchical, peer, mesh, star group shapes.
- [Workflows](/docs/multi-agent/workflows) — Sequential, parallel, loop execution.
- [Council & Voting](/docs/multi-agent/council-voting) — Majority, weighted, quorum decisions.
- [Negotiation](/docs/multi-agent/negotiation) — Contract-net, auction, bargaining.
- [Judge Agent](/docs/multi-agent/judge) — Arbiter for conflicting outputs.
- [Protocols](/docs/multi-agent/protocols) — Group communication patterns.
- [Communication](/docs/multi-agent/communication) — Agent-to-agent messaging.
- [Auto Team Builder](/docs/multi-agent/auto-team) — LLM-driven team composition.
- [Advanced](/docs/multi-agent/advanced) — Delegation, advanced topologies, swarm.

---

# Judge Agent Pattern

URL: https://tnsai.dev/docs/multi-agent/judge
Description: TnsAI.Coordination provides a judge agent pattern for evaluating and selecting the best output from multiple agents. A judge applies a policy to score candidate outputs and pick a winner. Package: com.tnsai.coordination.judge.

import { Callout } from 'fumadocs-ui/components/callout'

## Quick Start

```java
// Create a judge with LLM-based evaluation
JudgePolicy policy = new LLMJudgePolicy(llmClient, "Pick the most accurate and complete answer");

JudgeCoordinator judge = JudgeCoordinator.builder()
    .policy(policy)
    .build();

// Evaluate candidate outputs
List<CandidateOutput> candidates = List.of(
    new CandidateOutput("agent-1", "Paris is the capital of France."),
    new CandidateOutput("agent-2", "The capital of France is Paris, located on the Seine river."),
    new CandidateOutput("agent-3", "France's capital is Paris.")
);

JudgeResult result = judge.evaluate("What is the capital of France?", candidates);
System.out.println(result.getWinner().agentId());    // "agent-2"
System.out.println(result.getWinner().score());       // 0.95
System.out.println(result.getReasoning());            // LLM explanation
```

## JudgePolicy SPI

A `JudgePolicy` defines how candidates are scored and ranked. TnsAI ships with two built-in policies (threshold-based and LLM-based), and you can register your own via SPI. All judge policies implement this interface:

```java
public interface JudgePolicy {
    JudgeResult evaluate(String task, List<CandidateOutput> candidates);
}
```

Register custom policies via `META-INF/services/com.tnsai.coordination.judge.JudgePolicy`.

### ThresholdJudgePolicy

The simplest judge policy: it scores candidates on keyword overlap and length without calling an LLM, making it fast and free. Use it for automated testing or CI/CD pipelines where you just need a quick quality check.

```java
JudgePolicy policy = new ThresholdJudgePolicy(0.7); // minimum score threshold

JudgeResult result = policy.evaluate(task, candidates);
if (result.hasWinner()) {
    System.out.println("Winner: " + result.getWinner().agentId());
} else {
    System.out.println("No candidate met the threshold");
}
```

Best for: fast evaluation without LLM calls, CI/CD quality gates, automated testing.

### LLMJudgePolicy

This policy sends all candidate outputs to an LLM and asks it to score each one against your evaluation criteria. It provides the highest quality judgments but incurs an LLM API call. The LLM returns a score for each candidate and a reasoning explanation.

```java
JudgePolicy policy = new LLMJudgePolicy(llmClient, "Evaluate for accuracy, completeness, and clarity");

// With custom scoring rubric
JudgePolicy detailed = LLMJudgePolicy.builder()
    .llm(llmClient)
    .criteria("Evaluate for accuracy, completeness, and clarity")
    .scoringScale(10)         // score 1-10 instead of 0.0-1.0
    .requireReasoning(true)   // LLM must explain each score
    .model("claude-sonnet-4")
    .build();
```

| Parameter          | Default    | Description                     |
| ------------------ | ---------- | ------------------------------- |
| `llm`              | required   | LLMClient for evaluation        |
| `criteria`         | required   | Evaluation criteria prompt      |
| `scoringScale`     | 10         | Maximum score value             |
| `requireReasoning` | true       | Whether LLM must explain scores |
| `model`            | (from llm) | Model to use for evaluation     |

## JudgeCoordinator

The `JudgeCoordinator` is the main entry point for using the judge pattern. It either accepts pre-collected candidate outputs or runs agents itself, then passes the candidates to the policy for scoring.

```java
JudgeCoordinator judge = JudgeCoordinator.builder()
    .policy(policy)
    .timeout(Duration.ofSeconds(30))
    .build();

// Evaluate with pre-collected candidates
JudgeResult result = judge.evaluate(task, candidates);

// Or run agents and judge in one step
JudgeResult result = judge.runAndJudge(task, List.of(agent1, agent2, agent3));
```

### JudgeResult

The `JudgeResult` contains the winner, the full ranking of all candidates, the judge's reasoning, and individual scores.

```java
JudgeResult result = judge.evaluate(task, candidates);

result.getWinner();           // ScoredCandidate with highest score
result.hasWinner();           // true if at least one candidate scored
result.getRankings();         // All candidates sorted by score (descending)
result.getReasoning();        // Judge's explanation (from LLMJudgePolicy)
result.getScores();           // Map<String, Double> of agentId -> score
```

### ScoredCandidate

Each candidate in the result is wrapped in a `ScoredCandidate` that includes the agent ID, the output text, the normalized score, and optional per-candidate reasoning.

```java
ScoredCandidate winner = result.getWinner();
winner.agentId();     // Agent that produced this output
winner.output();      // The candidate output text
winner.score();       // Normalized score (0.0-1.0)
winner.reasoning();   // Per-candidate reasoning (if available)
```

## Integration with Topologies

The judge pattern is not a standalone feature -- it is designed to combine with any [Group Topology](/docs/multi-agent/topologies). Below are two common integration patterns.

### Parallel Generation + Judge

A common pattern is to have a parallel team generate multiple candidate answers, then use a judge to select the best one. This gives you the speed of parallel generation with the quality assurance of evaluation.

```java
Team team = Team.builder()
    .formation(TeamFormation.PARALLEL)
    .addMember(agent1, TeamRole.MEMBER)
    .addMember(agent2, TeamRole.MEMBER)
    .addMember(agent3, TeamRole.MEMBER)
    .build();

team.start();
List<String> outputs = team.executeAll(task);

JudgeCoordinator judge = JudgeCoordinator.builder()
    .policy(new LLMJudgePolicy(llmClient, "Pick the best answer"))
    .build();

List<CandidateOutput> candidates = IntStream.range(0, outputs.size())
    .mapToObj(i -> new CandidateOutput("agent-" + i, outputs.get(i)))
    .toList();

JudgeResult result = judge.evaluate(task, candidates);
```

### Iterative Refinement with Judge

For higher quality, run multiple rounds: generate candidates, judge them, feed the best back as context, and repeat. Each round improves on the previous best answer.

```java
String bestOutput = null;
for (int round = 0; round < 3; round++) {
    List<CandidateOutput> candidates = agents.stream()
        .map(a -> new CandidateOutput(a.getId(), a.chat(task + (bestOutput != null ? "\nPrevious best: " + bestOutput : ""))))
        .toList();

    JudgeResult result = judge.evaluate(task, candidates);
    bestOutput = result.getWinner().output();
}
```

---

# Negotiation

URL: https://tnsai.dev/docs/multi-agent/negotiation
Description: TnsAI.Coordination provides a pluggable negotiation framework with 4 built-in protocols, configurable concession strategies, and a unified NegotiationExecutor that resolves the correct protocol from configuration.

import { Callout } from 'fumadocs-ui/components/callout'

## NegotiationExecutor

The `NegotiationExecutor` is the main entry point for all negotiations. You give it agents, a configuration (protocol type, max rounds, timeout, starting offer), and it runs the negotiation from start to finish. It maintains a registry of `NegotiationProtocol` implementations keyed by `NegotiationType`, and dispatches negotiations to the matching protocol.

```java
NegotiationExecutor executor = new NegotiationExecutor();

// Built-in protocols are registered automatically:
// - BilateralNegotiation
// - MonotonicConcessionProtocol
// - AuctionAdapter (handles ENGLISH_AUCTION, DUTCH_AUCTION, SEALED_BID, VICKREY)
// - ContractNetAdapter

NegotiationOutcome outcome = executor.execute(
    List.of(buyerAgent, sellerAgent),
    new NegotiationConfig(
        NegotiationType.BILATERAL,
        10,                                      // maxRounds
        Duration.ofMinutes(5),                   // timeout
        50.0,                                    // reservationValue (walk-away point)
        100.0,                                   // initialOffer
        new LinearConcession()                   // concession strategy
    )
);
```

### Listeners

You can attach listeners to get notified when a negotiation starts and when it completes. This is useful for logging, monitoring, or triggering follow-up actions.

```java
executor.addListener(new NegotiationListener() {
    @Override
    public void onNegotiationStarted(NegotiationConfig config) { }

    @Override
    public void onNegotiationCompleted(NegotiationOutcome outcome) {
        System.out.println("Agreed: " + outcome.agreed());
    }
});
```

### Custom Protocols

If the built-in protocols don't fit your use case, you can implement your own `NegotiationProtocol` and register it with the executor.

Register a custom protocol to extend or replace built-in behavior:

```java
executor.register(new MyCustomProtocol()); // implements NegotiationProtocol
```

## Four Negotiation Protocols

TnsAI ships with four negotiation protocols that cover the most common multi-agent negotiation patterns, from simple two-party bargaining to competitive auctions.

### Bilateral

The simplest protocol: two agents take turns making offers and counter-offers until they agree or time runs out. This is the classic buyer-seller negotiation pattern. Each round, the current agent makes a concession based on its `ConcessionStrategy`.

```java
NegotiationConfig config = new NegotiationConfig(
    NegotiationType.BILATERAL,
    10,                          // max 10 rounds
    Duration.ofMinutes(5),
    50.0,                        // minimum acceptable value
    100.0,                       // starting offer
    new LinearConcession()
);

NegotiationOutcome result = executor.execute(List.of(agent1, agent2), config);
```

### MonotonicConcession

A variant of bilateral negotiation with a stricter rule: each party must make monotonically decreasing (or equal) offers. No party can raise their demand after conceding. Guarantees convergence when both parties concede.

```java
NegotiationConfig config = new NegotiationConfig(
    NegotiationType.MONOTONIC_CONCESSION,
    15,
    Duration.ofMinutes(3),
    30.0,
    100.0,
    new ExponentialConcession()
);
```

### Auction

Auctions allocate tasks or resources through competitive bidding. This is useful when multiple agents can do the same job and you want to find the best price or fit. The `AuctionAdapter` wraps the `Auction` class and handles all four auction types through a single negotiation interface.

```java
// Via NegotiationExecutor
NegotiationConfig config = new NegotiationConfig(
    NegotiationType.VICKREY,      // or ENGLISH_AUCTION, DUTCH_AUCTION, SEALED_BID
    10,
    Duration.ofMinutes(2),
    0.0,
    100.0,
    new LinearConcession()
);

NegotiationOutcome result = executor.execute(bidders, config);
```

See the dedicated Auction section below for direct usage.

### ContractNet

The Contract Net protocol is a structured approach where an initiator broadcasts a call for proposals to multiple agents, collects their bids, and selects the best one. It is the FIPA Contract Net protocol adapted for the negotiation framework. An initiator broadcasts a call for proposals, collects bids, selects a winner, and monitors execution.

```java
NegotiationConfig config = new NegotiationConfig(
    NegotiationType.CONTRACT_NET,
    5,
    Duration.ofMinutes(3),
    0.0,
    -1.0,                       // auto initial offer
    new LinearConcession()
);

NegotiationOutcome result = executor.execute(contractorAgents, config);
```

## Concession Strategies

A concession strategy controls how aggressively an agent lowers its asking price over successive rounds of negotiation. Different strategies produce different bargaining behaviors. The `ConcessionStrategy` functional interface computes the next offer value each round:

```java
@FunctionalInterface
public interface ConcessionStrategy {
    double nextOffer(int round, int maxRounds, double reservationValue,
                     double currentValue, List<NegotiationOffer> history);
}
```

### Built-in Strategies

TnsAI provides four concession strategies out of the box. You can also define a custom strategy using a lambda.

| Strategy       | Class                     | Behavior                                                                                             |
| -------------- | ------------------------- | ---------------------------------------------------------------------------------------------------- |
| Linear         | `LinearConcession`        | Constant concession rate per round. Concedes `(current - reservation) / remainingRounds` each round. |
| Exponential    | `ExponentialConcession`   | Concedes slowly at first, accelerates near deadline.                                                 |
| Tit-for-Tat    | `TitForTatConcession`     | Mirrors opponent's last concession amount. Cooperative if opponent cooperates.                       |
| Time-Dependent | `TimeDependentConcession` | Concession rate increases as deadline approaches.                                                    |

```java
// Linear: steady concession
ConcessionStrategy linear = new LinearConcession();

// Exponential: holds firm early, concedes more later
ConcessionStrategy exponential = new ExponentialConcession();

// Tit-for-Tat: reciprocates opponent's concession behavior
ConcessionStrategy tft = new TitForTatConcession();

// Custom lambda
ConcessionStrategy custom = (round, maxRounds, reservation, current, history) -> {
    double progress = (double) round / maxRounds;
    return current - (current - reservation) * progress * progress;
};
```

## NegotiationConfig

The configuration record that defines all parameters for a negotiation session: which protocol to use, how many rounds, the timeout, the walk-away point, and the concession strategy.

| Field                | Type                 | Description                                |
| -------------------- | -------------------- | ------------------------------------------ |
| `type`               | `NegotiationType`    | Protocol type (required)                   |
| `maxRounds`          | `int`                | Maximum negotiation rounds (\\>= 1)        |
| `timeout`            | `Duration`           | Overall negotiation timeout                |
| `reservationValue`   | `double`             | Minimum acceptable value (walk-away point) |
| `initialOffer`       | `double`             | Starting offer value (-1 for auto)         |
| `concessionStrategy` | `ConcessionStrategy` | Strategy for computing next offer          |

## NegotiationOutcome

The result of a completed negotiation. It tells you whether the agents reached an agreement, what the final offer was, how many rounds it took, and the reason for failure if no deal was reached.

| Field           | Type                     | Description                                            |
| --------------- | ------------------------ | ------------------------------------------------------ |
| `agreed`        | `boolean`                | Whether agreement was reached                          |
| `acceptedOffer` | `NegotiationOffer`       | The accepted offer (null if no agreement)              |
| `allOffers`     | `List<NegotiationOffer>` | All offers made during negotiation                     |
| `roundsUsed`    | `int`                    | Number of rounds conducted                             |
| `durationMs`    | `long`                   | Total negotiation duration                             |
| `finalState`    | `NegotiationState`       | Final state: `AGREED`, `FAILED`, `TIMEOUT`, `DEADLOCK` |
| `failureReason` | `String`                 | Reason for failure (null if agreed)                    |

## Auction Mechanism (Direct Usage)

While the `NegotiationExecutor` wraps auctions behind a uniform interface, you can also use the `Auction` class directly for full control over bidding strategies, reserve prices, and round mechanics.

### Auction Types

TnsAI supports four classic auction formats, each with different rules for bidding and pricing.

| Type         | Mechanism                                  | Winner Pays              |
| ------------ | ------------------------------------------ | ------------------------ |
| `ENGLISH`    | Ascending price, bidders outbid each other | Their bid price          |
| `DUTCH`      | Descending price, first to accept wins     | Current (accepted) price |
| `SEALED_BID` | Single sealed bid, highest wins            | Their bid price          |
| `VICKREY`    | Single sealed bid, highest wins            | Second-highest price     |

```java
AuctionResult result = Auction.builder()
    .type(Auction.Type.VICKREY)
    .task("Translate document to French")
    .bidders(List.of(translator1, translator2, translator3))
    .bidStrategy((agent, task, currentPrice, round, previousBids) -> {
        String response = agent.chat("Bid on: " + task + " (current: " + currentPrice + ")");
        return Double.parseDouble(response.trim());
    })
    .startingPrice(100.0)
    .reservePrice(20.0)
    .increment(10.0)         // price step for Dutch auction
    .maxRounds(10)
    .build()
    .execute();

if (result.success()) {
    System.out.println("Winner: " + result.winner().name());
    System.out.println("Bid: " + result.winningBid().value());
    System.out.println("Pays: " + result.payPrice());  // second-highest for Vickrey
}
```

### BidStrategy

The `BidStrategy` defines how each agent calculates its bid in each round. You implement this as a lambda or class that receives the agent, the task, the current price, the round number, and the history of previous bids.

```java
@FunctionalInterface
public interface BidStrategy {
    double calculateBid(Agent agent, String task, double currentPrice,
                        int round, List<Bid> previousBids);
}
```

Return `Double.NaN` to drop out of the auction. In English auctions, returning a value less than or equal to `currentPrice` also drops the bidder.

### Async Execution

Auctions can also run asynchronously so they don't block your main thread.

```java
CompletableFuture<AuctionResult> future = auction.executeAsync();
```

---

# Communication Protocols

URL: https://tnsai.dev/docs/multi-agent/protocols
Description: TnsAI provides annotation-driven inter-agent communication through ProtocolManager. Annotate your agent class with one or more communication paradigms and the framework auto-configures handlers, transports, and discovery.

import { Callout } from 'fumadocs-ui/components/callout'

## ProtocolManager

`ProtocolManager` is the central hub created during agent initialization. It scans annotations on the agent class, creates the matching protocol handlers, and provides a unified messaging API.

```java
// ProtocolManager is accessed via the agent
ProtocolManager pm = agent.getProtocolManager();

// Check what paradigms are active
Set<String> paradigms = pm.getParadigms(); // e.g. ["DIRECT", "PUBSUB"]
boolean hasBroadcast = pm.hasParadigm("BROADCAST");
```

Lifecycle: created in Agent constructor, auto-started if annotations present, stopped when the agent terminates.

## Five Communication Paradigms

TnsAI supports five ways for agents to talk to each other, from simple point-to-point messages to multi-agent debates. You choose a paradigm by adding the matching annotation to your agent class.

| Paradigm      | Annotation       | Handler                 | Pattern                       | Returns Response |
| ------------- | ---------------- | ----------------------- | ----------------------------- | ---------------- |
| DirectMessage | `@DirectMessage` | `DirectMessageHandler`  | Point-to-point                | Yes              |
| Broadcast     | `@Broadcast`     | `BroadcastHandler`      | One-to-many (single topic)    | No               |
| PubSub        | `@PubSub`        | `PubSubHandler`         | Multi-topic subscribe/publish | No               |
| RequestReply  | `@RequestReply`  | `RequestReplyHandler`   | Synchronous with timeout      | Yes              |
| Debate        | `@Debate`        | `DebateProtocolHandler` | Multi-agent deliberation      | No (async)       |

### DirectMessage

Point-to-point messaging. Supports both structured action execution and natural language LLM-powered messaging.

```java
@DirectMessage
public class OrderAgent extends Agent { }

// Structured action execution
Message response = pm.executeAction("receiver-id", "processOrder", Map.of(
    "orderId", "12345"
));

// With role targeting
Message response = pm.executeAction("receiver-id", "billing-role", "charge", params);

// Natural language (receiver's LLM parses and responds)
Message reply = pm.sendMessage("receiver-id", "Please summarize the quarterly report");
```

### Broadcast

One-to-many messaging on a single topic. Automatically discovers and delivers to all agents subscribed to the topic.

```java
@Broadcast(topic = "system-alerts", canSubscribe = true)
public class MonitorAgent extends Agent { }

// Broadcast to all subscribers
pm.broadcast(Map.of("level", "HIGH", "message", "CPU threshold exceeded"));
```

Agents with `canSubscribe = false` can publish but will not receive broadcasts.

### PubSub

Multi-topic publish/subscribe. Similar to Broadcast but supports multiple topics per agent and per-topic subscriber management.

```java
@PubSub(topics = {"alerts", "metrics", "deployments"})
public class DashboardAgent extends Agent { }

// Publish to a specific topic
pm.publish("alerts", Map.of("severity", "WARN", "source", "db-pool"));
pm.publish("metrics", Map.of("cpu", 85.2, "memory", 72.1));
```

The `PubSubHandler` tracks delivery results via `PublishResult`:

```java
PubSubHandler handler = ...;
PubSubHandler.PublishResult result = handler.getLastPublishResult();
// result.successCount(), result.failCount(), result.isFullyDelivered()
```

### RequestReply

Synchronous request-response with configurable timeout. Blocks the caller until a reply arrives or timeout is reached.

```java
@RequestReply(timeout = 30000) // 30 second timeout
public class QueryAgent extends Agent { }

// Send request and wait for reply
Message reply = pm.request("data-agent", "fetchReport", Map.of(
    "quarter", "Q4",
    "year", 2025
));
```

The handler manages a `CompletableFuture` per pending request and completes it when the reply arrives via `handleReply()`.

### Debate

Multi-agent deliberation protocol with round-based discussion, position submission, voting, and consensus detection.

```java
@Debate(maxRounds = 5, consensusThreshold = 0.66,
        strategy = Debate.ConsensusStrategy.WEIGHTED_CONFIDENCE,
        roundTimeoutMs = 60000)
public class DecisionAgent extends Agent { }

// Start a debate
DebateSession session = pm.startDebate(
    "Should we deploy to production?",
    List.of("dev-agent", "qa-agent", "ops-agent")
);

// Submit position (stance, confidence 1-10, reasoning)
pm.submitDebatePosition(session.getId(), "YES", 8, "All tests pass, staging looks good");

// Vote on another's position
pm.voteInDebate(session.getId(), "NO", false);

// Submit counter-argument
pm.counterArgument(session.getId(), "ops-agent",
    "Staging does not reflect prod traffic patterns", "Wait for load test results");
```

Consensus strategies: `MAJORITY`, `SUPERMAJORITY`, `UNANIMOUS`, `WEIGHTED_CONFIDENCE`. When consensus threshold is met, `DebateStatus.CONSENSUS` is set. If max rounds are exhausted without consensus, `DebateStatus.DEADLOCK` falls back to the highest-support stance.

## FIPA Protocols

FIPA (Foundation for Intelligent Physical Agents) is an international standard for agent communication. TnsAI implements these standards so your agents can interact using well-defined message types and interaction patterns recognized across the multi-agent systems community. The `com.tnsai.protocols.fipa` package provides FIPA ACL (Agent Communication Language) compliant messaging.

### FIPAPerformative

A performative describes the intent of a message -- whether the sender is informing, requesting, proposing, or refusing. TnsAI supports all 21 FIPA performatives organized by category:

| Category    | Performatives                                                                       |
| ----------- | ----------------------------------------------------------------------------------- |
| Assertive   | `INFORM`, `CONFIRM`, `DISCONFIRM`, `INFORM_IF`, `INFORM_REF`                        |
| Directive   | `REQUEST`, `REQUEST_WHEN`, `REQUEST_WHENEVER`, `QUERY_REF`, `QUERY_IF`, `SUBSCRIBE` |
| Commissive  | `PROPOSE`, `ACCEPT_PROPOSAL`, `REJECT_PROPOSAL`, `AGREE`, `REFUSE`, `CFP`           |
| Declarative | `CANCEL`, `FAILURE`, `NOT_UNDERSTOOD`, `PROPAGATE`, `PROXY`                         |

Each performative knows whether it is initiating (`isInitiating()`), responding (`isResponding()`), positive (`isPositive()`), or negative (`isNegative()`).

### FIPAMessage Builder

`FIPAMessage` is the structured message object that agents exchange. It is a Java record with a fluent builder and includes all standard FIPA ACL fields such as sender, receiver, content, protocol, and ontology.

```java
// Build a request message
FIPAMessage request = FIPAMessage.builder()
    .performative(FIPAPerformative.REQUEST)
    .sender("agent-1")
    .receiver("agent-2")
    .content("Please perform analysis")
    .protocol("fipa-request")
    .ontology("market-analysis")
    .replyBy(Instant.now().plus(Duration.ofMinutes(5)))
    .build();

// Create a reply from an existing message
FIPAMessage reply = request.createReply()
    .performative(FIPAPerformative.AGREE)
    .content("I will perform the analysis")
    .build();

// Convenience factory methods
FIPAMessage inform = FIPAMessage.inform("sender", "receiver", "Task complete");
FIPAMessage cfp = FIPAMessage.cfp("manager", "worker", "Translate document to French");
FIPAMessage query = FIPAMessage.queryIf("agent-a", "agent-b", "Is the service healthy?");
```

Key message utilities: `expectsReply()`, `isReply()`, `isReplyOverdue()`.

### ContractNetProtocol

The Contract Net protocol is a standard way to delegate tasks through competitive bidding: a manager broadcasts a "call for proposals," agents submit bids, and the manager picks the best one. It implements the FIPA Contract Net interaction protocol for task delegation through competitive bidding.

```java
ContractNetProtocol protocol = new ContractNetProtocol();

// Initiator creates CFP
FIPAMessage cfp = protocol.createCFP("manager", "Translate 500 pages to French");
protocol.processMessage(cfp);

// Participants respond with proposals
FIPAMessage proposal = protocol.createProposal(cfp, "Can do in 2 days for $500");
protocol.processMessage(proposal);

// Initiator selects winner
protocol.selectProposal("translator-agent");
FIPAMessage acceptance = protocol.createAcceptance(proposal);
protocol.processMessage(acceptance);

// Check state
protocol.isComplete();    // false until INFORM or FAILURE
protocol.isSuccessful();  // true after INFORM
protocol.getProposals();  // map of sender -> proposal
```

Protocol state machine: `INITIAL` -\\> `AWAITING_RESPONSE` -\\> `NEGOTIATING` -\\> `COMPLETED_SUCCESS` / `COMPLETED_FAILURE`.

## Transport Modes

Transport modes control how messages are physically delivered between agents. Agents in the same JVM use fast in-memory transport, while remote agents communicate over HTTP. Each protocol handler uses a `Transport` implementation for message delivery.

| Mode       | Class               | Use Case                              |
| ---------- | ------------------- | ------------------------------------- |
| `INTERNAL` | `InternalTransport` | Same JVM, in-memory (fastest)         |
| `HTTP`     | `HttpTransport`     | Remote agents via HTTP/REST           |
| `AUTO`     | (resolved)          | Auto-selects based on endpoint config |

```java
// Transport is configured via annotation attributes
@DirectMessage(endpoint = "http://remote:8080", transport = TransportMode.HTTP)
public class RemoteAgent extends Agent { }

// Internal transport (default when no endpoint specified)
@DirectMessage(transport = TransportMode.INTERNAL)
public class LocalAgent extends Agent { }

// Auto mode: resolves to INTERNAL if no endpoint, HTTP if endpoint given
@DirectMessage(transport = TransportMode.AUTO)
public class FlexibleAgent extends Agent { }
```

The `Transport` interface provides `send()` (synchronous), `sendAsync()` (callback-based), and `isSynchronous()` to let handlers decide whether to use futures for reply correlation.

---

# Group Topologies

URL: https://tnsai.dev/docs/multi-agent/topologies
Description: TnsAI.Coordination provides 8 group topologies for structuring multi-agent collaboration. Each topology has a dedicated builder and follows the lifecycle: create -> start -> execute -> stop.

import { Callout } from 'fumadocs-ui/components/callout'

## Team

A Team is the most common topology. It groups agents by role (lead, member, expert, reviewer) and executes tasks using a configurable formation (parallel, sequential, hierarchical, etc.). Think of it like organizing a project team where each person has a defined responsibility.

### TeamRole

Each agent in a team is assigned a role that determines its authority level and whether it actively participates in task execution.

| Role          | Authority | Participates | Description                                                |
| ------------- | --------- | ------------ | ---------------------------------------------------------- |
| `LEAD`        | Yes       | Yes          | Coordinates and delegates, final decision authority        |
| `CO_LEAD`     | Yes       | Yes          | Assists lead, takes over if lead unavailable               |
| `MEMBER`      | No        | Yes          | Executes assigned tasks                                    |
| `EXPERT`      | No        | Yes          | Subject matter expert, consulted for specialized knowledge |
| `REVIEWER`    | No        | Yes          | Validates output, quality control                          |
| `OBSERVER`    | No        | No           | Monitors but does not participate                          |
| `FACILITATOR` | Yes       | Yes          | Coordinates discussions (used in DEBATE formation)         |

### TeamFormation

The formation controls how the team executes its work. For example, `PARALLEL` runs all members at once for speed, while `SEQUENTIAL` passes context from one member to the next like a relay.

| Formation      | Description                               | Requires Lead | Ordered |
| -------------- | ----------------------------------------- | ------------- | ------- |
| `PARALLEL`     | All members work simultaneously           | No            | No      |
| `SEQUENTIAL`   | Members work in sequence, passing context | No            | Yes     |
| `HIERARCHICAL` | Lead coordinates and delegates            | Yes           | No      |
| `ADAPTIVE`     | Adapts based on task complexity           | No            | No      |
| `DEBATE`       | Members discuss and debate to consensus   | No            | No      |
| `ROUND_ROBIN`  | Members take turns adding perspective     | No            | Yes     |

### Builder

Use the fluent builder to create a team by specifying its name, mission, formation, and members with their roles.

```java
Team team = Team.builder()
    .name("Analysis Team")
    .mission("Analyze customer data and produce insights")
    .timeout(Duration.ofMinutes(30))
    .formation(TeamFormation.PARALLEL)
    .lead(coordinatorAgent)
    .member(analystAgent, TeamRole.EXPERT)
    .member(writerAgent, TeamRole.MEMBER)
    .member(reviewerAgent, TeamRole.REVIEWER)
    .autoStart(true)
    .build();
```

When `HIERARCHICAL` formation is used and no `LEAD` is explicitly set, the first member is auto-promoted.

## Pipeline

A Pipeline processes data through a series of stages in strict order, where each stage's output becomes the next stage's input. This is ideal for ETL (extract-transform-load) workflows or any process with a fixed sequence of steps. Includes per-stage metrics tracking.

```java
PipelineOrganization pipeline = PipelineOrganization.builder()
    .name("ETL Pipeline")
    .addStage("extract", extractorAgent)
    .addStage("transform", transformerAgent)
    .addStage("validate", validatorAgent)
    .addStage("load", loaderAgent)
    .autoStart(true)
    .build();
```

### Stage Record

A stage represents one step in the pipeline, linking a name to an agent. Each stage is a `record Stage(String name, String agentId, int index, UnaryOperator<String> transformFunction)`. The optional `transformFunction` preprocesses input before the agent sees it.

### PipelineMetrics

The pipeline automatically tracks how long each stage takes, how often it runs, and how often it fails. This helps you identify bottlenecks in your processing chain.

```java
PipelineMetrics metrics = pipeline.getMetrics();

// Per-stage stats
Optional<Duration> avgDuration = metrics.getAverageDuration("transform");
int executions = metrics.getExecutionCount("transform");
int failures = metrics.getFailureCount("transform");

// Bottleneck detection
Optional<String> bottleneck = metrics.getBottleneck(); // stage with highest avg duration

// Full summary
Map<String, String> summary = metrics.getSummary();
// {"extract": "executions=10, failures=0, avgMs=250", ...}
```

## HubSpoke

In a HubSpoke topology, one central "hub" agent distributes tasks to multiple "spoke" agents that do the actual work. This is useful when you have a coordinator that needs to fan out work to a pool of workers. The hub distributes tasks to spoke agents using configurable load balancing.

```java
HubSpoke hs = HubSpoke.builder()
    .name("Task Distributor")
    .hub(coordinatorAgent)
    .spoke(worker1)
    .spoke(worker2)
    .spoke(worker3)
    .loadBalancing(LoadBalancer.Strategy.LEAST_LOADED)
    .autoStart(true)
    .build();
```

### LoadBalancer Strategies

The load balancer decides which spoke agent gets the next task. Choose the strategy that best fits your workload pattern.

| Strategy       | Description                          |
| -------------- | ------------------------------------ |
| `ROUND_ROBIN`  | Cycles through spokes in order       |
| `LEAST_LOADED` | Picks spoke with fewest active tasks |
| `RANDOM`       | Random spoke selection               |

The `LoadBalancer` tracks active task counts per agent via `recordTaskAssigned()` and `recordTaskCompleted()`.

## Hierarchy

A Hierarchy models a tree-structured organization chart where tasks flow downward through delegation and issues flow upward through escalation. This is useful for modeling management structures or any scenario where agents have parent-child reporting relationships.

```java
Hierarchy hierarchy = Hierarchy.builder()
    .name("Engineering Org")
    .root(ctoAgent)
    .member(ctoAgent, teamLead1)
    .member(ctoAgent, teamLead2)
    .member(teamLead1, dev1)
    .member(teamLead1, dev2)
    .member(teamLead2, dev3)
    .autoStart(true)
    .build();
```

### DelegationChain

The `DelegationChain` records the history of who delegated what to whom, and who escalated issues back up the chain. This provides an audit trail for task flow through delegation and escalation events.

```java
DelegationChain chain = hierarchy.getDelegationChain();
chain.record("cto", "team-lead-1", "Review PR #42", DelegationChain.Type.DELEGATION);
chain.record("dev-1", "team-lead-1", "Need architecture review", DelegationChain.Type.ESCALATION);

List<DelegationChain.Entry> entries = chain.getEntries();
// Each entry: fromAgentId, toAgentId, task, type, timestamp
```

## Swarm

A Swarm is a decentralized topology inspired by biological swarm intelligence (like ant colonies or bird flocks). Instead of a central coordinator, agents communicate indirectly through "pheromones" and local neighbor interactions to solve problems collectively.

```java
Swarm swarm = Swarm.builder()
    .name("Explorer Swarm")
    .behavior(SwarmBehavior.EXPLORATION)
    .intelligence(SwarmIntelligence.antColony(0.5, 0.3))
    .neighborDetection(NeighborDetection.random())
    .neighborRadius(5)
    .pheromoneDecay(0.15)
    .add(agent1, agent2, agent3, agent4, agent5)
    .build();
```

### SwarmBehavior

The behavior determines how swarm agents interact with each other and their environment. Different behaviors suit different problem types.

| Behavior          | Uses Pheromones | Requires Neighbors | Best For                          |
| ----------------- | --------------- | ------------------ | --------------------------------- |
| `EXPLORATION`     | Yes             | No                 | Information gathering, search     |
| `FORAGING`        | Yes             | Yes                | Resource collection, optimization |
| `FLOCKING`        | No              | Yes                | Coordinated movement              |
| `AGGREGATION`     | Yes             | Yes                | Consensus building, clustering    |
| `TASK_ALLOCATION` | No              | No                 | Division of labor                 |
| `DIFFUSION`       | No              | Yes                | Distributed computation           |
| `QUORUM_SENSING`  | No              | Yes                | Collective decision making        |

### SwarmIntelligence

The swarm intelligence algorithm determines how individual agent results are combined into a collective answer. TnsAI provides several built-in algorithms:

```java
SwarmIntelligence.voting()              // Simple voting, first complete solution wins
SwarmIntelligence.consensus()           // Weighted averaging (max 10 iterations)
SwarmIntelligence.bestSelection()       // Best-of-N based on quality scores
SwarmIntelligence.antColony(0.5, 0.3)   // ACO with pheromoneWeight and explorationRate
SwarmIntelligence.iterativeRefinement() // Each agent improves on previous (max 5 iterations)
```

### PheromoneType

Pheromones are signals that agents leave behind to communicate indirectly with other agents. Different pheromone types convey different meanings.

| Type          | Default Intensity | Purpose                     |
| ------------- | ----------------- | --------------------------- |
| `ATTRACTION`  | 1.0               | Marks interesting locations |
| `REPULSION`   | 0.8               | Marks areas to avoid        |
| `TRAIL`       | 0.9               | Path markers for ACO        |
| `COMPLETION`  | 0.5               | Prevents redundant work     |
| `RECRUITMENT` | 1.2               | Calls for help              |
| `WARNING`     | 0.7               | Signals potential issues    |
| `QUALITY`     | 1.0               | Indicates solution quality  |
| `CUSTOM`      | 1.0               | User-defined                |

## Coalition

A Coalition is a temporary alliance of agents formed around a specific goal. Unlike a permanent team, a coalition forms dynamically, executes a task, distributes rewards based on each member's contribution, and then dissolves. This is useful when different tasks need different combinations of agents.

```java
Coalition coalition = Coalition.builder()
    .goal("Complete market analysis")
    .formation(CoalitionFormation.CAPABILITY_BASED)
    .rewardDistribution(RewardDistribution.SHAPLEY)
    .coalitionValue(100.0)
    .minMembers(2)
    .maxMembers(5)
    .add(agent1, agent2, agent3)
    .autoNegotiate(true)
    .build();
```

### CoalitionFormation

The formation strategy determines how agents are selected to join the coalition. Some strategies let agents volunteer, while others pick agents based on capabilities or reputation.

| Formation          | Selection-Based | Description                            |
| ------------------ | --------------- | -------------------------------------- |
| `VOLUNTARY`        | No              | Agents choose to participate           |
| `CAPABILITY_BASED` | Yes             | Selected by matching capabilities      |
| `REPUTATION_BASED` | Yes             | Selected by past performance           |
| `AUCTION`          | No              | Agents bid for participation           |
| `RANDOM`           | No              | Random selection from pool             |
| `INVITATION`       | Yes             | Coordinator invites specific agents    |
| `MERIT_BASED`      | Yes             | Combined capability + reputation score |

### RewardDistribution

After the coalition completes its goal, rewards are distributed among members. The strategy determines how fairly (or competitively) the total reward is split.

| Strategy            | Description                                                        |
| ------------------- | ------------------------------------------------------------------ |
| `EQUAL`             | Same share for all members                                         |
| `PROPORTIONAL`      | Based on contribution ratio                                        |
| `SHAPLEY`           | Fair division based on marginal contribution to all sub-coalitions |
| `NUCLEOLUS`         | Minimizes maximum dissatisfaction (10% floor + 90% proportional)   |
| `PERFORMANCE_BASED` | Based on task performance quality                                  |
| `SENIORITY_BASED`   | Higher share for earlier joiners                                   |
| `WINNER_TAKE_ALL`   | Best performer gets 70%, rest share 30%                            |

```java
Map<String, Double> contributions = Map.of("agent-1", 40.0, "agent-2", 35.0, "agent-3", 25.0);
Map<String, Double> rewards = RewardDistribution.SHAPLEY.calculate(100.0, contributions);
```

## Matrix

A Matrix topology models a dual-reporting structure where each agent can belong to multiple groups simultaneously. For example, a developer agent might belong to both the "Engineering" dimension (reporting to an engineering lead) and the "Product-A" dimension (reporting to a product lead). This mirrors how real-world matrix organizations work.

```java
MatrixOrganization matrix = MatrixOrganization.builder()
    .name("Product Org")
    .member(dev1)
    .member(dev2)
    .member(engLead)
    .member(productLead)
    .assign(dev1, "Engineering", engLead)
    .assign(dev1, "Product-A", productLead)
    .assign(dev2, "Engineering", engLead)
    .assign(dev2, "Product-B", productLead)
    .autoStart(true)
    .build();
```

Agents can be assigned to a dimension with or without a lead: `.assign(agent, "QA")` omits the lead parameter.

## NetworkMesh

A NetworkMesh is a peer-to-peer topology where agents connect directly to each other without a central coordinator. The mesh shape (full, partial, ring, or star) determines which agents can communicate directly.

```java
NetworkMesh mesh = NetworkMesh.builder()
    .name("Agent Network")
    .topology(Topology.FULL_MESH)
    .add(agent1, agent2, agent3, agent4)
    .autoStart(true)
    .build();
```

### Topology

The topology shape determines the connection pattern between agents in the mesh.

| Topology       | Description                               |
| -------------- | ----------------------------------------- |
| `FULL_MESH`    | Every agent connected to every other      |
| `PARTIAL_MESH` | Selective peer connections                |
| `RING`         | Circular chain                            |
| `STAR`         | One central agent connected to all others |

After `build()`, `initializeTopology()` is called automatically to wire up connections based on the chosen topology.

---

# Workflows

URL: https://tnsai.dev/docs/multi-agent/workflows
Description: TnsAI.Coordination provides two workflow executors: DAGExecutor for parallel dependency-graph execution and SagaExecutor for sequential execution with compensation (rollback) on failure.

import { Callout } from 'fumadocs-ui/components/callout'

## WorkflowStep

A workflow step represents one unit of work -- a single agent doing a single task. Steps can depend on other steps, have timeouts, and include compensation actions for rollback. The step is defined as an immutable record.

```java
public record WorkflowStep(
    String id,
    String name,
    Agent agent,
    List<String> dependencies,
    Optional<Predicate<WorkflowContext>> condition,
    Optional<WorkflowStep> compensation,
    long timeoutMs
)
```

### Creating Steps

Steps are created using factory methods and can be customized with dependencies, compensation actions, conditions, and timeouts.

```java
// Simple step (no dependencies)
WorkflowStep fetch = WorkflowStep.of("fetch-data", fetchAgent);

// Step with dependencies
WorkflowStep merge = WorkflowStep.of("merge", mergeAgent, List.of("fetch-a", "fetch-b"));

// Step with compensation (for saga pattern)
WorkflowStep book = WorkflowStep.of("book-flight", bookAgent)
    .withCompensation(WorkflowStep.of("cancel-flight", cancelAgent));

// Conditional step (only runs if predicate is true)
WorkflowStep notify = WorkflowStep.of("notify", notifyAgent)
    .withCondition(ctx -> ctx.has("step.merge.output"));

// Step with timeout
WorkflowStep slow = WorkflowStep.of("external-call", apiAgent)
    .withTimeout(30_000); // 30 seconds
```

`isRoot()` returns `true` when the step has no dependencies.

## WorkflowContext

The workflow context is a shared data store that all steps in a workflow can read from and write to. It holds initial parameters, intermediate results, and step outputs. It is thread-safe (uses `ConcurrentHashMap` internally) so multiple DAG steps running in parallel can access it safely.

```java
WorkflowContext context = new WorkflowContext("workflow-123");

// Store/retrieve arbitrary data
context.put("inputFile", "/data/report.csv");
String file = context.get("inputFile");
String fallback = context.getOrDefault("missing", "default-value");
boolean exists = context.has("inputFile");

// Step results are recorded automatically
context.recordStepResult("fetch", StepResult.success("fetch", "data...", 250));
WorkflowContext.StepResult result = context.getStepResult("fetch");
Map<String, WorkflowContext.StepResult> all = context.getAllStepResults();
```

You can pass initial data at construction:

```java
WorkflowContext context = new WorkflowContext("wf-1", Map.of(
    "region", "us-east-1",
    "maxRetries", 3
));
```

### StepResult

Each completed step produces a `StepResult` that records whether it succeeded or failed, the output or error message, and how long it took.

```java
public record StepResult(String stepId, boolean success, String output, long durationMs, String error) {
    static StepResult success(String stepId, String output, long durationMs);
    static StepResult failure(String stepId, String error, long durationMs);
}
```

## WorkflowResult

After a workflow finishes (whether it succeeds or fails), you get a `WorkflowResult` containing the overall success status, all step results, total duration, and any error message.

```java
public record WorkflowResult(
    String workflowId,
    boolean success,
    List<StepResult> stepResults,
    long totalDurationMs,
    String error
) {
    long completedCount();  // number of successful steps
    long failedCount();     // number of failed steps
}
```

## DAGExecutor

The DAG (Directed Acyclic Graph) executor is for workflows where some steps can run in parallel because they don't depend on each other. You declare each step's dependencies, and the executor automatically figures out what can run concurrently and what must wait. Steps with no dependencies run in parallel; dependent steps wait for all prerequisites to complete.

```java
WorkflowResult result = DAGExecutor.builder()
    .step("fetch-a", fetchAgentA)
    .step("fetch-b", fetchAgentB)
    .step("merge", mergeAgent, List.of("fetch-a", "fetch-b"))
    .step("report", reportAgent, List.of("merge"))
    .timeout(Duration.ofMinutes(10))
    .build()
    .execute(new WorkflowContext("dag-1"));
```

### How It Works

Here is what happens internally when the DAG executor runs your workflow:

1. **Validation**: On `build()`, the DAG is validated -- all dependency references must exist, and cycle detection runs via topological sort (Kahn's algorithm).
2. **Topological Sort**: Steps are ordered so that dependencies execute before dependents.
3. **Parallel Execution**: Root steps (no dependencies) start immediately on a cached thread pool. Each dependent step waits via `CompletableFuture.allOf()` on its prerequisites.
4. **Dependency Failure**: If a dependency fails, the dependent step is marked as failed without execution.
5. **Conditional Steps**: Steps with a `condition` predicate are evaluated before execution. If the condition returns `false`, the step is marked as `SKIPPED`.
6. **Context Propagation**: Each step receives a prompt that includes outputs from its dependency steps, enabling data flow through the DAG.

### Async Execution

If you don't want to block the calling thread, use `executeAsync()` to get a `CompletableFuture` that completes when the DAG finishes.

```java
CompletableFuture<WorkflowResult> future = dag.executeAsync(context);
future.thenAccept(result -> {
    System.out.println("DAG completed: " + result.success());
});
```

### Timeout

You can set both a global timeout for the entire DAG and individual timeouts per step. The overall DAG execution timeout defaults to 5 minutes. Individual steps can also have their own timeout set via `withTimeout()`.

## SagaExecutor

The Saga executor is for workflows where you need "all or nothing" semantics. Steps run in sequence, and if any step fails, the executor automatically runs compensation (undo) actions in reverse order for all previously completed steps. This is the saga pattern, commonly used for booking workflows (flights, hotels, cars) where a failure in one step means you need to cancel everything done so far.

```java
WorkflowResult result = SagaExecutor.builder()
    .step(WorkflowStep.of("book-flight", bookFlightAgent)
        .withCompensation(WorkflowStep.of("cancel-flight", cancelFlightAgent)))
    .step(WorkflowStep.of("book-hotel", bookHotelAgent)
        .withCompensation(WorkflowStep.of("cancel-hotel", cancelHotelAgent)))
    .step(WorkflowStep.of("book-car", bookCarAgent)
        .withCompensation(WorkflowStep.of("cancel-car", cancelCarAgent)))
    .step(WorkflowStep.of("confirm", confirmAgent))
    .build()
    .execute(new WorkflowContext("saga-1"));
```

### Compensation Behavior

Here is how the saga pattern handles failures and rollbacks:

- Steps execute in order. Each step's output is stored in context as `step.<id>.output`.
- On failure, compensation runs in **reverse order** for all completed steps.
- Steps without a compensation action are skipped during rollback.
- Compensation failures are logged but **do not stop** the compensation chain -- all compensations are attempted.
- Conditional steps (`withCondition()`) whose predicate returns `false` are skipped entirely.

### Async Execution

Like the DAG executor, sagas also support non-blocking execution via `CompletableFuture`.

```java
CompletableFuture<WorkflowResult> future = saga.executeAsync(context);
```

## Choosing Between DAG and Saga

Use this table to decide which executor fits your workflow. The key question is whether you need parallel execution or rollback-on-failure semantics.

| Aspect           | DAGExecutor                                  | SagaExecutor                           |
| ---------------- | -------------------------------------------- | -------------------------------------- |
| Execution        | Parallel where possible                      | Sequential                             |
| Failure handling | Partial results returned                     | Compensation (rollback)                |
| Use case         | Independent parallel tasks with merge points | Transactions that need undo on failure |
| Step ordering    | Dependency graph                             | Insertion order                        |
| Timeout          | Global + per-step                            | Per-step only                          |

---

# Observability

URL: https://tnsai.dev/docs/observability

import { Callout } from 'fumadocs-ui/components/callout'

> See also: **[Sampling](/docs/sampling)** — adaptive event sampling for production volume management (per-tenant rates, priority-aware, head-based).

## Quick Start

TnsAI provides structured logging out of the box. Create a logger for your agent and attach key-value metadata to every log entry for easy filtering and searching in your logging backend.

```java
// Structured logging
AgentLogger logger = AgentLogger.forAgent("agent-001");
logger.info("Processing request")
    .with("userId", userId)
    .with("action", "search")
    .log();
```

## OpenTelemetry Tracing

Distributed tracing with automatic span management:

```java
TnsAITelemetry telemetry = TnsAITelemetry.builder()
    .serviceName("my-agent-service")
    .serviceVersion("1.0.0")
    .otlpEndpoint("http://localhost:4317")
    .build();

OpenTelemetryTracer tracer = new OpenTelemetryTracer(telemetry.getTracer());

try (SpanScope scope = tracer.startSpan("process-request")) {
    scope.setAttribute("user.id", userId);
    scope.setAttribute("request.type", "search");
    // ... work happens here ...
    scope.setSuccess();
} // Span automatically closed

// Convenience wrapper
String result = tracer.trace("fetch-data", () -> fetchData(query));
```

## Prometheus Metrics

TnsAI provides pre-defined counters and histograms that track agent actions, LLM calls, token usage, and latency. These integrate with OpenTelemetry and can be exported to any compatible metrics backend.

```java
OpenTelemetryMetrics metrics = new OpenTelemetryMetrics(meter);

// Record agent actions
metrics.recordActionSuccess("search", 150);  // type, duration ms
metrics.recordActionFailure("write", 50, "timeout");

// Record LLM calls
metrics.recordLlmCall("openai", "gpt-4o", 1000, 500, 2300);  // provider, model, tokens, latency
metrics.recordLlmCallFailure("anthropic", "claude-3", 1500, "rate_limit");

// Gauges
metrics.setActiveAgents(5);
```

**Available metrics:**

| Metric                        | Type      | Description                |
| ----------------------------- | --------- | -------------------------- |
| `tnsai.agent.actions.total`   | Counter   | Total actions executed     |
| `tnsai.agent.actions.success` | Counter   | Successful actions         |
| `tnsai.agent.actions.failed`  | Counter   | Failed actions             |
| `tnsai.agent.action.duration` | Histogram | Action execution time (ms) |
| `tnsai.llm.calls.total`       | Counter   | Total LLM calls            |
| `tnsai.llm.tokens.prompt`     | Counter   | Input tokens consumed      |
| `tnsai.llm.tokens.completion` | Counter   | Output tokens generated    |
| `tnsai.llm.latency`           | Histogram | LLM response time (ms)     |
| `tnsai.agent.active`          | Gauge     | Currently active agents    |

## Structured Logging

The `AgentLogger` provides a fluent API for emitting structured log entries with arbitrary key-value metadata. It also includes convenience methods for common agent events like action starts, completions, and LLM calls.

```java
AgentLogger logger = AgentLogger.forAgent("agent-001");

// Fluent API
logger.info("Tool executed")
    .with("tool", "brave_search")
    .with("duration", 350)
    .log();

// Convenience methods
logger.logActionStart("searchPapers", params);
logger.logActionComplete("searchPapers", 1200, true);
logger.logLLMCall("gpt-4o", 500, 2100);

// Timed execution
String result = logger.timed("database-query", () -> db.query(sql));
```

## Themed Logging

Themed loggers wrap SLF4J and add fun, themed prefixes to log messages based on the event type. 7 built-in themes, 22 event types, custom theme support.

```java
// Create with theme
ThemedLogger logger = ThemedLogger.create("my-agent", LogThemes.STAR_WARS);

// Log events — each gets a themed prefix
logger.actionStart("Searching for documents");
// Output: [LIGHTSABER ON] Searching for documents

logger.actionComplete("Found 5 documents");
// Output: [THE FORCE SUCCEEDS] Found 5 documents

logger.error("Connection failed");
// Output: [SITH LORD DETECTED] Connection failed

// Startup/shutdown banners
logger.logStartup();
// Output: A long time ago in a galaxy far, far away... TnsAI awakens.
```

**Factory methods:**

```java
// With explicit theme
ThemedLogger logger = ThemedLogger.create("agent-001", LogThemes.LOTR);

// Default theme (no prefix)
ThemedLogger logger = ThemedLogger.create("agent-001");

// From class name
ThemedLogger logger = ThemedLogger.forClass(MyAgent.class, LogThemes.MATRIX);
```

**Available themes:**

| Theme                 | Name         | Example prefix                              |
| --------------------- | ------------ | ------------------------------------------- |
| `LogThemes.DEFAULT`   | `"default"`  | *(no prefix)*                               |
| `LogThemes.STAR_WARS` | `"starwars"` | `[LIGHTSABER ON]`, `[CONSULTING YODA]`      |
| `LogThemes.LOTR`      | `"lotr"`     | `[QUEST BEGINS]`, `[CONSULTING GANDALF]`    |
| `LogThemes.MATRIX`    | `"matrix"`   | `[ENTERING THE MATRIX]`, `[CALLING ORACLE]` |
| `LogThemes.PIRATE`    | `"pirate"`   | `[SETTING SAIL]`, `[CONSULTING DAVY JONES]` |
| `LogThemes.TURKISH`   | `"turkish"`  | `[IS BASLIYOR]`, `[AKIL DANIYOR]`           |
| `LogThemes.EMOJI`     | `"emoji"`    | Lightning, Robot, Trophy emojis             |

**22 event types** (`LogEvent` enum):

| Group           | Events                                                        |
| --------------- | ------------------------------------------------------------- |
| Agent lifecycle | `AGENT_START`, `AGENT_STOP`, `AGENT_IDLE`                     |
| Actions         | `ACTION_START`, `ACTION_COMPLETE`, `ACTION_ERROR`             |
| Planning        | `PLAN_START`, `PLAN_COMPLETE`, `GOAL_ACHIEVED`, `GOAL_FAILED` |
| LLM             | `LLM_CALL`, `LLM_RESPONSE`, `LLM_ERROR`, `TOOL_CALL`          |
| Communication   | `MESSAGE_SENT`, `MESSAGE_RECEIVED`                            |
| State           | `STATE_CHANGE`, `BELIEF_UPDATE`                               |
| General         | `INFO`, `WARNING`, `ERROR`, `DEBUG`, `SUCCESS`, `FAILURE`     |

**Custom themes and runtime switching:**

```java
// Implement LogTheme interface
LogTheme custom = new LogTheme() {
    public String name() { return "custom"; }
    public String format(LogEvent event, String message) {
        return "[MY-PREFIX] " + message;
    }
};

// Register for lookup by name
LogThemes.register(custom);
LogTheme theme = LogThemes.get("custom");

// Change theme at runtime
logger.setTheme(LogThemes.PIRATE);

// List and check themes
String[] names = LogThemes.listThemes();
boolean exists = LogThemes.hasTheme("starwars");
```

## Prometheus Exporter

If you use Prometheus for monitoring, this exporter spins up a lightweight HTTP server that serves metrics in the Prometheus text format. It syncs with the `Metrics` singleton and exposes all predefined agent and LLM counters.

```java
// Create and start
PrometheusMetricsExporter exporter = PrometheusMetricsExporter.builder()
    .port(9090)
    .build();
exporter.start();
// Metrics available at http://localhost:9090/metrics

// Record metrics
exporter.recordActionSuccess("search", 150);       // type, duration ms
exporter.recordActionFailure("write", "timeout", 50);
exporter.recordLlmCall("openai", "gpt-4o", 1000, 500, 2300);
exporter.recordLlmCallFailure("anthropic", "claude-3", "rate_limit", 1500);
exporter.setActiveAgents(5);

// One-liner startup
PrometheusMetricsExporter exporter = PrometheusMetricsExporter.createAndStart();

// Shutdown
exporter.stop();
```

**Environment variables:**

| Variable                   | Default | Description             |
| -------------------------- | ------- | ----------------------- |
| `TNSAI_PROMETHEUS_PORT`    | `9090`  | HTTP server port        |
| `TNSAI_PROMETHEUS_ENABLED` | `true`  | Enable/disable exporter |

```java
// Configure from environment
PrometheusMetricsExporter exporter = PrometheusMetricsExporter.builder()
    .fromEnvironment()
    .build();
```

**Predefined Prometheus metrics:**

| Metric                                | Type      | Labels                            |
| ------------------------------------- | --------- | --------------------------------- |
| `tnsai_agent_actions_total`           | Counter   | `status`                          |
| `tnsai_agent_actions_success_total`   | Counter   | --                                |
| `tnsai_agent_actions_failed_total`    | Counter   | `error_type`                      |
| `tnsai_llm_calls_total`               | Counter   | `provider`, `model`, `status`     |
| `tnsai_llm_calls_failed_total`        | Counter   | `provider`, `model`, `error_type` |
| `tnsai_llm_prompt_tokens_total`       | Counter   | `provider`, `model`               |
| `tnsai_llm_completion_tokens_total`   | Counter   | `provider`, `model`               |
| `tnsai_agent_active`                  | Gauge     | --                                |
| `tnsai_agent_action_duration_seconds` | Histogram | `action_type`                     |
| `tnsai_llm_latency_seconds`           | Histogram | `provider`, `model`               |

**Custom metrics:**

```java
Counter counter = exporter.getOrCreateCounter("my_counter", "My help text", "label1");
Gauge gauge = exporter.getOrCreateGauge("my_gauge", "My help text", "label1");
Histogram histogram = exporter.getOrCreateHistogram("my_histogram", "My help text", "label1");
```

## LLM Instrumentation

This module provides OpenTelemetry instrumentation specifically for LLM calls. It follows the GenAI semantic conventions, which means your traces are compatible with observability tools like Jaeger, Zipkin, and Grafana Tempo without any custom mapping.

```java
LlmInstrumentation llmInstr = new LlmInstrumentation(telemetry.getTracer());

// Trace a chat completion
ChatResponse response = llmInstr.traceChat("openai", "gpt-4", () -> {
    return client.chat(message);
});

// With request parameters
ChatResponse response = llmInstr.traceChat("openai", "gpt-4", 4096, 0.7, () -> {
    return client.chat(message);
});

// Trace streaming
Stream<ChatChunk> stream = llmInstr.traceStreamChat("anthropic", "claude-3", () -> {
    return client.streamChat(message);
});

// Trace embeddings
List<float[]> embeddings = llmInstr.traceEmbeddings("openai", "text-embedding-3", 10, () -> {
    return client.embed(inputs);
});
```

**GenAI semantic convention attributes:**

| Attribute                        | Description                                     |
| -------------------------------- | ----------------------------------------------- |
| `gen_ai.system`                  | LLM provider (openai, anthropic, etc.)          |
| `gen_ai.request.model`           | Model name                                      |
| `gen_ai.request.max_tokens`      | Max tokens requested                            |
| `gen_ai.request.temperature`     | Temperature setting                             |
| `gen_ai.request.top_p`           | Top-p setting                                   |
| `gen_ai.usage.prompt_tokens`     | Prompt tokens used                              |
| `gen_ai.usage.completion_tokens` | Completion tokens generated                     |
| `gen_ai.usage.total_tokens`      | Total tokens                                    |
| `gen_ai.response.finish_reasons` | Finish reason (stop, length, error)             |
| `gen_ai.operation.name`          | Operation type (chat, stream\_chat, embeddings) |

**TnsAI-specific attributes:**

| Attribute                | Description                 |
| ------------------------ | --------------------------- |
| `tnsai.agent.id`         | Agent identifier            |
| `tnsai.agent.name`       | Agent name                  |
| `tnsai.message.length`   | Input message length        |
| `tnsai.tool_use.enabled` | Whether tool use is enabled |
| `tnsai.tool_calls.count` | Number of tool calls        |

**Recording token usage and tool events on a span:**

```java
Span span = Span.current();
llmInstr.recordTokenUsage(span, 150, 50);
llmInstr.recordResponse(span, 150, 50, "stop");
llmInstr.recordToolUse(span, 3);
llmInstr.recordToolCallEvent(span, "brave_search", true);
```

**Span builder for full control:**

```java
Span span = llmInstr.spanBuilder("openai", "gpt-4")
    .operation("chat")
    .maxTokens(4096)
    .temperature(0.7)
    .topP(0.9)
    .agent("agent-001", "ResearchAgent")
    .messageLength(500)
    .toolUseEnabled(true)
    .start();
```

## Agent Instrumentation

Agent instrumentation adds tracing and metrics to agent operations without modifying the Agent class itself. It wraps calls like chat, action execution, and tool calls with OpenTelemetry spans, giving you end-to-end visibility into what your agents are doing.

```java
// Initialize
AgentInstrumentation instrumentation = new AgentInstrumentation(telemetry);
// Or with custom tracer/metrics
AgentInstrumentation instrumentation = new AgentInstrumentation(tracer, metrics);

// Register agents (increments active agent gauge)
instrumentation.registerAgent(agent);
```

**Traced operations:**

```java
// Trace chat
String response = instrumentation.traceChat(agent, "Hello", () -> {
    return agent.chat("Hello");
});

// Trace action execution
Object result = instrumentation.traceAction(agent, "searchPapers", params, () -> {
    return agent.executeAction("searchPapers", params);
});

// Trace tool call
String output = instrumentation.traceToolCall(agent, "brave_search", args, () -> {
    return tool.execute(args);
});

// Trace LLM call with GenAI conventions
ChatResponse resp = instrumentation.traceLlmCall(agent, "openai", "gpt-4", () -> {
    return llmClient.chat(messages);
});
```

**Span attributes per operation:**

| Operation         | Attributes                                                                   |
| ----------------- | ---------------------------------------------------------------------------- |
| `agent.chat`      | `agent.id`, `agent.name`, `message.length`, `message.preview`                |
| `agent.action`    | `agent.id`, `agent.name`, `action.name`, `action.parameters.count`           |
| `agent.tool_call` | `agent.id`, `tool.name`, `tool.arguments.count`                              |
| `agent.llm_call`  | `agent.id`, `gen_ai.system`, `gen_ai.request.model`, `gen_ai.operation.name` |

**Recording metrics directly:**

```java
instrumentation.recordLlmTokens("openai", "gpt-4", 150, 50, 2300);
instrumentation.recordLlmFailure("openai", "gpt-4", 1500, "rate_limit");
instrumentation.unregisterAgent(agent);  // decrements active agent gauge
```

## Agent-Specific Tracing

While the above instrumentation gives you OpenTelemetry-level visibility, agent-specific tracing goes deeper. It captures the full execution trace of a single agent run, including every LLM call, tool invocation, and guardrail check, along with quality scores you attach manually or automatically.

### AgentTrace

An `AgentTrace` represents a complete execution recording for one agent run. You start a trace, add observations as the agent works, and then complete it. This is the foundation for evaluation and debugging.

```java
AgentTrace trace = AgentTrace.start("agent-001", "research-task");

// Add observations during execution
trace.addObservation(Observation.span("llm-call")
    .input("Summarize the document")
    .output("The document describes...")
    .duration(Duration.ofMillis(2300))
    .metadata(Map.of("model", "claude-sonnet-4", "tokens", 1500))
    .build());

trace.addObservation(Observation.generation("tool-call")
    .input(Map.of("tool", "brave_search", "query", "quantum computing"))
    .output("Results: ...")
    .build());

trace.addObservation(Observation.event("guardrail-check")
    .metadata(Map.of("guardrail", "pii-filter", "passed", true))
    .build());

trace.complete();
```

### Observation Types

Observations are the building blocks of a trace. Each one records a specific event or operation during the agent's execution, and they come in three types depending on what happened.

| Type         | Constant                   | Description                                         |
| ------------ | -------------------------- | --------------------------------------------------- |
| `SPAN`       | `Observation.span()`       | Timed execution block (LLM call, tool execution)    |
| `GENERATION` | `Observation.generation()` | LLM text generation with input/output               |
| `EVENT`      | `Observation.event()`      | Point-in-time event (guardrail check, state change) |

Each observation supports: `input`, `output`, `duration`, `metadata`, `parentId` (for nesting), and `status`.

### Score

Scores let you attach quality measurements to a trace. You can use them for manual evaluation (did the agent answer correctly?) or automated evaluation (did the output contain PII?). Scores come in three types: numeric, boolean, and categorical.

```java
// Numeric score
trace.addScore(Score.numeric("relevance", 0.92));

// Boolean score
trace.addScore(Score.bool("contains_pii", false));

// Categorical score
trace.addScore(Score.categorical("sentiment", "positive"));
```

| Factory Method                   | Score Type  | Value           |
| -------------------------------- | ----------- | --------------- |
| `Score.numeric(name, value)`     | Numeric     | 0.0-1.0 double  |
| `Score.bool(name, value)`        | Boolean     | true/false      |
| `Score.categorical(name, value)` | Categorical | String category |

### TraceContext (ThreadLocal Nesting)

`TraceContext` uses a `ThreadLocal` to track which trace is active on the current thread. This means you can start nested spans anywhere in your code and they automatically link to the parent span, without passing the trace object through every method call.

```java
// Set current trace for this thread
TraceContext.set(trace);

// Get current trace (returns Optional)
AgentTrace current = TraceContext.current().orElseThrow();

// Nested spans auto-link to parent
try (var scope = TraceContext.startSpan("sub-operation")) {
    // This span's parentId is automatically set to the enclosing span
    doWork();
}

// Clear when done
TraceContext.clear();
```

### Guardrail SPI

Guardrails are safety checks that evaluate agent behavior during or after execution. Implement the `Guardrail` interface to define your own checks, such as ensuring the agent does not leak sensitive data or produce harmful content.

```java
public interface Guardrail {
    GuardrailResult evaluate(AgentTrace trace);
    String name();
}
```

### PiiGuardrail

The `PiiGuardrail` is a built-in guardrail that scans agent outputs for personally identifiable information such as email addresses, phone numbers, and social security numbers. Use it to prevent your agents from accidentally leaking sensitive data.

```java
PiiGuardrail piiGuard = new PiiGuardrail();
GuardrailResult result = piiGuard.evaluate(trace);

if (!result.passed()) {
    System.out.println("PII detected: " + result.findings());
    // Findings include: type (EMAIL, PHONE, SSN, etc.), location, severity
}
```

Wire guardrails into the agent:

```java
Agent agent = AgentBuilder.create()
    .model("claude-sonnet-4")
    .guardrail(new PiiGuardrail())
    .build();
```

## Health Checks

Health checks let you monitor the operational status of your application's components (database, cache, LLM providers, etc.). TnsAI provides an aggregated health registry that runs checks concurrently, handles timeouts gracefully, and produces HTTP-ready summaries suitable for load balancer probes.

**HealthStatus levels:**

| Status     | Severity | Description                         |
| ---------- | -------- | ----------------------------------- |
| `UP`       | 0        | Component functioning normally      |
| `DEGRADED` | 1        | Functioning with reduced capability |
| `UNKNOWN`  | 2        | Status cannot be determined         |
| `DOWN`     | 3        | Not functioning                     |

**HealthCheckResult factory methods:**

```java
HealthCheckResult.up()                        // healthy
HealthCheckResult.up("All good")              // healthy with message
HealthCheckResult.down()                      // unhealthy
HealthCheckResult.down("Connection refused")  // unhealthy with message
HealthCheckResult.down(exception)             // unhealthy from exception
HealthCheckResult.degraded("High latency")    // degraded
HealthCheckResult.unknown("Check timed out")  // unknown
HealthCheckResult.of(status, message)         // any status

// Add details (immutable — returns new instance)
HealthCheckResult result = HealthCheckResult.up()
    .withDetail("connections", 10)
    .withDetail("latency_ms", 50)
    .withDuration(duration);

// Inspect
result.getStatus();         // HealthStatus enum
result.getMessage();        // String or null
result.getDetails();        // Map<String, Object>
result.isHealthy();         // true if UP or DEGRADED
result.getDuration();       // Duration or null
result.toMap();             // Map for JSON serialization
result.combine(other);      // returns result with worse status
```

**HealthIndicator interface:**

```java
// Implement for custom components
public class DatabaseHealth implements HealthIndicator {
    public String getName() { return "database"; }
    public HealthCheckResult check() {
        return conn.isValid(5) ? HealthCheckResult.up() : HealthCheckResult.down();
    }
}

// Lambda shorthand — from boolean
HealthIndicator simple = HealthIndicator.of("cache", () -> cache.isConnected());

// Lambda shorthand — from supplier
HealthIndicator detailed = HealthIndicator.of("cache", () -> {
    return HealthCheckResult.up().withDetail("size", cache.size());
});

// Async and timeout support (default methods)
CompletableFuture<HealthCheckResult> future = indicator.checkAsync();
HealthCheckResult result = indicator.checkWithTimeout(Duration.ofSeconds(2));
```

**HealthRegistry:**

```java
HealthRegistry registry = HealthRegistry.getInstance();  // singleton

// Register indicators
registry.register(new ResourceHealthIndicator());
registry.register("custom", HealthIndicator.of("custom", () -> true));

// Check health
Optional<HealthCheckResult> single = registry.check("database");
HealthCheckResult all = registry.checkAll();                          // default 30s timeout
HealthCheckResult all = registry.checkAll(Duration.ofSeconds(10));    // custom timeout
CompletableFuture<HealthCheckResult> async = registry.checkAllAsync();
HealthCheckResult matched = registry.checkMatching("db-*");           // wildcard

// Quick status (2s timeout per indicator)
HealthStatus status = registry.getQuickStatus();  // UP, DOWN, DEGRADED, or UNKNOWN

// HTTP-ready summary
Map<String, Object> summary = registry.getHealthSummary();
// { "status": "UP", "timestamp": "...", "totalDurationMs": 45,
//   "components": { "database": {...}, "cache": {...} } }

// Management
registry.getIndicatorNames();  // Set<String>
registry.getIndicatorCount();  // int
registry.unregister("old");
registry.clear();
registry.shutdown();           // call on app shutdown
```

**ResourceHealthIndicator** -- monitors heap memory, CPU load, threads, and deadlocks:

```java
// Default thresholds (85% memory, 90% CPU)
registry.register(new ResourceHealthIndicator());

// Custom thresholds
registry.register(new ResourceHealthIndicator(0.9, 0.9));

// Direct queries
ResourceHealthIndicator res = new ResourceHealthIndicator();
double heapUsage = res.getHeapUsageRatio();   // 0.0-1.0
int threads = res.getThreadCount();
boolean deadlock = res.hasDeadlock();
```

Reports details: `heap.used.mb`, `heap.max.mb`, `heap.usage.percent`, `nonHeap.used.mb`, `cpu.availableProcessors`, `cpu.systemLoadAverage`, `cpu.loadPerProcessor`, `threads.current`, `threads.peak`, `threads.deadlocked`. Returns `DEGRADED` on high memory/CPU, `DOWN` on deadlock.

---

# Advanced: Structured Tracing

The tracing API (`com.tnsai.quality.tracing`) provides Langfuse-compatible structured traces for agent interactions.

### AgentTrace

Top-level trace representing one logical unit of work. Contains observations (forming a tree) and scores.

```java
AgentTrace trace = AgentTrace.builder("research-agent")
    .sessionId("session-456")
    .startTime(Instant.now())
    .metadata(Map.of("env", "production"))
    .build();
```

Key methods:

| Method                            | Description                                               |
| --------------------------------- | --------------------------------------------------------- |
| `addObservation(Observation)`     | Add an observation to the trace                           |
| `addScore(Score)`                 | Add a score to the trace                                  |
| `startSpan(String name)`          | Create a span builder with automatic parent linking       |
| `startGeneration(String name)`    | Create a generation builder with automatic parent linking |
| `rootObservations()`              | Get observations with no parent                           |
| `childrenOf(String parentId)`     | Get child observations of a parent                        |
| `scoresFor(String observationId)` | Get scores for a specific observation                     |

### Observation

Immutable observation within a trace. Observations form a tree via `parentId`.

Three types (`ObservationType`):

- **SPAN** -- generic timed operation (tool call, processing step)
- **GENERATION** -- LLM call with model-specific metadata (tokens, cost)
- **EVENT** -- point-in-time event with no duration

```java
// Span
Observation span = Observation.span("search-tool")
    .parentId(parentSpanId)
    .input("search query")
    .output("3 results found")
    .endTime(Instant.now())
    .metadata(Map.of("toolName", "brave-search"))
    .build();
trace.addObservation(span);

// Generation
Observation gen = Observation.generation("llm-call")
    .model("claude-sonnet-4-20250514")
    .inputTokens(1500)
    .outputTokens(800)
    .cost(0.0045)
    .prompt("What is...")
    .completion("The answer is...")
    .endTime(Instant.now())
    .build();
trace.addObservation(gen);

// Convenience: totalTokens() and durationMs()
gen.totalTokens(); // -> 2300
gen.durationMs();  // -> milliseconds between start and end
```

### TraceContext

Thread-local context for automatic parent-child nesting. Observations created within a scope automatically receive the pushed ID as their parent.

```java
TraceContext.setTrace(trace);

// Manual push/pop
TraceContext.push(spanId);
try {
    // observations created here have spanId as parent
} finally {
    TraceContext.pop();
}

// Try-with-resources (preferred)
try (var scope = TraceContext.scoped(spanId)) {
    // observations created here have spanId as parent
}

// Query
TraceContext.currentObservationId(); // peek at current parent
TraceContext.depth();                // nesting depth
TraceContext.clear();                // cleanup at end of request
```

### Score

Immutable score attached to a trace or observation for evaluation.

Three types (`ScoreType`):

- **NUMERIC** -- continuous value (0.0 to 1.0)
- **BOOLEAN** -- pass/fail (mapped to 1.0/0.0)
- **CATEGORICAL** -- labeled category

Three sources (`ScoreSource`):

- **HUMAN** -- manually assigned by a reviewer
- **MODEL** -- computed by an LLM-as-judge
- **HEURISTIC** -- computed by a deterministic rule

```java
// Numeric score
Score relevance = Score.numeric("relevance", 0.92, ScoreSource.MODEL);

// Boolean score
Score passed = Score.bool("toxicity-check", true, ScoreSource.HEURISTIC);

// Categorical score
Score sentiment = Score.categorical("sentiment", "positive", ScoreSource.MODEL);

// Bind to a specific observation
Score obsScore = relevance.forObservation(observationId);
trace.addScore(obsScore);
```

---

# Semantic Annotation Framework

URL: https://tnsai.dev/docs/reference/annotations/catalog

import { Callout } from 'fumadocs-ui/components/callout'

## What Does It Do?

The Semantic Annotation Framework allows you to define TnsAI role and agent metadata using semantically grouped annotations. Annotations are organized by purpose; the framework provides:

- **BDI Model** support: Beliefs, Desires, Intentions
- **Gaia Methodology** compatibility: Role, Responsibility definitions
- **Framework Portability**: JSON, YAML, JASON, JADE export
- **Type-Safe Configuration**: Type-safe configuration with nested annotations
- **Channel & Messaging**: Declarative channel adapters and message routing
- **Skills & Commands**: Slash commands and NL trigger patterns
- **Pipeline & Orchestration**: Processing pipelines and agent delegation
- **Observability & Quality**: Tracing, metrics, audit logging, quality gates
- **Security & Safety**: Pairing, input sanitization, content filtering

## When to Use?

| Scenario                                                 | Solution                                                                         |
| -------------------------------------------------------- | -------------------------------------------------------------------------------- |
| When you want to define your agent's knowledge and goals | BDI model with `@RoleSpec`                                                       |
| When you want to make web service calls                  | `@ActionSpec` + `@WebService` nested                                             |
| When you want to use LLM tool calling                    | `@ActionSpec(type = LLM)` + agent-level `.builtInTools(...)` / `.toolPojos(...)` |
| When you want to call an MCP server tool                 | `@ActionSpec` + `@MCPTool` nested                                                |
| When you want to add Retry/Circuit Breaker               | `@Resilience` annotation                                                         |
| When you want multi-agent coordination                   | `@Coordination` annotation                                                       |
| When you want agent memory management                    | `@Memory` annotation                                                             |
| When you want to export a role to another framework      | Use `RoleExporter`                                                               |
| When you want to bridge external messaging platforms     | `@ChannelSpec` + `@OnMessage`                                                    |
| When you want to define skills with NL routing           | `@SkillSpec` + `@Trigger`                                                        |
| When you want to build processing pipelines              | `@Pipeline` + `@PipelineStep`                                                    |
| When you want to delegate work to other agents           | `@Delegate` + `@Fallback`                                                        |
| When you want declarative workspace setup                | `@WorkspaceSpec` + `@ConfigProperty`                                             |
| When you want observability (tracing, metrics)           | `@Traced` + `@Metered`                                                           |
| When you want quality evaluation on output               | `@QualityGate` annotation                                                        |
| When you want audit logging                              | `@AuditLog` annotation                                                           |
| When you want to restrict access to paired contacts      | `@RequiresPairing` annotation                                                    |
| When you want input sanitization or output filtering     | `@Sanitize` + `@ContentFilter`                                                   |

## How Does It Work?

### Annotation Groups

Annotations in TnsAI are organized into logical groups, each handling a different aspect of agent behavior. Most groups support nested annotations, so you can configure complex behavior in a single, readable declaration.

```
@RoleSpec                 → BDI + Gaia metadata
├── @Responsibility       → Role responsibilities (nested)
└── @LLMSpec             → LLM configuration (nested)

@ActionSpec                   → Action definition
├── @WebService          → HTTP call config (nested, for WEB_SERVICE)
└── @MCPTool             → MCP server config (nested, for MCP_TOOL)
   (LLM type uses agent-level tool registration via
    AgentBuilder.builtInTools() / .toolPojos();
    per-action overrides go on @ActionSpec.llmSystemPrompt
    and @ActionSpec.llmTemperature)

@Communication           → Communication style
├── @Style               → Tone, formality (nested)
└── @Messaging           → Channels, topics (nested)

@Coordination            → Multi-agent coordination
└── @Consensus           → Voting mechanism (nested)

@Memory                  → Memory management
├── @Conversation        → Conversation memory (nested)
├── @Vector              → Vector memory (nested)
└── @Persistence         → Persistence (nested)

@Resilience              → Resilience
├── @Retry               → Retry policy (nested)
├── @CircuitBreaker      → Circuit breaker (nested)
└── @RateLimit           → Rate limiting (nested)

@Security                → Security
@Contract                → Design by Contract
@Lifecycle               → Lifecycle callbacks

@ChannelSpec             → Channel adapter definition
├── @OnMessage           → Message handler with filtering
├── @OnConnect           → Connection established
├── @OnDisconnect        → Connection lost
└── @RateLimited         → Rate limiting with strategies

@SkillSpec               → Skill definition with routing
├── @SlashCommand        → Slash command handler
└── @Trigger             → NL trigger pattern

@Pipeline                → Processing pipeline
├── @PipelineStep        → Pipeline stage
├── @Delegate            → Agent delegation
└── @Fallback            → Fallback handler

@WorkspaceSpec           → Declarative workspace
├── @SystemPrompt        → System prompt definition/injection
└── @ConfigProperty      → Configuration binding

@Traced                  → Trace span
@Metered                 → Metrics collection
@QualityGate             → Evaluation threshold
@AuditLog                → Audit trail

@RequiresPairing         → Restrict to approved contacts
@Sanitize                → Input sanitization
@ContentFilter           → Output filtering
```

## Usage

This section walks through each annotation with concrete examples. You can mix and match these annotations on your role and action classes as needed.

### 1. Role Definition (@RoleSpec)

`@RoleSpec` is the main annotation for defining an agent's identity and cognitive model. It brings together the BDI model (what the agent knows, wants, and plans to do), Gaia responsibilities (what it is accountable for), and LLM configuration into a single declaration on your role class.

```java
@RoleSpec(
    name = "ResearchAgent",
    description = "Agent that performs academic research",
    version = "1.0.0",

    // BDI Model
    beliefs = {"research_context", "available_sources"},
    desires = {"find_relevant_papers", "synthesize_findings"},
    intentions = {"search_databases", "analyze_papers"},

    // Gaia Responsibilities
    responsibilities = {
        @Responsibility(
            name = "Paper Search",
            description = "Search in academic databases",
            actions = {"searchPapers", "filterResults"}
        )
    },

    // LLM Configuration
    llm = @LLMSpec(
        provider = Provider.OLLAMA,
        model = "llama3",
        temperature = 0.7f,
        maxTokens = 4096
    )
)
public class ResearchRole extends Role {
    // ...
}
```

### 2. Action Definition

Actions are the things your agent can actually do. Each action has a type that determines how it executes -- locally in your JVM, via an HTTP call, through LLM tool calling, or by connecting to an MCP server. You annotate a method with `@ActionSpec` and specify the type along with any nested configuration it needs.

#### LOCAL Action (Direct Code)

A LOCAL action runs your own Java code directly. This is the simplest action type -- the method body is the implementation.

```java
@ActionSpec(
    type = ActionType.LOCAL,
    description = "Calculates the sum of numbers"
)
public double calculateSum(List<Double> numbers) {
    return numbers.stream().mapToDouble(d -> d).sum();
}
```

#### WEB\_SERVICE Action (REST API)

A WEB\_SERVICE action makes an HTTP request to an external REST API. You configure the endpoint, method, timeout, and authentication in the nested `@WebService` annotation, and TnsAI handles the HTTP call automatically -- the method body is not executed.

```java
@ActionSpec(
    type = ActionType.WEB_SERVICE,
    description = "Fetches weather data",
    webService = @WebService(
        endpoint = "https://api.weather.com/v1/current",
        method = HttpMethod.GET,
        timeout = 5000,
        auth = AuthType.BEARER,
        authTokenEnv = "WEATHER_API_KEY"
    )
)
public WeatherData getWeather(String city) {
    return null; // HTTP call is made automatically
}
```

#### LLM Action (Tool Calling)

An LLM action delegates work to an LLM with access to the agent's registered tools. The method's return value becomes the prompt sent to the LLM, which can then call any tool the agent was built with. Tools are configured at the agent level via `AgentBuilder.builtInTools(...)` and `.toolPojos(...)` — there is no per-action tool list.

Optional per-action overrides on `@ActionSpec` adjust the LLM call without touching the agent's global config:

```java
@ActionSpec(
    type = ActionType.LLM,
    description = "Performs research from academic sources",
    llmSystemPrompt = "You are a careful, citation-driven research assistant.",
    llmTemperature = 0.3f
)
public String researchTopic(String topic) {
    return "Please research: " + topic;
}
```

Then register the tools at the agent level:

```java
Agent agent = AgentBuilder.create()
    .llm(llmClient)
    .role(researchRole)
    .builtInTools(BuiltInTool.ACADEMIC_TOOLS, BuiltInTool.WEB_SEARCH_TOOLS)
    .build();
```

#### MCP\_TOOL Action (MCP Server)

An MCP\_TOOL action calls a tool hosted on a remote MCP (Model Context Protocol) server. You specify the server URL and tool name, and TnsAI handles the protocol communication for you.

```java
@ActionSpec(
    type = ActionType.MCP_TOOL,
    description = "Fetches cryptocurrency data",
    mcpTool = @MCPTool(
        serverUrl = "https://mcp.coingecko.com/mcp",
        toolName = "get_coin_price",
        apiKeyEnv = "COINGECKO_API_KEY"
    )
)
public CoinPrice getCoinPrice(String coinId) {
    return null; // Called via MCP
}
```

### 3. Resilience

`@Resilience` adds fault-tolerance to any action. You can configure automatic retries with exponential backoff, a circuit breaker that stops calling a failing service, timeouts, and a fallback method to call when everything else fails.

```java
@Resilience(
    retry = @Retry(
        maxAttempts = 3,
        backoffMs = 1000,
        multiplier = 2.0
    ),
    circuitBreaker = @CircuitBreaker(
        enabled = true,
        failureThreshold = 5,
        resetTimeoutMs = 30000
    ),
    timeout = 10000,
    fallback = "fallbackMethod"
)
public Result fetchData(String query) {
    // ...
}
```

### 4. Communication

`@Communication` controls how an agent expresses itself and interacts with channels. You can set the tone, formality level, verbosity, language, response format, and which messaging channels/topics the agent participates in.

```java
@Communication(
    style = @Style(
        tone = Tone.PROFESSIONAL,
        formality = Formality.FORMAL,
        verbosity = Verbosity.CONCISE
    ),
    messaging = @Messaging(
        channels = {"research-updates"},
        topics = {"papers", "findings"}
    ),
    language = "tr",
    responseFormat = ResponseFormat.MARKDOWN
)
public class ResearchRole extends Role { }
```

### 5. Coordination

`@Coordination` sets up multi-agent teamwork. One agent acts as the orchestrator (assigning tasks, gathering results), while others are workers with specific capabilities. You can also define a consensus strategy for decisions that require agreement among agents.

```java
// Orchestrator Agent
@Coordination(
    role = CoordinationRole.ORCHESTRATOR,
    workers = {"analyst-1", "analyst-2", "writer-1"},
    consensus = @Consensus(
        strategy = Strategy.MAJORITY,
        threshold = 0.6
    ),
    taskDistribution = TaskDistribution.CAPABILITY_BASED
)
public class TeamLeaderRole extends Role { }

// Worker Agent
@Coordination(
    role = CoordinationRole.WORKER,
    orchestrator = "team-leader",
    capabilities = {"analysis", "summarization"}
)
public class AnalystRole extends Role { }
```

### 6. Memory

`@Memory` configures how an agent retains information across interactions. You can enable conversation memory (recent message history), vector memory (semantic search over past knowledge), and persistence (saving state across restarts).

```java
@Memory(
    conversation = @Conversation(
        enabled = true,
        maxMessages = 100,
        strategy = Strategy.SLIDING_WINDOW
    ),
    vector = @Vector(
        enabled = true,
        dimensions = 1536,
        provider = "pinecone",
        topK = 5
    ),
    persistent = true,
    namespace = "research-agent"
)
public class ResearchRole extends Role { }
```

### 7. Security

`@Security` protects sensitive actions by requiring user approval, enforcing permissions, and enabling audit logging. Use it on any action that modifies critical data or accesses restricted resources.

```java
@Security(
    approvalRequired = true,
    approvalReason = "Critical data deletion operation",
    audit = AuditLevel.DETAILED,
    sensitive = true,
    requiredPermissions = {"admin", "data-manager"}
)
@ActionSpec(type = ActionType.LOCAL)
public void deleteUserData(String userId) {
    // ...
}
```

### 8. Contract (Design by Contract)

`@Contract` enforces preconditions, postconditions, and invariants on an action method. Preconditions are checked before execution, postconditions after, and invariants must hold at all times. This catches bugs early by validating assumptions at runtime.

```java
@Contract(
    preconditions = {
        "userId != null",
        "userId.length() > 0"
    },
    postconditions = {
        "result != null",
        "result.isValid()"
    },
    invariants = {
        "this.connectionPool.isHealthy()"
    }
)
@ActionSpec(type = ActionType.LOCAL)
public UserData fetchUser(String userId) {
    // ...
}
```

### 9. Lifecycle

`@Lifecycle` lets you hook into an agent's startup, shutdown, and error-handling phases. Annotate methods with the appropriate phase, and TnsAI calls them at the right time. Use the `order` parameter to control execution order when multiple methods share the same phase.

```java
public class ResearchAgent extends Agent {

    @Lifecycle(phase = Phase.INIT)
    public void loadResources() {
        // Load resources
    }

    @Lifecycle(phase = Phase.START, order = 1)
    public void connectToDatabase() {
        // Connect to database
    }

    @Lifecycle(phase = Phase.STOP)
    public void cleanup() {
        // Release resources
    }

    @Lifecycle(phase = Phase.ERROR)
    public void handleError(Throwable error) {
        // Error handling
    }
}
```

### 10. Channel & Messaging

Channel annotations let you build adapters for external messaging platforms and route messages declaratively. Use `@ChannelSpec` to register an adapter class, then `@OnMessage`, `@OnConnect`, and `@OnDisconnect` to handle events. `@RateLimited` protects any method from excessive calls.

#### @ChannelSpec (Channel Adapter)

`@ChannelSpec` marks a class as a channel adapter for SPI-based auto-discovery. It declares the channel name, any required configuration keys, and whether the channel supports bidirectional communication.

**Target:** TYPE

```java
@ChannelSpec(
    name = "telegram",
    description = "Telegram Bot API adapter",
    requiredConfig = {"bot_token"},
    bidirectional = true
)
public class TelegramChannel implements Channel { }
```

#### @OnMessage (Message Handler)

`@OnMessage` handles incoming messages with optional filtering by channel, sender, or content pattern. When multiple handlers match, the `priority` value determines execution order (higher first).

**Target:** METHOD

```java
@OnMessage(channel = "telegram", contentPattern = "^/help.*")
public void handleHelp(InboundMessage message) {
    // Handle help command from Telegram
}

@OnMessage  // matches all messages on all channels
public void handleAll(InboundMessage message) {
    // Global message handler
}
```

#### @OnConnect / @OnDisconnect (Connection Lifecycle)

`@OnConnect` is called when a channel connection is established. `@OnDisconnect` is called when a connection is lost or closed. Both can filter by channel name.

**Target:** METHOD

```java
@OnConnect(channel = "telegram")
public void onTelegramReady(String channelId) {
    log.info("Telegram connected: {}", channelId);
}

@OnDisconnect(channel = "slack")
public void onSlackDown(String channelId, String reason) {
    log.warn("Slack disconnected: {} - {}", channelId, reason);
}
```

#### @RateLimited (Rate Limiting)

`@RateLimited` applies rate limiting to a method or all methods of a class. You configure the maximum invocations per time window, an optional key for per-caller limiting, and a strategy for what happens when the limit is exceeded.

**Target:** METHOD, TYPE

```java
@RateLimited(max = 10, per = TimeUnit.MINUTES)
public void handleMessage(InboundMessage msg) {
    // Max 10 calls per minute (global)
}

@RateLimited(
    max = 100,
    per = TimeUnit.HOURS,
    key = "senderId",
    onLimit = RateLimited.Strategy.QUEUE
)
public Object executeAction(String action, Map<String, Object> params) {
    // 100 calls/hour per sender, excess queued
}
```

### 11. Skills & Commands

Skill annotations define self-contained units of functionality that can be activated by slash commands or natural language triggers. Use `@SkillSpec` on a class to register a skill, then annotate handler methods with `@SlashCommand` or `@Trigger` for routing.

#### @SkillSpec (Skill Definition)

`@SkillSpec` marks a class as a skill with metadata for routing and discovery. The `triggers` array provides keywords for natural language matching, and `priority` controls selection when multiple skills match.

**Target:** TYPE

```java
@SkillSpec(
    name = "weather",
    description = "Check weather for any location",
    triggers = {"weather", "forecast", "temperature"},
    priority = 10,
    requiresSession = true
)
public class WeatherSkill implements Skill { }
```

#### @SlashCommand (Slash Command Handler)

`@SlashCommand` registers a method as a handler for a specific slash command. The command string must include the leading slash. Set `hidden = true` to exclude it from help listings.

**Target:** METHOD

```java
@SlashCommand("/tools")
public String listTools() {
    return toolRegistry.describe();
}

@SlashCommand(value = "/model", description = "Switch LLM model")
public String switchModel(String modelName) {
    // Switch the active model
}

@SlashCommand(value = "/debug", description = "Debug internals", hidden = true)
public String debug() {
    return agent.dumpState();
}
```

#### @Trigger (Natural Language Trigger)

`@Trigger` defines a natural language pattern that activates a method. Patterns use `{placeholder}` syntax for argument extraction, or full regex when `regex = true`. The `confidence` threshold controls how strictly NL matching is applied.

**Target:** METHOD

```java
@Trigger("remind me to {task} at {time}")
public void setReminder(String task, String time) {
    // Extracted from: "remind me to buy milk at 5pm"
}

@Trigger(value = "translate .+ to \\w+", regex = true, confidence = 0.8)
public String translate(String input) {
    // Regex-based trigger with higher confidence threshold
}
```

### 12. Pipeline & Orchestration

Pipeline annotations let you define multi-step processing flows and delegate work to other agents. `@Pipeline` and `@PipelineStep` define ordered stages on a class. `@Delegate` forwards execution to another agent. `@Fallback` provides a recovery handler when things fail.

#### @Pipeline (Pipeline Definition)

`@Pipeline` defines a processing pipeline on a class. Methods within the class are marked as stages with `@PipelineStep`. By default, steps execute sequentially; set `sequential = false` to allow parallelization.

**Target:** TYPE

```java
@Pipeline(name = "ingest", description = "Message ingestion pipeline")
public class IngestPipeline {

    @PipelineStep(order = 1, name = "validate")
    public Message validate(Message msg) {
        // Validation logic
    }

    @PipelineStep(order = 2, condition = "message.length() > 0")
    public Message enrich(Message msg) {
        // Enrichment logic
    }

    @PipelineStep(order = 3)
    public String route(Message msg) {
        // Routing logic
    }
}
```

#### @PipelineStep (Pipeline Stage)

`@PipelineStep` marks a method as a stage within a `@Pipeline` class. The `order` value determines execution sequence (lower first). An optional `condition` expression (SpEL-style) causes the step to be skipped when it evaluates to false.

**Target:** METHOD

| Parameter | Type   | Default | Description                                     |
| --------- | ------ | ------- | ----------------------------------------------- |
| order     | int    | -       | REQUIRED: Execution order (lower first)         |
| name      | String | ""      | Step name for logging (defaults to method name) |
| condition | String | ""      | SpEL condition; step skipped when false         |

#### @Delegate (Agent Delegation)

`@Delegate` forwards execution of a method to another agent. You specify the target agent by ID or name. An optional `when` condition controls whether delegation occurs. The operation times out after `timeoutMs` milliseconds.

**Target:** METHOD

```java
@Delegate(agent = "research-agent", when = "topic == 'academic'")
public String handleResearch(String query) {
    // Delegated to research-agent when topic is academic
}

@Delegate(agent = "support-agent", timeoutMs = 60000)
public String handleSupport(String query) {
    // Always delegated, 60s timeout
}
```

#### @Fallback (Fallback Handler)

`@Fallback` designates a method as the recovery handler when the primary action fails. Use `forAction` to target a specific action, or leave it empty for a global fallback. The `maxRetries` parameter controls how many attempts are made before the fallback is invoked.

**Target:** METHOD

```java
@Fallback(forAction = "searchWeb")
public String searchFallback(String query, Exception error) {
    return "Search unavailable: " + error.getMessage();
}

@Fallback(maxRetries = 3)  // global fallback, retries 3 times first
public String globalFallback(String input, Exception error) {
    return "Something went wrong. Please try again.";
}
```

### 13. Workspace & Configuration

Workspace annotations provide declarative setup for agent environments. `@WorkspaceSpec` ties together channels, tools, and system prompts into a named workspace. `@SystemPrompt` defines or injects the system prompt. `@ConfigProperty` binds fields to configuration values.

#### @WorkspaceSpec (Declarative Workspace)

`@WorkspaceSpec` configures a complete workspace declaratively. It specifies which channels the workspace listens to, which tools are available, and an optional inline system prompt.

**Target:** TYPE

```java
@WorkspaceSpec(
    name = "support",
    channels = {"telegram", "slack"},
    systemPrompt = "You are a customer support agent.",
    tools = {"search", "ticket-create", "knowledge-base"}
)
public class SupportWorkspace { }
```

#### @SystemPrompt (System Prompt)

`@SystemPrompt` defines or injects a system prompt. On a class, it provides a static prompt with optional `{placeholder}` template variables. On a field, it marks the field for prompt injection. On a method, the return value supplies the prompt dynamically.

**Target:** TYPE, FIELD, METHOD

```java
// On class — static prompt with template variable
@SystemPrompt("You are a research assistant specializing in {domain}.")
public class ResearchAgent extends Agent { }

// On field — injectable prompt
@SystemPrompt
private String customPrompt;

// On method — dynamic prompt supplier
@SystemPrompt
public String buildPrompt() {
    return "Dynamic prompt based on " + currentState();
}
```

#### @ConfigProperty (Configuration Binding)

`@ConfigProperty` binds a field to a configuration value by dot-separated key path. If the key is missing at startup and no `defaultValue` is provided, the application fails to start when `required = true`.

**Target:** FIELD

```java
@ConfigProperty("sona.workspace.default.model")
private String defaultModel;

@ConfigProperty(value = "sona.max_tokens", defaultValue = "4096")
private int maxTokens;

@ConfigProperty(value = "sona.debug", defaultValue = "false", required = false)
private boolean debugMode;
```

### 14. Observability & Quality

Observability annotations add tracing, metrics, quality evaluation, and audit logging to methods without changing business logic. They integrate with TnsAI's SPI-based observability backend.

#### @Traced (Trace Span)

`@Traced` adds trace ID propagation and structured logging to a method. Each invocation creates a trace span with optional argument and result capture. The operation name defaults to `class.method` if not specified.

**Target:** METHOD

```java
@Traced
public String chat(String message) {
    // Automatic trace span: "MyAgent.chat"
}

@Traced(operationName = "tool-execution", includeArgs = true, includeResult = true)
public Object executeTool(String name, Map<String, Object> params) {
    // Custom span name, args and result logged
}
```

#### @Metered (Metrics Collection)

`@Metered` collects latency, call count, and error rate metrics for a method or all methods of a class. You can add custom tags and control whether a latency histogram is recorded.

**Target:** METHOD, TYPE

```java
@Metered
public String chat(String message) {
    // Automatic metrics: latency, count, error rate
}

@Metered(name = "tool.execution", tags = {"module=core"}, histogram = true)
public Object executeTool(String name) {
    // Custom metric name with tags
}
```

#### @QualityGate (Evaluation Threshold)

`@QualityGate` defines a minimum quality score for a method's output. The evaluator (resolved via SPI) scores the result, and the `onFail` strategy determines what happens when the score is below the threshold.

**Target:** METHOD

```java
@QualityGate(minScore = 0.8, evaluator = "relevance")
public String chat(String message) {
    // Logs warning if relevance < 0.8
}

@QualityGate(
    minScore = 0.9,
    evaluator = "factuality",
    onFail = QualityGate.OnFail.RETRY,
    maxRetries = 3
)
public String answerQuestion(String question) {
    // Retries up to 3 times if factuality < 0.9
}
```

#### @AuditLog (Audit Trail)

`@AuditLog` records method invocations to an audit trail. Each entry includes the action name, timestamp, caller identity, and optionally the method arguments and return value.

**Target:** METHOD

```java
@AuditLog(action = "user.approve")
public void approveUser(String userId) {
    // Audit entry: "user.approve" with timestamp
}

@AuditLog(action = "config.change", includeArgs = true, includeResult = true)
public void updateConfig(String key, String value) {
    // Audit entry includes args (key, value) and result
}
```

### 15. Security & Safety

Security annotations enforce access control, sanitize inputs, and filter outputs. They complement the `@Security` annotation (section 7) with fine-grained pairing requirements, parameter-level sanitization, and content-aware output filtering.

#### @RequiresPairing (Access Restriction)

`@RequiresPairing` restricts access to approved/paired contacts only. Place it on a class to protect all methods, or on individual methods for selective protection. Unapproved callers receive the configured rejection message.

**Target:** TYPE, METHOD

```java
@RequiresPairing
public class AdminSkill implements Skill {
    // All methods require pairing
}

@RequiresPairing(message = "You need to be approved first. Use /pair to request access.")
public void handleSensitiveAction(InboundMessage msg) {
    // Custom rejection message for unapproved users
}
```

#### @Sanitize (Input Sanitization)

`@Sanitize` sanitizes a method parameter before processing. Rules are applied in order to prevent injection attacks. Place it directly on the parameter you want to sanitize.

**Target:** PARAMETER

Available rules: `TRIM`, `STRIP_HTML`, `SHELL_ESCAPE`, `SQL_ESCAPE`, `STRIP_CONTROL`, `NORMALIZE_UNICODE`

```java
public void executeShell(@Sanitize(rules = {Rule.SHELL_ESCAPE}) String command) {
    // Shell metacharacters escaped
}

public void storeData(@Sanitize(rules = {Rule.STRIP_HTML, Rule.TRIM}) String input) {
    // HTML stripped, then trimmed
}

public void processInput(
    @Sanitize(rules = {Rule.STRIP_CONTROL, Rule.NORMALIZE_UNICODE, Rule.TRIM}) String text
) {
    // Control chars removed, unicode normalized, whitespace trimmed
}
```

#### @ContentFilter (Output Filtering)

`@ContentFilter` applies content filtering to a method's output. Filters run in order and can detect PII, toxicity, prompt injection attempts, and factual grounding issues. The `onViolation` strategy controls whether offending content is redacted, the entire response is blocked, or a warning is logged.

**Target:** METHOD

```java
@ContentFilter(filters = {Filter.PII_REDACTION})
public String chat(String message) {
    // PII automatically redacted from output
}

@ContentFilter(
    filters = {Filter.TOXICITY, Filter.PII_REDACTION, Filter.PROMPT_INJECTION},
    onViolation = ContentFilter.OnViolation.BLOCK
)
public String generateResponse(String prompt) {
    // Response blocked if any filter detects a violation
}
```

## Export System

TnsAI can export your annotated role definitions to other agent frameworks and formats. This is useful for interoperability -- you define your agent once using TnsAI annotations, then export it to whichever target platform you need.

### JSON Export

Exports the role as a JSON document. You can get it as a string or write it directly to a file.

```java
RoleExporter exporter = new JsonRoleExporter();
String json = exporter.export(myRole);
exporter.exportToFile(myRole, Path.of("role.json"));
```

### YAML Export

Exports the role as YAML, which is often easier to read and edit by hand than JSON.

```java
RoleExporter exporter = new YamlRoleExporter();
String yaml = exporter.export(myRole);
```

### JASON Export (BDI)

Exports the role as AgentSpeak code for the JASON BDI interpreter. This lets you run your TnsAI-defined agent logic in a traditional BDI execution environment.

```java
RoleExporter exporter = new JasonExporter();
String asl = exporter.export(myRole);
// Generates AgentSpeak code
```

### JADE Export

Exports the role as a JADE agent descriptor XML file. Use this when you need to deploy your agent in a JADE multi-agent platform.

```java
RoleExporter exporter = new JadeExporter();
String xml = exporter.export(myRole);
// Generates JADE agent descriptor
```

## API Reference

Complete parameter reference for every annotation in the framework. Use these tables when you need to know the exact parameter names, types, and defaults.

### @RoleSpec

Defines the agent's identity, BDI model, responsibilities, and LLM settings. This is the top-level annotation you place on a role class.

| Parameter        | Type               | Default    | Description       |
| ---------------- | ------------------ | ---------- | ----------------- |
| name             | String             | ""         | Role name         |
| description      | String             | ""         | Description       |
| version          | String             | "1.0.0"    | Version           |
| beliefs          | String\[]          | `{}`       | BDI beliefs       |
| desires          | String\[]          | `{}`       | BDI desires       |
| intentions       | String\[]          | `{}`       | BDI intentions    |
| responsibilities | @Responsibility\[] | `{}`       | Responsibilities  |
| capabilities     | String\[]          | `{}`       | Role capabilities |
| domains          | String\[]          | `{}`       | Working domains   |
| llm              | @LLMSpec           | @LLMSpec() | LLM configuration |

### @LLMSpec

Configures which LLM provider and model the agent uses, along with generation parameters. This is nested inside `@RoleSpec`.

| Parameter    | Type            | Default         | Description                                                         |
| ------------ | --------------- | --------------- | ------------------------------------------------------------------- |
| provider     | Provider (enum) | Provider.OLLAMA | LLM provider — enum: OLLAMA, OPENAI, ANTHROPIC, AZURE\_OPENAI, etc. |
| model        | String          | ""              | Model name (gpt-4o, claude-3-opus, llama3)                          |
| temperature  | float           | 0.7f            | Creativity (0.0-2.0)                                                |
| maxTokens    | int             | 4096            | Max token count                                                     |
| topP         | float           | 1.0f            | Nucleus sampling                                                    |
| systemPrompt | String          | ""              | Custom system prompt                                                |

### @ActionSpec

Marks a method as an executable action. The `type` parameter is required and determines how the action runs. Depending on the type, you provide a corresponding nested annotation for configuration.

| Parameter       | Type        | Default | Description                                                                          |
| --------------- | ----------- | ------- | ------------------------------------------------------------------------------------ |
| type            | ActionType  | -       | REQUIRED: LOCAL, WEB\_SERVICE, LLM, MCP\_TOOL                                        |
| description     | String      | ""      | Description                                                                          |
| excludeFromLLM  | boolean     | false   | If true, hidden from the LLM tool list (programmatic use only)                       |
| webService      | @WebService | -       | WEB\_SERVICE config (nested)                                                         |
| mcpTool         | @MCPTool    | -       | MCP\_TOOL config (nested)                                                            |
| llmSystemPrompt | String      | ""      | LLM-action-only: per-action system prompt override (empty = use LLM client default)  |
| llmTemperature  | float       | -1.0f   | LLM-action-only: per-action temperature override (negative = use LLM client default) |

### @WebService

Configures HTTP call details for WEB\_SERVICE actions. Nested inside `@ActionSpec`.

| Parameter    | Type       | Default  | Description          |
| ------------ | ---------- | -------- | -------------------- |
| endpoint     | String     | -        | HTTP endpoint URL    |
| method       | HttpMethod | GET      | HTTP method          |
| timeout      | int        | 30000    | Timeout (ms)         |
| auth         | AuthType   | NO\_AUTH | Authentication type  |
| authTokenEnv | String     | ""       | Bearer token env var |

### @MCPTool

Configures the connection to an MCP server for MCP\_TOOL actions. Nested inside `@ActionSpec`.

| Parameter | Type   | Default | Description     |
| --------- | ------ | ------- | --------------- |
| serverUrl | String | -       | MCP server URL  |
| toolName  | String | ""      | Tool name       |
| apiKeyEnv | String | ""      | API key env var |
| timeout   | int    | 30000   | Timeout (ms)    |

### @ChannelSpec

Marks a class as a channel adapter for SPI-based auto-discovery. Placed on channel implementation classes.

| Parameter      | Type      | Default | Description                                 |
| -------------- | --------- | ------- | ------------------------------------------- |
| name           | String    | -       | REQUIRED: Unique channel name (lowercase)   |
| description    | String    | ""      | Human-readable description                  |
| requiredConfig | String\[] | `{}`    | Required config keys for startup validation |
| bidirectional  | boolean   | true    | Supports bidirectional communication        |

### @OnMessage

Handles incoming messages with optional filtering by channel, sender, or content pattern.

| Parameter      | Type   | Default | Description                                 |
| -------------- | ------ | ------- | ------------------------------------------- |
| channel        | String | ""      | Channel name filter (empty = all channels)  |
| senderPattern  | String | ""      | Sender ID regex (empty = all senders)       |
| contentPattern | String | ""      | Message content regex (empty = all content) |
| priority       | int    | 0       | Processing priority (higher executes first) |

### @OnConnect

Called when a channel connection is established.

| Parameter | Type   | Default | Description                                |
| --------- | ------ | ------- | ------------------------------------------ |
| channel   | String | ""      | Channel name filter (empty = all channels) |

### @OnDisconnect

Called when a channel connection is lost or closed.

| Parameter | Type   | Default | Description                                |
| --------- | ------ | ------- | ------------------------------------------ |
| channel   | String | ""      | Channel name filter (empty = all channels) |

### @RateLimited

Applies rate limiting to a method or all methods of a class.

| Parameter | Type     | Default | Description                                             |
| --------- | -------- | ------- | ------------------------------------------------------- |
| max       | int      | -       | REQUIRED: Max invocations per time window               |
| per       | TimeUnit | SECONDS | Time unit for the rate window                           |
| key       | String   | ""      | Key expression for per-caller limiting (empty = global) |
| onLimit   | Strategy | REJECT  | Overflow strategy: REJECT, QUEUE, or DROP               |

### @SkillSpec

Marks a class as a skill with metadata for routing and discovery.

| Parameter       | Type      | Default | Description                                  |
| --------------- | --------- | ------- | -------------------------------------------- |
| name            | String    | -       | REQUIRED: Unique skill name (lowercase)      |
| description     | String    | ""      | Description for skill selection by router    |
| triggers        | String\[] | `{}`    | Keywords for NL routing                      |
| priority        | int       | 0       | Selection priority (higher takes precedence) |
| requiresSession | boolean   | true    | Whether an active session is required        |

### @SlashCommand

Registers a method as a slash command handler.

| Parameter   | Type    | Default | Description                                 |
| ----------- | ------- | ------- | ------------------------------------------- |
| value       | String  | -       | REQUIRED: Command string with leading slash |
| description | String  | ""      | Description shown in command listings       |
| hidden      | boolean | false   | Whether excluded from help/listings         |

### @Trigger

Defines a natural language trigger pattern for skill activation.

| Parameter  | Type    | Default | Description                                         |
| ---------- | ------- | ------- | --------------------------------------------------- |
| value      | String  | -       | REQUIRED: Trigger pattern (template or regex)       |
| regex      | boolean | false   | Whether pattern is regex (true) or template (false) |
| confidence | double  | 0.7     | Minimum NL matching confidence (0.0-1.0)            |

### @Pipeline

Defines a processing pipeline on a class.

| Parameter   | Type    | Default | Description                        |
| ----------- | ------- | ------- | ---------------------------------- |
| name        | String  | -       | REQUIRED: Pipeline name            |
| description | String  | ""      | Human-readable description         |
| sequential  | boolean | true    | Whether steps execute sequentially |

### @PipelineStep

Marks a method as a pipeline stage with execution order.

| Parameter | Type   | Default | Description                                     |
| --------- | ------ | ------- | ----------------------------------------------- |
| order     | int    | -       | REQUIRED: Execution order (lower first)         |
| name      | String | ""      | Step name for logging (defaults to method name) |
| condition | String | ""      | SpEL condition; step skipped when false         |

### @Delegate

Delegates execution to another agent with routing rules.

| Parameter | Type   | Default | Description                                    |
| --------- | ------ | ------- | ---------------------------------------------- |
| agent     | String | -       | REQUIRED: Target agent ID or name              |
| when      | String | ""      | Condition expression (empty = always delegate) |
| timeoutMs | long   | 30000   | Timeout in milliseconds                        |

### @Fallback

Fallback handler invoked when the primary action fails.

| Parameter  | Type   | Default | Description                                    |
| ---------- | ------ | ------- | ---------------------------------------------- |
| forAction  | String | ""      | Action name to cover (empty = global fallback) |
| maxRetries | int    | 0       | Max retries before invoking fallback           |

### @WorkspaceSpec

Declarative workspace definition with channels, tools, and system prompt.

| Parameter    | Type      | Default | Description                                   |
| ------------ | --------- | ------- | --------------------------------------------- |
| name         | String    | -       | REQUIRED: Workspace name (unique identifier)  |
| channels     | String\[] | `{}`    | Channels this workspace accepts messages from |
| systemPrompt | String    | ""      | Inline system prompt text                     |
| tools        | String\[] | `{}`    | Tool names available in this workspace        |

### @SystemPrompt

Defines or injects a system prompt. Supports `{placeholder}` template variables.

| Parameter | Type   | Default | Description                                       |
| --------- | ------ | ------- | ------------------------------------------------- |
| value     | String | ""      | Prompt template text (empty when on field/method) |

### @ConfigProperty

Binds a field to a configuration value by key path.

| Parameter    | Type    | Default | Description                               |
| ------------ | ------- | ------- | ----------------------------------------- |
| value        | String  | -       | REQUIRED: Config key path (dot-separated) |
| defaultValue | String  | ""      | Default if key missing (empty = required) |
| required     | boolean | true    | Fail startup if missing and no default    |

### @Traced

Adds trace ID propagation and structured logging to a method.

| Parameter     | Type    | Default | Description                                 |
| ------------- | ------- | ------- | ------------------------------------------- |
| operationName | String  | ""      | Custom span name (defaults to class.method) |
| includeArgs   | boolean | false   | Log method arguments in span                |
| includeResult | boolean | false   | Log return value in span                    |

### @Metered

Collects latency, call count, and error rate metrics.

| Parameter | Type      | Default | Description                                   |
| --------- | --------- | ------- | --------------------------------------------- |
| name      | String    | ""      | Custom metric name (defaults to class.method) |
| tags      | String\[] | `{}`    | Static tags in key=value format               |
| histogram | boolean   | true    | Record latency histogram                      |

### @QualityGate

Defines an evaluation quality threshold for method output.

| Parameter  | Type   | Default | Description                                 |
| ---------- | ------ | ------- | ------------------------------------------- |
| minScore   | double | -       | REQUIRED: Minimum score (0.0-1.0)           |
| evaluator  | String | -       | REQUIRED: Evaluator name (resolved via SPI) |
| onFail     | OnFail | LOG     | Failure action: LOG, RETRY, or REJECT       |
| maxRetries | int    | 2       | Max retries when onFail = RETRY             |

### @AuditLog

Records method invocations to an audit trail.

| Parameter     | Type    | Default | Description                           |
| ------------- | ------- | ------- | ------------------------------------- |
| action        | String  | -       | REQUIRED: Action name for audit entry |
| includeArgs   | boolean | false   | Log method arguments                  |
| includeResult | boolean | false   | Log return value                      |

### @RequiresPairing

Restricts access to approved/paired contacts only.

| Parameter | Type   | Default                            | Description              |
| --------- | ------ | ---------------------------------- | ------------------------ |
| message   | String | "Access denied. Pairing required." | Custom rejection message |

### @Sanitize

Sanitizes input parameters before processing.

| Parameter | Type    | Default  | Description                         |
| --------- | ------- | -------- | ----------------------------------- |
| rules     | Rule\[] | `{TRIM}` | Sanitization rules applied in order |

**Rule enum values:** `TRIM` (trim whitespace), `STRIP_HTML` (strip HTML tags), `SHELL_ESCAPE` (escape shell metacharacters), `SQL_ESCAPE` (escape SQL special characters), `STRIP_CONTROL` (remove null bytes and control characters), `NORMALIZE_UNICODE` (normalize to NFC form)

### @ContentFilter

Applies content filtering to method output.

| Parameter   | Type        | Default | Description                                |
| ----------- | ----------- | ------- | ------------------------------------------ |
| filters     | Filter\[]   | -       | REQUIRED: Content filters applied in order |
| onViolation | OnViolation | REDACT  | Violation action: REDACT, BLOCK, or WARN   |

**Filter enum values:** `PII_REDACTION` (redact personally identifiable information), `TOXICITY` (check for toxic/harmful content), `PROMPT_INJECTION` (detect prompt injection attempts), `GROUNDING` (ensure factual grounding against sources)

## Things to Note

These are common pitfalls to avoid when using the annotation framework, especially around how action types and nested annotations must match.

### Correct Usage

Always use the nested annotation that matches your action type. Configuration details go inside the nested annotation, not on `@ActionSpec` itself.

```java
// CORRECT: Using nested annotations
@ActionSpec(
    type = ActionType.WEB_SERVICE,
    webService = @WebService(endpoint = "https://api.example.com")
)
```

### Incorrect Usage

Do not put configuration properties directly on `@ActionSpec` -- this flat style is not supported and will cause a compile error.

```java
// INCORRECT: Using flat properties (old style, no longer supported)
@ActionSpec(
    type = ActionType.WEB_SERVICE,
    endpoint = "https://api.example.com"  // ERROR!
)
```

### Type Compatibility

Each action type uses different `@ActionSpec` fields:

- `LOCAL` → No nested annotation; the method body runs.
- `WEB_SERVICE` → Use the nested `@WebService(...)` for endpoint, method, auth, etc.
- `LLM` → No nested annotation; tool exposure is configured at the agent level via `AgentBuilder.builtInTools(...)` / `.toolPojos(...)`. Optional per-action overrides go on `@ActionSpec.llmSystemPrompt` and `@ActionSpec.llmTemperature`.
- `MCP_TOOL` → Use the nested `@MCPTool(serverUrl = ..., toolName = ..., ...)` for server connection.

## Related Topics

For deeper coverage of individual features mentioned in this guide, see these pages.

- [Action System](/docs/agents/fundamentals/action-system)
- [Role Definition](/docs/agents/fundamentals/roles)
- [LLM Tool Calling](/docs/capabilities/tools)
- [MCP Integration](/docs/mcp)
- [Resilience Patterns](/docs/agents/reliability/resilience)

---

# Annotations Reference

URL: https://tnsai.dev/docs/reference/annotations
Description: TnsAI ships its declarative annotation surface grouped by purpose — BDI (Belief-Desire-Intention), Gaia (roles + environment), channels, pipelines, security, observability, and core dispatch. The full catalog below documents every annotation, its fields, and example usage. Counts drift between releases; the catalog is the authoritative list.

import { Callout } from 'fumadocs-ui/components/callout'

- [Full Catalog](/docs/reference/annotations/catalog) — every annotation with field tables and
  usage examples.
- [Agents](/docs/agents) — annotations applied in practice.

---

# Configuration Reference

URL: https://tnsai.dev/docs/reference/configuration
Description: A consolidated reference for the knobs you can set on TnsAI from outside the code (env vars) or through builders. This page indexes the canonical sources rather than duplicating them — the goal is to tell you where to look, not to mirror state that will drift.

import { Callout } from 'fumadocs-ui/components/callout'

## Environment Variables

The framework reads environment variables for credentials. Each LLM provider has its own:

| Variable                                                                       | Used by          | Purpose                                                      |
| ------------------------------------------------------------------------------ | ---------------- | ------------------------------------------------------------ |
| `ANTHROPIC_API_KEY`                                                            | `tnsai-llm`      | Claude (Anthropic) API key                                   |
| `OPENAI_API_KEY`                                                               | `tnsai-llm`      | OpenAI / GPT API key                                         |
| `GEMINI_API_KEY`                                                               | `tnsai-llm`      | Google Gemini API key                                        |
| `NVIDIA_API_KEY`                                                               | `tnsai-llm`      | NVIDIA NIM API key (cloud `build.nvidia.com` or self-hosted) |
| `MISTRAL_API_KEY`                                                              | `tnsai-llm`      | Mistral La Plateforme key                                    |
| `GROQ_API_KEY`                                                                 | `tnsai-llm`      | Groq inference key                                           |
| `COHERE_API_KEY`                                                               | `tnsai-llm`      | Cohere API key                                               |
| `OPENROUTER_API_KEY`                                                           | `tnsai-llm`      | OpenRouter aggregator key                                    |
| `HUGGINGFACE_API_KEY`                                                          | `tnsai-llm`      | Hugging Face Inference API / Endpoints key                   |
| `XAI_API_KEY`                                                                  | `tnsai-llm`      | xAI Grok key                                                 |
| `DEEPSEEK_API_KEY`                                                             | `tnsai-llm`      | DeepSeek key                                                 |
| `PERPLEXITY_API_KEY`                                                           | `tnsai-llm`      | Perplexity Sonar key                                         |
| `TOGETHER_API_KEY`                                                             | `tnsai-llm`      | Together.ai key                                              |
| `FIREWORKS_API_KEY`                                                            | `tnsai-llm`      | Fireworks AI key                                             |
| `CEREBRAS_API_KEY`                                                             | `tnsai-llm`      | Cerebras inference key                                       |
| `REPLICATE_API_TOKEN`                                                          | `tnsai-llm`      | Replicate API token (note: `_TOKEN`, not `_KEY`)             |
| `DEEPINFRA_API_KEY`                                                            | `tnsai-llm`      | DeepInfra key                                                |
| `DATABRICKS_TOKEN`                                                             | `tnsai-llm`      | Databricks Mosaic AI token                                   |
| `WATSONX_API_KEY`, `WATSONX_PROJECT_ID`                                        | `tnsai-llm`      | IBM watsonx.ai credentials (both required)                   |
| `MINIMAX_API_KEY`                                                              | `tnsai-llm`      | MiniMax key                                                  |
| `ZHIPU_API_KEY`                                                                | `tnsai-llm`      | Zhipu AI key                                                 |
| `AZURE_OPENAI_API_KEY`, `AZURE_OPENAI_ENDPOINT`                                | `tnsai-llm`      | Azure OpenAI (both required)                                 |
| `BRAVE_API_KEY`                                                                | `tnsai-tools`    | Brave Search API key                                         |
| `TAVILY_API_KEY`                                                               | `tnsai-tools`    | Tavily Search API key                                        |
| `TELEGRAM_BOT_TOKEN`                                                           | `tnsai-channels` | Telegram Bot API token                                       |
| `EMAIL_IMAP_HOST`, `EMAIL_IMAP_USER`, `EMAIL_IMAP_PASSWORD`, `EMAIL_SMTP_HOST` | `tnsai-channels` | Email channel (IMAP poll + SMTP send)                        |
| `SLACK_APP_TOKEN`, `SLACK_BOT_TOKEN`                                           | `tnsai-channels` | Slack Socket Mode                                            |
| `DISCORD_BOT_TOKEN`                                                            | `tnsai-channels` | Discord Gateway                                              |
| `WHATSAPP_ACCESS_TOKEN`, `WHATSAPP_VERIFY_TOKEN`, `WHATSAPP_APP_SECRET`        | `tnsai-channels` | WhatsApp Cloud API                                           |

Self-hosted endpoints typically expose a `*_BASE_URL` companion variable (e.g. `NVIDIA_BASE_URL`, `OLLAMA_BASE_URL`) for pointing the client at your container.

The `ProviderEnvVarConsistencyTest` in `tnsai-llm` is the authoritative list — it fails CI on any drift between the variable name a client reads and what the module's README documents.

## AgentBuilder options

`AgentBuilder` is the canonical entry point for constructing agents. Read its [Javadoc](https://javadoc.io/doc/io.github.tansuasici/tnsai-core/latest/com/tnsai/agents/AgentBuilder.html) for the full method surface — every public method on the builder is a configurable option. The categories:

- **Identity** — `agentId`, `agentClass`, `displayName`
- **LLM** — `llm(LLMClient)`, plus per-call overrides via `temperature`, `topP`, `maxTokens`
- **Role + actions** — `role(Role)`, `roles(...)`, `capability(Capability)`
- **Tools** — `toolPojos(Object...)` (recommended), `dynamicTool(DynamicToolMethod)`, `dynamicTools(List)`
- **Memory + RAG** — `memoryConfig(MemoryConfig)` (declarative, mirrors `@MemorySpec`), `memoryStore(MemoryStore)` (custom store), `maxContextTokens(int)`, `knowledgeBase(KnowledgeBase)`
- **Resilience** — `cancellationToken(CancellationToken)`, `timeoutPolicy(TimeoutPolicy)`, `@Retry` / `@CircuitBreaker` honored from role annotations
- **Validation** — `relaxValidation(String code)` to skip a specific build-time check

## LLMClient configuration

Each `tnsai-llm` client has its own builder with provider-specific options (caching, beta headers, base-URL overrides, etc.). See the module's [README](https://github.com/TnsAI-Framework/TnsAI/tree/main/tnsai-llm) for the per-provider matrix.

## Server configuration

`tnsai-server` reads its bind address, port, auth mode, and tool-approval channel config either programmatically or from a `server.yml` file. See the [server module README](https://github.com/TnsAI-Framework/TnsAI/tree/main/tnsai-server).

## Related

- [Installation](/docs/start/installation) — initial env setup.
- [Help / FAQ](/docs/help/faq) — common credential / configuration questions.
- [Troubleshooting](/docs/help/troubleshooting) — diagnosing missing-credential failures.

---

# Glossary

URL: https://tnsai.dev/docs/reference/glossary
Description: Quick lookup for the abbreviations and TnsAI-specific terms that appear in the rest of the documentation. Linked terms go to the page that explains the concept in depth.

import { Callout } from 'fumadocs-ui/components/callout'

| Term                    | Definition                                                                                                                                                                |
| ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Action**              | A unit of work an agent can execute. Declared via `@ActionSpec` on a role method.                                                                                         |
| **Agent**               | Long-lived object that runs a BDI loop — perceives input, plans intentions, executes actions.                                                                             |
| **BDI**                 | Belief–Desire–Intention. The mental model TnsAI agents are built around.                                                                                                  |
| **Capability**          | A reusable contract (interface with default-method actions) a role can implement.                                                                                         |
| **Channel**             | Adapter for an external messaging platform — Telegram, Slack, CLI, Email, etc.                                                                                            |
| **FSM**                 | Finite State Machine. The agent lifecycle (`CREATED → STARTING → RUNNING → STOPPING → STOPPED` + `FAILED`) is one.                                                        |
| **Gaia**                | Multi-agent methodology that frames a system as roles, environment, and relationships. TnsAI's coordination module borrows from it.                                       |
| **GOAP**                | Goal-Oriented Action Planning. One of the planners under `tnsai-intelligence`.                                                                                            |
| **HTN**                 | Hierarchical Task Network — the other built-in planning style.                                                                                                            |
| **KB / Knowledge Base** | Vector + BM25-indexed corpus that backs `@KnowledgeSource` / `@Retrieval`.                                                                                                |
| **LAM**                 | Language-Action Model — the pattern that maps LLM output to typed action calls. See [LAM pattern](/docs/reference/lam-pattern).                                           |
| **MCP**                 | Model Context Protocol. Lets agents expose tools to (or consume tools from) MCP-compatible peers.                                                                         |
| **RAG**                 | Retrieval-Augmented Generation.                                                                                                                                           |
| **ReAct**               | Reasoning-and-Acting pattern: interleave LLM "thought" steps with tool calls.                                                                                             |
| **Role**                | Bundle of capabilities and actions an agent plays.                                                                                                                        |
| **SCOP**                | A separate Java multi-agent framework — see [scop-framework.netlify.app](https://scop-framework.netlify.app/). TnsAI ships an [integration bridge](/docs/integrate/scop). |
| **SPI**                 | Service Provider Interface — Java's `ServiceLoader` discovery pattern, the framework's main extensibility seam.                                                           |
| **ToT**                 | Tree of Thoughts — branching reasoning pattern that explores multiple lines before committing.                                                                            |

---

# Reference

URL: https://tnsai.dev/docs/reference
Description: Lookup material. You know what you want — find it fast.

import { Callout } from 'fumadocs-ui/components/callout'

## Pages

- [Annotations](/docs/reference/annotations) — Every annotation grouped by category (BDI, Gaia, channels, pipelines, security, observability, core dispatch).
- [Tool Catalog](/docs/reference/tool-catalog) — Quick lookup for the shipped POJO toolkits (full guide: [capabilities/tools/catalog](/docs/capabilities/tools/catalog)).
- [SPI](/docs/reference/spi) — Extension points for modular plug-ins.
- [LAM Pattern](/docs/reference/lam-pattern) — Language-Action Model coordination pattern.
- [Configuration](/docs/reference/configuration) — Environment variables, builder options.
- [Glossary](/docs/reference/glossary) — Terms and acronyms used across the docs.

---

# TnsAI as a Large Action Model (LAM) Framework

URL: https://tnsai.dev/docs/reference/lam-pattern

import { Callout } from 'fumadocs-ui/components/callout'

## Overview

TnsAI is a **Large Action Model (LAM) Framework** for Java - enabling agents that perceive, plan, and act in the world.

```
+-------------------------------------------------------------+
|                    LARGE ACTION MODEL                       |
+--------------+--------------+--------------+----------------+
|  PERCEPTION  |     GOAL     |     PLAN     |      ACT       |
|   (Belief)   |   (Desire)   |  (Intention) |    (Action)    |
+--------------+--------------+--------------+----------------+
|                     BDI ARCHITECTURE                        |
+-------------------------------------------------------------+
```

## LAM vs LLM

| Aspect           | LLM                    | LAM (TnsAI)            |
| ---------------- | ---------------------- | ---------------------- |
| **Output**       | Text generation        | Action execution       |
| **Capability**   | Language understanding | Real-world interaction |
| **Architecture** | Transformer            | BDI + Tools + Memory   |
| **State**        | Stateless              | Persistent beliefs     |
| **Learning**     | Pre-trained            | Runtime adaptation     |

## BDI to LAM Terminology Mapping

TnsAI uses BDI (Belief-Desire-Intention) architecture internally, which maps directly to LAM concepts:

| BDI Term      | LAM Term    | TnsAI Class           | Purpose                   |
| ------------- | ----------- | --------------------- | ------------------------- |
| **Belief**    | Perception  | `Belief`              | World state understanding |
| **Desire**    | Goal/Intent | `Desire`              | What to achieve           |
| **Intention** | Plan        | `Intention`           | How to achieve it         |
| **Action**    | Act         | `@ActionSpec` methods | Execution                 |

## LAM Architecture in TnsAI

### 1. Perception Layer (Beliefs)

```java
// Beliefs represent the agent's understanding of the world
protected List<Belief> getBeliefs() {
    return List.of(
        new Belief("Current date: " + LocalDate.now()),
        new Belief("User prefers concise responses"),
        new Belief("Available tools: web search, calculator")
    );
}
```

### 2. Goal Layer (Desires)

```java
// Desires represent what the agent wants to achieve
protected List<Desire> getDesires() {
    return List.of(
        Desire.goal("Provide accurate information"),
        Desire.goal("Complete tasks efficiently"),
        Desire.goal("Maintain user privacy")
    );
}
```

### 3. Planning Layer (Intentions)

```java
// Intentions represent the agent's current plans
protected List<Intention> getIntentions() {
    return List.of(
        new Intention("Research topic", "Use search tools"),
        new Intention("Summarize findings", "Extract key points")
    );
}
```

### 4. Action Layer (Execution)

```java
// Actions are executable methods
@ActionSpec(description = "Search the web for information")
public ActionResult searchWeb(String query, ActionResult result) {
    // Execution logic
    return result;
}
```

## LAM Patterns in TnsAI

### Hierarchical LAM (HLM)

Multi-level planning and execution:

```java
HierarchicalAgentOrchestrator orchestrator = HierarchicalAgentOrchestrator.builder()
    .strategicAgent(plannerAgent)      // Goal decomposition
    .tacticalAgent(coordinatorAgent)   // Sub-task planning
    .operationalAgents(workerAgents)   // Action execution
    .build();

HierarchicalResult result = orchestrator.execute("Build a web scraper");
```

### Large Reasoning Model (LRM)

Advanced reasoning capabilities:

```java
// Self-consistency reasoning
SelfConsistencyExecutor executor = SelfConsistencyExecutor.builder()
    .llm(client)
    .numPaths(5)
    .aggregation(Aggregation.MAJORITY_VOTE)
    .build();

// Tree of Thoughts exploration
TreeOfThoughtsExecutor tot = TreeOfThoughtsExecutor.builder()
    .llm(client)
    .pruning(PruningStrategy.BEAM_SEARCH)
    .maxDepth(5)
    .build();
```

## LAM Capabilities

### 1. Tool Use

```java
// 62 shipped POJO toolkits (~206 @Tool methods) for real-world interaction
@ActionSpec(type = ActionType.WEB_SERVICE)
public ActionResult fetchWeather(String city, ActionResult result) {
    return result;
}
```

### 2. Memory Persistence

```java
// Long-term memory for context retention
agent.getMemoryStore().store("user_preference", "concise");
```

### 3. Multi-Agent Coordination

```java
// Agent-to-Agent communication
A2AClient client = new A2AClient("https://other-agent.com/a2a");
TaskResponse response = client.sendTask("Research quantum computing");
```

### 4. Iterative Refinement

```java
// Ralph pattern for output quality
RefinementLoop loop = RefinementLoop.builder()
    .task("Convert Python to TypeScript")
    .completionCriteria(criteria)
    .maxIterations(10)
    .build();
```

## Why LAM Framework?

1. **Beyond Chat**: **Not** *just\*\*italic* conversation, but action execution
2. **Enterprise Ready**: Java-native, type-safe, production-ready
3. **Extensible**: 62 shipped POJO toolkits, custom POJOs, MCP integration
4. **Observable**: OpenTelemetry integration for monitoring
5. **Resilient**: Circuit breakers, retry policies, crash recovery

## Getting Started

```java
// Create a LAM agent
Agent agent = AgentBuilder.create()
    .llm(new GeminiClient("gemini-2.0-flash"))
    .role(new ResearcherRole())
    .belief(new Belief("accuracy", "Always verify sources"))
    .builtInTools(BuiltInTool.WEB_SEARCH_TOOLS)
    .build();

// Execute actions
String result = agent.chat("Research latest AI developments");
```

## References

- [8 LLM Architectures Explained](https://aiengineering.beehiiv.com/p/8-llm-architectures-clearly-explained) - LAM positioning
- [TnsAI GitHub](https://github.com/tnsai/tnsai) - Source code
- [BDI Architecture](https://en.wikipedia.org/wiki/Belief%E2%80%93desire%E2%80%93intention_software_model) - Theoretical foundation

---

# SPI Reference

URL: https://tnsai.dev/docs/reference/spi
Description: TnsAI.Core uses Java's ServiceLoader mechanism extensively for cross-module extensibility. SPI interfaces define contracts in the core module; implementations live in optional modules and are discovered at runtime via META-INF/services/ registration.

import { Callout } from 'fumadocs-ui/components/callout'

## How SPI Works in TnsAI

1. Core defines an interface (e.g., `CheckpointerProvider`)
2. An optional module implements it (e.g., `PostgresCheckpointerProvider`)
3. The implementation is registered in `META-INF/services/<interface-fqcn>`
4. At runtime, `ServiceLoader.load()` discovers all implementations
5. Core uses the implementation without compile-time dependency on the module

Many SPI interfaces also use the `Factory.discover()` pattern where a nested `Factory` interface has a static `discover()` method that returns null when no implementation is on the classpath.

## Core SPI Interfaces

### CheckpointerProvider

`com.tnsai.spi.CheckpointerProvider` -- pluggable storage backends for agent state checkpointing.

```java
public interface CheckpointerProvider {
    String name();
    default String description() { return name() + " checkpointer"; }
    boolean isAvailable();
    Checkpointer create(Map<String, Object> config);
    default int priority() { return 0; }
    default Optional<String> validateSpec(Map<String, Object> config) { return Optional.empty(); }
}
```

| Method                               | Description                                                           |
| ------------------------------------ | --------------------------------------------------------------------- |
| `name()`                             | Unique provider name (e.g., `"memory"`, `"postgres"`, `"sqlite"`)     |
| `isAvailable()`                      | Check if dependencies are present (e.g., JDBC driver)                 |
| `create(Map<String, Object> config)` | Create a `Checkpointer` with config (`url`, `path`, `username`, etc.) |
| `priority()`                         | Higher = preferred when multiple providers available                  |
| `validateSpec(config)`               | Validate config without creating -- returns error message or empty    |

**Registration:** `META-INF/services/com.tnsai.spi.CheckpointerProvider`

**Example implementation:**

```java
public class PostgresCheckpointerProvider implements CheckpointerProvider {
    @Override
    public String name() { return "postgres"; }

    @Override
    public boolean isAvailable() {
        try {
            Class.forName("org.postgresql.Driver");
            return true;
        } catch (ClassNotFoundException e) {
            return false;
        }
    }

    @Override
    public Checkpointer create(Map<String, Object> config) {
        String url = (String) config.get("url");
        return new PostgreSQLCheckpointer(url);
    }
}
```

### CheckpointerFactory

`com.tnsai.spi.CheckpointerFactory` is a singleton factory that discovers `CheckpointerProvider` implementations and provides a unified creation API.

```java
CheckpointerFactory factory = CheckpointerFactory.getInstance();

// List providers
List<String> available = factory.availableProviders();

// Create by name
Checkpointer cp = factory.create("postgres", Map.of(
    "url", "jdbc:postgresql://localhost/mydb",
    "username", "user",
    "password", "pass"
));

// Auto-select best available
Checkpointer cp = factory.createBest(Map.of("path", "./data"));

// Default in-memory
Checkpointer cp = factory.createInMemory();
```

Built-in providers: `"memory"` (priority -100, always available) and `"file"` (priority -50, JSON files).

### Tool registration is not an SPI

Since v0.6.0, tool discovery is no longer SPI-driven. There is no `ToolProvider` SPI, no global `ToolRegistry`, no auto-discovery. Tools are registered explicitly per agent via `AgentBuilder.builtInTools(BuiltInTool...)`, `AgentBuilder.toolPojos(Object...)`, or `AgentBuilder.dynamicTool(DynamicToolMethod)` — see [Tool Integration](/docs/capabilities/tools/registration).

The only "registry" left is each agent's per-instance `ToolMethodRegistry`, built at `AgentBuilder.build()` time from the explicitly-registered POJOs and dynamic tools. It is not a service, not a singleton, and not discoverable via `ServiceLoader`.

### CognitiveModel

`com.tnsai.spi.CognitiveModel` -- unified API for agent cognitive architectures (BDI, Reactive, Hybrid).

```java
public interface CognitiveModel {
    String getModelType();

    // Beliefs
    void addBelief(Belief belief);
    boolean removeBelief(String beliefContent);
    List<Belief> getBeliefs();
    List<Belief> queryBeliefs(String pattern);
    void clearBeliefs();

    // Goals
    void addGoal(Goal goal);
    boolean removeGoal(String goalId);
    List<Goal> getGoals();
    Optional<Goal> getTopGoal();

    // Intentions
    void addIntention(Intention intention);
    void completeIntention(String intentionId);
    List<Intention> getActiveIntentions();
    Optional<Intention> getCurrentIntention();

    // Reasoning cycle
    Optional<Action> reason(Map<String, Object> context);
    void reset();
    CognitiveState snapshot();
    void restore(CognitiveState state);

    // Factory methods
    static BDIModelBuilder bdi() { ... }
    static CognitiveModel reactive() { ... }
}
```

**Inner records:** `Belief(content, confidence, timestamp, metadata)`, `Goal(id, description, priority, status, parameters)`, `Intention(id, goalId, planDescription, status, steps, currentStep)`, `Action(type, description, parameters)`, `CognitiveState(beliefs, goals, intentions, metadata)`.

```java
CognitiveModel model = CognitiveModel.bdi()
    .withBelief("User prefers concise answers")
    .withGoal("Help the user effectively")
    .build();

model.addBelief(Belief.of("User is a developer", 0.9));
Optional<Action> next = model.reason(Map.of("input", "help me debug"));
```

### MessageBroker

`com.tnsai.spi.MessageBroker` -- abstraction for agent-to-agent message passing (direct, pub/sub, request-reply, broadcast).

```java
public interface MessageBroker {
    void send(String targetAgentId, Message message);
    void publish(String topic, Message message);
    String subscribe(String agentId, Consumer<Message> handler);
    String subscribeTopic(String topic, Consumer<Message> handler);
    void unsubscribe(String subscriptionId);
    CompletableFuture<Message> request(String targetAgentId, Message request);
    void broadcast(Message message);
    void close();
    static MessageBroker inMemory() { ... }
}
```

**`Message`** (record): `id`, `from`, `to`, `topic`, `payload (Object)`, `headers (Map)`, `timestamp`.

```java
MessageBroker broker = MessageBroker.inMemory();

broker.subscribe("agent-1", msg -> System.out.println("Got: " + msg.payload()));
broker.publish("tasks", Message.of("Process data"));

CompletableFuture<Message> reply = broker.request("agent-1", Message.of("status?"));
```

### ResilienceStrategy

`com.tnsai.spi.ResilienceStrategy` -- unified abstraction for resilience patterns (retry, circuit breaker, timeout, fallback).

```java
public interface ResilienceStrategy {
    <T> T execute(Callable<T> operation) throws Exception;
    default <T> T executeUnchecked(Supplier<T> operation) { ... }
    default void execute(Runnable operation) throws Exception { ... }
    default ResilienceStrategy andThen(ResilienceStrategy after) { ... }
    default String name() { ... }
    default boolean isHealthy() { return true; }
    default void reset() { }
    static Builder builder() { ... }
    static ResilienceStrategy noOp() { ... }
}
```

The builder composes retry, circuit breaker, timeout, and fallback:

```java
ResilienceStrategy strategy = ResilienceStrategy.builder()
    .retry(3, Duration.ofMillis(500))
    .circuitBreaker(5, Duration.ofSeconds(30))
    .timeout(Duration.ofSeconds(10))
    .fallback(() -> "default value")
    .build();

String result = strategy.execute(() -> riskyOperation());
```

Strategies can also be composed with `andThen`:

```java
ResilienceStrategy combined = retryStrategy.andThen(timeoutStrategy);
```

## Factory.discover() Pattern

Many SPI interfaces use an inner `Factory` interface with a static `discover()` method. Examples include:

- `EvalHandle.Factory.discover()` -- returns `null` if tnsai-quality is absent
- `FeedbackCollector.Factory.discover()` -- returns `null` if tnsai-intelligence is absent
- `ContextManagerHandle.Factory.discover()` -- returns `null` if tnsai-intelligence is absent
- `SecurityEnforcerHandle.Factory.discover()` -- returns `NOOP` if tnsai-security is absent

This pattern allows core code to initialize with no-op implementations when optional modules are not on the classpath:

```java
EvalHandle.Factory factory = EvalHandle.Factory.discover();
this.evalHandle = factory != null ? factory.create() : EvalHandle.NOOP;
```

## Related Documentation

- [Tools](/docs/capabilities/tools/registration) -- per-agent tool registration via `builtInTools` / `toolPojos` / `dynamicTool`
- [Action System](/docs/agents/fundamentals/action-system) -- ActionExecutor and typed executors
- [Resilience](/docs/agents/reliability/resilience) -- RetryPolicy and resilience configuration
- [Advanced Agent Features](/docs/agents/advanced) -- how SPI discoveries are used in Agent

---

# Tool Catalog Reference

URL: https://tnsai.dev/docs/reference/tool-catalog

import { Callout } from 'fumadocs-ui/components/callout'

> Quick lookup. Full documentation with per-toolkit method lists and usage examples: [capabilities/tools/catalog](/docs/capabilities/tools/catalog).

TnsAI ships **62 POJO toolkits** exposing roughly **206 `@Tool`-annotated methods** across 29 categories. Each toolkit is a plain class in `tnsai-tools` with public `@Tool` methods that the framework discovers reflectively at agent build time.

## Categories

`academic`, `ai`, `code`, `commerce`, `communication`, `crm`, `database`, `developer`, `diagram`, `document`, `file`, `finance`, `fintech`, `goose`, `knowledge`, `media`, `memory`, `productivity`, `project`, `realtime`, `scraping`, `search`, `social`, `system`, `trading`, `utility`, `visualization`.

## Finding a toolkit

The compile-safe path is the `BuiltInTool` enum — every entry maps a stable `toolName` to the FQCN of a backing POJO:

```java
import com.tnsai.enums.BuiltInTool;

// Register on an agent
Agent agent = AgentBuilder.create()
    .llm(llm)
    .role(role)
    .builtInTools(BuiltInTool.WEB_SEARCH_TOOLS, BuiltInTool.PDF_TOOLS)
    .build();

// Or instantiate directly
Object pdfToolkit = BuiltInTool.PDF_TOOLS.instantiate();

// Inspect catalog
for (var entry : BuiltInTool.values()) {
    System.out.println(entry.getToolName() + " — " + entry.getClassName());
}
```

## Inspecting registered tools at runtime

Each agent has a `ToolMethodDispatcher` whose `registry()` exposes every `@Tool` method registered with that agent:

```java
import com.tnsai.tools.method.ToolMethodRegistry;

ToolMethodRegistry registry = agent.getToolMethodDispatcher().registry();
for (var tool : registry.allMethods()) {
    System.out.println(tool.name() + " — " + tool.description());
}
```

For the complete list with per-toolkit method names, parameter shapes, and required environment variables, see [capabilities/tools/catalog](/docs/capabilities/tools/catalog).

---

# Sampling

URL: https://tnsai.dev/docs/sampling
Description: Production agents emit thousands of events per second — chat chunks, tool calls, memory writes, hook invocations. Sending all of them to a log aggregator buys a bigger Elasticsearch cluster every quarter; dropping everything to ERROR-only makes debugging impossible. The sampling SPI in tnsai-quality.sampling lets operators decide per-event whether to capture, drop, or down-sample, with policy choices that scale from \"dev — capture everything\" to \"production — errors always, INFO 1%\".

import { Callout } from 'fumadocs-ui/components/callout'

The SPI is `EventSamplingPolicy`. Default implementations (`AlwaysCapturePolicy`, `HeadBasedSamplingPolicy`, `PriorityBasedSamplingPolicy`, `ErrorAlwaysPolicy`) ship with the framework. Operators wire a policy into their event publisher via the `SamplingAgentEventPublisher` decorator.

## Quick start

```java
import com.tnsai.quality.sampling.*;
import com.tnsai.observability.events.AgentEventPublisher;

// 1. Pick a named policy shape (or build a custom one).
EventSamplingPolicy policy = SamplingPolicies.production();

// 2. Wrap your publisher.
AgentEventPublisher slf4j   = new Slf4jAgentEventPublisher();
AgentEventPublisher sampled = new SamplingAgentEventPublisher(slf4j, policy);

// 3. From this point on, every publish call goes through the policy.
agent.setEventPublisher(sampled);
```

## Decision shape

`SamplingDecision` is a sealed interface with three variants. Java 21 exhaustive `switch` makes the publisher's dispatch compile-checked:

| Decision                   | Meaning                                                                                                                               |
| -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
| `Capture`                  | Capture at full fidelity. No downstream rate adjustment needed.                                                                       |
| `Drop`                     | Discard the event. Never reaches the underlying publisher.                                                                            |
| `CaptureDownsampled(rate)` | Capture, but record the configured rate so downstream aggregators (Prometheus, Datadog) can extrapolate volume (`count += 1 / rate`). |

`Capture.INSTANCE` and `Drop.INSTANCE` are reusable singletons — most policies don't allocate a fresh decision per call.

## Default policies

### `AlwaysCapturePolicy`

The dev / single-tenant default — every event captured. Use the singleton: `AlwaysCapturePolicy.INSTANCE`.

```java
EventSamplingPolicy verbose = AlwaysCapturePolicy.INSTANCE;
```

### `HeadBasedSamplingPolicy`

Coin-flip per event at a fixed rate. Stateless, lock-free, the simplest production policy. Returns `CaptureDownsampled(rate)` when captured.

```java
EventSamplingPolicy p = new HeadBasedSamplingPolicy(0.10);   // 10%
```

Trade-off: no per-session affinity. A captured chat may have some chunks captured and others not. Use `TailBasedSamplingPolicy` (forthcoming) when you need every event from a captured session or none.

### `PriorityBasedSamplingPolicy`

Per-`EventLevel` rate. Default rates encode the production observability rule of thumb — never drop anything that's already a problem:

| Level      | Default rate |
| ---------- | ------------ |
| `CRITICAL` | 1.0 (always) |
| `ERROR`    | 1.0 (always) |
| `WARN`     | 1.0 (always) |
| `INFO`     | 0.1 (10%)    |
| `DEBUG`    | 0.01 (1%)    |

```java
// Defaults
EventSamplingPolicy p = PriorityBasedSamplingPolicy.withDefaults();

// Custom rates per level
Map<EventLevel, Double> overrides = Map.of(
        EventLevel.INFO, 0.5,
        EventLevel.DEBUG, 0.0           // hard-drop DEBUG
);
EventSamplingPolicy p = new PriorityBasedSamplingPolicy(overrides);
```

### `ErrorAlwaysPolicy`

Simpler than `PriorityBasedSamplingPolicy` — one switch on level, no per-level map:

- `WARN` / `ERROR` / `CRITICAL` → always `Capture`
- `INFO` / `DEBUG` → coin-flip at `infoDebugRate`

```java
EventSamplingPolicy p = new ErrorAlwaysPolicy(0.05);     // 5% INFO/DEBUG
EventSamplingPolicy d = ErrorAlwaysPolicy.withDefault(); // 10% INFO/DEBUG
```

Use when "is this a problem?" is the only distinction that matters for capture.

### `RuleBasedSamplingPolicy`

Ordered list of declarative rules, first match wins. Each rule combines an event-kind glob, a minimum level, an optional tenant filter, and an outcome (`CAPTURE` / `DROP` / `SAMPLE_AT_RATE`).

Glob shapes:

- `"*"` — matches any kind
- `"chat.*"` — matches one component after the prefix (`chat.chunk` ✓, `chat.tool.invoked` ✗)
- `"tool.invoked"` — exact match

```java
EventSamplingPolicy p = RuleBasedSamplingPolicy.builder()
        .capture("tool.invoked")                      // always capture
        .drop("internal.heartbeat")                   // hard-drop noise
        .sample("chat.chunk", 0.001)                  // 0.1% of chunks
        .capture("*", EventLevel.WARN, null)          // WARN+ everywhere
        .defaultDrop()                                // anything unmatched
        .build();
```

The programmatic builder is the canonical API; a YAML loader can produce the same `Rule` list at startup as a future enhancement.

### `AdaptiveSamplingPolicy`

Wraps a delegate policy and **degrades gracefully under measured backpressure**. The consumer supplies a `BackpressureSignal` — a `double` in `[0.0, 1.0]` representing "fraction of capacity in use" (queue depth, ack lag, Resilience4j RateLimiter saturation, …). The policy reads it and:

- below `lowWaterMark` (default 0.5) → delegate runs unchanged
- between low and high → linearly downsample the delegate's `Capture` decisions
- at or above `highWaterMark` (default 0.9) → drop everything below `criticalLevel` (default `ERROR`)

Critical-and-above events always survive, regardless of pressure.

```java
BackpressureSignal queueSignal = () -> queueDepth.get() / (double) MAX_DEPTH;

EventSamplingPolicy p = new AdaptiveSamplingPolicy(
        SamplingPolicies.production(),     // delegate
        queueSignal);                       // sane defaults: 0.5 / 0.9 / ERROR

// Or with custom thresholds:
EventSamplingPolicy p2 = new AdaptiveSamplingPolicy(
        delegate, queueSignal,
        0.4, 0.85, EventLevel.WARN);
```

Tracks `droppedDueToPressureCount()` for monitoring how often pressure forced a drop above what the delegate would have done.

## Named factories — `SamplingPolicies`

Three shapes for the most common operator intents:

| Factory                         | Returns                                      | When to use                            |
| ------------------------------- | -------------------------------------------- | -------------------------------------- |
| `SamplingPolicies.verbose()`    | `AlwaysCapturePolicy.INSTANCE`               | dev / staging / single tenant          |
| `SamplingPolicies.defaults()`   | `PriorityBasedSamplingPolicy.withDefaults()` | production with INFO/DEBUG distinction |
| `SamplingPolicies.production()` | `ErrorAlwaysPolicy.withDefault()`            | production with strict "errors-always" |

```java
EventSamplingPolicy p = SamplingPolicies.production();
```

## Per-tenant policies

`TenantPolicySamplingPolicy` dispatches each call to a per-tenant policy based on `EventContext.tenantId`. A multi-tenant agent group can apply different rates per tenant without the publisher knowing tenant boundaries.

Resolution order:

1. `EventContext.tenantId` from `SamplingInput.context`
2. Configured default policy — fallback for null / blank / unmatched

```java
EventSamplingPolicy dispatcher = TenantPolicySamplingPolicy.builder()
        .tenant("acme",   SamplingPolicies.verbose())            // power user — keep everything
        .tenant("globex", new ErrorAlwaysPolicy(0.001))          // chatty — aggressive sampling
        .defaultPolicy(SamplingPolicies.production())            // everyone else
        .build();
```

The dispatch happens on every call (no per-thread cache) so a single agent group serving multiple tenants doesn't leak rates across boundaries.

## Wiring into the publisher

`SamplingAgentEventPublisher` is a decorator over the `AgentEventPublisher` SPI in `tnsai-core`. Each publish call builds a `SamplingInput` from `event.eventType()` + `EventLevel` + `EventContext`, asks the policy, and publishes only when `SamplingDecision.shouldPublish()` is true.

```java
AgentEventPublisher base    = new Slf4jAgentEventPublisher();
AgentEventPublisher sampled = new SamplingAgentEventPublisher(base, policy);
```

Constructor variants:

| Constructor                           | Use when                                                        |
| ------------------------------------- | --------------------------------------------------------------- |
| `(delegate, policy)`                  | bare `publish(event)` calls fall back to `EventContext.empty()` |
| `(delegate, policy, contextSupplier)` | per-call tenant / agent context flows in via the supplier       |

Both `publish` overloads (level-aware and bare) route through the policy. Bare `publish(event)` defaults to `EventLevel.INFO`. Null event passes straight through to the delegate without consulting the policy.

## Composition with redaction

`SamplingAgentEventPublisher` and `RedactingAgentEventPublisher` are independent decorators — both implement the same SPI. Stack order chooses which runs first:

```java
// Order A: sample first, redact what survives.
//   Cheaper when drop rate is high — redactor only runs on captured events.
AgentEventPublisher pub = new RedactingAgentEventPublisher(
        new SamplingAgentEventPublisher(base, policy), redactor);

// Order B: redact every event, then sample.
//   Uniform redaction cost; slightly easier to debug ("what would have been emitted?").
AgentEventPublisher pub = new SamplingAgentEventPublisher(
        new RedactingAgentEventPublisher(base, redactor), policy);
```

Pick order A in production (where drop rate is meaningful) and order B when debugging redaction patterns.

## Rate limiting (last-resort backstop)

Sampling makes per-event capture/drop decisions; a **rate limiter** is a per-tenant volume cap that protects the aggregator when sampling alone isn't enough. The two compose: sampling reduces volume cheaply, the limiter bounds the survivors.

```java
import com.tnsai.quality.ratelimit.*;

// Per-tenant token bucket (Resilience4j-backed). Defaults are generous;
// configure tighter limits per noisy tenant.
EventRateLimiter limiter = Resilience4jEventRateLimiter.builder()
        .defaultLimit(1000, Duration.ofSeconds(1))           // 1000 events/sec
        .perTenant("noisy-tenant", 100, Duration.ofSeconds(1))
        .build();

AgentEventPublisher slf4j   = new Slf4jAgentEventPublisher();
AgentEventPublisher rate    = new RateLimitedAgentEventPublisher(slf4j, limiter);
AgentEventPublisher sampled = new SamplingAgentEventPublisher(rate, policy);
```

**Stack order matters.** `sampled → rate → sink` (above) lets the cheap sampling decision run first; the rate limiter only sees what survived. Reverse the stack (`rate → sampled → sink`) when the limiter's per-tenant cap matters more than sampling cost.

The limiter returns a `Decision`:

- `ACCEPT` → publish
- `DROP` → silently swallow (with a counter increment for monitoring)
- `DEFER` → opt-in deferred path; the default decorator drops, override `onDefer` to queue

`Resilience4jEventRateLimiter#flushDropCounters()` returns a per-tenant snapshot (and clears) so a periodic flusher can emit one `ratelimit.dropped` aggregate event per minute instead of suppressing the signal entirely.

## SPI summary

| Type                             | Role                                                                       |
| -------------------------------- | -------------------------------------------------------------------------- |
| `EventSamplingPolicy`            | The SPI — single method `sample(SamplingInput) → SamplingDecision`.        |
| `SamplingDecision`               | Sealed interface — `Capture`, `Drop`, `CaptureDownsampled(rate)`.          |
| `SamplingInput`                  | Per-call frozen view — `eventKind`, `level`, `context`.                    |
| `AlwaysCapturePolicy`            | Capture every event; the dev default.                                      |
| `HeadBasedSamplingPolicy`        | Coin-flip at fixed rate.                                                   |
| `PriorityBasedSamplingPolicy`    | Per-`EventLevel` rates with sensible defaults.                             |
| `ErrorAlwaysPolicy`              | WARN+ always; INFO/DEBUG at one rate.                                      |
| `RuleBasedSamplingPolicy`        | Declarative rule list (glob + level + tenant + outcome), first match wins. |
| `AdaptiveSamplingPolicy`         | Wraps a delegate; downsamples linearly under backpressure.                 |
| `SamplingPolicies`               | Named factories — `verbose`, `defaults`, `production`.                     |
| `TenantPolicySamplingPolicy`     | Per-tenant dispatcher.                                                     |
| `SamplingAgentEventPublisher`    | `AgentEventPublisher` decorator that consults the policy.                  |
| `EventRateLimiter`               | Per-tenant token-bucket SPI returning `ACCEPT` / `DROP` / `DEFER`.         |
| `Resilience4jEventRateLimiter`   | Default limiter — per-tenant configs, drop counters.                       |
| `RateLimitedAgentEventPublisher` | Decorator that consults the limiter before delegating.                     |

## Trade-offs

- **No per-session affinity** — `HeadBased` and `Priority` decide per event in isolation. A captured session may have some chunks dropped. `TailBasedSamplingPolicy` (forthcoming) buffers per session and decides at session end based on outcome (error / slow / expensive → capture; boring → drop).
- **`CaptureDownsampled` extrapolation cost** — downstream aggregators must know to scale counters by `1 / rate`. If your aggregator doesn't support per-event rates, use `Capture` (no rate) policies only.
- **Statistical variance** — at low rates over short windows, observed capture counts vary widely. Plan dashboards over windows large enough that the law of large numbers settles.
- **Cost vs. fidelity** — aggressive sampling reduces aggregator cost but loses signal for rare bugs. Production agents typically run `SamplingPolicies.production()` (10% INFO/DEBUG) until cost forces tighter rates.

## Roadmap

The sampling + rate-limiting SPIs are stable in `tnsai-quality.sampling` and `tnsai-quality.ratelimit`. Cost governance is documented in [Cost Governance](/docs/security/cost-governance). Pending work under [issue #82](https://github.com/TnsAI-Framework/TnsAI/issues/82):

- `TailBasedSamplingPolicy` — buffer per session, decide at session end based on outcome (matches OTel's tail-based trace sampling).
- YAML-driven rule loader for `RuleBasedSamplingPolicy` (programmatic builder is the canonical API; YAML is a producer).
- Grafana dashboard JSON + Prometheus Alertmanager rules for rate-limit drop counts and budget utilisation.

---

# Accountability

URL: https://tnsai.dev/docs/security/accountability
Description: Verifiable identity, audit-grade liability records, reputation, and agent-to-agent settlement. The framework's answer to: who is responsible for what an agent did, can it be re-verified later, and can it transact across trust boundaries?

import { Callout } from 'fumadocs-ui/components/callout'

The `com.tnsai.identity`, `com.tnsai.accountability`, and `com.tnsai.payment` packages in `tnsai-core` ship the primitives:

- **`AgentPrincipal` + `AgentPrincipalProvider`** — verifiable identity with version + runtime fingerprint + optional attestation
- **`AgentLiabilityRecord` + `LiabilitySink`** — append-only audit log of every dispatch
- **`AuthorityScope`** — pre-declared bounds (allowed actions, target systems, spend ceiling, validity window)
- **`ReputationLedger` + `ReputationScore`** — class-weighted aggregate score derived from liability history
- **`PaymentBroker` + `Quote` + `Settlement`** — agent-to-agent settlement with idempotency

Pairs with [Long-Running Runs](/docs/agents/reliability/long-running-runs) (every checkpoint can carry the principal that produced it) and [Cost Governance](/docs/security/cost-governance) (the `PaymentBroker` and `AuthorityScope.spendCeilingUSD` cooperate with `CostBudget` to bound spend).

## Why a separate layer

The framework already has rich observability — `LLMCallLog`, OpenTelemetry spans, hook events. That layer answers **what happened**. Accountability answers a different question: **who is responsible**, with attribution strong enough that an external auditor (insurance underwriter, compliance review, regulator) trusts the record.

Three forces are pushing this:

1. **Insurance / institutional governance** — AIUC-1 certified agent providers need agent identity, action audit, liability scoping, certification standard.
2. **Decentralized agent economy** — ERC-8004 (on-chain identity) and x402 (HTTP 402 micropayments) let agents prove identity and transact peer-to-peer without a central authority.
3. **Regulated deployments** — security-domain harnesses (XBOW-style benchmarks, code-review automation) produce findings with real-world consequences. Reproducibility and attribution become non-optional.

Without a first-class accountability SPI, every consumer rebuilds the same bookkeeping ad-hoc.

## Quick start

```java
import com.tnsai.identity.*;
import com.tnsai.accountability.*;
import com.tnsai.agents.AgentBuilder;

import java.nio.file.Path;
import java.util.List;

// 1. Mint a principal for the agent. LocalIdentityProvider is the
//    process-local default; external providers (ERC-8004, AIUC-1, X.509)
//    plug in via the same SPI.
AgentPrincipalProvider provider = new LocalIdentityProvider();
AgentPrincipal principal = provider.issue(
        AgentDescriptor.builder()
                .agentClass("com.example.ResearchAgent")
                .systemPrompt(systemPromptText)
                .toolNames(List.of("web.search", "github.create_pr"))
                .model("openai:gpt-5")
                .buildHash(buildHashFromManifest())
                .build());

// 2. Pick an audit sink. JSON-Lines on disk is a good default; swap in
//    a custom adapter for AIUC-1 / S3-with-object-lock when needed.
LiabilitySink sink = new FilesystemLiabilitySink(Path.of("/var/audit/agent.jsonl"));

// 3. Optionally narrow the scope. Auditors compare attempted actions
//    against this declared bound to flag scope violations.
AuthorityScope scope = new AuthorityScope(
        java.util.Set.of(com.tnsai.enums.ActionType.LLM,
                          com.tnsai.enums.ActionType.MCP_TOOL),
        java.util.Set.of("github.com/org/repo", "openai.com/v1"),
        java.util.Optional.of(new java.math.BigDecimal("50.00")),
        java.time.Duration.ofHours(1),
        java.time.Instant.now());

// 4. Wire on the AgentBuilder.
Agent agent = AgentBuilder.create()
        .id("research-agent-1")
        .llm(...)
        .role(myRole)
        .principal(principal)
        .liabilitySink(sink)
        .authorityScope(scope)
        .build();
```

From this point on, every `ActionExecutor.execute(...)` dispatch produces one `AgentLiabilityRecord` published to the configured sink — successful actions carry a result hash, failing actions carry an `ERROR:`-prefixed outcome.

## Identity

`AgentPrincipal` is the audit primitive. Four fields:

| Field                  | Meaning                                                                                                                                                                      |
| ---------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `id()`                 | Stable identifier across every dispatch from the same logical agent instance. UUID for `LocalIdentityProvider`; DID / on-chain address for external providers.               |
| `version()`            | Stamp that changes when the agent's **behaviour** changes (system prompt, tool list, model swap). Audit findings against a prior version don't apply once the version flips. |
| `attestation()`        | Optional cryptographic / external-system proof binding the identity to an outside trust root (ERC-8004, X.509, AIUC-1, JWT). Empty for `LocalIdentityProvider`.              |
| `runtimeFingerprint()` | Hash of build artefact + config. Two physical processes running the exact same binary with the exact same config produce the same fingerprint.                               |

`AgentPrincipalProvider` is the SPI for issuing and verifying. `LocalIdentityProvider` is the framework default — UUID-based, HMAC-SHA-256 signing, no external trust root. Suitable for single-tenant deployments where the framework is the trust boundary.

```java
// Mint and sign an identity.
LocalIdentityProvider p = new LocalIdentityProvider();
AgentPrincipal me = p.issue(spec);
byte[] sig = p.sign(me);

// Re-verify later (round-trips through the same instance only;
// cross-process verification needs an external attestation backend).
Optional<AgentPrincipal> verified = p.verify(me.id(), sig);
```

> **`AgentPrincipal` vs `AgentIdentity`.** The pre-existing `com.tnsai.models.agent.AgentIdentity` is a personality trait ("analytical", "friendly") used to shape system prompts. `AgentPrincipal` is a verifiable security primitive. They coexist — different concerns.

## Liability records

Every dispatch produces an `AgentLiabilityRecord`:

```java
public record AgentLiabilityRecord(
        String agentId,                   // from AgentPrincipal.id()
        String agentVersion,              // from AgentPrincipal.version()
        Instant invokedAt,
        Action action,                    // typed audit-grade descriptor
        AuthorityScope scope,             // bounds in force at invocation
        LiabilityClass liability,         // NONE / LOW / MEDIUM / HIGH / REGULATORY
        String correlationId,             // links to broader OTel trace / LLMCallLog
        Optional<String> outcome,         // result hash or ERROR: message
        Optional<byte[]> signature        // when the provider is signing records
) {}
```

The record is **append-only** by contract — implementations should enforce write-once semantics. `RecordingLiabilitySink` (in-memory, for tests) and `FilesystemLiabilitySink` (JSON-Lines on disk) ship as defaults; external adapters (`AIUC1LiabilitySink`, `S3LiabilitySink` with object-lock) follow the same SPI.

### Liability tiers

| Tier         | Use case                                                                                                                 |
| ------------ | ------------------------------------------------------------------------------------------------------------------------ |
| `NONE`       | Internal plumbing — log emit, counter increment. Auditors typically skip.                                                |
| `LOW`        | Cheap-to-reverse side effect — read-only external lookup, cache write.                                                   |
| `MEDIUM`     | Mutates state inside the framework or a system the framework controls — DB write, internal queue put.                    |
| `HIGH`       | External-system mutation with material consequence — public post, GitHub PR, build trigger.                              |
| `REGULATORY` | Regulated reach — payment, regulated data egress, anything covered by a compliance regime. Auditors review every record. |

The framework's default classification by dispatch type:

| `ActionType`                       | Default tier |
| ---------------------------------- | ------------ |
| `LOCAL`                            | `LOW`        |
| `LLM` / `MCP_TOOL` / `WEB_SERVICE` | `MEDIUM`     |

Operators with stricter classification needs override at the sink boundary (write a wrapping sink that reclassifies before persisting) or by attaching tier metadata via a custom action descriptor.

## Authority scope

`AuthorityScope` declares what the agent is allowed to do at dispatch time:

```java
AuthorityScope scope = new AuthorityScope(
        Set.of(ActionType.LLM, ActionType.MCP_TOOL),     // allowed action types
        Set.of("github.com/org/repo", "openai.com/v1"),  // allowed targets
        Optional.of(new BigDecimal("50.00")),            // spend ceiling USD
        Duration.ofHours(1),                              // validity window
        Instant.now());

scope.permits(ActionType.LLM);            // true
scope.permits(ActionType.LOCAL);          // false (not in whitelist)
scope.permitsTarget("evil.com");          // false
scope.isExpired(Instant.now().plus(Duration.ofHours(2)));  // true
```

Empty whitelist = permissive (caller chose not to narrow). Populated whitelist = explicit membership check. Auditors compare each record's `action` against `scope` to flag violations; the default `InMemoryReputationLedger` counts scope violations as misses.

`AuthorityScope.unrestricted(validFor)` is the maximally-permissive default — auditable but unconstraining.

## Reputation

`ReputationLedger` derives a normalised `[0.0, 1.0]` score from an agent's liability history:

```java
LiabilitySink sink = new FilesystemLiabilitySink(Path.of("audit.jsonl"));
ReputationLedger ledger = new InMemoryReputationLedger(sink);

ReputationScore score = ledger.scoreOf("research-agent-1");
// score.score() = 1.0 - (weighted_misses / weighted_total)
// score.recordCount() = how many records the score was computed from
```

The default `InMemoryReputationLedger` weighting:

- **Pass** — outcome is non-error AND action is permitted by its declared scope (action type + target).
- **Miss** — anything else (error outcome, scope-violating action, target violation).
- **Class weights** — `NONE`/`LOW` = 1, `MEDIUM` = 3, `HIGH` = 9, `REGULATORY` = 27. One regulatory miss outweighs many low-tier misses.

Unknown agents (no records) get `1.0` — innocent until proven. Operators with stricter onboarding override in a custom ledger.

```java
// Walk recent history for a stale-score check.
List<AgentLiabilityRecord> last24h = ledger.historyOf(
        "research-agent-1", Duration.ofHours(24));
```

## Payment

`PaymentBroker` is the SPI for agent-to-agent settlement. Two-phase contract: `quote` prices the work, `settle` moves funds.

```java
PaymentBroker broker = new NoOpPaymentBroker();   // 0.00 USD, instant settlement

Service work = new Service("github.create_pr", "request",
        BigDecimal.ONE, Optional.empty(), Map.of());

Quote q = broker.quote(payerPrincipal, payeePrincipal, work);
Settlement result = broker.settle(q);

switch (result) {
    case Settlement.Settled s -> /* funds moved; s.transactionId() */;
    case Settlement.AlreadySettled a -> /* idempotency replay */;
    case Settlement.Rejected r -> /* broker policy refused */;
    case Settlement.Expired e -> /* quote past expiresAt */;
}
```

Idempotency is built in: `Quote.idempotencyKey` is opaque to the framework, broker-derived. A second `settle` call with the same key returns `Settlement.AlreadySettled` carrying the original `transactionId` — retrying a flaky network call doesn't double-charge.

`NoOpPaymentBroker` is the default for internal multi-agent (no real money flow). External brokers — `X402PaymentBroker` (HTTP 402 micropayment), `StripePaymentBroker`, ledger-backed adapters — plug into the same SPI.

## What's not in this layer

Out of scope for the framework primitives, tracked separately:

- **ERC-8004 / x402 / AIUC-1 actual adapter implementations** — open per-adapter follow-up issues; the SPI is stable today
- **UI for liability record viewing / reputation dashboard** — `LiabilitySink.query` is the data plane; UI is downstream
- **KMS integration for cryptographic key management** — `LocalIdentityProvider` uses an in-process HMAC key; KMS-backed providers ship as separate adapters
- **Capability-level liability declaration** (`@Capability(liability = HIGH)`) — annotation surface enhancement; tracked separately

## See Also

- **[Long-Running Runs](/docs/agents/reliability/long-running-runs)** — checkpoint records can carry `AgentPrincipal` for audit continuity across resumes
- **[Cost Governance](/docs/security/cost-governance)** — `AuthorityScope.spendCeilingUSD` is the per-scope counterpart to `CostBudget`'s tenant-wide spend ledger
- **[Approvals and Annotations](/docs/security/approvals-and-annotations)** — `@ApprovalRequired` works alongside accountability; approvals gate access, liability records track the gated action
- **[Redaction](/docs/security/redaction)** — `Action.parameters` snapshots must be redaction-safe; the redaction layer is the source of truth for what can leave the trust boundary

---

# Security & Approvals

URL: https://tnsai.dev/docs/security/approvals-and-annotations
Description: TnsAI provides a layered security model for agent actions: approval tokens for human-in-the-loop gating, AG-UI interrupts for blocking agent execution until a user responds, input/output guardrails for validation and sanitization, and a declarative @Security annotation that combines audit, access control, and encryption policies in one place.

import { Callout } from 'fumadocs-ui/components/callout'

## @ApprovalRequired and ApprovalToken

Some actions are too dangerous to run automatically -- deleting data, sending emails, or making payments. The `@ApprovalRequired` annotation marks these actions so the framework blocks execution until a human explicitly approves it through an `ApprovalToken`. This is the foundation of human-in-the-loop safety in TnsAI.

### Annotation

Place `@ApprovalRequired` on any action method that should not run without human consent. The `reason` field is shown to the approver so they understand why approval is needed.

```java
@Retention(RetentionPolicy.RUNTIME)
@Target(ElementType.METHOD)
public @interface ApprovalRequired {
    String reason() default "Sensitive operation";
}
```

Usage:

```java
@ActionSpec(type = ActionType.LOCAL, description = "Delete user account permanently")
@ApprovalRequired(reason = "Permanent data deletion")
public void deleteUser(String userId) {
    // Only runs if an approved token is presented
}
```

### ApprovalToken

An `ApprovalToken` is a time-limited, agent-and-action-scoped credential that tracks the approval state. It is created when a protected action is invoked, sent to a human for review, and then approved or rejected. Tokens are bound to a specific agent and action, time-limited, and optionally single-use. The lifecycle is:

1. Action annotated with `@ApprovalRequired` is invoked.
2. Token created with `agentId`, `actionName`, and expiry.
3. Token sent to an approval system (UI, CLI, API).
4. Human approves or rejects.
5. If approved, the action executes.
6. If `singleUse`, the token transitions to `CONSUMED`.

#### Status Enum

A token moves through these states during its lifecycle. Once it leaves `PENDING`, it cannot go back.

| Status     | Description                   |
| ---------- | ----------------------------- |
| `PENDING`  | Awaiting human decision       |
| `APPROVED` | Approved by human             |
| `REJECTED` | Rejected by human             |
| `EXPIRED`  | TTL elapsed before decision   |
| `CONSUMED` | Single-use token already used |

#### Builder

Create tokens with the builder for full control over expiration, single-use behavior, and metadata. The `agentId` and `actionName` fields scope the token so it cannot be reused for a different action.

```java
ApprovalToken token = ApprovalToken.builder()
    .agentId("agent-1")
    .actionName("delete-user")
    .parameters(Map.of("userId", "123"))
    .timeToLive(Duration.ofMinutes(5))   // default: 5 minutes
    .singleUse(true)                     // default: true
    .requestedBy("system")
    .reason("User requested account deletion")
    .tokenId("custom-id")               // optional, auto-generated UUID if omitted
    .build();
```

Factory shorthand:

```java
ApprovalToken token = ApprovalToken.create("agent-1", "delete-user", Duration.ofMinutes(5));
```

#### State Mutations

These methods transition the token between states. Each returns `true` if the transition was valid, `false` if it was not (for example, trying to approve an already-expired token).

```java
// Approve -- only valid (not expired/consumed/rejected) tokens can be approved
boolean ok = token.approve("admin@example.com");

// Reject -- only PENDING, non-expired tokens can be rejected
boolean ok = token.reject("admin@example.com", "Too risky");

// Consume -- marks single-use tokens as CONSUMED after execution
boolean ok = token.consume();
```

#### Query Methods

Use these to check the current state of a token. The `isValid()` method is a convenient shorthand that checks the token is not expired, consumed, or rejected.

```java
token.isApproved();        // status == APPROVED
token.isPending();         // status == PENDING (auto-checks expiry)
token.isRejected();        // status == REJECTED
token.isExpired();         // Instant.now().isAfter(expiresAt)
token.isConsumed();        // status == CONSUMED
token.isValid();           // !expired && !consumed && !rejected
token.getRemainingTime();  // Duration until expiry (Duration.ZERO if past)
token.matches("agent-1", "delete-user"); // checks agentId + actionName
```

### ApprovalTokenStore

In a real application, tokens need to be stored so they survive between requests and can be looked up when the human responds. `ApprovalTokenStore` is the persistence interface, and three implementations ship with TnsAI for different deployment scenarios.

| Implementation               | Use case                      |
| ---------------------------- | ----------------------------- |
| `InMemoryApprovalTokenStore` | Development and testing       |
| `RedisApprovalTokenStore`    | Distributed systems           |
| `DatabaseApprovalTokenStore` | SQL-backed persistent storage |

#### Store API

The store manages the full token lifecycle: creating, saving, looking up, approving, rejecting, validating, and cleaning up tokens.

```java
ApprovalTokenStore store = new InMemoryApprovalTokenStore();

// Create tokens
ApprovalToken token = store.createToken("agent-1", "delete-data", Duration.ofMinutes(5));
ApprovalToken token = store.createToken("agent-1", "delete-data",
    Map.of("userId", "123"), Duration.ofMinutes(5));

// Or save a manually built token
store.save(token);

// Lookup
Optional<ApprovalToken> found = store.getToken(tokenId);

// Approve / Reject via store
store.approve(tokenId, "admin@example.com");
store.reject(tokenId, "admin@example.com", "Not authorized");

// Validate and consume atomically -- checks approved + not expired + agent/action match
boolean valid = store.validateAndConsume(tokenId, "agent-1", "delete-data");

// List pending tokens
List<ApprovalToken> pending = store.listPending();
List<ApprovalToken> agentPending = store.listPendingForAgent("agent-1");

// Cleanup
int cleaned = store.cleanupExpired();
boolean deleted = store.delete(tokenId);
int total = store.count();
```

#### Full Workflow Example

This example shows the complete approval flow from token creation to action execution, as it would work in a web application with an approval UI.

```java
// 1. Create and store token
ApprovalTokenStore store = new InMemoryApprovalTokenStore();
ApprovalToken token = store.createToken("agent-1", "delete-user", Duration.ofMinutes(5));

// 2. Send token ID to approval UI (REST endpoint, WebSocket, etc.)
sendToApprovalUI(token.getTokenId(), token.getActionName(), token.getReason());

// 3. Human approves via UI -> callback hits server
store.approve(token.getTokenId(), "admin@example.com");

// 4. Action executor validates before running
boolean canExecute = store.validateAndConsume(
    token.getTokenId(), "agent-1", "delete-user"
);

if (canExecute) {
    executeAction();
}
```

## InterruptService (AG-UI Pattern)

When your agent runs in a UI-connected environment, you may want to pause execution mid-task and ask the user a question before continuing. `InterruptService` implements this pattern: it blocks the agent thread, emits an event to the frontend, waits for the user's response, and then resumes the agent with the result.

### Flow

Here is the step-by-step sequence of how an interrupt works, from the agent requesting it to the user responding and the agent resuming.

1. Agent calls `requestInterrupt()` before a critical action.
2. Service creates an interrupt; the agent thread **blocks**.
3. AG-UI emits `RUN_FINISHED { outcome: "interrupt", interrupt: {...} }`.
4. Frontend shows an approval dialog.
5. User approves or rejects.
6. Frontend POSTs to `/v1/runs/{runId}/resume`.
7. Service completes the future; the agent thread **unblocks**.
8. Agent continues or aborts based on the result.

### Constructors

Create an `InterruptService` with an optional listener (which emits events to the frontend) and an optional default timeout for how long to wait for a human response.

```java
// Default: no listener, 5-minute timeout
InterruptService service = new InterruptService();

// With listener for AG-UI event emission
InterruptService service = new InterruptService(request -> {
    agUiAdapter.emitInterrupt(request);
});

// With listener and custom default timeout
InterruptService service = new InterruptService(listener, Duration.ofMinutes(10));
```

The listener receives an `InterruptRequest` record:

```java
public record InterruptRequest(
    String interruptId,
    InterruptReason reason,
    Map<String, Object> payload,
    Duration timeout,
    Instant createdAt
) {}
```

### InterruptReason Enum

The reason tells the frontend what kind of interrupt this is, so it can show the appropriate UI (an approval dialog, a file upload prompt, an error recovery form, etc.).

| Value              | AG-UI wire value     | Use case                                    |
| ------------------ | -------------------- | ------------------------------------------- |
| `HUMAN_APPROVAL`   | `"human_approval"`   | Sensitive actions (delete, send, pay)       |
| `UPLOAD_REQUIRED`  | `"upload_required"`  | Missing files or parameters                 |
| `POLICY_VIOLATION` | `"policy_violation"` | Rate limit, budget, access control override |
| `ERROR_RECOVERY`   | `"error_recovery"`   | API errors needing human guidance           |
| `CONFIRMATION`     | `"confirmation"`     | Multi-step wizard confirmation              |
| `CUSTOM`           | `"custom"`           | Anything else                               |

Parsing: `InterruptReason.fromValue("human_approval")` returns `HUMAN_APPROVAL`. Unknown strings return `CUSTOM`.

### Requesting Interrupts

There are two ways to request an interrupt: blocking (the agent thread waits) and non-blocking (you get a `CompletableFuture`).

**Blocking (synchronous)** -- blocks the calling thread until resolved or timed out:

```java
InterruptResult result = service.requestInterrupt(
    InterruptReason.HUMAN_APPROVAL,
    Map.of("action", "delete_user", "userId", "123")
);
// Thread blocks here until user responds or timeout

if (result.shouldProceed()) {
    deleteUser("123");
} else {
    throw new ActionRejectedException(result.getRejectionReason());
}
```

With custom timeout:

```java
InterruptResult result = service.requestInterrupt(
    InterruptReason.HUMAN_APPROVAL,
    Map.of("action", "delete_user", "userId", "123"),
    Duration.ofMinutes(10)
);
```

**Non-blocking (async)** -- returns a `CompletableFuture`:

```java
CompletableFuture<InterruptResult> future = service.requestInterruptAsync(
    InterruptReason.CONFIRMATION,
    Map.of("summary", orderSummary),
    Duration.ofMinutes(5)
);

future.thenAccept(result -> {
    if (result.isApproved()) {
        submitOrder();
    }
});
```

### Resuming and Cancelling

These methods are called from your backend endpoint when the user responds in the frontend. The `resume` call unblocks the waiting agent thread with the user's decision.

```java
// Approve
service.resume(interruptId, true, Map.of("approver", "admin"));

// Reject
service.resume(interruptId, false, Map.of("reason", "Too risky"));

// Cancel programmatically
service.cancel(interruptId);
```

### Query Methods

Check whether an interrupt is still waiting for a response, retrieve its details, or count all active interrupts.

```java
service.isPending(interruptId);                 // true if active and not expired
Optional<InterruptRequest> req =
    service.getPendingInterrupt(interruptId);    // details or empty
int count = service.getPendingCount();           // auto-cleans expired
```

### InterruptResult

When an interrupt resolves, the agent receives an `InterruptResult` that tells it the outcome: approved, rejected, timed out, cancelled, or error. Use `shouldProceed()` as the primary check for whether to continue with the action.

#### Outcome Enum

These are the possible outcomes of an interrupt. Only `APPROVED` results in `shouldProceed()` returning `true`.

| Outcome     | Description                |
| ----------- | -------------------------- |
| `APPROVED`  | User approved the action   |
| `REJECTED`  | User rejected the action   |
| `TIMED_OUT` | No response within timeout |
| `CANCELLED` | Cancelled programmatically |
| `ERROR`     | An exception occurred      |

#### Factory Methods

Create `InterruptResult` instances for each outcome. These are typically called by the `InterruptService` internally, but you can use them in tests.

```java
InterruptResult.approved(interruptId, Map.of("approver", "admin"));
InterruptResult.rejected(interruptId, Map.of("reason", "Not safe"));
InterruptResult.timedOut(interruptId);
InterruptResult.cancelled(interruptId);
InterruptResult.error(interruptId, "Connection failed");
```

#### Query Methods

These methods let you inspect the result and extract relevant details like the approver's identity or rejection reason.

```java
result.isApproved();         // outcome == APPROVED
result.isRejected();         // outcome == REJECTED
result.isTimedOut();         // outcome == TIMED_OUT
result.isCancelled();        // outcome == CANCELLED
result.isError();            // outcome == ERROR
result.shouldProceed();      // true only if APPROVED

result.getInterruptId();     // the interrupt ID
result.getOutcome();         // Outcome enum value
result.getPayload();         // Map<String, Object>, never null
result.getErrorMessage();    // error message (null unless ERROR)
result.getRejectionReason(); // payload.get("reason") as String, null if not rejected
result.getApprover();        // payload.get("approver") as String
```

## @InputGuardrail

Before an action runs, you want to make sure the input is safe and well-formed. `@InputGuardrail` lets you declare validation rules (length limits, required patterns, injection detection) and sanitization steps (trimming whitespace, escaping HTML) that are applied automatically before the action method is called.

### Fields

These fields control what validation and sanitization is applied to the input. Most fields are optional -- only configure what you need.

| Field                | Type                | Default  | Description                                                           |
| -------------------- | ------------------- | -------- | --------------------------------------------------------------------- |
| `maxLength`          | `int`               | `0`      | Maximum input length in characters. 0 = no limit.                     |
| `minLength`          | `int`               | `0`      | Minimum input length in characters.                                   |
| `validators`         | `Class<?>[]`        | `{}`     | Validator classes (must implement `InputValidator`).                  |
| `sanitizers`         | `Class<?>[]`        | `{}`     | Sanitizer classes applied in order (must implement `InputSanitizer`). |
| `blockPatterns`      | `String[]`          | `{}`     | Regex patterns to reject (prompt injection protection).               |
| `allowPatterns`      | `String[]`          | `{}`     | Regex patterns input must match. Non-matching input is rejected.      |
| `jsonSchema`         | `String`            | `""`     | JSON schema path or inline schema for JSON input validation.          |
| `detectInjection`    | `boolean`           | `true`   | Enable prompt injection detection.                                    |
| `injectionThreshold` | `double`            | `0.7`    | Injection detection sensitivity (0.0--1.0). Higher = stricter.        |
| `moderateContent`    | `boolean`           | `false`  | Enable content moderation.                                            |
| `blockCategories`    | `ContentCategory[]` | `{}`     | Content categories to block.                                          |
| `onFailure`          | `FailureAction`     | `REJECT` | What to do when validation fails.                                     |
| `errorMessage`       | `String`            | `""`     | Custom error message on failure.                                      |
| `logFailures`        | `boolean`           | `true`   | Log validation failures.                                              |

### ContentCategory Enum

When content moderation is enabled, you can block specific categories of harmful content. These match standard content moderation taxonomies.

`HATE`, `VIOLENCE`, `SEXUAL`, `SELF_HARM`, `HARASSMENT`, `DANGEROUS`, `ILLEGAL`

### FailureAction Enum

When validation fails, this determines what happens next. Choose based on how strict your use case requires.

| Value      | Behavior                         |
| ---------- | -------------------------------- |
| `REJECT`   | Reject input and throw exception |
| `SANITIZE` | Sanitize the input and continue  |
| `WARN`     | Log a warning and continue       |
| `REVIEW`   | Request human review             |

### Built-in Validators

TnsAI ships with these validators out of the box. You can also implement your own by implementing the `InputValidator` interface.

`NotEmpty`, `MaxLength`, `NoInjection`, `SafeContent`, `JsonSchema`

### Built-in Sanitizers

These sanitizers transform the input to remove or escape potentially harmful content. They are applied in the order you list them.

`TrimWhitespace`, `HtmlEscape`, `NormalizeUnicode`, `RemoveControlChars`

### Example

```java
@ActionSpec(type = ActionType.LOCAL, description = "Process user message")
@InputGuardrail(
    maxLength = 10000,
    validators = {NotEmpty.class, NoInjection.class},
    sanitizers = {TrimWhitespace.class, HtmlEscape.class},
    blockPatterns = {"ignore previous", "system prompt"},
    injectionThreshold = 0.8,
    onFailure = FailureAction.REJECT
)
public String processMessage(String message) {
    // message is validated and sanitized before this runs
}
```

Role-level default:

```java
@InputGuardrail(
    maxLength = 5000,
    validators = {NotEmpty.class},
    detectInjection = true
)
public class UserInputRole extends Role {
    // All actions in this role inherit these guardrails
}
```

## @OutputGuardrail

Just as you validate input before processing, you should validate output before returning it to the user. `@OutputGuardrail` lets you enforce format requirements, mask personally identifiable information (PII), check for hallucinations, and moderate content -- all declaratively through an annotation.

### Fields

These fields control output validation, PII masking, hallucination detection, and what to do when validation fails.

| Field                    | Type                | Default | Description                                                             |
| ------------------------ | ------------------- | ------- | ----------------------------------------------------------------------- |
| `format`                 | `OutputFormat`      | `TEXT`  | Expected output format.                                                 |
| `schema`                 | `String`            | `""`    | JSON schema path or inline schema.                                      |
| `maxTokens`              | `int`               | `0`     | Maximum output length in tokens. 0 = no limit.                          |
| `maxChars`               | `int`               | `0`     | Maximum output length in characters.                                    |
| `minChars`               | `int`               | `0`     | Minimum output length in characters.                                    |
| `validators`             | `Class<?>[]`        | `{}`    | Validator classes (must implement `OutputValidator`).                   |
| `postProcessors`         | `Class<?>[]`        | `{}`    | Post-processor classes (must implement `OutputParser`).                 |
| `maskPII`                | `boolean`           | `false` | Enable PII detection and masking.                                       |
| `piiTypes`               | `PIIType[]`         | `{}`    | PII types to mask.                                                      |
| `moderateContent`        | `boolean`           | `false` | Enable content moderation on output.                                    |
| `blockCategories`        | `ContentCategory[]` | `{}`    | Content categories to filter (reuses `InputGuardrail.ContentCategory`). |
| `checkHallucination`     | `boolean`           | `false` | Check for hallucinations (requires context).                            |
| `hallucinationThreshold` | `double`            | `0.8`   | Hallucination confidence threshold (0.0--1.0).                          |
| `onFailure`              | `FailureAction`     | `RETRY` | What to do when validation fails.                                       |
| `maxRetries`             | `int`               | `3`     | Maximum retry attempts on failure.                                      |
| `fallback`               | `String`            | `""`    | Fallback value if all retries fail.                                     |
| `logFailures`            | `boolean`           | `true`  | Log validation failures.                                                |
| `includeSpec`            | `boolean`           | `false` | Include validation metadata in response.                                |

### OutputFormat Enum

The expected format of the action's output. When set, the framework validates that the output conforms to this format.

`TEXT`, `JSON`, `MARKDOWN`, `HTML`, `XML`, `YAML`, `CSV`

### PIIType Enum

When PII masking is enabled, specify which types of personal information to detect and redact from the output.

`EMAIL`, `PHONE`, `SSN`, `CREDIT_CARD`, `ADDRESS`, `NAME`, `DOB`, `IP_ADDRESS`, `PASSPORT`, `DRIVER_LICENSE`

### FailureAction Enum

When output validation fails, this determines the recovery behavior. `RETRY` is the default because LLMs often produce valid output on the second attempt.

| Value      | Behavior                        |
| ---------- | ------------------------------- |
| `RETRY`    | Retry generation                |
| `FALLBACK` | Return fallback value           |
| `REJECT`   | Throw exception                 |
| `TRUNCATE` | Return partial/truncated output |
| `REVIEW`   | Request human review            |

### Built-in Validators

TnsAI ships with these output validators. You can also implement the `OutputValidator` interface for custom checks.

`NotEmpty`, `JsonValid`, `SchemaCompliant`, `NoHallucination`, `NoPII`, `SafeContent`

### Example

```java
@ActionSpec(type = ActionType.LLM, description = "Generate response")
@OutputGuardrail(
    format = OutputFormat.JSON,
    schema = "response-schema.json",
    maxTokens = 1000,
    validators = {NoHallucination.class, NoPII.class},
    maskPII = true,
    piiTypes = {PIIType.EMAIL, PIIType.PHONE, PIIType.SSN},
    onFailure = FailureAction.RETRY,
    maxRetries = 3,
    fallback = "{\"error\": \"Unable to generate response\"}"
)
public String generateResponse(String query) {
    // Response is validated before returning to the caller
}
```

Role-level default:

```java
@OutputGuardrail(
    format = OutputFormat.MARKDOWN,
    maxTokens = 2000,
    checkHallucination = true
)
public class ContentRole extends Role {
    // All actions inherit these output guardrails
}
```

## @Security

For actions that need multiple security controls at once, `@Security` bundles everything into a single annotation: approval gating, audit logging, access control (who can call this action), encryption of parameters and results, and idempotency protection. This avoids the need to stack multiple separate annotations on the same method.

### Fields

Each field controls one aspect of the security policy. All fields are optional with safe defaults -- only enable what your action needs.

| Field                 | Type         | Default | Description                                                                              |
| --------------------- | ------------ | ------- | ---------------------------------------------------------------------------------------- |
| `approvalRequired`    | `boolean`    | `false` | Require human approval before execution.                                                 |
| `approvalReason`      | `String`     | `""`    | Reason shown to the approver.                                                            |
| `audit`               | `AuditLevel` | `NONE`  | Audit logging level.                                                                     |
| `sensitive`           | `boolean`    | `false` | Marks action as handling sensitive data (masked in logs/traces).                         |
| `maskFields`          | `String[]`   | `{}`    | Field names to mask in logs for sensitive data.                                          |
| `allowedCallers`      | `String[]`   | `{}`    | Agent IDs allowed to call this action. Empty = all allowed.                              |
| `allowedRoles`        | `String[]`   | `{}`    | Role names allowed to call this action. Empty = all allowed.                             |
| `requiredPermissions` | `String[]`   | `{}`    | Required permission names.                                                               |
| `encryptParams`       | `boolean`    | `false` | Encrypt action parameters in transit.                                                    |
| `encryptResult`       | `boolean`    | `false` | Encrypt action result in transit.                                                        |
| `securityTimeout`     | `int`        | `0`     | Max execution time (ms) before automatic abort. 0 = no timeout.                          |
| `idempotent`          | `boolean`    | `false` | Whether the action can be safely replayed. Non-idempotent actions get replay protection. |

### AuditLevel Enum

Choose how much detail to record in audit logs. Higher levels capture more information but generate more log volume.

| Level      | What is logged                          |
| ---------- | --------------------------------------- |
| `NONE`     | Nothing                                 |
| `BASIC`    | Action name and caller only             |
| `STANDARD` | Action name, caller, and parameters     |
| `DETAILED` | Everything including results            |
| `FULL`     | Full audit with timing and stack traces |

### Example -- Method Level

This example shows a high-security action that requires approval, detailed audit logging, restricted access, and a timeout.

```java
@ActionSpec(type = ActionType.LOCAL, description = "Delete user account")
@Security(
    approvalRequired = true,
    approvalReason = "Permanent data deletion",
    audit = AuditLevel.DETAILED,
    sensitive = true,
    maskFields = {"password", "ssn"},
    allowedCallers = {"admin-agent"},
    requiredPermissions = {"user:delete"},
    securityTimeout = 30000
)
public void deleteUser(String userId) {
    // Requires approval, full audit trail, 30s security timeout
}
```

### Example -- Type Level

Apply `@Security` to a role class to set default policies for all its actions. Individual methods can still override these defaults.

```java
@Security(
    audit = AuditLevel.BASIC,
    allowedCallers = {"admin-agent", "supervisor-agent"},
    sensitive = true,
    encryptParams = true,
    encryptResult = true
)
public class AdminRole extends Role {
    // All actions inherit these security policies
}
```

### Combining with Other Annotations

For maximum protection, layer multiple security annotations. This example shows a financial transaction protected by approval, full audit, input validation, and output PII masking -- defense in depth.

```java
@ActionSpec(type = ActionType.WEB_SERVICE, description = "Transfer funds")
@Security(
    approvalRequired = true,
    approvalReason = "Financial transaction",
    audit = AuditLevel.FULL,
    sensitive = true,
    maskFields = {"accountNumber"},
    requiredPermissions = {"finance:transfer"},
    idempotent = false
)
@InputGuardrail(
    validators = {NotEmpty.class},
    detectInjection = true,
    onFailure = FailureAction.REJECT
)
@OutputGuardrail(
    maskPII = true,
    piiTypes = {PIIType.CREDIT_CARD, PIIType.SSN},
    onFailure = FailureAction.REJECT
)
public String transferFunds(String fromAccount, String toAccount, double amount) {
    // Protected by approval, input validation, output masking, and full audit
}
```

---

# Code Review Harness

URL: https://tnsai.dev/docs/security/code-review-harness
Description: Agent-driven, idempotent, fan-out-safe security review pipeline (TNS-291). Takes a codebase, runs static matchers as a pre-filter, invokes an LLM agent on each candidate file, and produces structured ReviewFindings exportable as SARIF, JSON, or Markdown.

import { Callout } from 'fumadocs-ui/components/callout'

The framework's answer to: **how do I run a continuous security review over my repo without hand-rolling the orchestration, idempotency, fan-out, and output format?**

The `com.tnsai.quality.security.harness` package in `tnsai-quality` ships the primitives:

- **`CodeReviewPipeline`** — orchestrator + builder
- **`PipelineStage`** — SPI; one impl per `scan / process / revalidate / enrich / export`
- **`MatcherSpi`** — pluggable static-analysis matchers (regex, AST, dependency-version, …)
- **`ReviewAgentSpi`** — pluggable agent backends (default: `ReviewAgent.claudeOpus(LLMClient)`)
- **`PipelineStateStore`** — disk persistence with deepsec-shaped layout
- **`FileRecord`** / **`ReviewFinding`** / **`AgentRun`** — canonical data records
- **`Severity`** / **`ExportFormat`** / `BuiltInMatchers`

Inspired by [Vercel-Labs deepsec](https://github.com/vercel-labs/deepsec); pattern adapted, code not ported.

## Why a separate primitive

Three forces motivate the harness as a first-class layer:

1. **Continuous security scan needs orchestration, not just a tool call.** Wiring "walk repo → filter → call LLM → parse JSON → de-dup → SARIF" by hand for every consumer is friction that buries the use case. The harness ships it.
2. **Static matchers are cheap pre-filters; agents are expensive deep checks.** Sending every file through an LLM is wasteful and noisy. The two-stage `scan` + `process` shape keeps cost tractable: matchers seed candidates, agents adjudicate.
3. **Cross-checking reduces false positives.** A single agent's output has a non-trivial false-positive rate. The optional `revalidate` stage runs a second agent against the first agent's findings and keeps only the ones a different model also agrees with.

## Quick start

```java
import com.tnsai.quality.security.harness.*;
import com.tnsai.llm.LLMClient;

CodeReviewPipeline pipeline = CodeReviewPipeline.builder()
    .projectRoot(Path.of("/work/myrepo"))
    .stateStore(new FileSystemPipelineStateStore(Path.of(".tnsai/security")))
    .matcher(BuiltInMatchers.sqlInjection())
    .matcher(BuiltInMatchers.commandInjection())
    .matcher(BuiltInMatchers.hardcodedSecrets())
    .agent(ReviewAgent.claudeOpus(llmClient))
    .stage(new ScanStage())
    .stage(new ProcessStage())
    .stage(new EnrichStage())
    .stage(new ExportStage(ExportFormat.SARIF))
    .build();

pipeline.run();   // executes every registered stage in order
```

After the run, look in `.tnsai/security/<projectId>/`:

```text
project.json                 # project metadata
files/<path-hash>.json       # one FileRecord per flagged file
runs/<epoch>-<stage>.json    # stage-execution audit trail
reports/findings.sarif       # exporter output
```

## Pipeline stages

| Stage        | What it does                                                                                                                                                                  | Idempotent skip key                                                              |
| ------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------- |
| `scan`       | Walks the repo, runs every `MatcherSpi` against every (filtered) file. Persists a `FileRecord` for files with at least one matcher hit.                                       | `(path, contentHash, matcherSet)` — unchanged content + same matcher set ⇒ skip  |
| `process`    | Iterates flagged files, invokes the first registered agent on each, persists findings + an `AgentRun` per call.                                                               | `(agentId, agentVersion, inputHash)` — successful run with the same tuple ⇒ skip |
| `revalidate` | Runs the second registered agent (when present) over files that already have findings. Drops agent-only findings the cross-agent rejects; keeps matcher-only findings always. | Same `(agentId, agentVersion, inputHash)` rule applied to the cross-agent        |
| `enrich`     | Adds `cweName` + `cweUrl` to every finding's metadata using the built-in CWE catalog.                                                                                         | Already-enriched findings are skipped                                            |
| `export`     | Serialises every finding to SARIF / JSON / Markdown. Multiple `ExportStage(format)` instances can be registered.                                                              | Always overwrites the report                                                     |

## Built-in matchers

Ten regex-based matchers covering common CWEs across Java / Python / JavaScript / TypeScript:

| Matcher id               | CWE     | Default severity | Targets                                                                                            |
| ------------------------ | ------- | ---------------- | -------------------------------------------------------------------------------------------------- |
| `sql-injection`          | CWE-89  | HIGH             | `Statement.execute*("..." + var)`, `String.format("SELECT ...", ...)`, Python f-string SQL         |
| `path-traversal`         | CWE-22  | HIGH             | `new File(input + ...)`, `Path.of(input + ...)`, literal `../` traversal                           |
| `command-injection`      | CWE-78  | CRITICAL         | `Runtime.exec(input + ...)`, `subprocess(shell=True)`, `child_process.exec` with template literals |
| `hardcoded-secrets`      | CWE-798 | CRITICAL         | AWS / GitHub / OpenAI / Anthropic / Google / Slack tokens + `password = "..."` + bearer            |
| `weak-crypto`            | CWE-327 | MEDIUM           | `MessageDigest.getInstance("MD5")`, `Cipher.getInstance("DES")`, `hashlib.md5`                     |
| `cross-site-scripting`   | CWE-79  | HIGH             | `.innerHTML = userVar`, `dangerouslySetInnerHTML`, `document.write`                                |
| `unsafe-deserialization` | CWE-502 | CRITICAL         | `ObjectInputStream`, `pickle.loads`, `yaml.load` (no `SafeLoader`)                                 |
| `ssrf`                   | CWE-918 | HIGH             | HTTP client receiving `req.body.url` or `requests.get(request.json[...])`                          |
| `xxe`                    | CWE-611 | HIGH             | XML factories without `setFeature(...)` / `IS_SUPPORTING_EXTERNAL_ENTITIES`                        |
| `eval-on-input`          | CWE-95  | CRITICAL         | `eval(req.*)`, `exec(input(...))`, `new Function(var)`                                             |

`BuiltInMatchers.all()` returns the full list in stable order. Custom matchers extend `RegexMatcher` (regex-based) or implement `MatcherSpi` directly (AST / dependency-version / import-graph).

## Agent backends

The default agent is `ReviewAgent.claudeOpus(llmClient)` — an instance of `ReviewAgent` (a concrete `ReviewAgentSpi`) backed by any `tnsai-llm` `LLMClient`. The agent prompts the LLM with the file path, candidate-matcher list, and file content, and asks for a JSON array of findings matching the `ReviewFinding` schema.

The output is parsed defensively: malformed JSON yields an empty list and a WARN log (not a throw), so one bad response never kills the stage. Markdown ` ```json ... ``` ` fences are stripped if the model ignores the "no fences" instruction.

| Factory                                   | Id            | Notes                                                   |
| ----------------------------------------- | ------------- | ------------------------------------------------------- |
| `ReviewAgent.claudeOpus(LLMClient)`       | `claude-opus` | Recommended default                                     |
| `ReviewAgent.gpt(LLMClient)`              | `gpt`         | Useful as the second agent for `revalidate` cross-check |
| `new ReviewAgent(id, version, LLMClient)` | custom        | For routing + version-pinning                           |

The first registered agent is the `process` backend; the second (when present) is used by `revalidate`. Mix-and-match across providers is the intended pattern — the cross-check is most valuable when the two agents come from different model families.

## State store + idempotency

`FileSystemPipelineStateStore` persists everything under `<baseDir>/<projectId>/`:

- **Atomic writes** — temp file + `Files.move(ATOMIC_MOVE)` so a partial write never replaces a good one
- **Per-path locking** — concurrent writers for the same `FileRecord` serialise; the rest fan-out
- **Path-hashed filenames** — `files/<sha256-of-path>.json`; same path always lands in the same file regardless of OS encoding quirks
- **Crash-safe restart** — interrupting the JVM mid-run leaves the store in a consistent state; the next invocation picks up where it left off

`FileRecord` carries:

- `path` (relative)
- `contentHash` (SHA-256 of the file)
- `candidateMatchers` (ids that fired during scan)
- `findings` (canonical, deduplicated by id)
- `runs` (history of every agent invocation; cache key for the `process` stage)

## Output formats

| Format     | When to use                                                                                                                                    |
| ---------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
| `SARIF`    | Default — feeds GitHub Code Scanning, Azure DevOps, GitLab SAST. The driver, rules, and result properties are populated for the consumer's UI. |
| `JSON`     | TnsAI's canonical shape — the same `ReviewFinding` records the SDK uses. Best for in-house tooling.                                            |
| `MARKDOWN` | Human reading — one section per file with severity, lines, description, evidence code blocks, mitigation, CWE reference link.                  |

```java
.stage(new ExportStage(ExportFormat.SARIF))
.stage(new ExportStage(ExportFormat.JSON))
.stage(new ExportStage(ExportFormat.MARKDOWN))
```

Stage ids encode the format (`export-sarif`, `export-json`, `export-markdown`), so multiple instances coexist.

### SARIF severity mapping

| `Severity`         | SARIF level |
| ------------------ | ----------- |
| `INFO`             | `note`      |
| `LOW`, `MEDIUM`    | `warning`   |
| `HIGH`, `CRITICAL` | `error`     |

## Cost containment (v1)

`process.maxFiles` and `process.maxFindings` config keys cap the work the `process` stage does in a single invocation:

```java
.config("process.maxFiles", "200")
.config("process.maxFindings", "50")
```

Deeper integration with [`CostBudget`](/docs/security/cost-governance) — pipeline-wide USD cap with per-stage breakdown via the `Hook<PreAction>` enforcement layer — is a follow-up issue. The lightweight cap above keeps a single pipeline run bounded today.

## Pairs with

- [Sandbox](/docs/security/sandbox) — agent invocations can run inside the framework's sandbox primitive when the consumer wraps the `LLMClient` accordingly. The harness itself is process-local; sandbox is the inner ring.
- [Cost Governance](/docs/security/cost-governance) — full `CostBudget` integration is the in-flight piece (see "Cost containment" above).
- [Accountability](/docs/security/accountability) — every `AgentRun` carries the agent id + version; downstream consumers can join on `correlationId` to merge harness audit with the rest of the agent activity stream.

## What's not in v1 (deferred to follow-ups)

- **Custom matcher DSL** — built-in catalog ships; user-defined matchers are a v2.
- **Multi-language full coverage** — Java / Python / JS / TS / Go / Ruby / PHP are scanned; Rust / C++ / C# / Kotlin matchers come as additive extensions.
- **Auto-fix generation** — findings include a `mitigation` suggestion, but a PR-applying agent is its own issue.
- **GitHub Action wrapper** — the harness ships a CLI-shaped surface; turning it into a published Action is separate tooling.
- **CodeQL / Semgrep rule import** — only the native catalog in v1; importing community rule sets is a v2.
- **Cloud / hosted variant** — self-hosted only.
- **Full `CostBudget` Hook integration** — see "Cost containment".

## See also

- [Sandbox](/docs/security/sandbox) — isolated execution primitive used as the inner ring for any per-agent code execution.
- [Cost Governance](/docs/security/cost-governance) — `CostBudget` shape; full integration is planned.
- [Accountability](/docs/security/accountability) — `AgentLiabilityRecord` for joining harness audit with the rest of the agent timeline.

---

# Cost Governance

URL: https://tnsai.dev/docs/security/cost-governance
Description: Cap LLM spend per tenant, agent, or capability with declarative budgets backed by a pluggable spend store. Pairs with LLM observability — every captured LLMCallLog carries a cost estimate; the cost-governance layer reads cumulative spend and enforces a policy when a budget is exhausted.

import { Callout } from 'fumadocs-ui/components/callout'

> See also: **[Cost Tracking](/docs/capabilities/llm/cost-tracking)** — the older `CostAwareLLMClient` system focused on client-side spend control. Use cost-tracking for self-contained client-edge enforcement; use cost-governance when budgets need to span multiple clients / agents / processes against a shared spend ledger.

## Why a separate layer

Capturing cost (`LLMCallLog.cost()`) tells you what's been spent. Cost governance answers the next question: **what should happen when a tenant's daily budget is exhausted?** Three policies cover the realistic answers:

| Policy      | Behavior                                                             | Use case                                                                         |
| ----------- | -------------------------------------------------------------------- | -------------------------------------------------------------------------------- |
| `SOFT_WARN` | Emit warning event + Prometheus alert; let the call through          | Budget is informational (analytics, not throttling)                              |
| `HARD_STOP` | Block any new LLM call that would push spend over budget             | Interactive chat where user should see "budget exceeded" immediately             |
| `DEFER`     | Queue the call for the next period; notify via `OnNotification` hook | Non-interactive workloads that can wait (overnight batch, async research agents) |

The framework ships the data model and the evaluator. Wiring it into the LLM call hot path uses the hook system from issue #60.

## Building blocks

```java
package com.tnsai.quality.cost;

// Declarative budget — what the limit is + what to do when exhausted.
public record CostBudget(
        String tenantId,
        Optional<String> agentId,
        Optional<String> capabilityId,
        BigDecimal periodBudgetUSD,
        Duration period,
        BudgetPolicy onExceed) { ... }

// Where spend is bucketed for accumulation.
public record CostScope(
        String tenantId,
        Optional<String> agentId,
        Optional<String> capabilityId) { ... }

// SPI — store cumulative spend per scope per rolling window.
public interface CostBudgetStore {
    void record(CostScope scope, BigDecimal amountUSD);
    BigDecimal currentSpend(CostScope scope, Duration period);
    void clear();
}

// Stateless evaluator — query the store, package as a snapshot.
public final class CostBudgetEvaluator {
    public BudgetState evaluate(CostBudget budget);
}

// Snapshot — budget + spend + status (HEALTHY / WARNING / EXHAUSTED).
public record BudgetState(
        CostBudget budget,
        BigDecimal spentUSD,
        BigDecimal remainingUSD,
        double fractionConsumed,
        Status status,           // HEALTHY < 80%, WARNING 80–100%, EXHAUSTED ≥ 100%
        Instant evaluatedAt) { ... }
```

## Quick start

Define a tenant-wide daily cap, record spend after each LLM call, and read the status before each call:

```java
import com.tnsai.quality.cost.*;
import java.math.BigDecimal;
import java.time.Duration;

CostBudgetStore store = new InMemoryCostBudgetStore();
CostBudgetEvaluator eval = new CostBudgetEvaluator(store);

CostBudget acmeDaily = CostBudget.tenantWide(
        "acme",
        new BigDecimal("50.00"),     // $50/day cap
        Duration.ofDays(1),
        BudgetPolicy.HARD_STOP);

// After each captured LLMCallLog (e.g. from CapturingLLMClient),
// fold the cost into the store.
LLMCallLog log = ...; // from your LLMCallPublisher
store.record(
        CostScope.tenant(log.context().tenantId().orElse("default")),
        log.cost().totalUSD());

// Before the next call, check the budget.
BudgetState state = eval.evaluate(acmeDaily);
switch (state.status()) {
    case HEALTHY -> { /* proceed */ }
    case WARNING -> logger.warn("Budget at {}% — consider throttling",
            (int)(state.fractionConsumed() * 100));
    case EXHAUSTED -> throw new IllegalStateException(
            "Tenant acme over $" + acmeDaily.periodBudgetUSD() + " daily cap");
}
```

## Hierarchical scopes

Budgets nest: tenant-wide caps the whole tenant, per-agent caps a single agent inside it, per-capability caps a specific `@Capability`:

```java
CostBudget tenantCap = CostBudget.tenantWide("acme",
        new BigDecimal("500.00"), Duration.ofDays(1), BudgetPolicy.HARD_STOP);

CostBudget summarizerCap = CostBudget.forAgent("acme", "summarizer-agent",
        new BigDecimal("50.00"), Duration.ofDays(1), BudgetPolicy.SOFT_WARN);

// Spend recorded at finest scope rolls up to coarser-scoped queries.
store.record(CostScope.full("acme", "summarizer-agent", "extractive-summary"),
        new BigDecimal("0.42"));

// Both budgets see the $0.42 — the tenant query rolls up; the agent
// query matches exactly.
eval.evaluate(tenantCap).spentUSD();      // $0.42
eval.evaluate(summarizerCap).spentUSD();  // $0.42
```

`CostScope.contains(other)` defines the rollup rule: a scope's populated fields must all match the recorded scope; absent fields wildcard.

## Status thresholds

`BudgetState.Status` follows the SRE 80% rule of thumb:

- **`HEALTHY`** — spend under 80% of the cap
- **`WARNING`** — 80% – 100% (raise the flag)
- **`EXHAUSTED`** — 100%+ (cap reached or breached)

Match thresholds to your alerting policy: `WARNING` typically becomes a Prometheus alert; `EXHAUSTED` triggers `BudgetPolicy.HARD_STOP` enforcement when you wire the enforcement hook.

## Stores

`InMemoryCostBudgetStore` ships as the default — process-local, fine for dev / single-tenant. The SPI accepts custom backends:

- **Redis** — distributed across multiple TnsAI server instances; respected globally
- **Postgres** — strong durability + queryable history for audit
- **ClickHouse / DuckDB** — analytical workloads where you also want time-series cost reports

A Redis adapter ships as a focused follow-up. The SPI is stable so consumer-side adapters can be written today.

## Enforcement

`CostBudgetEnforcer` is a `Hook<PreAction>` that reads recorded spend before every `ActionType.LLM` dispatch and applies the budget's `onExceed` policy. It runs at priority `-100` (before normal-priority hooks) so a budget-blocked call never reaches argument-scrubbing or observability hooks that would do real work.

```java
package com.tnsai.quality.cost;

CostBudgetStore store = new InMemoryCostBudgetStore(Duration.ofHours(24));

// Resolve the most-specific budget for an in-flight LLM call.
Function<PreAction, Optional<CostBudget>> resolver = event ->
        Optional.ofNullable(budgetByTenant.get(event.context().tenantId().orElse(null)));

// Optional pre-call estimator. When zero (default), the enforcer fires
// after the cap is crossed by recorded spend; with a real estimator it
// can block borderline calls before they go out.
Function<PreAction, BigDecimal> estimator = event -> BigDecimal.ZERO;

CostBudgetEnforcer enforcer = new CostBudgetEnforcer(store, resolver, estimator);

// Wire alongside security/audit hooks via the standard dispatcher.
hookDispatcher.register(enforcer);
```

**Policy outcomes:**

- `SOFT_WARN` → log a warning and allow the call (budget is informational)
- `HARD_STOP` → return `HookResult.Block` with `Severity.HIGH` and a structured "budget exceeded" reason
- `DEFER` → also blocks (the framework owns no queue), but the block reason explicitly tells the consumer "DEFER policy requested but no framework queue is configured; consumer must catch and reschedule" — typically caught and routed onto a consumer-owned background worker.

The enforcer's matcher restricts it to `ActionType.LLM`; tool / web-service / local-method dispatches never invoke it.

## Composing with sampling and redaction

All three observability decorators (`SamplingAgentEventPublisher`, `RedactingAgentEventPublisher`, custom `LLMCallPublisher`) compose freely. A typical production stack:

```
LLM call → CapturingLLMClient (LLMCallLog with cost)
                    ↓
       publish to LLMCallPublisher chain:
                    ↓
       Spend recorder → store.record(scope, cost)
                    ↓
       Sampling decorator → drop high-volume noise
                    ↓
       Redaction decorator → scrub PII before sink
                    ↓
       Final sink (LangFuse / Loki / S3 / Slack)
```

Cost recording happens **before** sampling so the spend ledger is always complete — sampling decisions are about log volume, not budget accuracy.

## Future work

- `RedisCostBudgetStore` — distributed store for multi-process deployments
- Prometheus metrics derivation: `tnsai_llm_cost_usd_total{tenant,agent,capability}`, `tnsai_llm_budget_utilized_ratio`, `tnsai_llm_budget_exceeded_total`
- Grafana dashboard JSON bundled with the framework
- Prometheus Alertmanager rule examples (80% / 100% utilization)
- YAML budget config loader (avoids programmatic builder per tenant)

## See Also

- **[LLM Observability](/docs/capabilities/llm/observability)** — `LLMCallLog` is the source of cost data; without capture, there's nothing to govern
- **[Cost Tracking](/docs/capabilities/llm/cost-tracking)** — older client-edge `CostAwareLLMClient` + `BudgetManager` system
- **[Sampling](/docs/sampling)** — companion observability decorator; sampling decisions never affect spend recording
- **[Redaction](/docs/security/redaction)** — pair cost-governance dashboards with redaction so spend reports don't leak prompt content

---

# Encryption

URL: https://tnsai.dev/docs/security/encryption

import { Callout } from 'fumadocs-ui/components/callout'

> **Status:** Placeholder. Content currently lives inline in [approvals-and-annotations](/docs/security/approvals-and-annotations) and [enforcement](/docs/security/enforcement). A dedicated page will extract the AES-256-GCM, envelope encryption, and key rotation sections.

## Quick Reference

TnsAI uses **AES-256-GCM** for at-rest encryption of sensitive fields. Keys are managed via an envelope pattern (data key + master key).

See [Enforcement](/docs/security/enforcement) for the current encryption section.

## Planned Content

- AES-256-GCM setup.
- Envelope encryption (data encryption key + master key).
- Key rotation.
- Integration with external KMS.

---

# Security

URL: https://tnsai.dev/docs/security/enforcement
Description: The Quality module provides a layered security framework: annotation-driven access control and encryption (SecurityEnforcer), content moderation (PatternBasedModerator), prompt injection detection (PromptInjectionDetector), sandboxed execution, audit logging, and input validation (ValidationService).

import { Callout } from 'fumadocs-ui/components/callout'

## SecurityEnforcer

Runtime enforcement of policies defined via the `@Security` annotation. Handles access control, parameter/result encryption (AES-256-GCM), security timeouts, field masking, and audit logging.

```java
// Auto-generated encryption key
SecurityEnforcer enforcer = new SecurityEnforcer();

// Custom encryption config
EncryptionConfig config = EncryptionConfig.fromBase64Key(myKey);
SecurityEnforcer enforcer = new SecurityEnforcer(config);

// Access control
enforcer.enforceAccessControl(action, role, callerAgentId, callerRoles);

// Parameter encryption (if @Security(encryptParams=true))
Map<String, Object> secureParams = enforcer.processParameters(action, role, params);

// Execute with timeout (if @Security(securityTimeout=5000))
Object result = enforcer.executeWithTimeout(action, role, () -> doExecute());

// Result encryption (if @Security(encryptResult=true))
Object secureResult = enforcer.processResult(action, role, result);

// Mask fields for logging (if @Security(maskFields={"password","token"}))
Map<String, Object> safeParams = enforcer.maskForLogging(action, role, params);
```

### Access Control Checks

When an action is invoked, the enforcer reads the `@Security` annotation from the action method (or falls back to the role class) and checks three layers in order. If any layer rejects the caller, a `SecurityException` is thrown.

1. **allowedCallers**: Whitelist of agent IDs permitted to invoke the action
2. **allowedRoles**: Set of role names -- caller must have at least one
3. **requiredPermissions**: Delegated to a `PermissionProvider` SPI implementation (discovered via `ServiceLoader`)

All three throw `SecurityException(ViolationType.UNAUTHORIZED)` on failure.

### EncryptionConfig

The `EncryptionConfig` manages the AES-256-GCM encryption keys used by `SecurityEnforcer` to encrypt and decrypt action parameters and results. You can generate a random key or import an existing one from Base64.

```java
// Generate a random key
EncryptionConfig config = EncryptionConfig.generateKey();

// From Base64-encoded key
EncryptionConfig config = EncryptionConfig.fromBase64Key(base64String);

// Encrypt/decrypt via SecurityEnforcer
String encrypted = enforcer.encrypt("sensitive data");
String decrypted = enforcer.decrypt(encrypted);
// Format: Base64(IV[12 bytes] + ciphertext + authTag[16 bytes])
```

### PermissionProvider SPI

If your application uses a custom permission backend (like an RBAC database or an external authorization service), you can integrate it with TnsAI through the `PermissionProvider` SPI. The enforcer discovers your implementation via `ServiceLoader` and delegates permission checks to it.

```java
public interface PermissionProvider {
    boolean hasPermission(String agentId, String permission);
    static PermissionProvider discover() { /* ServiceLoader lookup */ }
}
```

If no implementation is on the classpath, permission checks are skipped with a debug log.

## ContentModerator / PatternBasedModerator

The content moderator scans text for harmful or unwanted content using regex patterns. It checks user input, model output, and tool results against configurable categories (violence, hate speech, PII, spam, etc.) and returns a decision: allow, warn, block, or flag for review.

### Moderation Categories (11)

The moderator organizes harmful content into 11 categories. Each category has its own set of detection patterns and can be individually enabled or disabled.

| Category         | Description                                |
| ---------------- | ------------------------------------------ |
| `VIOLENCE`       | Violent threats, bomb/terrorism references |
| `SELF_HARM`      | Self-harm methods, suicide references      |
| `HATE`           | Hate speech indicators                     |
| `HARASSMENT`     | Personal attacks, stalking, doxxing        |
| `SPAM`           | Commercial spam patterns                   |
| `ILLEGAL`        | Hacking, counterfeiting, piracy            |
| `PII`            | Email, phone, SSN, credit card patterns    |
| `SEXUAL`         | Sexual content                             |
| `TOXIC`          | General toxicity                           |
| `MISINFORMATION` | Factual inaccuracies                       |
| `OTHER`          | Uncategorized                              |

### ModerationResult Actions

After checking content, the moderator returns one of four actions based on how the content scored against your configured thresholds.

| Action   | Meaning                                      |
| -------- | -------------------------------------------- |
| `ALLOW`  | Content passes all checks                    |
| `WARN`   | Score exceeds warn threshold but below block |
| `BLOCK`  | Score exceeds block threshold                |
| `REVIEW` | Flagged for manual review                    |

### Usage

You can create a moderator with sensible defaults, use a strict preset for high-risk applications, or fully customize the patterns and thresholds.

```java
// Default patterns (block >= 0.8, warn >= 0.5)
ContentModerator moderator = PatternBasedModerator.withDefaults();

// Strict mode (block >= 0.6, warn >= 0.3)
ContentModerator strict = PatternBasedModerator.strict();

// Custom configuration
ContentModerator custom = PatternBasedModerator.builder()
    .addPattern(Category.TOXIC, "badword1|badword2", 0.9)
    .addPattern(Category.SPAM, "(buy now|free offer)", 0.7)
    .blockThreshold(0.8)
    .warnThreshold(0.5)
    .checkPII(true)
    .enabledCategories(Set.of(Category.VIOLENCE, Category.PII, Category.SPAM))
    .build();

// Check content
ModerationResult result = moderator.checkInput(userMessage);
if (result.isBlocked()) {
    logger.warn("Blocked: {}", result.getReason());
}

// Separate checks for input, output, and tool results
ModerationResult inputResult = moderator.checkInput(userMessage);
ModerationResult outputResult = moderator.checkOutput(modelResponse);
ModerationResult toolResult = moderator.checkToolResult("shell", shellOutput);
// Tool results use less strict checking (mainly PII detection)
```

## PromptInjectionDetector

Detects prompt injection attacks using regex pattern matching across 6 injection types.

### Injection Types

The detector recognizes six categories of prompt injection attacks. Each category has multiple regex patterns that catch common variations.

| Type                     | Detection Patterns                                                              |
| ------------------------ | ------------------------------------------------------------------------------- |
| `SYSTEM_PROMPT_OVERRIDE` | "Ignore previous instructions", "Disregard all prior", "Override system prompt" |
| `JAILBREAK`              | "DAN mode", "Do Anything Now", "Developer mode enabled", "Remove restrictions"  |
| `ROLE_MANIPULATION`      | "You are now\...", "Pretend you are", "Act as if", "Assume the role of"         |
| `INSTRUCTION_BYPASS`     | "Skip safety filters", "Bypass restrictions", "Without restrictions"            |
| `CONTEXT_MANIPULATION`   | Fake `[SYSTEM]` delimiters, markdown system injection, code block system tags   |
| `DATA_EXFILTRATION`      | "Repeat your instructions", "Output your system prompt", "Dump your memory"     |

### Usage

Create a detector with a preset sensitivity, or customize it with your own patterns and thresholds. The detector returns a detailed result with matched patterns and confidence scores.

```java
// Standard detector (threshold: 0.6)
PromptInjectionDetector detector = PromptInjectionDetector.standard();

// Strict detector (threshold: 0.5)
PromptInjectionDetector strict = PromptInjectionDetector.strict();

// Throwing detector (raises SecurityException on detection)
PromptInjectionDetector throwing = PromptInjectionDetector.throwing();

// Custom configuration
PromptInjectionDetector custom = PromptInjectionDetector.builder()
    .addPattern(InjectionType.JAILBREAK, "my-custom-pattern", 0.9)
    .confidenceThreshold(0.5)
    .throwOnDetection(true)
    .build();

// Check input
InjectionDetectionResult result = detector.detect(userInput);
if (result.isInjectionDetected()) {
    System.out.println("Types: " + result.getDetectedTypes());
    System.out.println("Patterns: " + result.getMatchedPatterns());
    if (result.shouldBlock()) {
        // Block at detector's configured threshold
    }
}

// Convenience methods
boolean safe = detector.isSafe(input);       // No injection detected
boolean block = detector.shouldBlock(input);  // Exceeds threshold
```

## SandboxedExecutor

Executes untrusted code in a restricted environment using `SandboxSpec` constraints.

```java
SandboxSpec spec = SandboxSpec.builder()
    .timeout(Duration.ofSeconds(30))
    .maxMemory(256_000_000)   // 256 MB
    .allowNetwork(false)
    .allowFileRead(true)
    .allowFileWrite(false)
    .build();

SandboxedExecutor executor = new SandboxedExecutor(spec);
Object result = executor.execute(() -> runUntrustedCode());
```

## Audit Infrastructure

### AuditEvent and AuditStore

The audit system records security-relevant events (access decisions, encryption operations, moderation results) for compliance and debugging. You can choose between in-memory storage for development or file-based storage for production.

```java
// In-memory (bounded, for development/testing)
AuditStore store = new InMemoryAuditStore();

// File-based (persistent)
AuditStore store = new FileAuditStore(Path.of("/var/log/tnsai/audit.json"));
```

## ValidationService

Comprehensive input validation with configurable limits and dangerous pattern detection.

### Detected Dangerous Patterns

The validation service checks input strings against common attack patterns. When `blockDangerousPatterns` is enabled (the default), inputs containing these patterns are rejected.

| Pattern Type      | Examples                                          |
| ----------------- | ------------------------------------------------- |
| SQL Injection     | `UNION SELECT`, `DROP TABLE`, `--`, `INSERT INTO` |
| Command Injection | `$(...)`, backticks, `; rm`, `; curl`, `; sudo`   |
| Path Traversal    | `../`, `..\\`, `%2e%2e%2f`                        |
| XSS               | `<script>`, `javascript:`, `onclick=`, `<iframe>` |

### Usage

The validation service provides multiple presets: a permissive default, a strict mode for high-security applications, and a fully customizable builder.

```java
// Default settings
ValidationService validator = ValidationService.withDefaults();

// Strict settings
ValidationService strict = ValidationService.strict();
// maxStringLength: 10,000  maxParameterCount: 20
// maxNestingDepth: 5       maxArraySize: 100

// Custom configuration
ValidationService validator = ValidationService.builder()
    .maxStringLength(10_000)
    .maxParameterCount(50)
    .maxNestingDepth(5)
    .blockDangerousPatterns(true)
    .allowedFields(Set.of("name", "query", "path"))
    .addPattern("email", Pattern.compile("^[^@]+@[^@]+$"))
    .build();

// Validate string input
ValidationResult result = validator.validateString(userInput, "message");
if (!result.isValid()) {
    result.getErrors().forEach(System.err::println);
}

// Validate parameters (recursive, checks nesting depth)
ValidationResult paramResult = validator.validateParameters(params);

// Validate-and-throw
validator.validateOrThrow(input, "userInput");  // throws SecurityException

// Sanitization (escapes HTML, removes null bytes, truncates)
String safe = validator.sanitize(rawInput);

// Quick danger check
boolean dangerous = validator.containsDangerousPatterns(input);
```

### Default Limits

These are the default and strict configuration limits. The strict preset is recommended for applications that accept untrusted user input.

| Setting                  | Default | Strict |
| ------------------------ | ------- | ------ |
| `maxStringLength`        | 100,000 | 10,000 |
| `maxParameterCount`      | 100     | 20     |
| `maxNestingDepth`        | 10      | 5      |
| `maxArraySize`           | 1,000   | 100    |
| `blockDangerousPatterns` | true    | true   |

## SecretFilter (Pre-LLM Scrubbing)

Automatically detects and redacts secrets from content before it reaches the LLM. Uses 59 regex patterns covering API keys, tokens, passwords, connection strings, and other credentials. Package: `com.tnsai.quality.security`.

```java
SecretFilter filter = new SecretFilter();

String sanitized = filter.scrub(userInput);
// "My key is sk-abc123xyz" -> "My key is [REDACTED:API_KEY]"

// Check without redacting
boolean hasSecrets = filter.containsSecrets(userInput);

// Get detailed findings
List<SecretFinding> findings = filter.scan(userInput);
for (SecretFinding finding : findings) {
    System.out.println(finding.type());      // e.g., "AWS_ACCESS_KEY"
    System.out.println(finding.location());  // character offset
}
```

**Detected secret types include:**

| Category       | Examples                                                         |
| -------------- | ---------------------------------------------------------------- |
| Cloud          | AWS access keys, GCP service accounts, Azure connection strings  |
| API Keys       | OpenAI, Anthropic, Stripe, Twilio, SendGrid, Slack tokens        |
| Auth           | JWT tokens, OAuth secrets, Bearer tokens, Basic auth credentials |
| Database       | JDBC URLs with passwords, MongoDB URIs, Redis URLs               |
| Crypto         | Private keys (RSA, EC), SSH keys, PGP keys                       |
| Infrastructure | Docker registry credentials, Kubernetes secrets, Terraform state |

Wire into an agent to automatically scrub all inputs:

```java
Agent agent = AgentBuilder.create()
    .model("claude-sonnet-4")
    .secretFilter(new SecretFilter())
    .build();
// All user inputs and tool results are scrubbed before reaching the LLM
```

## PermissionLevel (3-Tier Permissions)

TnsAI uses a simple three-tier permission model to control which tools an agent can execute. This is the foundation for the tool approval system -- each tool is assigned a permission level that determines whether it runs automatically, requires user confirmation, or is blocked entirely.

```java
public enum PermissionLevel {
    ALWAYS,  // Tool executes without any approval
    ASK,     // Requires user confirmation before execution
    DENY     // Tool is blocked entirely
}
```

Configure permissions per tool:

```java
Map<String, PermissionLevel> permissions = Map.of(
    "file_read", PermissionLevel.ALWAYS,
    "file_write", PermissionLevel.ASK,
    "shell", PermissionLevel.ASK,
    "git_write", PermissionLevel.DENY
);

ToolFilter toolFilter = ToolFilter.fromPermissions(permissions);
agent.setToolCallFilter(toolFilter);
```

## ToolFilter (Glob Allow/Deny)

The `ToolFilter` lets you define allow and deny rules using glob patterns, so you can control tool access with wildcard matching instead of listing every tool individually. This is useful when you have many tools and want broad category-level permissions.

```java
ToolFilter filter = ToolFilter.builder()
    .allow("file_*")           // allow all file tools
    .allow("search_*")         // allow all search tools
    .deny("file_write")        // except file_write
    .deny("shell")             // block shell access
    .defaultPermission(PermissionLevel.ASK)  // ask for unlisted tools
    .build();

agent.setToolCallFilter(filter);
```

Rules are evaluated in order: first matching rule wins. The `defaultPermission` applies when no glob pattern matches.

```java
// Check a tool explicitly
PermissionLevel level = filter.getPermission("file_read");  // ALWAYS
PermissionLevel level = filter.getPermission("shell");      // DENY
PermissionLevel level = filter.getPermission("unknown");    // ASK (default)
```

---

# Security

URL: https://tnsai.dev/docs/security
Description: Control what agents can do, encrypt sensitive data, and defend against prompt injection.

import { Callout } from 'fumadocs-ui/components/callout'

## Pages

- [Approvals and Annotations](/docs/security/approvals-and-annotations) — `@ApprovalRequired`, `@Security`, per-action gates.
- [Enforcement](/docs/security/enforcement) — `SecurityEnforcer`, access control, audit logging.
- [Encryption](/docs/security/encryption) — AES-256-GCM at-rest, envelope encryption.
- [Prompt Injection](/docs/security/prompt-injection) — Detection and mitigation.
- [Redaction](/docs/security/redaction) — `Redactor` SPI, default pattern catalog, per-tenant policy dispatch, audit events.
- [Cost Governance](/docs/security/cost-governance) — `CostBudget` per tenant / agent / capability, `BudgetState` snapshots, hierarchical scope rollup, hard-stop / soft-warn / defer policies.
- [Accountability](/docs/security/accountability) — `AgentPrincipal` verifiable identity, `AgentLiabilityRecord` audit trail, `AuthorityScope` bounds, `ReputationLedger`, `PaymentBroker` for agent-to-agent settlement.
- [Sandbox](/docs/security/sandbox) — `Sandbox` SPI for isolated execution, `ProcessSandbox` / `ContainerSandbox` backends, `SandboxPool`, per-execute observability events.
- [Server Hardening](/docs/security/server-hardening) — `tnsai-server` HTTP/WS surface: bind policy, Bearer auth, Origin allowlist, per-session capability tokens, workspace allowlist for `/api/index`.
- [Code Review Harness](/docs/security/code-review-harness) — `CodeReviewPipeline` (deepsec pattern), `MatcherSpi` + 10 built-in CWE matchers, `ReviewAgentSpi` for LLM-driven review, `FileSystemPipelineStateStore`, SARIF / JSON / Markdown exporters.

---

# Prompt Injection Defense

URL: https://tnsai.dev/docs/security/prompt-injection

import { Callout } from 'fumadocs-ui/components/callout'

> **Status:** Placeholder. Content currently lives inline in [enforcement](/docs/security/enforcement). A dedicated page will extract detection and mitigation sections.

## Quick Reference

Prompt injection is a user-supplied input that attempts to override the agent's system prompt or steal credentials. TnsAI provides heuristic and model-based detection.

See [Enforcement](/docs/security/enforcement) for the current prompt-injection section.

## Planned Content

- Heuristic patterns (role override, credential extraction, instruction smuggling).
- Model-based detection via a dedicated classifier.
- Output sanitization.
- Defense-in-depth: input filter + system prompt hardening + output review.

---

# Redaction

URL: https://tnsai.dev/docs/security/redaction
Description: The Quality module ships a pluggable redaction layer that scrubs PII and secrets out of every framework boundary that could leak — log lines, trace attributes, memory writes, captured LLM prompts, agent events. Redaction is always-on when the decorator is wired in; opt-out is per-tenant per-sink, never the default.

import { Callout } from 'fumadocs-ui/components/callout'

The redactor SPI is `Redactor` in `com.tnsai.quality.redaction`. The default implementation `PatternRedactor` ships with a 14-pattern catalog covering email, phone, credit card, SSN, TC Kimlik, API keys, JWT, AWS keys, IBAN, IP, bearer tokens, and SSH private-key blocks. Operators wire the redactor into framework sinks via decorator types (`RedactingMemoryStore`, `RedactingAgentEventPublisher`).

## Quick start

```java
import com.tnsai.quality.redaction.*;

// 1. Build a redactor with the default pattern catalog.
Redactor redactor = PatternRedactor.withDefaults();

// 2. Wrap the agent's memory store and event publisher.
MemoryStore           memory = new RedactingMemoryStore(new InMemoryStore(), redactor);
AgentEventPublisher   events = new RedactingAgentEventPublisher(new Slf4jAgentEventPublisher(), redactor);

// 3. From this point on, every write through these sinks is scrubbed.
agent.setMemoryStore(memory);
agent.setEventPublisher(events);
```

## Pattern catalog

`RedactionPatterns.defaults()` returns the patterns below. Each is exported as a constant on `RedactionPatterns` (e.g. `RedactionPatterns.EMAIL`) so you can subset or augment.

| Pattern                 | Severity | Example                                           |
| ----------------------- | -------- | ------------------------------------------------- |
| `email`                 | MEDIUM   | `user@example.com`                                |
| `phone_e164`            | MEDIUM   | `+15551234567`                                    |
| `credit_card`           | CRITICAL | `4111-1111-1111-1111` (Luhn-validated)            |
| `us_ssn`                | CRITICAL | `123-45-6789`                                     |
| `tc_kimlik`             | CRITICAL | 11-digit Turkish national ID (checksum-validated) |
| `openai_api_key`        | HIGH     | `sk-...`                                          |
| `anthropic_api_key`     | HIGH     | `sk-ant-...`                                      |
| `github_pat`            | HIGH     | `ghp_...`                                         |
| `aws_access_key`        | HIGH     | `AKIA...`                                         |
| `jwt`                   | HIGH     | three base64url segments separated by `.`         |
| `iban`                  | MEDIUM   | `DE89370400440532013000`                          |
| `ipv4`                  | LOW      | `10.0.0.1`                                        |
| `bearer_token`          | HIGH     | `Bearer <token>` in headers                       |
| `ssh_private_key_block` | CRITICAL | `-----BEGIN ... PRIVATE KEY-----` blocks          |

Numeric patterns (credit card, TC Kimlik) include checksum validators so order IDs that *look* like credit-card numbers don't get scrubbed. The placeholder shape is `[REDACTED:<pattern_name>]` and is itself inert against every default pattern — a placeholder cannot be re-redacted on a later pass.

## Where redaction fires

| Sink                          | Decorator                      | What gets scrubbed                                                                         |
| ----------------------------- | ------------------------------ | ------------------------------------------------------------------------------------------ |
| Conversation memory writes    | `RedactingMemoryStore`         | `addMessage(role, content)`, `addMessage(Map)`, `search(query, …)`                         |
| Agent event log               | `RedactingAgentEventPublisher` | All 13 `TnsAIEvent` variants — every user-input-bearing field rebuilt with redacted values |
| Search queries against memory | `RedactingMemoryStore`         | `search(query, limit)` — query scrubbed before backend dispatch                            |

Reads (`getHistory`, `getRecentHistory`) pass through unchanged because content stored via these decorators is **already** redacted at write time; double-scrubbing wastes cycles.

## Composition

Every redactor type below implements the same `Redactor` SPI, so they slot into each other freely.

### Multiple redactor pipeline

`CompositeRedactor` chains redactors in order; the output text of one feeds the next. Useful for stacking a fast pattern matcher with a slower LLM-classifier or Presidio bridge.

```java
Redactor pipeline = CompositeRedactor.of(
        PatternRedactor.withDefaults(),                    // fast, regex-based
        new LLMClassifierRedactor(haiku, "pii-policy"));   // slower, NER-based (when shipped)
```

Order matters: the first redactor's `[REDACTED:...]` placeholders pass through every later redactor unchanged, so a downstream classifier doesn't get to "explain" what was already scrubbed.

### Per-tenant policies

`TenantPolicyRedactor` dispatches each call to a per-tenant redactor based on the active `RedactionContext`. Resolution order:

1. `RedactionContext.tenantPolicyId()` — explicit override
2. `EventContext.tenantId()` from the framework's correlation context
3. Configured default redactor — fallback for unmatched / blank ids

```java
Redactor everything   = PatternRedactor.withDefaults();
Redactor emailOnly    = new PatternRedactor(List.of(RedactionPatterns.EMAIL));
Redactor pciOnly      = new PatternRedactor(List.of(RedactionPatterns.CREDIT_CARD,
                                                   RedactionPatterns.US_SSN));

Redactor dispatcher = TenantPolicyRedactor.builder()
        .tenant("acme",   emailOnly)        // acme: minimal redaction
        .tenant("globex", pciOnly)          // globex: PCI-only
        .defaultRedactor(everything)        // everyone else: full catalog
        .build();
```

The dispatch happens on every call (no per-thread cache) so a single agent group serving multiple tenants doesn't leak across tenant boundaries.

### Audit trail

`AuditingRedactor` decorates any redactor and emits a `RedactionAuditEvent` to a `RedactionAuditListener` whenever findings exist. Aggregates pattern counts + highest severity + framework correlation context — the redacted content itself never appears in the audit event.

```java
Redactor base    = PatternRedactor.withDefaults();
Redactor audited = new AuditingRedactor(base, event -> {
    metrics.counter("redaction.applied",
            "scope", event.scope().name(),
            "severity", event.highestSeverity().name())
           .increment(event.totalFindings());
    if (event.isCritical()) {
        slack.alert("CRITICAL pii redaction in tenant " + event.eventContext().tenantId());
    }
});
```

`AuditingRedactor` IS a `Redactor`, so it stacks anywhere — inside `TenantPolicyRedactor` for per-tenant audit, or wrapping the dispatcher itself for cross-tenant audit.

A broken audit listener cannot break redaction itself: exceptions thrown inside `onRedaction(...)` are caught and logged at WARN.

## Custom patterns

Build your own `RedactionPattern` and pass it to a `PatternRedactor`:

```java
import java.util.regex.Pattern;
import com.tnsai.quality.redaction.*;

RedactionPattern medicalRecordId = RedactionPattern.of(
        "medical_record_id",
        "MRN-\\d{8}",                  // pattern (compiled to Pattern internally)
        Severity.CRITICAL);

RedactionPattern licensePlate = RedactionPattern.of(
        "license_plate_tr",
        "\\b\\d{2}\\s?[A-Z]{1,3}\\s?\\d{2,4}\\b",
        Severity.MEDIUM,
        plate -> plate.length() >= 7);  // optional validator filters false positives

List<RedactionPattern> myCatalog = new java.util.ArrayList<>(RedactionPatterns.defaults());
myCatalog.add(medicalRecordId);
myCatalog.add(licensePlate);

Redactor custom = new PatternRedactor(myCatalog);
```

The validator runs *after* the regex matches and lets you check shape rules (Luhn checksum, range, character class) that regex alone can't express. Patterns without a validator accept every regex match.

## SPI summary

| Type                           | Role                                                                           |
| ------------------------------ | ------------------------------------------------------------------------------ |
| `Redactor`                     | The SPI. Three methods: `scrubString`, `scrubValue`, `maybeContainsSensitive`. |
| `PatternRedactor`              | Default implementation — regex-driven, stateless, thread-safe.                 |
| `RedactionPatterns`            | Constants for the 14 default patterns + `defaults()` list.                     |
| `RedactionResult`              | Output of `scrubString` — text + findings.                                     |
| `RedactionFinding`             | One match — pattern name, offsets, placeholder, severity.                      |
| `RedactionContext`             | Per-call context — `EventContext`, scope, tenant policy id.                    |
| `RedactionScope`               | Enum of sinks — `LOG_ATTR`, `MEMORY_WRITE`, `LLM_PROMPT`, etc.                 |
| `Severity`                     | LOW / MEDIUM / HIGH / CRITICAL.                                                |
| `CompositeRedactor`            | Chain of redactors.                                                            |
| `TenantPolicyRedactor`         | Per-tenant dispatcher.                                                         |
| `AuditingRedactor`             | Decorator that fires `RedactionAuditEvent`s.                                   |
| `RedactingMemoryStore`         | `MemoryStore` decorator.                                                       |
| `RedactingAgentEventPublisher` | `AgentEventPublisher` decorator.                                               |

## Invariants

The framework's property test suite (`RedactorPropertyTest`) verifies four invariants that every `Redactor` implementation must hold:

1. **Idempotency** — `redact(redact(x)).text == redact(x).text`. A sink that double-scrubs (memory write then log emit) doesn't drift.
2. **Pattern survival** — no input PII fragment appears in the redacted output.
3. **Placeholder safety** — the literal placeholder `[REDACTED:foo]` does not match any default pattern.
4. **Length bounded** — output length ≤ input length + (max placeholder length × findings count).

Custom redactors that ship into framework sinks should add coverage to the same suite.

## Trade-offs

- **False positives** — phone-regex matches tracking IDs that look like phones. Default placeholder preserves visual shape; tighten per-pattern with a validator.
- **False negatives** — rare PII formats (driver's licence, medical record IDs) are not in the default catalog. Plug in custom patterns or the (forthcoming) classifier redactor.
- **Performance cost** — on hot paths with huge payloads (1MB+ LLM responses), redaction adds measurable latency. Big-payload paths should run through async / buffered publish.
- **Data loss risk** — aggressive redaction may scrub legitimate content (base64 images, opaque IDs that look like tokens). Tune per tenant.

## Roadmap

The redaction SPI lives in `tnsai-quality` and is stable. Pending work tracked under [issue #80](https://github.com/TnsAI-Framework/TnsAI/issues/80):

- `LLMClassifierRedactor` — opt-in classifier using any `LLMClient` for non-pattern PII (names, addresses, health info).
- `PresidioRedactor` — bridge to Microsoft Presidio for state-of-the-art NER.
- YAML policy loader — `tenant-redaction-policies.yaml` parser to populate `TenantPolicyRedactor` from config.
- `OnNotification` hook integration — fire on critical-severity findings (depends on the hook system in #60).

---

# Sandbox

URL: https://tnsai.dev/docs/security/sandbox
Description: Run untrusted code through a lightweight, isolated, fast primitive. The framework's answer to: how do I run LLM-generated code or untrusted shell commands without giving them access to the host's filesystem, network, or unbounded resources?

import { Callout } from 'fumadocs-ui/components/callout'

The `com.tnsai.quality.sandbox` package in `tnsai-quality` ships the primitives:

- **`Sandbox`** — interface: `start / execute / stop / terminate` + `AutoCloseable`
- **`SandboxSpec`** — declarative config (image, env, fsMounts, networkPolicy, resourceLimits, workingDir, warmable)
- **`SandboxResult`** — exit code, stdout/stderr, ResourceUsage, timedOut flag
- **`SandboxFactory`** — SPI for backend selection (process / container / wasm / future firecracker)
- **`ResourceLimits`** — cpu / memory / disk / timeout / maxProcesses
- **`NetPolicy`** — sealed: `DenyAll` / `AllowList(hosts)` / `Inherit`
- **`SandboxPool`** — warmable-instance reuse for high-concurrency workloads
- **`ObservedSandbox` + `SandboxExecutionListener`** — per-execute observability events

Pairs with [Accountability](/docs/security/accountability) (sandbox events correlate with `AgentLiabilityRecord`s on the same correlationId) and the future `tnsai-tools` refactor (existing `PythonExecutionTools` / `JsExecutionTools` move to the SPI in a follow-up).

## Why a separate primitive

Three forces motivate sandbox as a first-class layer:

1. **LLM-generated code is untrusted by definition.** Even when an agent is benevolent, hallucinated `rm -rf /` happens. Process boundary alone doesn't protect the host filesystem; we need FS jail + env scrubbing + timeout enforcement.
2. **Per-instance cost matters.** Multi-agent fan-out (TNS-294 group), code review (TNS-291 deepsec harness), Agents-of-Chaos benchmarks — all push concurrent sandbox counts into the hundreds. Tencent CDB-style targets (60ms cold start, 5MB RAM) become the budget.
3. **Backend selection is deployment-specific.** A laptop dev wants speed (ProcessSandbox); a CI runner wants real isolation (ContainerSandbox); a production microVM host wants Firecracker. The framework ships an SPI so the same calling code targets all three.

## Quick start

```java
import com.tnsai.quality.sandbox.*;
import java.time.Duration;

// 1. Pick a backend. preferred() auto-selects the highest-priority
//    one available; explicit selection via byId(...) when you want
//    a specific backend.
SandboxFactory factory = SandboxFactory.preferred();
// or: SandboxFactory factory = SandboxFactory.byId("container");

// 2. Build a spec. Defaults: deny-all network, standard resource
//    limits (1 CPU, 256MB, 30s, 64 maxProcs), workingDir = backend
//    default, warmable = false.
SandboxSpec spec = SandboxSpec.builder()
        .image("python:3.12-slim")          // backend-specific; "" for ProcessSandbox
        .resourceLimits(ResourceLimits.standard())
        .networkPolicy(NetPolicy.denyAll())
        .build();

// 3. Run a command. The sandbox is created lazily by create();
//    each execute() runs to completion or to the timeout budget.
try (Sandbox sb = factory.create(spec)) {
    SandboxResult r = sb.execute(Command.shell("python -c 'print(1+1)'"));
    System.out.println("exit=" + r.exitCode() + " stdout=" + r.stdoutString());
}
```

## Backend choice tree

| Backend                  | Cold start        | RAM/instance    | Network isolation              | Use when                              |
| ------------------------ | ----------------- | --------------- | ------------------------------ | ------------------------------------- |
| `process`                | \~50–150ms        | \~10–30MB       | **Not enforced** (host-shared) | Dev / CI / portable fallback          |
| `container`              | \~150–400ms       | \~30–80MB       | Real (`--network=none`)        | Production code-exec, untrusted shell |
| `wasm` (v1 stub)         | sub-50ms (target) | sub-MB (target) | Capability-gated               | Pyodide / Wasmer adapter (follow-up)  |
| `firecracker` (deferred) | \~125ms           | \~5MB           | microVM-isolated               | Linux production at scale             |

`SandboxFactory.preferred()` picks the highest-priority backend whose `available()` returns true:

| Backend     | priority                 |
| ----------- | ------------------------ |
| process     | 10                       |
| wasm        | 75 (when adapter ships)  |
| container   | 50                       |
| firecracker | 100 (when adapter ships) |

## Resource limits

```java
ResourceLimits limits = new ResourceLimits(
        1.0,                            // cpuShares (1.0 = one full core)
        256,                            // memoryMb
        128,                            // diskMb (scratch space)
        Duration.ofSeconds(30),         // timeout (per-execute wall clock)
        64);                            // maxProcesses (0 = no limit)
```

Validation rejects:

- `cpuShares <= 0`
- `memoryMb <= 0`
- `diskMb < 0`
- `timeout` zero or negative — sandbox without a deadline is a footgun
- `maxProcesses < 0`

Presets:

- `ResourceLimits.minimal()` — 0.25 CPU / 64MB / 16MB disk / 5s / 32 procs (policy checks, regex eval)
- `ResourceLimits.standard()` — 1 CPU / 256MB / 256MB disk / 30s / 64 procs (typical code-exec)
- `ResourceLimits.of(cpu, memMb, timeoutSec)` — convenience for the common shape

## Network policy

```java
NetPolicy.denyAll();                                  // recommended default
NetPolicy.allow(List.of("github.com", "api.openai.com:443"));
NetPolicy.inherit();                                  // sandbox inherits host network
```

| Policy      | ProcessSandbox                                         | ContainerSandbox                                |
| ----------- | ------------------------------------------------------ | ----------------------------------------------- |
| `DenyAll`   | Logged, NOT enforced (JVM child inherits host network) | `--network=none` — real                         |
| `AllowList` | Logged, NOT enforced                                   | Rejected at create time (deferred to follow-up) |
| `Inherit`   | Default, no warning                                    | `--network=host`                                |

ProcessSandbox is honest about its limits — it logs WARN at create time when the requested policy isn't enforceable, rather than silently downgrading. Real network isolation requires `container` or `firecracker`.

## Filesystem mounts

```java
SandboxSpec.builder()
        .fsMount(FsMount.readOnly(Path.of("./inputs"), Path.of("/data")))
        .fsMount(FsMount.readWrite(Path.of("./outputs"), Path.of("/work")))
        // …
        .build();
```

| Backend          | Read-only                      | Read-write                                               |
| ---------------- | ------------------------------ | -------------------------------------------------------- |
| ProcessSandbox   | Copy-in (host file → jail dir) | Rejected at create (copy-in can't propagate writes back) |
| ContainerSandbox | `--mount type=bind,readonly`   | `--mount type=bind`                                      |

Sandbox path MUST be absolute — the sandbox sees its filesystem rooted at `/`.

## Pool reuse

For high-concurrency workloads, reuse warm instances through a pool:

```java
SandboxPool pool = new SandboxPool(
        SandboxFactory.preferred(),
        spec.toBuilder().warmable(true).build(),
        /* maxSize */ 16,
        Duration.ofSeconds(2));

try (SandboxPool.Lease lease = pool.borrow()) {
    SandboxResult r = lease.execute(Command.of("python", "task.py"));
    // ... lease.close() returns sandbox to the pool
}

pool.close();   // drains every idle sandbox
```

The pool degrades gracefully when full + timeout-exceeded (creates a non-pooled sandbox so callers never block forever); explicit `Lease.terminate()` evicts a sandbox the caller has reason to mark unhealthy.

## Observability

Every `execute()` emits a `SandboxExecutionEvent` to the wired listener:

```java
SandboxExecutionListener listener = event ->
        log.info("[sandbox] backend={} image={} exit={} timeoutMs={} cpuMs={}",
                event.backend(), event.image(), event.exitCode(),
                event.resourceUsage().wallClockMs(), event.resourceUsage().cpuMillis());

try (Sandbox sb = new ObservedSandbox(
        factory.create(spec),
        listener,
        factory.id())) {
    sb.execute(Command.shell("python -c 'print(1)'"));
}
```

The event carries the sandboxId, backend, image, argv, exit, timedOut flag, ResourceUsage, and the network-policy class name (`DenyAll` / `Inherit` / `AllowList`). Listener exceptions are caught + logged so observability outages never break execution.

## Pairs with accountability

Sandbox events correlate with [`AgentLiabilityRecord`](/docs/security/accountability) entries on the same correlation id — a single audit timeline for "agent X attempted action Y inside sandbox Z, used N CPU-ms, exited with code C". Operators wire both listeners on the same agent and downstream consumers join on `correlationId`.

## What's not in v1 (deferred to follow-ups)

- **`FirecrackerSandbox`** — Linux microVM backend; child issue
- **WASM runtime adapters** — Pyodide / Wasmer / Bun WASM; child issues per language
- **`AllowList` enforcement on ContainerSandbox** — needs custom network + iptables; v2
- **GPU sandbox** — model inference inside sandbox; v3
- **Multi-tenant resource quota** — per-tenant aggregate limits; v2
- **Snapshot/restore** — running sandbox state save; v2
- **`tnsai-tools` refactor** — `PythonExecutionTools` / `JsExecutionTools` move to the SPI; child issue (current implementations document the gap explicitly via "WARNING: not a sandbox" headers)

## See also

- [Accountability](/docs/security/accountability) — sandbox events correlate with `AgentLiabilityRecord` for the same dispatch
- [Approvals and Annotations](/docs/security/approvals-and-annotations) — `@ApprovalRequired` works alongside sandboxing (approvals gate access; sandbox bounds the blast radius)
- [Enforcement](/docs/security/enforcement) — `SecurityEnforcer` runs OUTSIDE the sandbox; sandbox is the inner ring

---

# Server Hardening

URL: https://tnsai.dev/docs/security/server-hardening
Description: tnsai-server exposes the framework over HTTP + WebSocket. Every input — HTTP body, WS frame, query param — is potentially attacker-controlled if the listener is reachable. The hardening surface in com.tnsai.server.security (TNS-302) is built on five concentric defences, every one of which is

import { Callout } from 'fumadocs-ui/components/callout'

**enabled by default**:

1. **Bind policy** — listener binds to loopback unless explicitly opted out.
2. **Bearer auth** — every HTTP request and WS upgrade requires a token.
3. **Origin allowlist** — the WS upgrade rejects unrecognised browser origins.
4. **Per-session capability tokens** — only the client that created a session
   can drive it.
5. **Workspace allowlist** — `/api/index` accepts only paths under the
   configured workspace roots, with a file-count cap.

The five layers are independent. A misconfiguration of any one (e.g. you
forget to set a Bearer token) does not collapse the others.

## Bind policy

Default: `127.0.0.1` (loopback). Opt in to a non-loopback bind explicitly:

```bash
# Loopback (default — no flag)
java -jar tnsai-server.jar

# Bind to all interfaces
java -jar tnsai-server.jar --host 0.0.0.0 --allow-public

# Bind to a specific interface
java -jar tnsai-server.jar --host 192.168.1.10 --allow-public
```

| Source | Variable / flag                                 |
| ------ | ----------------------------------------------- |
| CLI    | `--host <addr>` / `--allow-public`              |
| Env    | `TNSAI_HOST=<addr>` / `TNSAI_ALLOW_PUBLIC=true` |

Without `--allow-public`, a non-loopback host crashes the JVM at startup with
`IllegalStateException`. This is intentional — the default refuses to make
anything reachable beyond the host machine.

A non-loopback bind also requires a Bearer token (next section); starting
with `--allow-public` and no `TNSAI_TOKEN` is rejected.

## Bearer authentication

Every HTTP request and every WS upgrade must carry
`Authorization: Bearer <TNSAI_TOKEN>` when a token is configured. Missing or
mismatched tokens return `401`. Health probes (`/health`, `/health/live`,
`/health/ready`) are explicitly exempt so orchestrators can probe without a
secret.

```bash
TNSAI_TOKEN=$(openssl rand -hex 32) \
  java -jar tnsai-server.jar
```

When `TNSAI_TOKEN` is unset, auth is disabled — but **only valid combined
with the loopback bind**. The constructor of `TnsServer` rejects the
non-loopback + no-token combination.

Token comparison is constant-time over equal-length values. Length itself
is not secret, so length-mismatch shortcuts; value-byte timing is the part
that matters.

## Origin allowlist (WebSocket)

Browsers attach `Origin: <scheme://host[:port]>` on every cross-origin WS
handshake. The default `OriginPolicy.loopback()` admits:

- Missing or `null` Origin (native non-browser clients).
- `http://localhost[:port]`, `https://localhost[:port]`, and the `127.0.0.1`
  / `::1` variants.

Any other Origin gets `403 FORBIDDEN_ORIGIN` at upgrade time. Add explicit
production origins via:

```bash
TNSAI_ALLOWED_ORIGINS="https://app.tnsai.dev,https://staging.tnsai.dev" \
  java -jar tnsai-server.jar
```

The matcher is case-insensitive and not prefix-greedy: `localhost.evil.com`
is rejected even though it has `localhost` as a substring.

## Per-session capability tokens

The first request to touch a `sessionId` mints a random 32-byte URL-safe
token. The server returns it via the `X-Session-Capability` response header
(HTTP) or via the WS upgrade response. Every subsequent request with the
same `sessionId` must present the same token; otherwise `403 FORBIDDEN_SESSION`.

This is the layer that prevents one client on the same listener from
attaching to another client's session and reading their tool outputs.

### HTTP transport

```bash
# 1. First call — server mints a capability and echoes it.
curl -i -X POST http://127.0.0.1:7777/api/index \
  -H "Authorization: Bearer $TNSAI_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"sessionId":"alice-1","path":"./project"}'
# < X-Session-Capability: AbCdEf...

# 2. Subsequent calls present that capability.
curl -X POST http://127.0.0.1:7777/api/search \
  -H "Authorization: Bearer $TNSAI_TOKEN" \
  -H "X-Session-Capability: AbCdEf..." \
  -H "Content-Type: application/json" \
  -d '{"sessionId":"alice-1","query":"hello"}'
```

### WebSocket transport

The capability is supplied as a query string on the upgrade URL:

```text
ws://127.0.0.1:7777/chat?sessionId=alice-1&capability=AbCdEf...
```

The first WS upgrade for a sessionId mints; the response carries
`X-Session-Capability`. Subsequent upgrades must echo the same value or get
`403`. After upgrade, the connection is bound to its sessionId — frames
that try to drive a different sessionId on the same socket are rejected
with `FORBIDDEN_SESSION`.

Tokens are in-memory; a server restart invalidates every capability, which
matches the in-memory nature of `SessionManager` itself.

## Workspace allowlist

`/api/index` accepts a directory path from the request body. Without an
allowlist, that endpoint is a "give me your disk" gadget — a hostile client
can `POST /api/index {"path":"/"}` then `POST /api/search` to exfiltrate
contents.

The default `WorkspaceConfig.cwd()` allows only paths under the JVM's
working directory. Configure additional roots:

```bash
# Multiple roots (PATH-separator)
TNSAI_WORKSPACE_ROOT="/srv/projects/foo:/srv/projects/bar" \
  java -jar tnsai-server.jar

# Lower the file-count cap
TNSAI_WORKSPACE_MAX_FILES=10000 \
  java -jar tnsai-server.jar
```

Each request path is canonicalised via `Path.toRealPath()` before the
allowlist check. Symlinks that escape the workspace are resolved and then
rejected — there is no way to sneak content in via a child of an allowed
directory whose ancestor is a symlink to `/etc`.

`/api/index` also rejects `400 OUTSIDE_WORKSPACE` when the directory tree
exceeds `maxFileCount` (default 50,000). The walk short-circuits on the
first count past the cap, so the rejection is constant-time in the size of
the offending tree.

## Programmatic configuration

If you embed `TnsServer` in another process, every layer is composable on
the builder:

```java
TnsServer server = TnsServer.builder()
    .port(7777)
    .bindPolicy(new BindPolicy("0.0.0.0", true))
    .authConfig(AuthConfig.withToken(System.getenv("TNSAI_TOKEN")))
    .originPolicy(OriginPolicy.with(
            "https://app.tnsai.dev",
            "https://staging.tnsai.dev"))
    .workspaceConfig(WorkspaceConfig.roots(
            Path.of("/srv/projects/foo"),
            Path.of("/srv/projects/bar")).withMaxFileCount(20_000))
    .llmClientSupplier(...)
    .build();
server.start();
```

## Acceptance reference

The five reject paths covered by the integration test:

| Reject case                                | Status                          | Code                |
| ------------------------------------------ | ------------------------------- | ------------------- |
| HTTP without Bearer token                  | `401`                           | `UNAUTHENTICATED`   |
| HTTP with mismatched session capability    | `403`                           | `FORBIDDEN_SESSION` |
| `/api/index` with path outside workspace   | `400`                           | `OUTSIDE_WORKSPACE` |
| WS upgrade with disallowed Origin          | `403`                           | `FORBIDDEN_ORIGIN`  |
| Non-loopback bind without `--allow-public` | startup `IllegalStateException` | —                   |

## What's not in v1

- **Capability rotation** — tokens live for the lifetime of the server.
  Rotation hooks are a follow-up issue.
- **Admin role** — every capability owns its own session and only its own
  session. There is no `/api/audit?all=true` carve-out yet.
- **Token persistence** — capabilities are in-memory; a server restart
  invalidates every session.
- **CIDR allowlist on the bind side** — the policy is binary loopback /
  public; finer-grained network ACLs are out of scope.

## See also

- [Sandbox](/docs/security/sandbox) — isolated execution primitive used by tools that
  agents drive after the listener has authenticated their request.
- [Approvals and Annotations](/docs/security/approvals-and-annotations) — per-action
  approval gates, applied after auth + capability admit the request.
- [Cost Governance](/docs/security/cost-governance) — `CostBudget` per tenant / agent,
  applied after auth identifies who the request belongs to.

---

# Server Advanced Features

URL: https://tnsai.dev/docs/server/advanced
Description: Declarative hooks, agent factory, idle shutdown management, and the RAG pipeline (indexing, chunking, retrieval).

import { Callout } from 'fumadocs-ui/components/callout'

## Declarative Hook System

The `com.tnsai.server.hooks` package provides a YAML-driven hook system that attaches lifecycle callbacks to agents without writing Java code.

### Architecture Overview

```text
hooks.yaml --> DeclarativeHookRegistry --> ToolCallListener on Agent
                     |                          |
                     |                    PRE_TOOL_USE / POST_TOOL_USE
                     |
               DeclarativeHookExecutor
                     |
              COMMAND (shell) / LOG (message)
```

### HookEvent

Lifecycle events that hooks can listen for:

| Event           | Description            |
| --------------- | ---------------------- |
| `PRE_TOOL_USE`  | Before a tool executes |
| `POST_TOOL_USE` | After a tool executes  |
| `ON_START`      | When the agent starts  |
| `ON_STOP`       | When the agent stops   |
| `ON_ERROR`      | When an error occurs   |

Parsing supports both camelCase (`preToolUse`) and UPPER\_SNAKE (`PRE_TOOL_USE`) from YAML.

### HookActionType

| Type      | Description             |
| --------- | ----------------------- |
| `COMMAND` | Execute a shell command |
| `LOG`     | Log a message           |

### HookAction

Record defining what to execute when a hook fires.

```java
// Factory methods
HookAction cmd = HookAction.command("echo 'tool finished'");
HookAction log = HookAction.log("Tool ${tool} is about to execute");
```

Fields: `type` (HookActionType), `command` (for COMMAND), `message` (for LOG). Supports `${variable}` placeholders that are substituted at execution time.

### HookMatcher

Matches tool names against patterns. Case-insensitive using `Locale.ROOT`.

```java
// Exact match
HookMatcher exact = new HookMatcher("shell");
exact.matches("shell");    // true
exact.matches("Shell");    // true (case-insensitive)

// Glob wildcard
HookMatcher glob = new HookMatcher("file_*");
glob.matches("file_read");  // true
glob.matches("file_write"); // true
glob.matches("shell");      // false

// Match all tools
HookMatcher all = HookMatcher.ALL; // pattern "*"
```

### HookConfig

A single hook definition, typically parsed from YAML.

```java
// Record: HookConfig(event, matcher, action)
HookConfig config = new HookConfig(
    HookEvent.PRE_TOOL_USE,
    new HookMatcher("file_*"),
    HookAction.log("Tool ${tool} is about to execute")
);
```

### HookExecutor Interface

```java
@FunctionalInterface
public interface HookExecutor {
    void execute(HookAction action, Map<String, String> context);
}
```

### DeclarativeHookExecutor

Default executor that runs shell commands (with 30-second timeout) and logs messages. All execution is best-effort: failures are logged but never propagate.

Variable substitution replaces `${key}` placeholders with values from the context map.

### DeclarativeHookRegistry

Parses YAML hook definitions and registers them as runtime listeners on agents.

**YAML schema:**

```yaml
hooks:
  - event: preToolUse
    matcher: "file_*"
    action:
      type: log
      message: "Tool ${tool} is about to execute"

  - event: postToolUse
    matcher: "shell"
    action:
      type: command
      command: "echo 'shell tool finished'"

  - event: onStart
    action:
      type: log
      message: "Agent started"
```

**Loading and applying hooks:**

```java
// Parse from YAML
InputStream yaml = new FileInputStream("hooks.yaml");
DeclarativeHookRegistry registry = DeclarativeHookRegistry.fromYaml(yaml);

// Parse with custom executor
DeclarativeHookRegistry registry = DeclarativeHookRegistry.fromYaml(yaml, customExecutor);

// Programmatic creation
DeclarativeHookRegistry registry = DeclarativeHookRegistry.of(hookConfigs);

// Apply to an agent (registers ToolCallListener + fires ON_START)
registry.applyTo(agent);
```

**Manual event firing:**

```java
// Fire a generic event
registry.fireEvent(HookEvent.ON_START, Map.of("agent", agentId));

// Fire a tool-specific event (checks HookMatcher against tool name)
registry.fireToolEvent(HookEvent.PRE_TOOL_USE, "shell",
    Map.of("event", "preToolUse"));
```

**Querying hooks:**

```java
List<HookConfig> all = registry.getHooks();
List<HookConfig> preHooks = registry.getHooksForEvent(HookEvent.PRE_TOOL_USE);
```

When applied to an agent via `applyTo()`, the registry creates a `ToolCallListener` that:

- Fires `PRE_TOOL_USE` hooks in `onToolCallStart`
- Fires `POST_TOOL_USE` hooks in `onToolCallComplete`
- Fires `ON_ERROR` hooks when `success` is false

## Agent Factory

`AgentFactory` (`com.tnsai.server.agent`) creates agents for chat sessions with built-in tools and configurable LLM providers.

### Built-in Tools

Every agent created by `AgentFactory` includes:

| Tool            | Description                                      |
| --------------- | ------------------------------------------------ |
| `ShellTool`     | Shell command execution with risk classification |
| `FileReadTool`  | Read file contents (auto-approved)               |
| `FileWriteTool` | Write file contents (requires approval)          |

### Creating Agents

```java
// Setup
Supplier<LLMClient> llmSupplier = () -> new AnthropicClient("claude-sonnet-4-20250514");
AgentFactory factory = new AgentFactory(llmSupplier);

// Or with a custom working directory
AgentFactory factory = new AgentFactory(llmSupplier, Path.of("/project"));

// Simple creation (role name only)
Agent agent = factory.createAgent("agent-001", "assistant");

// With additional tools (e.g., MCP proxy tools)
Agent agent = factory.createAgent("agent-001", "developer", additionalTools);

// With provider and model override
Agent agent = factory.createAgent("agent-001", "developer",
    "ollama", "glm-5:cloud", additionalTools);

// Full configuration with custom goal and domain
Agent agent = factory.createAgent("agent-001", "developer",
    "ollama", "glm-5:cloud",
    "Write clean, tested code for the TnsAI framework",
    "tnsai-core",
    additionalTools);

// Multi-role agent from RoleDef list
Agent agent = factory.createAgent("agent-001", "assistant",
    "ollama", "glm-5:cloud", roleDefs, additionalTools);
```

### Built-in Role Goals

| Role        | Goal                                                                                          |
| ----------- | --------------------------------------------------------------------------------------------- |
| `assistant` | Help users by answering questions. Use tools to read files, execute commands, and write code. |
| `developer` | Write clean, well-tested code. Use shell and file tools to explore and implement.             |
| `reviewer`  | Analyze code for bugs, style, and improvements. Read files and run tests.                     |
| `architect` | Design systems and make technical decisions. Explore codebase structure.                      |
| `tester`    | Write tests and find edge cases. Use shell tools to run tests.                                |
| (other)     | Perform the role of `{name}` effectively. Use available tools as needed.                      |

### Provider Resolution

When `provider` is specified:

- `"ollama"` -- creates `OllamaClient` with the given model (default: `"glm-5:cloud"`)
- Any other value -- falls back to the default `LLMClient` supplier with a warning

## Idle Shutdown Manager

`IdleShutdownManager` (`com.tnsai.server.health`) auto-shuts down the server after a configurable idle period when no active WebSocket connections exist (ADR-2: hybrid auto-daemon).

```java
IdleShutdownManager manager = new IdleShutdownManager(
    connectionManager,           // WsConnectionManager instance
    Duration.ofMinutes(30),      // idle timeout
    () -> server.stop()          // shutdown action
);

manager.start();
// If no connections exist at start, schedules shutdown

// Called by WebSocket handlers
manager.onConnectionOpened();  // cancels pending shutdown
manager.onConnectionClosed();  // schedules shutdown if no connections remain

manager.stop();  // cleanup
```

Behavior:

- On connection open: cancels any pending shutdown
- On connection close: if no active connections remain, schedules shutdown after timeout
- On timeout: re-checks for connections before shutting down (double-check safety)
- Uses a daemon thread (`tns-idle-shutdown`) for the scheduler

## RAG Pipeline

The RAG (Retrieval-Augmented Generation) system in `com.tnsai.server.rag` provides file indexing, code-aware chunking, and hybrid retrieval.

### RagService

Orchestrates the full RAG pipeline for a single session. Thread-safe: indexing is serialized via a lock; reads (search) are concurrent.

```java
RagService rag = new RagService();

// Index a directory
rag.indexDirectory(Path.of("/project/src"), progress ->
    System.out.printf("Indexing: %d/%d - %s%n",
        progress.indexedFiles(), progress.totalFiles(), progress.currentFile()));

// Search the knowledge base
List<SearchResult> results = rag.search("authentication logic", 5);

// Build a context-augmented prompt
String augmentedPrompt = rag.buildContextPrompt("How does auth work?", 3);
// Returns:
// [Relevant code context]
// --- file: src/main/java/Auth.java (lines 10-30) ---
// <chunk content>
//
// [User question]
// How does auth work?
```

**Document management:**

```java
// Add a document manually
String docId = rag.addDocument("Some text content",
    Map.of("source", "manual", "tag", "notes"));

// Remove a document
boolean removed = rag.removeDocument(docId);

// List all managed documents
List<RagService.DocumentInfo> docs = rag.listDocuments();
// DocumentInfo(id, preview, contentLength, metadata)

// Get a specific document
Optional<Document> doc = rag.getDocument(docId);

// Get index status
IndexStatus status = rag.getStatus();
// IndexStatus(fileCount, chunkCount, lastIndexedAt, indexedPath)

// Clear everything
rag.clear();
```

### FileIndexer

Walks a directory tree and indexes supported source files into a `KnowledgeBase`.

**Features:**

- Respects `.gitignore` and `.tnsignore` patterns
- Skips binary files and known build/dependency directories
- SHA-256 content hashing for incremental re-indexing (unchanged files are skipped)
- Progress reporting via callback

**Supported file extensions:** `java`, `ts`, `tsx`, `js`, `jsx`, `py`, `md`, `json`, `yml`, `yaml`, `xml`, `html`, `css`, `sh`, `sql`, `go`, `rs`, `rb`, `kt`, `scala`, `c`, `cpp`, `h`

**Skipped directories:** `.git`, `node_modules`, `build`, `dist`, `target`, `.idea`, `.vscode`, `.gradle`, `__pycache__`, `vendor`, `.next`, `out`, `coverage`, `.svn`, `.hg`

**Maximum file size:** 512 KB

```java
FileIndexer indexer = new FileIndexer();

FileIndexer.IndexResult result = indexer.index(
    Path.of("/project"),
    knowledgeBase,
    bm25Stream,
    progress -> System.out.println(progress.currentFile())
);
// IndexResult(fileCount, chunkCount)

// Force full re-index on next run
indexer.clearHashes();
```

### CodeChunker

Splits source code files into semantically meaningful chunks for RAG indexing. Each chunk is a `Document` with metadata: `file`, `startLine`, `endLine`, `language`.

**Chunking strategies by language:**

| Language               | Strategy                                               |
| ---------------------- | ------------------------------------------------------ |
| Java, Kotlin, Scala    | Class and method boundary detection via regex          |
| TypeScript, JavaScript | Function, class, and arrow-function boundary detection |
| Markdown               | Heading boundary detection                             |
| Everything else        | Fixed-size line groups (max 100 lines)                 |

Files under 100 lines are kept as a single chunk.

```java
// Chunk a file
List<Document> chunks = CodeChunker.chunk(
    Path.of("src/Auth.java"),  // file path (metadata)
    fileContent,                // file content string
    "java"                      // language key
);

// Detect language from filename
String lang = CodeChunker.detectLanguage("Auth.java");    // "java"
String lang = CodeChunker.detectLanguage("app.tsx");       // "tsx"
String lang = CodeChunker.detectLanguage("README.md");     // "md"
String lang = CodeChunker.detectLanguage("unknown.xyz");   // "text"
```

### Hybrid Retrieval

`RagService` uses a `HybridRetriever` that combines two retrieval streams:

- **BM25Stream** (weight 0.6) -- keyword-based BM25 scoring
- **KnowledgeBaseStream** (weight 0.4) -- vector similarity from the `KnowledgeBase`

Results are merged and ranked by weighted score.

## Cross-References

- [WebSocket Protocol](/docs/server/websocket) -- WebSocket v1 protocol for client communication
- [Tool Approval](/docs/server/tool-approval) -- tool approval flow via WebSocket
- [RAG Pipeline](/docs/capabilities/rag/pipeline) -- RAG pipeline overview
- [Observability](/docs/observability) -- evaluation hooks and tracing

---

# Server

URL: https://tnsai.dev/docs/server
Description: Run TnsAI as a backend — WebSocket API, session management, human-in-the-loop tool approval.

import { Callout } from 'fumadocs-ui/components/callout'

## Pages

- [WebSocket](/docs/server/websocket) — The v1 WebSocket protocol.
- [Tool Approval](/docs/server/tool-approval) — Human-gated destructive operations.
- [Advanced](/docs/server/advanced) — Scaling, persistence, multi-tenant deployment.

> For production RAG pipelines, see [RAG Pipeline](/docs/capabilities/rag/pipeline).

---

# Tool Approval

URL: https://tnsai.dev/docs/server/tool-approval
Description: The server implements a risk-based tool approval system that classifies tool calls by danger level, checks them against the session's autonomy setting, and either auto-approves or blocks the agent's virtual thread until the user responds via WebSocket.

import { Callout } from 'fumadocs-ui/components/callout'

## ToolRisk Enum

Every tool call is classified into a risk level before the autonomy matrix is consulted. Risk levels range from safe read-only operations to dangerous destructive commands, and this classification determines whether the user needs to approve the action.

| Risk          | Label    | `requiresApproval()` | `isRejected()` | Examples                                  |
| ------------- | -------- | -------------------- | -------------- | ----------------------------------------- |
| `READ_ONLY`   | low      | false                | false          | `file_read`, `git` (status, log, diff)    |
| `BUILD_TEST`  | low      | false                | false          | `mvn test`, `npm run build`               |
| `WRITE`       | medium   | true                 | false          | `file_write`                              |
| `DESTRUCTIVE` | high     | true                 | false          | `git_write` (reset, force push), `rm -rf` |
| `NETWORK`     | medium   | true                 | false          | `curl`, `wget`                            |
| `ESCALATION`  | critical | true                 | **true**       | `sudo`, `su` -- always rejected           |
| `UNKNOWN`     | high     | true                 | false          | Unrecognized commands                     |

Shell commands are classified by `ShellTool.classifyCommand()`, which inspects the command string for destructive patterns, network tools, and privilege escalation keywords.

## AutonomyLevel Enum

The autonomy level controls how much freedom the agent has to execute tools without asking. At `FULL_AUTO`, the agent handles most things itself; at `MANUAL`, every tool call requires explicit user approval. Choose based on how much you trust the agent and the sensitivity of the environment.

| Level        | LOW risk | MEDIUM risk | HIGH risk | CRITICAL risk |
| ------------ | -------- | ----------- | --------- | ------------- |
| `FULL_AUTO`  | Auto     | Auto        | Auto      | Ask           |
| `SUPERVISED` | Auto     | Auto        | Ask       | Ask           |
| `CAUTIOUS`   | Auto     | Ask         | Ask       | Ask           |
| `MANUAL`     | Ask      | Ask         | Ask       | Ask           |

`ESCALATION` risk is always rejected outright -- it never reaches the approval matrix.

Change the autonomy level at runtime:

```json
{"v": 1, "type": "set_autonomy", "sessionId": "s1", "level": "CAUTIOUS"}
```

The server broadcasts an `autonomy_changed` event and updates all existing `WsToolApprovalFilter` instances for that session.

## Approval Flow

This diagram shows the decision path for every tool call, from initial risk classification through to the final approve/reject decision. Understanding this flow is essential for building a client that handles tool approval correctly.

```
Agent calls tool
       |
       v
WsToolApprovalFilter.isAllowed(toolName, args)
       |
       +-- ESCALATION? --> REJECT (broadcast error, audit "rejected")
       |
       +-- autonomyLevel.requiresApproval(risk) == false? --> AUTO-APPROVE (audit "auto_approved")
       |
       +-- trustedTools.contains(toolName)? --> AUTO-APPROVE (audit "trusted")
       |
       v
Send tool_approve_request to client
       |
       v
Block virtual thread (CompletableFuture.get, 120s timeout)
       |
       +-- User sends "approve"  --> ALLOW (audit "approved")
       +-- User sends "reject"   --> DENY  (audit "rejected")
       +-- User sends "always"   --> ALLOW + add to trustedTools (audit "approved")
       +-- Timeout (120s)        --> DENY  (audit "timeout", broadcast APPROVAL_TIMEOUT error)
```

## WsToolApprovalFilter

The `WsToolApprovalFilter` is the server-side component that bridges the tool approval system with the WebSocket protocol. It implements `ToolCallFilter` from tnsai-core and is created automatically for each session+agent pair.

```java
// Created automatically when a chat event is processed
WsToolApprovalFilter filter = new WsToolApprovalFilter(
    sessionId, agentId, connectionManager, auditService);
filter.setAutonomyLevel(session.getAutonomyLevel());
agent.setToolCallFilter(filter);
```

Key behaviors:

- **CompletableFuture blocking**: The filter creates a `CompletableFuture<Boolean>` per pending approval. The agent's virtual thread calls `future.get(120, SECONDS)`. This blocks the virtual thread without pinning OS threads.
- **120-second timeout**: If the user does not respond within 2 minutes, the tool call is rejected and an `APPROVAL_TIMEOUT` error is broadcast.
- **Always-trust**: When the user sends `decision: "always"`, the tool name is added to a `Set<String> trustedTools`. Future calls to that tool in this session skip the approval dialog.
- **Cancellation**: `cancelAll()` completes all pending futures with `false`, unblocking any waiting agents. Called on `cancel` events and server shutdown.

## AuditService

Every tool call decision -- whether auto-approved, manually approved, rejected, or timed out -- is recorded in a per-session audit trail. This provides full accountability for what the agent did and why, which is essential for debugging and compliance.

```java
// AuditService holds up to 500 entries (bounded ConcurrentLinkedDeque)
AuditService audit = session.getAuditService();

// Record an entry
audit.record(new AuditEntry(
    toolCallId, agentId, toolName,
    risk.label(),   // "low", "medium", "high", "critical"
    decision,       // "auto_approved", "approved", "rejected", "trusted", "timeout"
    timestamp,
    summary
));

// Query
List<AuditEntry> all = audit.getEntries();
List<AuditEntry> recent = audit.getEntries(10);  // last 10
```

When the trail exceeds 500 entries, the oldest entry is dropped. Each audit entry is also broadcast as an `audit_entry` WebSocket event for real-time UI display.

The audit trail is accessible via the REST API:

```
GET /api/audit?sessionId=my-session
```

## Tool Risk Classification

The filter uses a combination of tool name matching and command string analysis to determine the risk level. Shell commands get the most detailed classification because they can execute arbitrary system commands.

```java
private ToolRisk classifyToolRisk(String toolName, Map<String, Object> arguments) {
    if ("shell".equals(toolName)) {
        // Delegates to ShellTool.classifyCommand() for fine-grained analysis
        return ShellTool.classifyCommand(arguments.get("input").toString());
    }
    if ("file_write".equals(toolName)) return ToolRisk.WRITE;
    if ("file_read".equals(toolName)) return ToolRisk.READ_ONLY;
    if ("git".equals(toolName))       return ToolRisk.READ_ONLY;
    if ("git_write".equals(toolName)) return ToolRisk.DESTRUCTIVE;
    return ToolRisk.UNKNOWN;  // Unknown tools treated as high risk
}
```

Shell commands get the most detailed classification, examining the command string for patterns like `rm -rf`, `sudo`, `curl`, `git push --force`, etc.

## Declarative YAML Lifecycle Hooks

Lifecycle hooks let you run custom actions (like pre-deploy checks or notification scripts) in response to server events, without writing any Java code. You define them in YAML, and the server executes them automatically when the matching event occurs.

### HookConfig

Each hook has a name, the event it listens for, a glob matcher to filter which tools or agents trigger it, and an action to execute. Here is a complete example configuration.

```yaml
hooks:
  - name: "pre-deploy-check"
    event: "tool.execute"
    matcher: "deploy*"
    action:
      type: "command"
      command: "scripts/pre-deploy-check.sh"
    enabled: true

  - name: "audit-shell"
    event: "tool.execute"
    matcher: "shell"
    action:
      type: "log"
      message: "Shell command executed: {{toolName}} with args: {{args}}"
    enabled: true

  - name: "notify-on-error"
    event: "agent.error"
    matcher: "*"
    action:
      type: "command"
      command: "scripts/notify-error.sh {{agentId}} {{error}}"
    enabled: true
```

### HookMatcher

Hooks use glob patterns to match against tool names or event types. This lets you target specific tools, groups of tools, or all events with a single rule.

```yaml
matcher: "file_*"        # matches file_read, file_write
matcher: "deploy*"       # matches deploy, deploy-staging
matcher: "*"             # matches everything
matcher: "shell"         # exact match
```

### Hook Action Types

There are two action types. Both support `{{variable}}` placeholders that are replaced with actual values at execution time.

| Type      | Description                                                                                            |
| --------- | ------------------------------------------------------------------------------------------------------ |
| `command` | Execute a shell command. Supports `{{variable}}` placeholders for tool name, arguments, agent ID, etc. |
| `log`     | Write a structured log entry. Supports the same placeholders.                                          |

### DeclarativeHookExecutor

The `DeclarativeHookExecutor` runs matched hooks when events fire. Command hooks run asynchronously and have a configurable timeout (default: 30 seconds). Failed hooks are logged but never block agent execution, so a broken hook script will not take down your agent.

### DeclarativeHookRegistry

The `DeclarativeHookRegistry` manages all registered hooks and matches them against incoming events. You can also enable or disable individual hooks at runtime without restarting the server.

```java
DeclarativeHookRegistry registry = DeclarativeHookRegistry.fromConfig(config);

// Match hooks for an event
List<HookConfig> matched = registry.match("tool.execute", "shell");

// Enable/disable hooks at runtime
registry.setEnabled("pre-deploy-check", false);

// List all hooks
List<HookConfig> all = registry.getAll();
```

---

# WebSocket Protocol

URL: https://tnsai.dev/docs/server/websocket
Description: The Server module is a Javalin-based backend that bridges frontends (CLI, IDE, web) to the TnsAI agent framework via WebSocket. It provides multi-agent sessions, real-time streaming, risk-based tool approval, hybrid RAG search, and an audit trail.

import { Callout } from 'fumadocs-ui/components/callout'

## Quick Start

Start a TnsAI server with just a few lines of code. The server opens a WebSocket endpoint that clients can connect to for real-time agent interaction.

```java
TnsServer server = TnsServer.builder()
    .port(7777)
    .llmClientSupplier(() -> LLMClientFactory.create("openai", "gpt-4o", 0.7f))
    .idleTimeout(Duration.ofMinutes(30))
    .build();
server.start();
// WebSocket endpoint: ws://localhost:7777/chat
```

The builder requires a `Supplier<LLMClient>` -- the factory is called once per agent creation. Default port is 7777. An idle shutdown manager auto-terminates the server after 30 minutes of inactivity.

## Connection Lifecycle

Understanding the connection lifecycle helps you build reliable clients. Here is what happens from the moment a client connects to when it disconnects.

1. Client opens a WebSocket connection to `/chat`
2. `WsHandler.onConnect` fires, registers the connection with `IdleShutdownManager`
3. Client sends a `chat` event with a `sessionId` -- the server creates a `SessionInfo` (with a default assistant agent) on first access
4. The connection is registered to that session in `WsConnectionManager` -- multiple connections can share one session
5. On disconnect, `onClose` removes the connection and notifies the idle manager

Every message must carry the protocol version (`v: 1`) and a `sessionId`. The server rejects messages with mismatched versions (`PROTOCOL_MISMATCH` error).

## Protocol v1 Events

### Client to Server

These are the events that a client can send to the server. Every event must include `v: 1` (protocol version) and a `sessionId`.

| Type           | Fields                                                               | Description                                                                 |
| -------------- | -------------------------------------------------------------------- | --------------------------------------------------------------------------- |
| `chat`         | `message`                                                            | Send a user message to the session's active agent                           |
| `agent_add`    | `role`, `provider?`, `model?`, `goal?`, `domain?`, `name?`, `roles?` | Add a new agent to the session                                              |
| `agent_remove` | `agentId`                                                            | Remove an agent from the session                                            |
| `tool_approve` | `toolCallId`, `decision`                                             | Respond to a tool approval request (`approve`, `reject`, `always`)          |
| `cancel`       | --                                                                   | Cancel the running chat task and pending approvals                          |
| `mcp_tools`    | `tools: McpToolDef[]`                                                | Register MCP server tools for the session                                   |
| `mcp_result`   | `toolCallId`, `result?`, `error?`                                    | Return the result of an MCP proxy tool call                                 |
| `set_autonomy` | `level`                                                              | Change the autonomy level (`FULL_AUTO`, `SUPERVISED`, `CAUTIOUS`, `MANUAL`) |
| `kb_add`       | `content`, `metadata?`                                               | Add a document to the session knowledge base                                |
| `kb_search`    | `query`, `limit?`, `threshold?`                                      | Search the knowledge base                                                   |
| `kb_list`      | --                                                                   | List all documents in the knowledge base                                    |
| `kb_remove`    | `documentId`                                                         | Remove a document from the knowledge base                                   |

### Server to Client

These are the events that the server sends to connected clients. Your client should handle at least `token`, `done`, and `error` for basic functionality.

| Type                   | Fields                                                                      | Description                               |
| ---------------------- | --------------------------------------------------------------------------- | ----------------------------------------- |
| `token`                | `agentId`, `content`                                                        | A streamed content token from the LLM     |
| `tool_start`           | `agentId`, `toolCallId`, `tool`, `args`, `risk`                             | Tool execution has begun                  |
| `tool_approve_request` | `agentId`, `toolCallId`, `tool`, `risk`, `summary`                          | User approval required for a tool call    |
| `tool_result`          | `agentId`, `toolCallId`, `tool`, `duration`, `result`                       | Tool execution completed                  |
| `agent_state`          | `agentId`, `status`, `role`, `provider?`, `model?`                          | Agent status change (`thinking`, `idle`)  |
| `team_update`          | `agents: AgentInfo[]`, `strategy`                                           | Full roster update after agent add/remove |
| `done`                 | `usage: {tokens, cost}`                                                     | LLM response stream completed             |
| `error`                | `message`, `code?`                                                          | Error event with optional error code      |
| `mcp_call`             | `toolCallId`, `server`, `tool`, `args`                                      | Request the client to execute an MCP tool |
| `index_start`          | `path`, `totalFiles`                                                        | Directory indexing started                |
| `index_progress`       | `indexedFiles`, `totalFiles`, `currentFile`                                 | Indexing progress update                  |
| `index_done`           | `fileCount`, `chunkCount`                                                   | Indexing completed                        |
| `autonomy_changed`     | `level`                                                                     | Autonomy level was updated                |
| `audit_entry`          | `toolCallId`, `agentId`, `tool`, `risk`, `decision`, `timestamp`, `summary` | Audit trail entry                         |
| `kb_added`             | `documentId`                                                                | Document added to knowledge base          |
| `kb_search_result`     | `results: KbSearchHit[]`                                                    | Knowledge base search results             |
| `kb_list_result`       | `documents: KbDocInfo[]`, `totalCount`                                      | Knowledge base document listing           |
| `kb_removed`           | `documentId`, `found`                                                       | Document removal result                   |

## Session Management

Sessions are the server's way of keeping track of ongoing conversations. Each session has its own set of agents, an audit trail, and an autonomy level. The `SessionManager` creates a session automatically the first time a client sends a message with a given `sessionId`.

```java
// SessionInfo holds:
// - AgentGroup (one or more agents)
// - AuditService (per-session audit trail)
// - AutonomyLevel (default: SUPERVISED)
// - createdAt / lastActivity timestamps

SessionInfo session = sessionManager.getOrCreate("my-session-id");
session.getGroup().getMembers();  // List<Agent>
session.getAutonomyLevel();       // SUPERVISED
session.getAuditService();        // Per-session audit trail
```

Each session has its own `RagService`, lazily created on first access via `sessionManager.getRag(sessionId)`.

## Multi-Agent Support

A session can contain multiple agents with different roles and LLM configurations. You can add and remove agents dynamically at runtime using WebSocket events, which is useful for building team-based workflows where different agents handle different parts of a task.

```json
// Add an agent with specific LLM configuration
{
  "v": 1,
  "type": "agent_add",
  "sessionId": "sess-1",
  "role": "reviewer",
  "provider": "anthropic",
  "model": "claude-sonnet-4-20250514",
  "name": "Code Reviewer",
  "goal": "Review code for security issues"
}
```

Agent IDs are generated as `{name}-{8hexchars}` (e.g., `Code Reviewer-abcd1234`). After each add/remove, the server broadcasts a `team_update` event with the full agent roster, including each agent's tools, roles, and LLM configuration.

Multi-role agents can be created by providing a `roles` array instead of a single `goal`/`domain`:

```json
{
  "v": 1,
  "type": "agent_add",
  "sessionId": "sess-1",
  "role": "fullstack",
  "roles": [
    {"name": "frontend", "goal": "Build React UIs", "domain": "web"},
    {"name": "backend", "goal": "Build REST APIs", "domain": "server"}
  ]
}
```

## MCP Tool Injection

If your frontend application connects to MCP servers (like a filesystem or database server), you can register those tools with the TnsAI server session. The server wraps each tool as a `McpProxyTool` and injects them into all agents, enabling a proxy pattern where the server orchestrates and the client executes.

```json
// Client sends mcp_tools event
{
  "v": 1,
  "type": "mcp_tools",
  "sessionId": "sess-1",
  "tools": [
    {
      "server": "filesystem",
      "name": "read_file",
      "description": "Read a file",
      "inputSchema": {"type": "object", "properties": {"path": {"type": "string"}}}
    }
  ]
}
```

When the LLM invokes an MCP tool, the server sends an `mcp_call` event to the client. The client executes the tool via its local MCP connection and returns the result via `mcp_result`:

```
Server -> Client:  mcp_call {toolCallId: "abc", server: "filesystem", tool: "read_file", args: {path: "/src/App.tsx"}}
Client -> Server:  mcp_result {toolCallId: "abc", result: "import React from 'react';..."}
```

The `McpProxyTool` uses a `CompletableFuture` to block the agent's virtual thread until the client responds.

## Knowledge Base Events

Each session has an optional knowledge base for Retrieval-Augmented Generation (RAG). The `kb_*` events let clients add, search, list, and remove documents in real time, which the agent can then use as context when answering questions.

```json
// Add a document
{"v": 1, "type": "kb_add", "sessionId": "s1",
 "content": "React hooks must follow the rules of hooks...",
 "metadata": {"source": "docs", "topic": "react"}}

// Search
{"v": 1, "type": "kb_search", "sessionId": "s1",
 "query": "rules of hooks", "limit": 5, "threshold": 0.3}

// List all documents
{"v": 1, "type": "kb_list", "sessionId": "s1"}

// Remove a document
{"v": 1, "type": "kb_remove", "sessionId": "s1", "documentId": "doc-abc123"}
```

Search results include `documentId`, `content`, `score`, and `metadata` for each hit. The `kb_list_result` event returns document previews (first 100 chars) and content lengths.

## WsHandler Internals

This section covers the internal workings of the WebSocket handler for contributors and advanced users. The `WsHandler` processes all WebSocket events on the Javalin thread, then dispatches chat work to a virtual-thread executor for non-blocking concurrency.

- **Chat routing**: Messages are routed to the first agent in the group. The agent runs `streamChatWithTools`, which handles the full LLM-tool-LLM loop.
- **RAG injection**: Before sending a message to the LLM, the handler checks if the session has indexed content. If so, it calls `rag.buildContextPrompt(message, 5)` to prepend relevant code context.
- **Cancellation**: `cancel` events interrupt the running virtual thread and cancel all pending approval futures.
- **Keepalive**: Plain `"ping"` text frames are silently ignored.

## REST Endpoints

In addition to the WebSocket endpoint, the server exposes REST APIs for health checking, code search indexing, and audit trail retrieval. These are useful for monitoring, CI/CD integration, and administrative tasks.

| Method | Path                | Description                                      |
| ------ | ------------------- | ------------------------------------------------ |
| GET    | `/health`           | Aggregated health status (200/503)               |
| GET    | `/health/live`      | Liveness probe (always 200)                      |
| GET    | `/health/ready`     | Readiness probe (server + components)            |
| GET    | `/api/info`         | Server info (version, protocol, active sessions) |
| POST   | `/api/index`        | Trigger directory indexing                       |
| GET    | `/api/index/status` | Current index status                             |
| POST   | `/api/search`       | Search the index                                 |
| DELETE | `/api/index`        | Clear the index                                  |
| GET    | `/api/audit`        | Retrieve audit trail for a session               |

## Connection Example

Here is a complete JavaScript example showing how to connect to the server, send a chat message, handle streamed tokens, and respond to tool approval requests.

```javascript
const ws = new WebSocket("ws://localhost:7777/chat");

ws.onopen = () => {
  // Send a chat message
  ws.send(JSON.stringify({
    v: 1,
    type: "chat",
    sessionId: "my-session",
    message: "Explain the WebSocket protocol"
  }));
};

ws.onmessage = (event) => {
  const data = JSON.parse(event.data);
  switch (data.type) {
    case "token":
      process.stdout.write(data.content);
      break;
    case "tool_approve_request":
      // Auto-approve for this example
      ws.send(JSON.stringify({
        v: 1,
        type: "tool_approve",
        sessionId: data.sessionId,
        toolCallId: data.toolCallId,
        decision: "approve"
      }));
      break;
    case "done":
      console.log("\n[Done]", data.usage);
      break;
    case "error":
      console.error("[Error]", data.message);
      break;
  }
};
```

---

# CLI

URL: https://tnsai.dev/docs/sona/channels/cli
Description: The CLI channel turns your terminal into a Sona client. Two modes:

import { Callout } from 'fumadocs-ui/components/callout'

- **REPL** (default) — interactive prompt with JLine: arrow-history, slash commands, streaming token render.
- **JSON** — newline-delimited JSON on stdin/stdout, suitable for pipelines, e2e smoke tests, and shell automation.

Unlike Telegram, the CLI channel has **no auth handshake** — your terminal **is** the authority. If you don't want the keys to your daemon in `~/.sona/`, don't run `sona chat`.

## REPL mode

```bash
sona chat
```

This opens a JLine session against your local daemon. You'll see the user name prompt and a streaming reply:

```text
sona › what time is it in istanbul?
The current time in Istanbul is 03:14 UTC+3 (assuming standard time).
sona › /exit
```

Built-in slash commands (intercepted **locally** by the REPL — they never reach the gateway):

| Command          | Effect                                                          |
| ---------------- | --------------------------------------------------------------- |
| `/exit`, `/quit` | Close the REPL, return to your shell. The daemon keeps running. |
| `/clear`         | ANSI clear-screen. History is preserved.                        |

Anything else flows to the gateway as a `UnifiedMessage` — including text that starts with `/`, in case you've registered a skill that matches a slash prefix.

### Workspace selection

By default `sona chat` uses the `default` workspace. To pick a specific one:

```bash
sona chat --workspace personal
```

If the workspace name doesn't match, the CLI prints the list of available workspaces and exits.

### Streaming

REPL mode streams the LLM reply token-by-token as it arrives. There is one `assistant: ` prefix per turn, then the deltas concatenate inline, then a newline closes the line. No per-token re-render, no flicker — the assistant prefix is emitted once on the first chunk and the cursor walks forward.

Internally this uses the framework's `StreamingChannelAdapter` mixin (TNS-440); the gateway routes every chunk through `sendChunk(UnifiedChunk.text(...))` and a final `UnifiedChunk.done(...)`. See [How It Works](/docs/sona/how-it-works#channel-adapter-tnsai-channels) for the SPI surface.

## JSON mode

```bash
sona chat --json
```

This is the same Gateway-to-channel path, but the wire format is **newline-delimited JSON** on both directions. Useful for piping into Sona from another program, recording golden transcripts in tests, or driving the daemon from a script without an interactive terminal.

### Input

One JSON object per line, each with at least a `text` field:

```json
{"text": "hi"}
{"text": "what time is it in istanbul?"}
```

Other fields on the object are reserved for future use; today they're ignored.

### Output

Streaming responses come back as a **sequence of chunk records**, terminated by a `done: true` envelope (TNS-458):

```json
{"type":"chunk","content":"Hello","done":false}
{"type":"chunk","content":" ","done":false}
{"type":"chunk","content":"world","done":false}
{"type":"chunk","content":"","done":true}
```

Pipeline-friendly consumers can stream the body by concatenating `content` fields until `done: true`; one-shot consumers can buffer until `done` then read the response in one go.

Errors come back as a single error record:

```json
{"type":"error","message":"invalid JSON: Unexpected character (' ')..."}
```

Sona writes one record per inbound message; a single `sona chat --json` invocation can handle multiple turns in the same conversation by keeping the process open and piping more `text` lines in.

### Example: shell pipeline

```bash
printf '%s\n' \
  '{"text":"hi"}' \
  '{"text":"summarise the TnsAI framework in one paragraph"}' \
  | sona chat --json \
  | jq -r 'select(.type=="chunk") | .content' \
  | tr -d '\n'
```

This streams two turns through Sona, extracts the content chunks, and strips chunk-boundary newlines so you get the assistant's reply as flowing text.

## Sessions

CLI sessions are scoped to your local `$USER` — every `sona chat` invocation reuses the same `(channelId=cli, senderId=<your-username>)` pair and thus the same chat history. Killing and re-running `sona chat` resumes the same conversation.

If you want a fresh conversation, today the cleanest way is:

```bash
rm ~/.sona/sessions/cli:<your-username>.json
```

A `/new` slash command is tracked as a future improvement.

## When to use which mode

| Use case                                                   | Mode |
| ---------------------------------------------------------- | ---- |
| Quick interactive question                                 | REPL |
| Pair-programming with Sona, multi-turn                     | REPL |
| Shell pipeline / `xargs` over a CSV                        | JSON |
| E2E test that asserts response shape                       | JSON |
| Recording a golden transcript for a regression suite       | JSON |
| Driving Sona from another program (Python script, Node, …) | JSON |

JSON mode never prints prompts, banners, ANSI colour, or anything other than JSON records — safe to pipe.

## Comparison to Telegram

|           | Telegram                                | CLI                                        |
| --------- | --------------------------------------- | ------------------------------------------ |
| Auth      | Pairing code + owner approval           | Local user (no handshake)                  |
| Streaming | Yes (sends one envelope per N tokens)   | Yes (REPL) / Yes (JSON, per-chunk records) |
| Media     | Photos, documents, voice (inbound)      | No                                         |
| Threads   | Yes (per-chat threading)                | One conversation per `$USER`               |
| Use case  | Phone / off-machine                     | Local dev, CI smoke tests, scripts         |
| Latency   | Network round-trip via Telegram servers | Direct stdin/stdout                        |

The two adapters are **independent**. You can run both at the same time — they're separate sessions even for "the same" person.

---

# Email

URL: https://tnsai.dev/docs/sona/channels/email
Description: Sona's email channel polls an IMAP inbox for new mail and replies through an SMTP server. It's the right pick when you want Sona reachable from any device a person already uses email on — phones, work laptops, shared accounts — without installing a new app.

import { Callout } from 'fumadocs-ui/components/callout'

If you ran the [Quickstart](/docs/sona/quickstart), the wizard skipped this channel; email isn't enabled by default because the configuration involves real credentials and a deliberate sender allowlist. This page walks through enabling it from scratch.

## Choose an account

Use a mailbox dedicated to Sona rather than your personal inbox. The channel reads every unread message in `INBOX` and treats matching senders as conversation partners — pointing it at a busy account is noisy and risks accidentally replying to a real human.

Common setups:

- **Gmail** — works with an [app password](https://support.google.com/accounts/answer/185833) (requires 2-Step Verification on the account). The plain account password will not work; Google blocks IMAP password auth for security.
- **iCloud Mail** — same story: turn on 2-Step Verification, then create an [app-specific password](https://support.apple.com/HT204397).
- **Self-hosted / cohosted IMAP** (Migadu, Fastmail, Zoho, your company server) — usually accepts the regular login password directly.

Whichever you pick, write down four things: **IMAP host + port**, **SMTP host + port**, **username** (typically the email address), and the **password / app password** for IMAP. SMTP usually accepts the same credentials, but you can override per direction if your provider gives you a separate token.

## Wire it into Sona

Edit `~/.sona/sona.yml` and add a `channels.email` block. Gmail example:

```yaml
# ~/.sona/sona.yml
channels:
  email:
    imap_host: imap.gmail.com
    imap_port: 993
    imap_user: sona-bot@example.com
    imap_password: ${EMAIL_IMAP_PASSWORD}   # the app password
    imap_ssl: true
    imap_folder: INBOX

    smtp_host: smtp.gmail.com
    smtp_port: 587
    smtp_user: sona-bot@example.com
    smtp_password: ${EMAIL_SMTP_PASSWORD}   # often the same value
    smtp_start_tls: true
    from_address: sona-bot@example.com

    sender_allowlist:
      - you@example.com

    poll_interval_seconds: 60
```

The `${VAR}` form reads from the environment at boot, keeping the secret out of the YAML file. Drop the credentials into your shell profile or systemd unit:

```bash
export EMAIL_IMAP_PASSWORD="abcd efgh ijkl mnop"   # Gmail app password format
export EMAIL_SMTP_PASSWORD="$EMAIL_IMAP_PASSWORD"
```

Generic non-Gmail provider with IMAPS + SMTPS (port 465) looks the same, just swap hosts and flip `smtp_start_tls: false` if your SMTP wants implicit TLS instead.

### Field reference

| Field                        | Default                   | Notes                                                                                               |
| ---------------------------- | ------------------------- | --------------------------------------------------------------------------------------------------- |
| `imap_host`, `imap_port`     | —                         | Required. 993 for IMAPS, 143 for STARTTLS-then-plain.                                               |
| `imap_user`, `imap_password` | —                         | Required. App password for Gmail/iCloud.                                                            |
| `imap_ssl`                   | `true`                    | `true` for port 993, `false` for 143.                                                               |
| `imap_folder`                | `INBOX`                   | Override if Sona should poll a label/folder instead.                                                |
| `smtp_host`, `smtp_port`     | —                         | Required. 587 for STARTTLS, 465 for SMTPS.                                                          |
| `smtp_user`, `smtp_password` | falls back to IMAP creds  | Set only if SMTP needs a different login.                                                           |
| `smtp_start_tls`             | `true`                    | `true` for 587, `false` for 465 (which negotiates TLS at connect time).                             |
| `from_address`               | falls back to `smtp_user` | Useful when SMTP auth account ≠ display address (e.g. shared-inbox setups).                         |
| `sender_allowlist`           | `[]`                      | **Critical** — see below. Empty list means "drop all inbound mail".                                 |
| `poll_interval_seconds`      | `60`                      | How often the channel re-checks IMAP. Lower for tighter feedback; higher to spare your IMAP server. |

## Sender allowlist — read this carefully

Email is the most spammable channel Sona supports. To keep the gateway off the agentic-reply-bot spam list, the channel **silently drops every inbound message whose `From` address is not on `sender_allowlist`** (case-insensitive). A blank allowlist drops everything.

This is intentional. If you remove an address you no longer trust, future mail from that account stops reaching Sona without you having to redeploy. If you forget to add an address, your test message lands in the inbox but never reaches the agent and you get no reply — which is the failure mode you want.

Add every sender you expect to chat with:

```yaml
    sender_allowlist:
      - you@example.com
      - partner@example.com
      - oncall+sona@company.example.com
```

Wildcards aren't supported; list each address. If a teammate forwards mail to Sona under their own address, that's the address to allowlist, not the original sender's.

## First message

Restart the daemon (`sona stop && sona`). On a healthy start you'll see a log line like:

```text
INFO  c.t.c.email.EmailChannel [] - Email adapter started — polling imap.gmail.com:993 every 60s
```

Send a message from one of the allowlisted addresses to `sona-bot@example.com`. Within the poll window, Sona reads it, runs the agent, and replies. Replies use the original `Message-ID` for `In-Reply-To` so they thread correctly in your mail client.

The pairing flow that Telegram uses doesn't apply here — the allowlist is the trust boundary, so every allowlisted address is already paired implicitly.

## Operational notes

- The channel marks messages as read after processing. If you also want them moved out of the inbox, set up a server-side rule keyed off the `From: sona-bot@…` outbound header.
- `poll_interval_seconds: 60` is a sensible default. Going below 30 seconds rarely helps and starts to look like a misbehaving client to some providers; going above 300 makes replies feel asynchronous in a bad way.
- IMAP IDLE (push) is not used. Sona's deliberate poll-only design means transient connection drops don't strand inbound mail — the next poll catches up.
- `from_address` controls only what arrives in recipients' inboxes; SMTP servers may still rewrite the envelope sender. Test against the actual provider before assuming the `From` you set is the `From` users see.

## Troubleshooting

**`Authentication failed` on startup.** Gmail and iCloud both reject regular account passwords for IMAP. Confirm you're using an app password and that 2-Step Verification is on for the account.

**Sona logs `Email adapter started` but inbound messages never reach the agent.** Two usual suspects: (1) the sender address isn't on `sender_allowlist` — the channel drops these silently. (2) the message is in a folder other than `imap_folder` (Gmail's "Promotions" / "Updates" tabs apply server-side filters; explicitly route inbound Sona mail to `INBOX` via a Gmail rule).

**Outbound mail fails with `530 Authentication required`.** Either `smtp_user` / `smtp_password` is wrong, or your SMTP host requires STARTTLS that isn't enabled — flip `smtp_start_tls: true` if you're on port 587.

**Replies arrive but don't thread in the original conversation.** Some clients use the `References` header, others use `In-Reply-To`; Sona sets both. If threading still breaks, check that your mail client honours the standard headers — Outlook web has had on-and-off bugs here.

**Mail looks fine in the IMAP server but the agent never sees it.** Check `~/.sona/sona.log` for `Email adapter` lines. The most common silent drop is allowlist mismatch on a forwarded address: the `From` Sona sees is the forwarder, not the original sender.

If something else breaks, `sona doctor` checks the IMAP and SMTP probes at startup and prints what failed. For framework-side bugs, file an issue against [TnsAI.Channels](https://linear.app/tnsai) with the relevant log lines.

---

# Channels

URL: https://tnsai.dev/docs/sona/channels
Description: A channel is one way humans talk to Sona. The framework's tnsai-channels module defines the SPI; each adapter is one implementation that translates a platform-specific transport into the gateway's UnifiedMessage / UnifiedResponse records.

import { Callout } from 'fumadocs-ui/components/callout'

You can run more than one channel at the same time — Sona starts every channel listed under `channels:` in `~/.sona/sona.yml` at boot.

## Shipped today

These adapters are production-ready and live in the framework / Sona repos:

- [Telegram](/docs/sona/channels/telegram) — long-poll Bot API, threads, media, pairing flow. **The default channel** — Sona ships with it enabled out of the box.
- [CLI](/docs/sona/channels/cli) — interactive REPL (`sona chat`) or newline-delimited JSON for scripting (`sona chat --json`). No external dependencies; useful for local dogfooding, headless deployment smoke tests, and shell pipelines.
- [Email (IMAP/SMTP)](/docs/sona/channels/email) — async inbox poll + SMTP send, thread tracking via `Message-ID`. Framework adapter and Sona-side YAML config landed in framework 0.10.4 ([TNS-354](https://linear.app/tnsai/issue/TNS-354)).

## Framework adapter shipped, Sona-side wiring in progress

Each of these landed as a `tnsai-channels` adapter in framework 0.10.4. Sona discovers them at boot via SPI (`ChannelDiscovery.discoverFactories`), but the daemon still needs its own YAML config block for the channel to be useful — that wiring is the follow-up task tracked alongside each row.

| Channel           | Framework PR                                      | Sona-side wiring                                  | Status                                     |
| ----------------- | ------------------------------------------------- | ------------------------------------------------- | ------------------------------------------ |
| Slack             | [TNS-350](https://linear.app/tnsai/issue/TNS-350) | [TNS-502](https://linear.app/tnsai/issue/TNS-502) | Socket Mode WebSocket + `chat.postMessage` |
| Discord           | [TNS-351](https://linear.app/tnsai/issue/TNS-351) | [TNS-503](https://linear.app/tnsai/issue/TNS-503) | Gateway v10 WebSocket + heartbeat          |
| WhatsApp Business | [TNS-352](https://linear.app/tnsai/issue/TNS-352) | [TNS-504](https://linear.app/tnsai/issue/TNS-504) | Cloud API webhook + HMAC verify            |

Once a Sona-side wiring task lands, its setup guide follows here. Drop a thumbs-up on the Linear issue if you need one urgently or open a discussion on the [Sona repo](https://github.com/TnsAI-Framework/TnsAI.Sona/discussions).

## How channels compose

Every adapter sits behind the same `ChannelAdapter` SPI, so the gateway code is identical for all of them. The flow:

1. Adapter receives a platform-specific message (Telegram update, Slack event, stdin line, …).
2. Adapter translates it into a `UnifiedMessage` with a `channelId` (`"telegram"`, `"cli"`, …) and a `senderId`.
3. Gateway routes by `(channelId, senderId)` to a session; the session runs the agent.
4. Gateway hands the reply back as a `UnifiedResponse`; adapter renders it to the platform.

The [How It Works](/docs/sona/how-it-works) page has a Mermaid sequence diagram that traces one Telegram message end-to-end. The same diagram applies to every adapter — only the first and last hops change.

## Writing your own

The framework's [Channels](/docs/channels) reference covers the SPI surface (`ChannelAdapter`, `StreamingChannelAdapter`, `UnifiedMessage`, `UnifiedResponse`). Sona will auto-discover any `ChannelAdapter` SPI on the classpath at boot — `META-INF/services/com.tnsai.channels.api.ChannelAdapter`.

To use a custom adapter with Sona, drop the JAR into `~/.sona/lib/`, restart the daemon, and add its `channels:` config block to `sona.yml`. No Sona-side code change.

---

# Telegram

URL: https://tnsai.dev/docs/sona/channels/telegram
Description: Telegram is Sona's default channel. The adapter long-polls the Bot API, so you don't need a public IP, a tunnel, or a webhook server — Sona pulls updates over an outbound HTTPS connection.

import { Callout } from 'fumadocs-ui/components/callout'

If you ran the [Quickstart](/docs/sona/quickstart), you already did most of this. This page covers the same flow in more detail and adds the bits the wizard skips.

## Get a bot token

1. Open Telegram, search for **[@BotFather](https://t.me/BotFather)**, start a chat.
2. Send `/newbot`.
3. Pick a display name — what users will see in the chat header. Anything goes.
4. Pick a username — must be globally unique and end in `_bot` (e.g. `myname_sona_bot`).
5. BotFather replies with the token. It looks like `123456789:ABC-DEF1234ghIkl-zyx57W2v1u123ew11`.

Keep the token secret — anyone with it can impersonate your bot.

Optional but useful, still in BotFather:

- `/setdescription` — short blurb users see before the first message
- `/setabouttext` — long description on the bot's profile page
- `/setuserpic` — bot avatar
- `/setcommands` — populate the slash-command menu (see [slash commands](#slash-commands) below)
- `/setjoingroups` and `/setprivacy` — group behaviour (default: groups disabled, "privacy mode" on so the bot only sees commands and mentions)

## Wire it into Sona

Either re-run `sona init` (which writes the token into `~/.sona/sona.yml` for you and verifies it via `getMe`), or edit the file directly:

```yaml
# ~/.sona/sona.yml
channels:
  telegram:
    token: ${TELEGRAM_BOT_TOKEN}   # or paste the literal string
```

The `${VAR}` form makes Sona read from the environment at boot — you keep the token out of the config file but still tie it to the channel config. If you'd rather paste the literal token, that works too.

Restart the daemon:

```bash
sona stop
sona
```

On a healthy start you should see:

```text
INFO  c.t.c.t.TelegramAdapter [] - Telegram adapter started — listening for messages
```

If you see `Unauthorized` in the logs, the token is wrong or revoked — re-issue via BotFather `/token` and try again.

## First message + pairing

Open `t.me/<your_bot_username>` and send `hi`.

Sona doesn't trust strangers. The first message from an unknown sender returns a **pairing code**:

```text
You: hi
Bot: Hi! I don't recognize you yet. Please share this pairing code
     with my owner to get access: 483921
```

You **are** the owner. Approve from another terminal:

```bash
sona pair list           # lists pending requests
sona pair approve 483921 # by code
# or
sona pair approve telegram:123456789  # by scoped id
```

Now send another message — the bot replies through the LLM.

Pairing is per-`(channelId, senderId)`. Approving `telegram:123` does **not** approve `cli:123` or `slack:123`. This is intentional — channels are tenant boundaries, not just routing labels.

## Slash commands

Telegram's slash-command menu helps users discover what a bot can do. Set the menu from BotFather:

```text
/setcommands
@your_bot_username
help - What can this bot do?
new - Start a new conversation
stop - Stop the current task
```

Sona doesn't currently parse slash commands into special behaviours — they flow into the gateway like any other text. If you want a command to do something specific, register a [skill](/docs/sona/how-it-works#skill-router-comtnsaisonaskill) whose `match()` looks for the `/help` prefix.

## Threads, groups, and multi-user chats

By default Telegram bots only see DMs and messages that mention them. To use Sona in a group:

1. In BotFather: `/mybots` → pick your bot → **Bot Settings** → **Group Privacy** → **Turn off**. (This lets the bot see every message in the group, not just `@mentions`.)
2. Add the bot to the group.
3. Anyone in the group can now talk to it — but pairing applies per-user, so you'll need to approve each user that should be allowed to use the LLM budget.

For 1:1 DMs Sona's session boundary is the user's Telegram ID; conversations across days resume from where they left off (until the daemon is restarted with `sona uninstall`, or you explicitly purge `~/.sona/sessions/`).

## Media

The adapter accepts inbound photos, documents, and voice notes; they're attached to the `UnifiedMessage` as `Attachment` records. What happens next depends on whether your configured LLM is vision-capable:

| Attachment          | Vision LLM (Claude Sonnet, GPT-4o, Gemini Vision)                                                    | Text-only LLM               |
| ------------------- | ---------------------------------------------------------------------------------------------------- | --------------------------- |
| Photo               | Encoded inline; passed to the LLM as image input                                                     | Logged, not sent to the LLM |
| Document (PDF, txt) | If a tool's wired for it (`tnsai-tools` PDF / text), the agent can ask for content; otherwise logged | Same                        |
| Voice note          | Not yet wired — tracked in `tnsai-tools` STT toolkit                                                 |                             |

For outbound media, the adapter renders `UnifiedResponse.attachments` as Telegram media — Sona doesn't ship outbound-media skills today, but the SPI supports it.

## Privacy + audit

Sona logs every inbound message + the LLM reply to `~/.sona/sona.log` (and the per-session JSON in `~/.sona/sessions/`). The Telegram chat metadata (user ID, message ID, timestamps) is in the audit log; the LLM's API call to the provider follows the provider's own retention policy.

If you need a redaction filter on the way in — e.g. strip emails or credit-card numbers before they reach the LLM — see the [framework's redaction guide](/docs/security/prompt-injection). Wiring it through Sona is one extra config block; no code change.

## Troubleshooting

| Symptom                                            | Cause                                           | Fix                                                           |
| -------------------------------------------------- | ----------------------------------------------- | ------------------------------------------------------------- |
| `Unauthorized` in logs                             | Token wrong / revoked                           | Re-issue via BotFather `/token`, re-run `sona init`           |
| `Conflict: terminated by other getUpdates request` | Another process is polling the same bot         | Stop the other process; only one consumer per token           |
| Bot is added to a group but doesn't respond        | Group privacy is on                             | BotFather → bot settings → group privacy → turn off           |
| Message reaches Sona logs but no reply             | LLM key is invalid, or budget is exhausted      | Check `sona logs --grep ERROR -i` for the provider's response |
| First-message pairing code never arrives           | Network drop right after the inbound message    | Send again; pairing is per-sender, you'll get a fresh code    |
| `sona pair approve` says "no such code"            | Daemon restarted between code-issue and approve | Issue a new one by sending a new message                      |

## Removing the bot

To stop using a bot but keep its token alive for future use:

```yaml
# ~/.sona/sona.yml
channels: {}     # empty map = no adapters at boot
```

then `sona stop && sona`. The bot exists on Telegram's side until you `/deletebot` via BotFather.

---

# How Sona Works

URL: https://tnsai.dev/docs/sona/how-it-works
Description: Sona is mostly routing. The LLM, the tool calls, the streaming — all of that is tnsai-core doing its job. Sona's contribution is deciding which agent should hear an inbound message, keeping a session for each (channel, sender) pair, and enforcing a pairing policy so strangers can't burn your LLM budget.

import { Callout } from 'fumadocs-ui/components/callout'

This page traces one Telegram message end-to-end, then zooms in on the pieces.

## The round-trip

```mermaid
sequenceDiagram
    participant U as User
    participant TG as Telegram
    participant TA as TelegramAdapter
    participant GW as Gateway
    participant WS as Workspace
    participant SE as Session
    participant AG as Agent
    participant LLM as LLM Provider

    U->>TG: "summarise this article"
    TG->>TA: long-poll update
    TA->>GW: UnifiedMessage (channelId="telegram")
    GW->>GW: pairingPolicy.check(scopedId)
    GW->>WS: route by channel (default: "default")
    WS->>SE: getOrCreate(scopedId)
    SE->>AG: streamChatWithTools(text, chunkSink)
    AG->>LLM: chat(systemPrompt + history + tools)
    LLM-->>AG: stream chunks
    AG-->>SE: chunks via chunkSink
    SE->>SE: append to response
    AG->>SE: complete
    SE->>GW: send UnifiedResponse
    GW->>TA: adapter.send(response)
    TA->>TG: sendMessage
    TG->>U: rendered reply
```

The same diagram applies to the CLI channel (`sona chat`) — swap `TelegramAdapter` for `CliChannel`. Channel-specific quirks (Telegram threading, Telegram media, CLI ANSI colour) all happen **inside** the adapter; the gateway never sees them.

## The pieces

### Channel adapter (`tnsai-channels`)

The framework defines a small SPI in `tnsai-channels`:

```text
ChannelAdapter           ← every adapter implements this
└── StreamingChannelAdapter ← adapters that want per-token delivery add this mixin
```

A channel adapter wraps a platform-specific transport and produces `UnifiedMessage` records on the way in / consumes `UnifiedResponse` records on the way out. The TnsAI framework owns the SPI; Sona is a consumer that registers the channels it cares about with the gateway:

| Adapter           | Lives in                     | Status                                              |
| ----------------- | ---------------------------- | --------------------------------------------------- |
| `TelegramAdapter` | `tnsai-sona`                 | Production — long-polling Bot API                   |
| `CliChannel`      | `tnsai-channels` (framework) | Production — REPL + ndJSON modes, streaming-capable |

Adapters for Slack, Discord, WhatsApp, Email are tracked in Linear under the public-alpha epic.

### Gateway (`com.tnsai.sona.gateway.Gateway`)

The Gateway is the **fan-in** point — every adapter hands `UnifiedMessage` to the same `onMessage(...)` callback. From there:

```mermaid
flowchart LR
    M[UnifiedMessage] --> P{Pairing<br/>policy}
    P -->|approved| R[Workspace router]
    P -->|unknown| C[Pairing code reply]
    R --> S[Session getOrCreate]
    S --> A[Agent<br/>streamChatWithTools]
    A --> L[LLM call]
    A --> SC[Streaming chunks]
    SC -->|adapter is StreamingChannelAdapter| AD[adapter.sendChunk]
    A --> RP[Final UnifiedResponse]
    RP --> SX[adapter.send]
```

Key behaviours:

- **Pairing policy** — first message from an unknown sender gets a 6-digit code. The owner approves via `sona pair approve` (or by scoped ID). Unapproved senders never reach the LLM.
- **Workspace router** — a workspace is a named LLM + system-prompt + tool-allowlist bundle. A workspace can declare `channels: [telegram]` to bind itself to a subset of channels; the default workspace catches everything not claimed. A workspace can also point at a project directory via `project_dir` — see **Project context** below.
- **Per-session agent** — every `(channelId, senderId)` pair gets its own agent instance. State (chat history, BDI beliefs) lives in the `Session`; the agent reads it before each turn and writes it back after. This is what lets two users on the same workspace have completely independent conversations.
- **Project context** — when a workspace declares `project_dir: "<path>"`, Sona reads `AGENTS.md` (falling back to `CLAUDE.md` / `README.md` with lowercase + dotfile variants) from that directory at session boot and **augments** the configured `system_prompt` with the parsed intro + sections under a stable `## Project context (auto-loaded from AGENTS.md)` anchor. The operator's prompt always leads. Missing or malformed file → silent fallback (DEBUG log; session still boots). Empty `project_dir` keeps pre-feature behaviour byte-for-byte: same role body, same `AgentSpec` digest, same principal id.
- **Streaming forwarding** — when the registered adapter is a `StreamingChannelAdapter` (currently just `CliChannel`), each token from `Agent.streamChatWithTools` is forwarded through `sendChunk(UnifiedChunk.text(...))` as it arrives, terminated by one `UnifiedChunk.done(...)`. The final `UnifiedResponse` still fires through `send(...)` for audit / non-streaming downstreams; the framework `CliChannel` suppresses the duplicate render.

### BDI loop (`tnsai-core`)

The agent itself is a `tnsai-core` `Agent` — Sona doesn't subclass it. Each turn it runs a Believe-Desire-Intend cycle:

```mermaid
flowchart TD
    I[Incoming text] --> B[Believe<br/>read session memory]
    B --> D[Desire<br/>derive goals from role + context]
    D --> P[Plan<br/>pick actions or LLM-call]
    P --> X[eXecute<br/>tool call or LLM stream]
    X --> R[Reflect<br/>update beliefs]
    R --> O[Outbound text]
```

For Sona's "single-role assistant" use case this collapses to **read history → call LLM → stream reply**, with the planner short-circuiting to "just call the LLM" when no tool match scores high enough.

### Memory model

Sona inherits the framework's memory backends. Currently the daemon uses **per-session in-memory history with periodic checkpoint** — when the daemon shuts down cleanly via `sona stop`, sessions persist to `~/.sona/sessions/<scopedId>.json`. The next start rehydrates them.

The framework also ships **sliding-window**, **summary**, **hybrid** and **tiered** memory strategies (see [Intelligence → Context](/docs/capabilities/intelligence/context)). They're not yet wired into Sona by default; the dogfood path is the one above. Tracked as a follow-up.

### Skill router (`com.tnsai.sona.skill`)

Before a message reaches the LLM, the Gateway checks the `SkillRegistry`:

```mermaid
flowchart LR
    M[Inbound text] --> R[SkillRegistry.match]
    R -->|match score > threshold| S[Skill.execute]
    S --> RP[Direct reply]
    R -->|no match| A[Agent path<br/>LLM call]
```

A `Skill` is a small named capability — an inbound regex / classifier matches, the skill runs, the reply goes back without an LLM call (or with a constrained one). Skills are useful for high-frequency intents (`/help`, `weather <city>`, `define <word>`) where you want deterministic latency and no token spend.

The built-in skill catalog is currently small (`/help`, owner admin commands). The full **dogfood catalog** is tracked in the Linear backlog and intentionally not bundled until the SPI is more settled.

### Session, sender, scoped identity

Sessions are keyed by a `ChannelScopedId` — `channelId:senderId`. This means:

- The same Telegram user talking to Sona via two different bot tokens is **two separate sessions** (different `channelId` if you ever ran two bots).
- The same person on Telegram and CLI is **two separate sessions** — different `channelId`, even if profile preferences live elsewhere.
- Pairing approvals are scoped — approving `telegram:12345` does not approve `discord:12345`.

The intent is "channel-as-tenant"; the scoped ID is the framework's primitive for this.

### Sessions vs profiles

| Concept     | Lives in                           | Lifetime                                                                                                               |
| ----------- | ---------------------------------- | ---------------------------------------------------------------------------------------------------------------------- |
| **Session** | `~/.sona/sessions/<scopedId>.json` | Per `(channel, sender)`. Holds chat history + BDI state. Survives daemon restarts.                                     |
| **Profile** | `~/.sona/profiles/<scopedId>.json` | Per `(channel, sender)`. Holds user preferences (preferred model, tool allowlist, language). Survives daemon restarts. |
| **Config**  | `~/.sona/sona.yml`                 | One file. Workspaces, LLM, channels, MCP, skills. Loaded at start.                                                     |

`sona uninstall` deletes all three. `sona init` only touches the config.

## Where the framework ends and Sona begins

If you're reading the source to mine patterns, the boundary is clean:

| Sona code does                               | Framework code does                                        |
| -------------------------------------------- | ---------------------------------------------------------- |
| Channel registration order, per-channel auth | `ChannelAdapter` SPI + `Gateway` callbacks                 |
| Pairing policy + pairing codes               | `ChannelScopedId` + `ChannelContext`                       |
| Workspace ↔ channel routing                  | `Agent.streamChatWithTools`                                |
| Skill matching + executing                   | Tool execution, `ToolCallListener`, MCP plumbing           |
| Sessions on disk (`SessionStore`)            | Memory backends, message rendering                         |
| `sona.yml` parsing + `sona init` wizard      | `AgentBuilder`, `LLMClient`, all 62 `BuiltInTool` toolkits |

If any line in "Sona code" looks reusable for your own project, lift the pattern — none of it depends on Sona-specific internals.

## Going further

- The Sona repo has source you can read end-to-end in an hour — start with `SonaMain` and `Gateway`. CLAUDE.md in the repo root is the entrypoint for AI coding sessions and doubles as a fast tour.
- For framework details: [Agents](/docs/agents) → [Capabilities](/docs/capabilities) → [Channels](/docs/channels).
- The dogfood "why" perspective is at [Sona — Dogfood Personal Assistant](/docs/community/examples/sona).

---

# Sona

URL: https://tnsai.dev/docs/sona
Description: Sona is the personal AI assistant the TnsAI maintainers run for themselves. It is the first concrete consumer of the framework — every TnsAI module that can't carry Sona's workload is a feature we haven't finished.

import { Callout } from 'fumadocs-ui/components/callout'

This section walks you from "zero" to "talking to your own Sona over Telegram", then explains the architecture underneath.

## Pages

- [Quickstart](/docs/sona/quickstart) — Install Sona, point it at a Telegram bot, send your first message. ≈ 5 minutes.
- [How It Works](/docs/sona/how-it-works) — Gateway, workspaces, BDI loop, channel adapter SPI, skill router. Diagrams included.
- [Channels](/docs/sona/channels) — Per-channel setup guides. [Telegram](/docs/sona/channels/telegram), [CLI](/docs/sona/channels/cli), and [Email](/docs/sona/channels/email) ship today; Slack / Discord / WhatsApp are framework-side ready, Sona-side wiring in progress.
- [Machine-readable mode (`--json`)](/docs/sona/json-mode) — Envelope contract for orchestrating Sona from scripts and parent agents. Every command, one shape.

## Related

- [Sona — Dogfood Personal Assistant](/docs/community/examples/sona) — "Why does Sona exist?" perspective.
- [TnsAI Quickstart](/docs/start/quickstart) — Build an agent **inside** your own code (not as a daemon).
- [Channels](/docs/channels) — The SPI the Telegram adapter implements.
- [TnsAI.Sona repo](https://github.com/TnsAI-Framework/TnsAI.Sona) — Source + the authoritative `README.md` and `CHANGELOG.md`.

---

# Machine-readable mode

URL: https://tnsai.dev/docs/sona/json-mode
Description: Every non-streaming sona command accepts --json and emits a single-line envelope. Streaming commands (chat) emit one envelope per chunk (ndJSON). This is the surface you wire orchestrators, CI pipelines, and parent agents against — no scraping of human text, no fragile regex.

import { Callout } from 'fumadocs-ui/components/callout'

## Envelope shape

One of two shapes, picked by success or failure:

```json
{ "ok": true,  "command": "status",   "result": { "running": true, "pid": 12345 } }
{ "ok": false, "command": "uninstall", "error": { "code": "confirmation_required", "message": "uninstall is destructive; --json requires explicit --yes to skip the prompt." } }
```

Fields:

- `ok` (boolean) — true on success, false on error.
- `command` (string) — the subcommand that wrote the envelope (`status`, `update`, `uninstall`, …). Matches what you typed after `sona`.
- `result` (object, success only) — command-specific payload. See the table below for the per-command shape.
- `error.code` (string, error only) — machine-readable identifier. Stable across releases.
- `error.message` (string, error only) — human-readable explanation. May change between releases; do not match against it.

Exit code reflects the same semantics: `0` for success, non-zero for errors. The exit code is the orchestrator's primary signal; the envelope is the explanation.

## Commands

| Command                           | Success `result` keys                                                              | Notable error codes                                      |
| --------------------------------- | ---------------------------------------------------------------------------------- | -------------------------------------------------------- |
| `sona --version --json`           | `version`, `jre`                                                                   | —                                                        |
| `sona --help --json`              | `version`, `commands[]`, `globalOptions[]`                                         | —                                                        |
| `sona status --json`              | `running`, `pid`, `reason`                                                         | — (exit 1 when not running)                              |
| `sona stop --json`                | `stopped`, `pid`, `used_force`                                                     | `signal_rejected`, `timeout`, `interrupted`              |
| `sona init --json`                | `configExists`, `configPath`                                                       | `interactive_required` (with `--add-channel`)            |
| `sona update --json`              | `action` (one of `installed` / `already_latest`), `from`, `to`, `current`          | `endpoint_unusable`, `no_download_url`, `install_failed` |
| `sona update --check --json`      | `action: "check"`, `current`, `latest`, `url`, `upToDate`                          | `endpoint_unusable`                                      |
| `sona update --rollback --json`   | `action: "rolled_back"`                                                            | `no_previous_version`, `rollback_failed`                 |
| `sona uninstall --dry-run --json` | `dryRun: true`, `paths[]`                                                          | —                                                        |
| `sona uninstall --yes --json`     | `dryRun: false`, `removed`, `failed`, `skipped`, `removedPaths[]`, `failedPaths[]` | `confirmation_required`, `daemon_stop_failed`            |
| `sona doctor --json`              | `schemaVersion`, `overall` (one of `ok` / `warn` / `fail`), `checks[]`             | — (own document, not the envelope)                       |
| `sona logs --count --json`        | `count`                                                                            | —                                                        |
| `sona chat --json`                | per-chunk records (ndJSON; see [CLI channel](/docs/sona/channels/cli))             | —                                                        |
| `sona search --json "<q>"`        | `query`, `total`, `hits[]`                                                         | —                                                        |
| `sona config show --json`         | masked sona.yml as a `config` map                                                  | —                                                        |
| `sona cost --json [WINDOW]`       | `window`, `totalUsd`, `byProvider`, `byWorkspace`                                  | —                                                        |

> `doctor --json` predates the envelope contract (TNS-466 axis 2). It emits its own document with `schemaVersion`, `overall`, and `checks`. The shape is stable; do not assume the standard envelope.

## Examples

### Health check in a CI script

```bash
if ! sona doctor --json | jq -e '.overall == "ok"' > /dev/null; then
  echo "Sona not healthy; aborting deploy"
  exit 1
fi
```

### Upgrade only when a newer version exists

```bash
LATEST=$(sona update --check --json | jq -r '.result.latest')
CURRENT=$(sona update --check --json | jq -r '.result.current')
if [ "$LATEST" != "$CURRENT" ]; then
  sona update --json | jq .
fi
```

### Audit what `uninstall` would touch

```bash
sona uninstall --dry-run --json | jq '.result.paths[]'
# → "/Users/me/.sona"
# → "/Users/me/.local/bin/sona"
# → "/Users/me/.local/share/sona"
```

### Read config state without triggering the wizard

```bash
sona init --json | jq '{exists: .result.configExists, path: .result.configPath}'
# → { "exists": true, "path": "/Users/me/.sona/sona.yml" }
```

`init --json` is a **query**. The interactive wizard is never triggered; only `configExists` + `configPath` are reported. To bootstrap from scratch, fall back to interactive `sona init` and let the operator walk the prompts. `init --add-channel --json` is rejected with `interactive_required` for the same reason — the channel walkthrough is multi-prompt and doesn't translate to a single envelope.

## Destructive commands and --json

`sona uninstall` is destructive. In `--json` mode, the prompt would be silently skipped; we refuse rather than allow that. `--json` alone returns `confirmation_required` (exit 1):

```json
{ "ok": false, "command": "uninstall", "error": { "code": "confirmation_required", "message": "..." } }
```

Pass `--yes` explicitly to opt in:

```bash
sona uninstall --yes --json
```

`--dry-run --json` is always safe (no filesystem touch) and does not need `--yes`.

## Streaming vs. one-shot

This page covers **one-shot** commands. Streaming commands (`sona chat --json`) emit ndJSON — one envelope per chunk, terminated by a `done: true` record. See the [CLI channel](/docs/sona/channels/cli) page for the per-chunk shape and a worked example.

## Stability

- `ok`, `command`, `result`/`error` keys: **stable**. Renaming would break every orchestrator. Treated as semver-major.
- `error.code` values: **stable** within a minor release. New codes may appear in minor releases; existing codes never change spelling.
- `result` payload keys per command: **additive**. New keys may appear; existing keys keep their meaning.
- `error.message` text: **unstable**. Read for humans; never match against.

See [Sona how-it-works](/docs/sona/how-it-works) for the architecture context, or the [TnsAI.Sona release notes](https://github.com/TnsAI-Framework/TnsAI.Sona/blob/main/CHANGELOG.md) for the per-version changelog.

---

# Sona Quickstart

URL: https://tnsai.dev/docs/sona/quickstart
Description: Five minutes from a fresh shell to a Telegram message round-tripping through your own agent. You need:

import { Callout } from 'fumadocs-ui/components/callout'

- macOS, Linux, or WSL on x64 or aarch64
- An LLM API key — Anthropic, OpenAI, Google, or a local Ollama
- A Telegram account (the bot is free; no card on file)

You do **not** need Java pre-installed — the installer bundles a JRE if you don't already have Java 21+ on your `PATH`.

## 1. Install (30 seconds)

```bash
curl -fsSL https://tnsai.dev/install/sona.sh | bash
```

This drops the JAR into `~/.sona/`, bundles Eclipse Temurin JRE 21 if your system Java is \\< 21, writes a config template, and puts `sona` on your `PATH`.

Verify:

```bash
sona --version
```

You should see the current version + the JRE path.

## 2. Get a Telegram bot token (90 seconds)

In Telegram, open a chat with [@BotFather](https://t.me/BotFather):

1. `/newbot`
2. Pick a display name (e.g. `My Sona`)
3. Pick a username — must end in `_bot` (e.g. `my_sona_bot`)
4. Copy the token BotFather sends back. It looks like `123456:ABC-DEF...`.

## 3. Run `sona init` (90 seconds)

```bash
sona init
```

The wizard asks for, in order:

| Prompt             | What to paste                                                              |
| ------------------ | -------------------------------------------------------------------------- |
| LLM provider       | `anthropic`, `openai`, `google`, or `ollama`                               |
| Model              | e.g. `claude-sonnet-4-20250514`, `gpt-4o`, `gemini-2.0-flash`, `llama3.1`  |
| API key            | Your provider key (or blank if you'd rather use `$ANTHROPIC_API_KEY` etc.) |
| Telegram bot token | The string from step 2                                                     |

The wizard writes `~/.sona/sona.yml`, then runs a health check (LLM `ping` + Telegram `getMe`). If both pass you're ready.

## 4. Start the daemon (30 seconds)

```bash
sona
```

The first time you run it, the daemon will log a banner like:

```text
TnsAI Sona v0.3.0
Workspaces: 1
Channels:   1
```

The process stays running in the foreground. Open another terminal for the rest of this guide; `sona stop` shuts it down.

## 5. Send your first message (60 seconds)

Open your bot's chat in Telegram (`t.me/<your_bot_username>`) and send anything — `hi`.

Sona replies with a **pairing code** because it doesn't know you yet:

```text
You: hi
Bot: Hi! I don't recognize you yet. Please share this pairing code
     with my owner to get access: 483921
```

You **are** the owner, so approve yourself from another terminal:

```bash
sona pair list           # show pending requests
sona pair approve 483921 # by code
```

(`sona pair approve telegram:<your_telegram_user_id>` also works if you'd rather skip the code.)

Send another message:

```text
You: Summarize what TnsAI is in one paragraph.
Bot: TnsAI is a modular Java framework for building AI agents…
```

That's it — your daemon is processing a message, dispatching to an LLM, and replying through the Telegram adapter.

## What just happened

Three TnsAI modules carried the round-trip:

1. **[`tnsai-channels`](/docs/channels)** — The `TelegramAdapter` translated an incoming Telegram update into a `UnifiedMessage`.
2. **`tnsai-core`** — The session's `Agent` ran one BDI cycle: pulled belief state from the session, picked an action, called the LLM through `tnsai-llm`, streamed the reply back.
3. **[`tnsai-channels`](/docs/channels)** again — The adapter rendered the agent's `UnifiedResponse` as a Telegram message.

Sona's only job is the **routing** in the middle: workspace selection, session lookup, pairing-policy enforcement, audit log. See [How It Works](/docs/sona/how-it-works) for the full picture.

## Next steps

- **Talk to it from a terminal instead** — `sona chat` opens a local REPL (JLine, streaming, slash commands). Add `--json` for newline-delimited JSON suitable for piping.
- **Tail the logs** — `sona logs -f` follows `~/.sona/sona.log` in real time. `sona logs --grep ERROR -i` for case-insensitive filtering.
- **Add tools** — Sona ships with all 62 `tnsai-tools` POJO toolkits available. Toggle a per-user allowlist in `~/.sona/sona.yml` under `workspaces.<name>.tools`.
- **Add MCP servers** — Drop a `mcp_servers:` block in `sona.yml` and Sona will register them through `tnsai-mcp` at start-up.
- **Point a workspace at a project** — Set `project_dir` on a workspace and Sona auto-loads `AGENTS.md` (falling back to `CLAUDE.md` / `README.md`) from that directory into the agent's system prompt at session boot. See [Project context below](#project-context-via-agentsmd).
- **Drive Sona from a script** — Every command takes `--json` and emits the same envelope. See the [machine-readable mode reference](/docs/sona/json-mode).
- **Stop** — `sona stop` (SIGTERM) or `sona stop --force` (SIGKILL).
- **Uninstall** — `sona uninstall --yes` removes `~/.sona/` and the `sona` launcher.

## Project context via AGENTS.md

When a workspace's `project_dir` points at a code repo, Sona reads that repo's `AGENTS.md` ([agents.md spec](https://agents.md/)) at session boot and augments the workspace's `system_prompt` with the parsed intro + sections. The operator's prompt always leads; the project context is appended, never substituted.

```yaml
# ~/.sona/sona.yml
workspaces:
  code:
    system_prompt: "You are a coding assistant."
    project_dir: "/Users/me/repos/my-project"   # opt-in
```

Fallback order is `AGENTS.md` → `CLAUDE.md` → `README.md` (with lowercase + dot-prefixed variants). If none exist, the system prompt is unchanged — session boot never fails because of a missing or malformed context file. Project context is opt-in: a workspace without `project_dir` boots byte-for-byte the same as before (same role body, same agent principal id).

## Troubleshooting

| Symptom                                             | Likely cause                                                | Fix                                                                         |
| --------------------------------------------------- | ----------------------------------------------------------- | --------------------------------------------------------------------------- |
| `sona init` says "LLM health check failed"          | Wrong key, wrong model name, or no network                  | Re-run `sona init` and double-check the key                                 |
| `sona init` says "Telegram getMe failed"            | Wrong bot token or token revoked                            | Re-issue via BotFather `/token`, re-run `sona init`                         |
| Bot doesn't respond at all                          | Daemon isn't running, or another instance has the bot token | `sona status` to check; only one daemon can poll a token at a time          |
| `command not found: sona`                           | Installer couldn't write to your PATH                       | Re-source your shell rc (`source ~/.zshrc`) or add `~/.local/bin` to `PATH` |
| Pairing code reply but approval doesn't take effect | Code typo, or daemon restarted between issue and approve    | Send a fresh message; a new code is issued, then approve that one           |

For deeper digging, see the [Sona repo's README](https://github.com/TnsAI-Framework/TnsAI.Sona/blob/main/README.md) — it's the authoritative reference and tracks command changes as they ship.

---

# Architecture Overview

URL: https://tnsai.dev/docs/start/architecture-overview

import { Callout } from 'fumadocs-ui/components/callout'

## Module Dependency Graph

Twelve modules on one lockstep version. Dependencies form four layers
plus a meta artifact (BOM) that pins them all.

![Module Dependency Graph — TnsAI 12 modules across Foundation / Capabilities / Composed / Application + BOM meta layer](https://tnsai.dev/assets/diagrams/architecture-modules.svg)

<details>
<summary>ASCII fallback (for terminal / plain-text readers)</summary>

```
FOUNDATION
  tnsai-core            Agent · Role · Action · Events · SPI · ToolMethodRegistry
                        (no inter-module dependencies)
       │
       ▼
CAPABILITIES — depend on tnsai-core
  tnsai-llm             LLMClient impls for 20+ providers · prompt caching
  tnsai-coordination    Group topologies · council · voting
  tnsai-tools           POJO toolkits across web · file · DB · code · media · fintech · …
  tnsai-mcp             Model Context Protocol client + server
  tnsai-quality         Observability + security enforcement
       │
       ▼
COMPOSED — depend on core + 1+ capability
  tnsai-intelligence    → core, coordination
                          RAG · planning · reasoning · context
  tnsai-channels        → core, coordination
                          Telegram · CLI · Email · Slack · Discord · WhatsApp
                          adapters via SPI
  tnsai-evaluation      → core, intelligence, quality
                          Benchmarks · quality gates · evaluators
  tnsai-integration     → core, coordination, evaluation
                          SCOPBridge + framework adapters
       │
       ▼
APPLICATION
  tnsai-server          → core, llm, coordination, quality
                          WebSocket backend · RAG service · tool execution

META
  tnsai-bom             Bill of Materials — pins all 12 modules to one version
```

</details>

## Core Concepts

### Agent Lifecycle

![Agent lifecycle: a user message flows through agent.chat into Memory, System Prompt, and LLM call. The LLM response branches into either a text reply (returned to the user) or a tool call routed to ActionExecutor. ActionExecutor dispatches one of four ActionType handlers (LOCAL, WEB\_SERVICE, LLM, MCP\_TOOL), and the tool result loops back to the LLM for multi-turn execution.](https://tnsai.dev/assets/diagrams/agent-lifecycle.svg)

<details>
<summary>ASCII fallback (for terminal / plain-text readers)</summary>

```
User Message
    │
    ▼
agent.chat(message)
    │
    ├──  Memory         append to conversation history
    ├──  System Prompt  identity + roles + invariants + state
    └──  LLM call       request with tool definitions
                                │
                                ▼
                        LLM Response
                                │
                ┌───────────────┴───────────────┐
                │                               │
                ▼                               ▼
            Text reply                      Tool Call
                │                               │
                │                               ▼
                │                         ActionExecutor
                │                               │
                │                               │   ActionType:
                │                               ├──  LOCAL        Java method on Role
                │                               ├──  WEB_SERVICE  HTTP / REST endpoint
                │                               ├──  LLM          LLM dispatch via ToolMethodDispatcher
                │                               └──  MCP_TOOL     Model Context Protocol tool
                │                               │
                │                               ▼
                │                         Tool Result
                │                               │
                │                               ▼
                │                     back to LLM (multi-turn loop)
                │
                ▼
          return to user
```

</details>

### Action Routing

Actions are discovered from Roles via `@ActionSpec` annotations. The `ActionExecutor` routes each action to the correct executor based on `ActionType`:

| Type          | Source                                                | Example                                                                                     |
| ------------- | ----------------------------------------------------- | ------------------------------------------------------------------------------------------- |
| `LOCAL`       | Java method on Role                                   | `@ActionSpec(type = ActionType.LOCAL) String greet(String name)`                            |
| `WEB_SERVICE` | HTTP / REST endpoint                                  | `@ActionSpec(type = ActionType.WEB_SERVICE) + @WebService(...)`                             |
| `LLM`         | LLM dispatch using the agent's `ToolMethodDispatcher` | `@ActionSpec(type = ActionType.LLM)` + agent-level `.builtInTools(...)` / `.toolPojos(...)` |
| `MCP_TOOL`    | Model Context Protocol server tool                    | `@ActionSpec(type = ActionType.MCP_TOOL) + @MCPTool(serverUrl = "...")`                     |

### Extension Points (SPI)

TnsAI uses Java's `ServiceLoader` pattern for modular extensibility:

| SPI Interface                     | Module       | Purpose                |
| --------------------------------- | ------------ | ---------------------- |
| `LLMClientProvider`               | Core         | Register LLM providers |
| `CheckpointerProvider`            | Core         | State persistence      |
| `PlannerHandle.Factory`           | Intelligence | Planning algorithms    |
| `ReasoningStrategyHandle.Factory` | Intelligence | Reasoning patterns     |
| `ContextManagerHandle.Factory`    | Intelligence | Decision tracing       |
| `EvalHandle.Factory`              | Quality      | Evaluation hooks       |
| `SecurityEnforcerHandle.Factory`  | Quality      | Security policies      |

Register implementations in `META-INF/services/<interface-name>`.

### Agent Internal Architecture

The `Agent` facade composes a small set of focused collaborators — each
owns one slice of behavior, so extending or replacing one piece doesn't
ripple through the whole class. If you're subclassing `Agent` or
swapping out one of these collaborators via SPI, this is the map.

![Agent internal architecture: the Agent facade composes focused collaborators — AgentOrchestrator (chat loop, tool-call routing, KB integration), AgentStreamingSupport (streaming chat + ChatChunk events), AgentCapabilities (planning, reasoning, eval, feedback, environment, variant, resilience), AgentCognitiveSupport, AgentHierarchyManager (parent/child relationships), AgentMessagingHandler (inter-agent messaging via communication SPI), AgentGroupManager (group membership).](https://tnsai.dev/assets/diagrams/agent-internal.svg)

<details>
<summary>ASCII fallback (for terminal / plain-text readers)</summary>

```
Agent                       identity · lifecycle · template methods · facade
   │
   ├── AgentOrchestrator    chat · streaming hooks · tool-call loop · KB
   │
   ├── AgentCapabilities    planning · reasoning · eval · feedback ·
   │                        environment · variant · resilience
   │
   ├── AgentCognitiveSupport
   │                        internal cognitive support (public for visibility)
   │
   ├── AgentHierarchyManager
   │                        parent / child relationships
   │
   ├── AgentStreamingSupport
   │                        streaming chat + ChatChunk events
   │
   ├── AgentMessagingHandler
   │                        inter-agent messaging via communication SPI
   │
   └── AgentGroupManager    group membership
```

</details>

## Design Principles

1. **Annotation-first** — `@ActionSpec`, `@AgentSpec`, `@ChannelSpec` over programmatic config
2. **SPI for extensibility** — modules register via `META-INF/services/`
3. **Composition over inheritance** — Agent delegates to focused managers
4. **Immutability** — records for data, `List.of()`, `Map.of()`
5. **Thread safety** — `ConcurrentHashMap`, `CopyOnWriteArrayList`, virtual threads

---

# Start Here

URL: https://tnsai.dev/docs/start
Description: New to TnsAI? This section gets you from zero to a running agent in under 10 minutes.

import { Callout } from 'fumadocs-ui/components/callout'

## Pages

- [Installation](/docs/start/installation) — Add TnsAI to your Maven or Gradle project.
- [Quickstart](/docs/start/quickstart) — Build your first agent with a role, a tool, and streaming output.
- [Architecture Overview](/docs/start/architecture-overview) — How the modules fit together.
- [Module Overview](/docs/start/module-overview) — Which modules to pull in for your use case.

## After this section

- Deep-dive into a single agent → [Agents](/docs/agents)
- Add tools, planning, RAG, LLM tuning → [Capabilities](/docs/capabilities)
- Coordinate multiple agents → [Multi-Agent](/docs/multi-agent)
- Observe, validate, secure, evaluate → [Observability](/docs/observability), [Validation](/docs/validation), [Security](/docs/security), [Evaluation](/docs/evaluation)
- Run as a backend → [Server](/docs/server), [MCP](/docs/mcp), [Channels](/docs/channels), [Integrate](/docs/integrate)
- Build an end-to-end example → [Tutorials](/docs/tutorials)

---

# Installation

URL: https://tnsai.dev/docs/start/installation
Description: Add TnsAI to your project. The recommended path is via the BOM — one version number covers every module.

import { Callout } from 'fumadocs-ui/components/callout'

All `tnsai-*` artifacts are published to Maven Central under the
`io.github.tansuasici` group. Browse them at
[central.sonatype.com/namespace/io.github.tansuasici](https://central.sonatype.com/namespace/io.github.tansuasici).

## Prerequisites

- Java 21+
- Maven 3.9+ or Gradle 8+

## Maven (recommended — via BOM)

Import the BOM once in `<dependencyManagement>`, then list the modules you need in `<dependencies>` without any `<version>` tag:

```xml
<dependencyManagement>
    <dependencies>
        <dependency>
            <groupId>io.github.tansuasici</groupId>
            <artifactId>tnsai-bom</artifactId>
            <version>0.12.0</version>
            <type>pom</type>
            <scope>import</scope>
        </dependency>
    </dependencies>
</dependencyManagement>

<dependencies>
    <dependency>
        <groupId>io.github.tansuasici</groupId>
        <artifactId>tnsai-core</artifactId>
    </dependency>
    <dependency>
        <groupId>io.github.tansuasici</groupId>
        <artifactId>tnsai-llm</artifactId>
    </dependency>
    <!-- pick any other tnsai-* module — versions come from the BOM -->
</dependencies>
```

**To upgrade**: change the single `<version>` on `tnsai-bom`. Every `tnsai-*` module moves together.

## Gradle

```kotlin
dependencies {
    implementation(platform("io.github.tansuasici:tnsai-bom:0.12.0"))
    implementation("io.github.tansuasici:tnsai-core")
    implementation("io.github.tansuasici:tnsai-llm")
    // pick any other tnsai-* module — versions come from the BOM
}
```

## Available modules

| Module               | What it provides                                                                                                                                                                                                                       |
| -------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `tnsai-core`         | Agent lifecycle, action routing, tools, capabilities, streaming                                                                                                                                                                        |
| `tnsai-llm`          | LLM providers — Anthropic, OpenAI, Gemini, NVIDIA, Mistral, Groq, Cohere, OpenRouter, Hugging Face, xAI, DeepSeek, Perplexity, Together, Fireworks, Cerebras, Replicate, DeepInfra, Databricks, watsonx, MiniMax, Zhipu, Azure, Ollama |
| `tnsai-intelligence` | RAG, knowledge bases, planning, reasoning, context management                                                                                                                                                                          |
| `tnsai-coordination` | Multi-agent groups, topologies, negotiation, workflows                                                                                                                                                                                 |
| `tnsai-quality`      | OpenTelemetry, Prometheus, prompt injection detection, sandboxing                                                                                                                                                                      |
| `tnsai-evaluation`   | G-Eval, release gates, regression detection                                                                                                                                                                                            |
| `tnsai-mcp`          | Model Context Protocol — client, server, stdio/SSE/streamable-HTTP transports                                                                                                                                                          |
| `tnsai-tools`        | 62 POJO toolkits (\~206 `@Tool` methods) across web, file, DB, code, media, fintech, …                                                                                                                                                 |
| `tnsai-channels`     | Telegram / CLI / Email / Slack / Discord / WhatsApp channel adapters via SPI                                                                                                                                                           |
| `tnsai-integration`  | External framework bridges and integration tests                                                                                                                                                                                       |
| `tnsai-server`       | HTTP/WebSocket agent server (Javalin-based)                                                                                                                                                                                            |

Detailed per-module guidance → [Module Overview](/docs/start/module-overview).

## Without the BOM (not recommended)

If for some reason you want to pin module versions individually:

```xml
<dependency>
    <groupId>io.github.tansuasici</groupId>
    <artifactId>tnsai-core</artifactId>
    <version>0.12.0</version>
</dependency>
```

This works but you're now responsible for keeping every `tnsai-*` dep on the same version. BOM mismatch is the #1 source of runtime surprises — stick with the BOM unless you have a specific reason.

## Logging — pick an SLF4J backend

TnsAI uses [SLF4J](https://www.slf4j.org) for all internal logging.
SLF4J is a **facade** — TnsAI itself only depends on `slf4j-api`, the
interface layer. You must add a logging **implementation** to your
own project, otherwise SLF4J prints

```
SLF4J: No SLF4J providers were found.
SLF4J: Defaulting to no-operation (NOP) logger implementation.
```

at startup and you'll see no log output at all.

This is the standard Java pattern (Spring, Jackson, Apache HTTP
Client, etc. all behave the same way) — libraries don't ship a log
backend so they don't conflict with whatever you're already using.

Pick one based on your context:

| Context                          | Recommended                 | Notes                                                   |
| -------------------------------- | --------------------------- | ------------------------------------------------------- |
| **Production**, no Spring Boot   | `logback-classic`           | Battle-tested, async, structured logging                |
| **Production**, with Spring Boot | *nothing — already there*   | Spring Boot Starter brings logback-classic transitively |
| **CLI / small tool**             | `slf4j-simple`              | Plain console output, one extra dep                     |
| **Tests only**                   | `slf4j-simple` (test scope) | What most TnsAI examples use                            |
| **No logs wanted**               | `slf4j-nop`                 | Silently drops every log call                           |

### Logback (recommended for production)

```xml
<dependency>
    <groupId>ch.qos.logback</groupId>
    <artifactId>logback-classic</artifactId>
    <version>1.5.18</version>
</dependency>
```

### SLF4J Simple (tests / CLI)

```xml
<dependency>
    <groupId>org.slf4j</groupId>
    <artifactId>slf4j-simple</artifactId>
    <version>2.0.17</version>
    <scope>test</scope>
</dependency>
```

Drop `<scope>test</scope>` if you want it on the production
classpath too.

### Silence all logging

```xml
<dependency>
    <groupId>org.slf4j</groupId>
    <artifactId>slf4j-nop</artifactId>
    <version>2.0.17</version>
</dependency>
```

## Environment variables

| Variable            | Purpose               |
| ------------------- | --------------------- |
| `ANTHROPIC_API_KEY` | Claude API key        |
| `OPENAI_API_KEY`    | OpenAI API key        |
| `BRAVE_API_KEY`     | Brave Search API key  |
| `TAVILY_API_KEY`    | Tavily Search API key |

For the full list, see [reference/configuration](/docs/reference/configuration).

## Next

- [Quickstart](/docs/start/quickstart) — Build your first agent in five minutes.
- [Module Overview](/docs/start/module-overview) — Which modules you need for your use case.

---

# Module Overview

URL: https://tnsai.dev/docs/start/module-overview

import { Callout } from 'fumadocs-ui/components/callout'

## Module Summary

| Module           | Purpose                                                     | Key Classes                                                                         |
| ---------------- | ----------------------------------------------------------- | ----------------------------------------------------------------------------------- |
| **Core**         | Agent lifecycle, roles, actions, events, SPI, tool dispatch | Agent, AgentBuilder, Role, ActionExecutor, ToolMethodRegistry, ToolMethodDispatcher |
| **LLM**          | Provider-agnostic LLM abstraction + streaming               | LLMClient, ChatRequest, ChatResponse, ChatChunk                                     |
| **Coordination** | Multi-agent groups, protocols, voting                       | AgentGroup, ProtocolManager, LeaderElection                                         |
| **Intelligence** | Planning, reasoning, RAG, learning                          | PlannerHandle, ReasoningStrategy, KnowledgeBase                                     |
| **Quality**      | Observability, security, validation                         | AgentTrace, SecurityEnforcer, InvariantChecker                                      |
| **Evaluation**   | Evaluators, benchmarks, quality gates                       | Evaluator, BenchmarkRunner, QualityGate, EvalTest                                   |
| **Tools**        | 62 POJO toolkits (\~206 @Tool methods) across 29 categories | CsvTools, JsonTools, PdfTools, WebSearchTools, AcademicTools, …                     |
| **MCP**          | Model Context Protocol client/server                        | McpClient, McpServer, McpTransport, TnsAIToolProvider                               |
| **Server**       | WebSocket backend, RAG pipeline, tool approval              | TnsServer, ServerFileTools, ServerShellTools, ServerGitTools                        |
| **Channels**     | External messaging adapters                                 | ChannelAdapter, InboundMessage, OutboundMessage                                     |
| **Integration**  | Cross-module integration tests                              | End-to-end scenarios                                                                |
| **Docs**         | Documentation (this repo)                                   | —                                                                                   |
| **Wiki**         | Living knowledge base (Obsidian)                            | —                                                                                   |

## When to Use What

### Minimal Agent (Core + LLM)

```xml
tnsai-core + tnsai-llm
```

Chat-capable agent with roles and actions. No tools, no coordination.

### Agent with Tools (+ Tools)

```xml
tnsai-core + tnsai-llm + tnsai-tools
```

Agent can search the web, read files, execute code, query databases.

### Multi-Agent System (+ Coordination)

```xml
tnsai-core + tnsai-llm + tnsai-coordination
```

Agent groups, leader election, negotiation protocols, task delegation.

### Smart Agent (+ Intelligence)

```xml
tnsai-core + tnsai-llm + tnsai-intelligence
```

GOAP/HTN planning, ReAct/ToT reasoning, RAG, learning from feedback.

### Production Agent (+ Quality + Evaluation)

```xml
tnsai-core + tnsai-llm + tnsai-quality + tnsai-evaluation
```

OpenTelemetry tracing, security enforcement, automated evaluation, quality gates.

### Full Stack (+ Server)

```xml
All modules + tnsai-server
```

WebSocket API, session management, RAG pipeline, tool approval UI.

## Module Dependencies

| Module       | Depends On                                                 |
| ------------ | ---------------------------------------------------------- |
| Core         | — (foundation)                                             |
| LLM          | Core                                                       |
| Coordination | Core                                                       |
| Intelligence | Core                                                       |
| Quality      | Core                                                       |
| Evaluation   | Core, Quality                                              |
| Tools        | Core                                                       |
| MCP          | Core                                                       |
| Server       | Core, LLM, Coordination, Intelligence, Quality, Tools, MCP |
| Channels     | Core                                                       |
| Integration  | All                                                        |

---

# Quickstart

URL: https://tnsai.dev/docs/start/quickstart
Description: Build your first TnsAI agent in 5 minutes. Prerequisites: Installation.

import { Callout } from 'fumadocs-ui/components/callout'

## Your First Agent

### Option 1: AgentBuilder (no subclassing)

```java
import com.tnsai.agents.AgentBuilder;
import com.tnsai.roles.Role;
import com.tnsai.annotations.ActionSpec;
import com.tnsai.enums.ActionType;
import com.tnsai.llm.providers.AnthropicClient;
import com.tnsai.models.role.RoleIdentity;
import com.tnsai.models.role.Responsibility;
import com.tnsai.models.role.CoreDuty;
import com.tnsai.identity.LocalIdentityProvider;
import com.tnsai.identity.AgentDescriptor;          // identity RECORD — not annotations.AgentSpec
import com.tnsai.accountability.RecordingLiabilitySink;
import com.tnsai.accountability.AuthorityScope;
import java.time.Duration;
import java.util.List;

// 1. Define a role with actions
public class AssistantRole extends Role {
    @Override public RoleIdentity getIdentity() {
        return new RoleIdentity("assistant",
            "Friendly conversational agent", "support");
    }
    @Override public List<Responsibility> getResponsibilities() {
        return List.of(new CoreDuty("greet",
            "Greet users by name"));
    }

    @ActionSpec(type = ActionType.LOCAL,
                description = "Greet the user by name")
    public String greet(String name) {
        return "Hello, " + name + "!";
    }
}

// 2. Mint identity + accountability wiring (REQUIRED by AgentBuilder.build())
var principal = new LocalIdentityProvider().issue(
    AgentDescriptor.builder()
        .agentClass("com.example.AssistantAgent")
        .systemPrompt("You are a friendly assistant.")
        .toolNames(List.of())
        .model("claude-sonnet-4-20250514")
        .build());
var sink  = new RecordingLiabilitySink();              // in-memory; FilesystemLiabilitySink in prod
var scope = AuthorityScope.unrestricted(Duration.ofHours(1));

// 3. Build and use the agent
var agent = AgentBuilder.create()
    .llm(new AnthropicClient("claude-sonnet-4-20250514"))
    .role(new AssistantRole())
    .principal(principal)
    .liabilitySink(sink)
    .authorityScope(scope)
    .build();

agent.start();
String response = agent.chat("Hi, my name is Alice");
System.out.println(response);
```

> **Required.** The `AgentBuilder` path needs the accountability trio — `principal`, `liabilitySink`, and `authorityScope`. Without all three, `build()` throws `IllegalStateException` (TNS-298); the framework ships no silent no-op default. `RecordingLiabilitySink` is in-memory — use `FilesystemLiabilitySink` for production audit trails. See [Accountability](/docs/security/accountability) for the full identity / liability / scope model.

### Option 2: Annotated Agent (subclass)

```java
import com.tnsai.agents.Agent;
import com.tnsai.annotations.AgentSpec;
import com.tnsai.llm.LLMClient;
import com.tnsai.llm.providers.AnthropicClient;
import com.tnsai.roles.Role;
import java.util.List;

@AgentSpec(description = "A helpful research assistant")
public class ResearchAgent extends Agent {

    @Override
    protected LLMClient getLLM() {
        return new AnthropicClient("claude-sonnet-4-20250514");
    }

    @Override
    protected List<Role> getRoles() {
        return List.of(new AssistantRole());
    }
}

// Use it
var agent = new ResearchAgent();
agent.start();
String answer = agent.chat("What is quantum computing?");
```

## Adding Tools

> For brevity the examples below omit the `.principal(...)`, `.liabilitySink(...)`, and `.authorityScope(...)` calls — assume the `principal`, `sink`, and `scope` from Option 1. Every `AgentBuilder.build()` still requires them.

Register shipped POJO toolkits by enum constant — compile-safe:

```java
import com.tnsai.enums.BuiltInTool;

var agent = AgentBuilder.create()
    .llm(new AnthropicClient("claude-sonnet-4-20250514"))
    .role(new AssistantRole())
    .builtInTools(
        BuiltInTool.WEB_SEARCH_TOOLS,   // brave_search, duckduckgo, wikipedia, …
        BuiltInTool.PDF_TOOLS,          // pdf_extract_text, pdf_metadata, …
        BuiltInTool.MARKDOWN_TOOLS      // markitdown
    )
    .build();
```

Each enum entry registers every `@Tool`-annotated method on the backing POJO. The LLM sees them all (e.g. `brave_search`, `pdf_extract_text`, `markitdown`) and picks one per call.

For your own toolkits, register the POJO instances directly:

```java
var agent = AgentBuilder.create()
    .llm(llm)
    .role(role)
    .toolPojos(new MyDomainTools(), new MyAnalyticsTools())
    .build();
```

See [Tools / Catalog](/docs/capabilities/tools/catalog) for the full shipped catalog and [Custom Tools](/docs/capabilities/tools/custom-tools) for the `@Tool` annotation pattern.

## Streaming

```java
agent.streamChatWithTools("Summarize this paper", chunk -> {
    if (chunk.isContent()) {
        System.out.print(chunk.getContent());
    }
});
```

## Structured Output

```java
record WeatherInfo(String city, double temperature, String condition) {}

WeatherInfo weather = agent.chatWithFormat(
    "What's the weather in Istanbul?",
    WeatherInfo.class,
    3 // max retries
);
```

## Next Steps

- [Architecture Overview](/docs/start/architecture-overview) — How the modules fit together.
- [Module Overview](/docs/start/module-overview) — What each module provides.
- [Agents](/docs/agents) — Deep dive into a single agent's lifecycle and behavior.
- [Capabilities: Tools](/docs/capabilities/tools) — Using and creating tools.
- [Production: Evaluation](/docs/evaluation) — Testing agent quality.

---

# Tutorial: Agent with Tool Approval

URL: https://tnsai.dev/docs/tutorials/agent-with-tool-approval
Description: A full code walkthrough is still being written. In the meantime this page describes the shape and points at the framework pieces you can wire up today. Open a discussion if you start the implementation and hit something missing.

import { Callout } from 'fumadocs-ui/components/callout'

## Goal

An agent that uses destructive tools (shell, `git` writes, DB mutations) safely by routing them through a human approval channel before they execute.

## How approval flows through the framework

```text
LLM proposes tool call ──▶ ToolCallFilter (per-session)
                              │
                              ├─ tool.requiresConfirmation() == false ─▶ execute immediately
                              │
                              └─ tool.requiresConfirmation() == true  ─▶ pause + emit approval event
                                                                          │
                                                                          ▼
                                                                  Approval channel
                                                                  (WebSocket UI / webhook / chat /custom)
                                                                          │
                                                                          ▼
                                                                  Approve / Deny / Modify
                                                                          │
                                                                          ▼
                                                            Resume tool execution or skip
```

## Building blocks you'd compose

- **Tool risk metadata** — declare `requiresConfirmation()` on your `@Tool` method or POJO, plus `getRiskLevel()` so the approval UI can surface the right context.
- **`tnsai-server`** — runs the WebSocket protocol that streams pending approval events and accepts the operator's response.
- **`ToolCallFilter`** — the per-session interceptor that pauses execution and waits for the approval channel. The default filter blocks indefinitely; production setups wire either a websocket-backed `WsToolApprovalFilter` or an HTTP-webhook variant.
- **Tracing** — every approval / denial / modification emits a `DecisionTracer` event you can ship to OpenTelemetry.

## Related

- [Reference: Agent reliability](/docs/agents/reliability) — error handling, guardrails, retries.
- [Server module](https://github.com/TnsAI-Framework/TnsAI/tree/main/tnsai-server) — websocket protocol + approval-channel internals.

---

# Tutorial: Reusable Capabilities

URL: https://tnsai.dev/docs/tutorials/capability-pattern
Description: Build an editorial agent that can summarise, translate, and classify sentiment — without writing a single body for those action methods. This tutorial walks through the @Capability pattern, showing composition, override, and the common mistakes the framework prevents.

import { Callout } from 'fumadocs-ui/components/callout'

## Goal

At the end of the tutorial you will have:

1. Three reusable capability interfaces (`Summarizer`, `Translator`, `SentimentClassifier`) that can be shared across any number of roles.
2. An `EditorRole` that composes all three — **zero method bodies** for dispatched actions.
3. A `StrictEditor` variant that overrides one capability with a deterministic local implementation — demonstrating the role-wins dedupe rule.
4. A minimal test exercising the agent end-to-end.

Total work: about 40 lines of interface code, 15 lines of role code, 15 lines of test.

## Prerequisites

- [Installation](/docs/start/installation)
- An LLM provider configured (any one of Anthropic, OpenAI, Ollama, etc.)
- Familiarity with [Roles](/docs/agents/fundamentals/roles) and the [Action System](/docs/agents/fundamentals/action-system)
- Recommended: skim [Capabilities](/docs/agents/fundamentals/capabilities) first for the conceptual overview

## Step 1 — Define the Capabilities

Each capability is an interface annotated `@Capability`. The methods carry their usual `@ActionSpec` metadata; the default bodies throw `Actions.dispatchedByFramework()` so bypass calls fail loudly instead of silently returning `null`.

Per-action LLM overrides (system prompt, temperature) live directly on `@ActionSpec` — no nested annotation needed.

### `Summarizer`

```java
package com.example.tutorial.capabilities;

import com.tnsai.actions.Actions;
import com.tnsai.annotations.ActionSpec;
import com.tnsai.capabilities.Capability;
import com.tnsai.enums.ActionType;

@Capability
public interface Summarizer {

    @ActionSpec(
        type = ActionType.LLM,
        description = "Summarise the given text in one short paragraph of at most 60 words.",
        llmSystemPrompt = "You are a concise summariser. Output plain text only, no preamble.",
        llmTemperature = 0.2f
    )
    default String summarize(String text) {
        throw Actions.dispatchedByFramework();
    }
}
```

### `Translator`

```java
package com.example.tutorial.capabilities;

import com.tnsai.actions.Actions;
import com.tnsai.annotations.ActionSpec;
import com.tnsai.capabilities.Capability;
import com.tnsai.enums.ActionType;

@Capability
public interface Translator {

    @ActionSpec(
        type = ActionType.LLM,
        description = "Translate the given text to the target language, preserving tone.",
        llmSystemPrompt = "You are a precise translator. Output only the translation.",
        llmTemperature = 0.1f
    )
    default String translate(String text, String targetLanguage) {
        throw Actions.dispatchedByFramework();
    }
}
```

### `SentimentClassifier`

```java
package com.example.tutorial.capabilities;

import com.tnsai.actions.Actions;
import com.tnsai.annotations.ActionSpec;
import com.tnsai.capabilities.Capability;
import com.tnsai.enums.ActionType;

@Capability
public interface SentimentClassifier {

    @ActionSpec(
        type = ActionType.LLM,
        description = "Classify the sentiment of the input as POSITIVE, NEGATIVE, or NEUTRAL.",
        llmSystemPrompt = "Output exactly one of: POSITIVE, NEGATIVE, NEUTRAL. Nothing else.",
        llmTemperature = 0.0f
    )
    default String classifySentiment(String text) {
        throw Actions.dispatchedByFramework();
    }
}
```

Each capability is now a first-class reusable contract. Any role in your codebase can gain these abilities by `implements`-ing the interface.

## Step 2 — Compose Them Onto a Role

The role class is where agent state lives (memory, history, lifecycle). Capabilities are added by implementing their interfaces — no method bodies needed:

```java
package com.example.tutorial.roles;

import com.example.tutorial.capabilities.Summarizer;
import com.example.tutorial.capabilities.Translator;
import com.example.tutorial.capabilities.SentimentClassifier;
import com.tnsai.annotations.RoleIdentity;
import com.tnsai.models.role.Responsibility;
import com.tnsai.roles.Role;

import java.util.List;

@RoleIdentity(
    name = "Editor",
    goal = "Produce clean, readable articles in any target language"
)
public class EditorRole extends Role implements Summarizer, Translator, SentimentClassifier {

    @Override
    public List<Responsibility> getResponsibilities() {
        return List.of(
            new Responsibility("Condense source material", "High"),
            new Responsibility("Render outputs in the requested language", "High"),
            new Responsibility("Gauge reader sentiment on drafts", "Medium")
        );
    }

    // Agent state + lifecycle methods go here. No capability bodies — inherited.
}
```

`ActionDiscovery` walks the interface chain at role initialisation, picks up the three `@ActionSpec` methods from the capability interfaces, and registers them as the role's actions. The LLM sees all three as tools it can call; dispatch flows through `ActionExecutor` → `LLMRoleExecutor` exactly as it would for a concrete `@ActionSpec` method.

## Step 3 — Wire Into an Agent

```java
import com.example.tutorial.roles.EditorRole;
import com.tnsai.agents.Agent;
import com.tnsai.agents.AgentBuilder;
import com.tnsai.llm.LLMClientFactory;
import com.tnsai.roles.Role;

public class TutorialMain {

    public static void main(String[] args) {
        Agent editor = AgentBuilder.create()
            .llm(LLMClientFactory.create("openai", "gpt-4o", 0.3f))
            .role(Role.create(EditorRole.class))
            .build();

        // The LLM now has three tools available: summarize, translate, classifySentiment.
        String reply = editor.chat(
            "Please summarise this in one line, then translate the summary to Turkish: "
                + "The framework pattern eliminates boilerplate by moving dispatched method "
                + "contracts into reusable interfaces. Roles compose them without bodies."
        );

        System.out.println(reply);
    }
}
```

Run it; the LLM picks `summarize`, then picks `translate`, and produces the combined output. Your codebase contains zero lines of `return null;` — the contracts are expressed once, on the capability interfaces.

## Step 4 — Override When You Need Deterministic Behaviour

A role that implements a capability can *also* declare its own version of the method. When that happens, the role's declaration wins; the capability's default is skipped. Use this when a specific role needs deterministic, non-LLM behaviour for one of the contracted actions:

```java
package com.example.tutorial.roles;

import com.example.tutorial.capabilities.Summarizer;
import com.tnsai.annotations.ActionSpec;
import com.tnsai.annotations.RoleIdentity;
import com.tnsai.enums.ActionType;
import com.tnsai.models.role.Responsibility;
import com.tnsai.roles.Role;

import java.util.List;

@RoleIdentity(
    name = "StrictEditor",
    goal = "Truncate-first summarise (deterministic, no LLM)"
)
public class StrictEditor extends Role implements Summarizer {

    @Override
    public List<Responsibility> getResponsibilities() {
        return List.of(new Responsibility("Produce identical output for identical input", "High"));
    }

    // Overrides Summarizer.summarize — type flips from LLM to LOCAL.
    @Override
    @ActionSpec(
        type = ActionType.LOCAL,
        description = "Truncate to the first 60 characters (no LLM, reproducible)."
    )
    public String summarize(String text) {
        return text.length() <= 60 ? text : text.substring(0, 60) + "...";
    }
}
```

When `ActionDiscovery` scans `StrictEditor`, it records the class-declared `summarize` signature on the first pass, then skips `Summarizer.summarize` on the capability-interface pass. Exactly one `summarize` action ends up in the role's metadata, and its `ActionType` is `LOCAL` rather than `LLM`. The LLM still sees an action called `summarize`; invoking it just runs the deterministic body instead of hitting the model.

## Step 5 — Test

```java
import com.example.tutorial.roles.EditorRole;
import com.tnsai.actions.ActionDiscovery;
import com.tnsai.metadata.ActionMetadata;
import com.tnsai.metadata.DiscoveredRoleActions;
import org.junit.jupiter.api.Test;

import java.util.Set;
import java.util.stream.Collectors;

import static org.junit.jupiter.api.Assertions.*;

class EditorRoleCapabilityTest {

    @Test
    void editorGainsAllThreeCapabilityActions() {
        DiscoveredRoleActions discovered = ActionDiscovery.discoverActions(EditorRole.class);

        Set<String> actionNames = discovered.getActions().stream()
            .map(ActionMetadata::getName)
            .collect(Collectors.toSet());

        assertEquals(
            Set.of("summarize", "translate", "classifySentiment"),
            actionNames,
            "Role must gain actions from every @Capability interface it implements"
        );
    }
}
```

`ActionDiscovery.discoverActions` is synchronous and pure — it works at unit-test time without an LLM client. A passing test here proves the composition works before you ever hit the network.

## Common Mistakes the Framework Catches

Two misuses of `@Capability` fail fast at discovery time with an `IllegalStateException`. Knowing them up front saves a debugging round:

1. **Abstract method (no `default` body)** — a capability method without a default forces every adopter to write a body, defeating the whole point. The error message names the offending interface and method, and points to `default { throw Actions.dispatchedByFramework(); }` as the fix.
2. **`ActionResult` parameter on a capability method** — capabilities are pure dispatch. If a method needs post-processing on the LLM's raw response, keep that method on the concrete role class (the legacy pattern) rather than on the capability interface.

The validator message is explicit in both cases; you don't have to guess what went wrong.

## What Not to Turn Into a Capability

- **`ActionType.LOCAL` methods** — they have real bodies the framework invokes via reflection. They don't suffer from the `return null` problem.
- **Methods with `ActionResult` parameters** — rejected by the validator by design.
- **One-off methods used by a single role** — extracting a capability interface for one consumer adds indirection without reuse benefit. Inline is fine.

## Related

- [Capabilities](/docs/agents/fundamentals/capabilities) — concept reference, validation rules, migration table
- [Action System](/docs/agents/fundamentals/action-system) — how `ActionExecutor` routes dispatched calls
- [Roles](/docs/agents/fundamentals/roles) — declaring roles, `@RoleIdentity`, responsibilities

## Implementation References

- `com.tnsai.capabilities.Capability` — marker annotation
- `com.tnsai.actions.Actions.dispatchedByFramework()` — exception-returning helper
- `com.tnsai.actions.ActionDiscovery` — two-pass discovery (class methods, then `@Capability` interface methods)

---

# Tutorial: Customer Support Bot

URL: https://tnsai.dev/docs/tutorials/customer-support-bot
Description: A full code walkthrough for this tutorial is still being written. In the meantime this page describes the shape of the system and links to the framework pieces you'd compose to build it. If you start the implementation and hit something missing, open a discussion — concrete questions accelerate the write-up.

import { Callout } from 'fumadocs-ui/components/callout'

## Goal

A support agent that:

1. Answers product questions from a knowledge base via RAG.
2. Creates tickets through a webhook when it can't find a confident answer.
3. Escalates to a human reviewer when the user is frustrated.

## Architecture

```text
User message ──▶ SupportRole
                    │
                    ├──▶ @Retrieval(knowledge="support_kb")   ◀── KnowledgeBase (RAG)
                    │
                    ├──▶ @Tool create_ticket(...)             ◀── HTTP webhook
                    │
                    └──▶ @Tool escalate(...)                  ◀── HumanReviewSink
                                                                  (sentiment-aware)
```

## Building blocks you'd compose

- **Knowledge base** — ingest your support docs as a `KnowledgeBase` and consume it from the role with `@KnowledgeSource` + `@Retrieval`. See [RAG guide](/docs/capabilities/rag).
- **Tools** — define `create_ticket` and `escalate` as `@Tool`-annotated methods on a POJO; register via `AgentBuilder.toolPojos(...)`. See [Tools guide](/docs/capabilities/tools).
- **Sentiment-aware escalation** — wrap the inbound message in a guardrail (`@InputGuardrail`) or pre-step that runs a sentiment classifier and short-circuits to `escalate` when frustration is detected.
- **Observability** — enable tracing with `@Traced` on action methods so each ticket / escalation lands in your trace exporter.

## Related

- [Guides: RAG](/docs/capabilities/rag)
- [Guides: Tools](/docs/capabilities/tools)

---

# Tutorial: Declarative input/output guardrails

URL: https://tnsai.dev/docs/tutorials/declarative-guardrails
Description: Validate, sanitise, and bound every action's parameters and return value through annotations alone — no per-action if (input.length() > N) throw … plumbing in your Role. The framework's InputGuardrailEnforcer and OutputGuardrailEnforcer enforce the contract at dispatch time, before and after the action body runs.

import { Callout } from 'fumadocs-ui/components/callout'

## Prerequisites

- [Installation](/docs/start/installation)
- A Role with at least one `@ActionSpec` method

## The role

Full source lives at `tnsai-integration/src/main/java/com/tnsai/integration/scop/examples/UserInputRole.java` and is exercised by `UserInputRoleGuardrailIntegrationTest`. The shape:

```java
@RoleSpec(
    name = "UserInputAgent",
    description = "Validates and sanitises user-supplied text.",
    responsibilities = {
        @Responsibility(
            name = "InputHardening",
            actions = {"summarize", "formatJson", "adminCommand"})
    }
)
@InputGuardrail(
    // Class-level default — inherited by every action below
    maxLength = 16384,
    blockPatterns = {
        "(?i)ignore\\s+previous\\s+instructions",
        "(?i)disregard\\s+the\\s+system\\s+prompt",
        "<script\\b"
    },
    onFailure = FailureAction.REJECT
)
public class UserInputRole extends Role {
    // ... actions below
}
```

## Pattern 1 — Class-level default + per-action inheritance

The `@InputGuardrail` on the **class** sets a baseline that every `@ActionSpec` method inherits unless it declares its own. This is the cleanest way to express "every action this role exposes must reject prompt-injection attempts and oversized payloads."

```java
@ActionSpec(
    type = ActionType.LOCAL,
    description = "Summarize user-provided text in plain language."
)
@OutputGuardrail(maxChars = 2000, onFailure = OutputGuardrail.FailureAction.TRUNCATE)
public String summarize(@Param(name = "text") String text) {
    return "Summary of " + text.substring(0, Math.min(text.length(), 64)) + "…";
}
```

`summarize` has no `@InputGuardrail` of its own, so the framework applies the class default: anything containing `(?i)ignore previous instructions` is rejected, anything over 16KB is rejected. Output is bounded separately to 2000 chars via `@OutputGuardrail`; oversize output is silently truncated rather than failing the call.

## Pattern 2 — Method-level override + SANITIZE policy

When a specific action needs different rules, declare `@InputGuardrail` on the method. **Method-level wins entirely — no merging.** This is deliberate: merging produces surprising behaviour when one annotation tightens what another loosens.

```java
@ActionSpec(
    type = ActionType.LOCAL,
    description = "Format a JSON-shaped payload."
)
@InputGuardrail(
    minLength = 2,
    maxLength = 4096,
    allowPatterns = {"^\\s*[\\{\\[]"},      // must start with { or [
    onFailure = FailureAction.SANITIZE
)
@OutputGuardrail(maxChars = 4096, onFailure = OutputGuardrail.FailureAction.TRUNCATE)
public String formatJson(@Param(name = "json") String json) {
    return json; // pretty-print in real life
}
```

`SANITIZE` strips C0 control characters (preserving `\t` / `\n` / `\r`) and truncates to `maxLength`. The action body still runs — but it sees a cleaned value rather than failing.

## Pattern 3 — REVIEW disposition for human-in-loop gating

When you want a violation to **route to a human** rather than block silently, use `onFailure = FailureAction.REVIEW`. The thrown `GuardrailViolationException` carries a `Cause.NEEDS_REVIEW` marker so callers can route to an approval queue.

```java
@ActionSpec(
    type = ActionType.LOCAL,
    description = "Issue a privileged admin command."
)
@InputGuardrail(
    maxLength = 64,
    allowPatterns = {"^[a-z][a-z0-9_-]{0,62}$"},
    onFailure = FailureAction.REVIEW,
    errorMessage = "Admin commands must be lowercase identifiers; routing to human review."
)
@OutputGuardrail(
    maxChars = 256,
    onFailure = OutputGuardrail.FailureAction.FALLBACK,
    fallback = "(redacted: admin output exceeded budget)"
)
public String adminCommand(@Param(name = "command") String command) {
    return "executed: " + command;
}
```

A consumer catches `ActionExecutionException` (the dispatcher wraps `GuardrailViolationException` as category `VALIDATION`), inspects the typed cause, and routes to a queue:

```java
try {
    Object result = agent.executeAction("adminCommand", Map.of("command", "REBOOT_NODE"));
} catch (ActionExecutionException ex) {
    if (ex.getCause() instanceof GuardrailViolationException gve
        && gve.guardrailCause() == GuardrailViolationException.Cause.NEEDS_REVIEW) {
        approvalQueue.submit(gve.actionName(), gve.parameterName(), ex.getMessage());
    } else {
        throw ex;
    }
}
```

## Failure dispositions reference

### `@InputGuardrail.onFailure`

| Value      | What happens                                                                                                              |
| ---------- | ------------------------------------------------------------------------------------------------------------------------- |
| `REJECT`   | Throws `GuardrailViolationException` (wrapped as `ActionExecutionException(VALIDATION)`). Action body never runs.         |
| `WARN`     | Logs the violation; passes the original value through unchanged. Useful for telemetry on borderline values.               |
| `SANITIZE` | Strips C0 control chars (preserving `\t` / `\n` / `\r`) and truncates to `maxLength`. Action body sees the cleaned value. |
| `REVIEW`   | Throws with `Cause.NEEDS_REVIEW` so callers can route to a human-approval queue.                                          |

### `@OutputGuardrail.onFailure`

| Value      | What happens                                                                                                               |
| ---------- | -------------------------------------------------------------------------------------------------------------------------- |
| `REJECT`   | Throws `GuardrailViolationException` (same wrapping as input).                                                             |
| `TRUNCATE` | Clips a too-long value to `maxChars`. Falls back to `FALLBACK` for too-short values.                                       |
| `FALLBACK` | Returns the configured `fallback` string.                                                                                  |
| `RETRY`    | **Phase 1**: degrades to `REJECT` with a self-documenting message — the enforcer doesn't have a re-execution callback yet. |
| `REVIEW`   | Throws with `Cause.NEEDS_REVIEW`.                                                                                          |

## What's enforced today

| Annotation field                                          | Enforced in Phase 1                                                                |
| --------------------------------------------------------- | ---------------------------------------------------------------------------------- |
| `minLength` / `maxLength` (input)                         | ✓                                                                                  |
| `minChars` / `maxChars` (output)                          | ✓                                                                                  |
| `blockPatterns` (regex denylist)                          | ✓                                                                                  |
| `allowPatterns` (regex allowlist)                         | ✓                                                                                  |
| `onFailure` disposition                                   | ✓                                                                                  |
| `fallback` (output)                                       | ✓                                                                                  |
| `errorMessage` (custom log line)                          | ✓                                                                                  |
| `logFailures`                                             | ✓                                                                                  |
| `validators` / `sanitizers` (`Class[]`)                   | Phase 2 (needs SPI)                                                                |
| `jsonSchema`                                              | Phase 2                                                                            |
| `detectInjection` / `moderateContent` / `blockCategories` | Phase 2 (needs content-moderation provider)                                        |
| `maxTokens`                                               | Phase 2 (needs tokenizer)                                                          |
| `maskPII` / `piiTypes`                                    | Phase 2 (will plug into [#80](https://github.com/TnsAI-Framework/TnsAI/issues/80)) |
| `checkHallucination`                                      | Phase 2 (LLM-driven)                                                               |
| `format` per `OutputFormat` variant                       | Phase 2                                                                            |
| `RETRY` re-execution                                      | Phase 2 (needs callback wiring)                                                    |

The `@InputGuardrail` and `@OutputGuardrail` annotations themselves already document every field; this table records what the **runtime enforcer** does with them today, not what the annotation surface promises.

## Where it sits in the pipeline

Inside `ActionExecutor.executeInternal`:

```
1. Approval check
2. Security access control + parameter encryption
3. @BeforeAction transformation
4. ActionValidator.validateParameters (basic schema)
5. ActionContract.validateInvariants + preconditions
6. Invariant precondition checks
7. ► InputGuardrailEnforcer.enforce  ← input guardrails
8. Action body execution (LOCAL / WEB_SERVICE / LLM / MCP_TOOL)
9. ► OutputGuardrailEnforcer.enforce ← output guardrails
10. Postcondition + post-execution invariants
```

Both enforcers translate `GuardrailViolationException` into `ActionExecutionException(VALIDATION)` at the dispatcher's edge, so existing consumer error handlers that match on `ActionExecutionException` continue to work without changes.

## Reference

- `@InputGuardrail` annotation — `tnsai-core/src/main/java/com/tnsai/annotations/InputGuardrail.java`
- `@OutputGuardrail` annotation — `tnsai-core/src/main/java/com/tnsai/annotations/OutputGuardrail.java`
- `GuardrailViolationException` — `tnsai-core/src/main/java/com/tnsai/guardrails/GuardrailViolationException.java`
- `InputGuardrailEnforcer` / `OutputGuardrailEnforcer` — `tnsai-core/src/main/java/com/tnsai/guardrails/`
- `UserInputRole` — `tnsai-integration/src/main/java/com/tnsai/integration/scop/examples/UserInputRole.java`
- `UserInputRoleGuardrailIntegrationTest` — `tnsai-integration/src/test/java/com/tnsai/integration/scop/examples/`

---

# Tutorial: Declarative RAG with @KnowledgeSource and @Retrieval

URL: https://tnsai.dev/docs/tutorials/declarative-rag
Description: Wire knowledge sources to a Role and retrieve from them on every action call — through annotations alone. No manual Embedding embed = ...; vectorStore.search(query, k) plumbing in your role; the framework binds and dispatches.

import { Callout } from 'fumadocs-ui/components/callout'

## Prerequisites

- [Installation](/docs/start/installation)
- A directory of text files (`.txt`, `.md`, `.json`, `.yaml`, `.csv`) you want the role to ground its answers in
- `tnsai-intelligence` on the runtime classpath (the SPI implementation lives there; `tnsai-core` ships only the SPI interface)

## The role

Full source lives at `tnsai-integration/src/main/java/com/tnsai/integration/scop/examples/ResearchRole.java` and is exercised by `ResearchRoleRagIntegrationTest`. The shape:

```java
@RoleSpec(
    name = "ResearchAgent",
    description = "Answers questions and summarises with citations from a local research corpus.",
    responsibilities = {
        @Responsibility(name = "QA", actions = {"answer"}),
        @Responsibility(name = "Summarisation", actions = {"summarise"})
    }
)
@KnowledgeSources({
    @KnowledgeSource(
        name = "research-corpus",
        type = KnowledgeSource.KnowledgeType.FILE,
        path = "target/_research-corpus/papers"),
    @KnowledgeSource(
        name = "research-faq",
        type = KnowledgeSource.KnowledgeType.FILE,
        path = "target/_research-corpus/faq")
})
public class ResearchRole extends Role {
    // ... actions below
}
```

## Pattern 1 — Class-level @KnowledgeSource(s)

Declare every corpus the role should ingest at the class level. Phase 1 ships one source loader (`KnowledgeType.FILE`); each declaration is read once at first action invocation and the resulting documents are cached for the lifetime of the JVM keyed by Role class.

```java
@KnowledgeSources({
    @KnowledgeSource(name = "research-corpus", type = FILE, path = "papers/"),
    @KnowledgeSource(name = "research-faq",    type = FILE, path = "faq/")
})
```

Each `path` may point to a single file or a directory; for a directory, every text file under it (recursively) is ingested as one document. Binary formats need their own loader (deferred — `.pdf` / `.docx` will get loaders in Phase 2).

## Pattern 2 — Method-level @Retrieval per action

Pick the strategy + tuning knobs that suit each action. The dispatcher reads the annotation, runs the strategy against the role's binding, and injects the formatted context into the action's `context` map under `RetrievalSpi.RETRIEVED_CONTEXT_KEY` (`"_rag_context"`) **before** the action body runs.

```java
@ActionSpec(
    type = ActionType.LOCAL,
    description = "Answer a research question with citations from the corpus."
)
@Retrieval(
    strategy = Retrieval.Strategy.HYBRID,
    topK = 5,
    minScore = 0.0,
    contextFormat = "[Source: ${source}, score=${score}]\n${content}\n\n",
    onFailure = Retrieval.FallbackAction.CONTINUE
)
public String answer(@Param(name = "question") String question) {
    // The framework injects the formatted context under
    // _rag_context BEFORE this body runs. A real impl would read it
    // via the agent's context map.
    return "Answering: " + question;
}
```

The query for retrieval is the action's first `String`-typed parameter (Phase 1 convention; Phase 2 will add `@Retrieval(queryParam = "...")` for explicit selection).

## Pattern 3 — Mixing strategies in one role

Different actions justify different retrieval shapes. `answer` benefits from HYBRID for proper-noun precision + paraphrase recall; `summarise` is a topic-descriptor lookup where SEMANTIC alone gives tighter focus.

```java
@ActionSpec(type = ActionType.LOCAL, description = "Summarise corpus segments for a topic.")
@Retrieval(
    strategy = Retrieval.Strategy.SEMANTIC,
    topK = 3,
    contextFormat = "<<< ${source}\n${content}\n>>>\n",
    onFailure = Retrieval.FallbackAction.CONTINUE
)
public String summarise(@Param(name = "topic") String topic) {
    return "Summarising: " + topic;
}
```

## Reading the injected context

Inside the action body (or in a downstream LLM executor), pull the formatted context from the framework's context map:

```java
Map<String, Object> context = ...; // ActionExecutor passes this in
String retrieved = (String) context.get(RetrievalSpi.RETRIEVED_CONTEXT_KEY);
Integer count   = (Integer) context.get(RetrievalSpi.RETRIEVED_DOCUMENT_COUNT_KEY);

if (count != null && count > 0) {
    promptBuilder.append("Reference material:\n").append(retrieved).append("\n\n");
}
```

The two-key pattern (formatted text + document count) lets callers distinguish "retrieval ran but found nothing" (count == 0) from "retrieval was skipped" (key absent).

## What's enforced today

| Annotation field                                                    | Phase 1 behaviour                                                                                                           |
| ------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------- |
| `@KnowledgeSource.type`                                             | `FILE` only (URL / VECTOR\_DB / DATABASE / WEB\_SEARCH deferred)                                                            |
| `@KnowledgeSource.path`                                             | Directory or single file; `.txt` / `.md` / `.markdown` / `.json` / `.yaml` / `.yml` / `.csv`                                |
| `@KnowledgeSource.enabled = false`                                  | Source skipped                                                                                                              |
| `@KnowledgeSource.name`                                             | Used as document-id prefix and in `${source}` template                                                                      |
| `@KnowledgeSources` (`@Repeatable` container)                       | All entries ingested in declaration order                                                                                   |
| `@Retrieval.strategy`                                               | `SEMANTIC` / `KEYWORD` / `HYBRID` live; `GRAPH` / `MULTI_QUERY` / `HIERARCHICAL` / `TEMPORAL` fall back to `SEMANTIC` + log |
| `@Retrieval.topK` / `.minScore`                                     | ✓                                                                                                                           |
| `@Retrieval.contextFormat`                                          | `${content}` / `${source}` / `${score}` placeholders                                                                        |
| `@Retrieval.onFailure`                                              | `CONTINUE` (default — log + skip) and `FAIL` (rethrow); `USE_CACHE` / `RETRY_SIMPLE` degrade to `CONTINUE`                  |
| Method-level vs class-level resolution                              | Method wins; both can be absent                                                                                             |
| `@Retrieval.rerank` / `.queryExpansion` / `.deduplicate` / `.cache` | Phase 2                                                                                                                     |
| `@Retrieval(queryParam = "...")` explicit query selection           | Phase 2                                                                                                                     |
| `@MemorySpec` / `@VectorMemory` runtime resolution                  | Phase 2                                                                                                                     |
| Embedding model selection                                           | Phase 2 (Phase 1 uses a deterministic-hash function for testability)                                                        |
| Chunking                                                            | Phase 2 (Phase 1 ingests files whole)                                                                                       |
| `@ContextCompaction`                                                | Phase 2                                                                                                                     |

## Where it sits in the pipeline

Inside `ActionExecutor.executeInternal`:

```
1. Approval check
2. Security access control + parameter encryption
3. @BeforeAction transformation
4. ActionValidator.validateParameters
5. ActionContract.validateInvariants + preconditions
6. Invariant precondition checks
7. @InputGuardrail enforcement (if tnsai-core has #171's Phase 1 wired)
8. ► RetrievalSpi.find().ifPresent(spi -> spi.enforce(...))  ← @Retrieval here
9. Action body execution (LOCAL / WEB_SERVICE / LLM / MCP_TOOL)
10. @OutputGuardrail enforcement
11. Postcondition + post-execution invariants
```

The `RetrievalSpi.find()` lookup is cached in an `AtomicReference` so the discovery cost is paid at most once per JVM. If `tnsai-intelligence` isn't on the classpath, the optional returns empty and the pipeline silently skips — so a consumer that doesn't want declarative RAG simply doesn't depend on intelligence.

## Wiring the role into an Agent

```java
Agent agent = AgentBuilder.create()
    .role(new ResearchRole())
    .llm(new OpenAIClient("gpt-4o-mini"))
    .build();

Object answer = agent.executeAction(
    "answer",
    Map.of("question", "What is RAG?"));
```

No corpus pre-load call needed — `RoleRagBinding.forRole(ResearchRole.class)` runs lazily on the first action invocation that triggers retrieval.

## Reference

- `@KnowledgeSource` annotation — `tnsai-core/src/main/java/com/tnsai/annotations/KnowledgeSource.java`
- `@KnowledgeSources` (`@Repeatable` container) — `tnsai-core/src/main/java/com/tnsai/annotations/KnowledgeSources.java`
- `@Retrieval` annotation — `tnsai-core/src/main/java/com/tnsai/annotations/Retrieval.java`
- `RetrievalSpi` — `tnsai-core/src/main/java/com/tnsai/rag/RetrievalSpi.java`
- `RoleRagBinding` / `DefaultRetrievalSpi` / `LocalFileSourceLoader` — `tnsai-intelligence/src/main/java/com/tnsai/intelligence/rag/binding/`
- `ResearchRole` cookbook — `tnsai-integration/src/main/java/com/tnsai/integration/scop/examples/ResearchRole.java`
- `ResearchRoleRagIntegrationTest` — `tnsai-integration/src/test/java/com/tnsai/integration/scop/examples/`

---

# Tutorial: Declarative resilience with @Traced, @Metered, and @Fallback

URL: https://tnsai.dev/docs/tutorials/declarative-resilience
Description: Wrap every action dispatch with structured tracing, latency + counter metrics, and automatic recovery on failure — through annotations alone. The framework's ActionExecutor applies the decorators in canonical order; your action body stays focused on the business logic.

import { Callout } from 'fumadocs-ui/components/callout'

## Prerequisites

- [Installation](/docs/start/installation)
- A Role with at least one `@ActionSpec` method that calls something flaky (external API, slow disk, untrusted input)

## The role

Full source lives at `tnsai-integration/src/main/java/com/tnsai/integration/scop/examples/PaymentRole.java` and is exercised by `PaymentRoleResilienceIntegrationTest`. The shape:

```java
@RoleSpec(
    name = "PaymentAgent",
    description = "Charges accounts via a flaky external API; falls back to queueing on failure.",
    responsibilities = {
        @Responsibility(name = "Payments", actions = {"charge"})
    }
)
public class PaymentRole extends Role {
    // ... primary action + fallback method below
}
```

## The decorator stack

For each annotated action, `ActionExecutor.executeInternal` wraps the dispatch like this:

```
open Traced span      ← MDC trace id + structured span.start log
  ↓
start Metered timer   ← record latency + counter on close
  ↓
PRIMARY EXECUTION     ← your action body (or LOCAL/WEB_SERVICE/LLM/MCP executor)
  ↓
on success → record success on metered + traced, return result
on failure → invoke @Fallback (with retries), record outcome,
             return fallback's value or rethrow
  ↓
finally: close metered (record entry) + traced (emit span.end)
```

Every annotation is independent — declare any subset.

## Pattern 1 — `@Traced` for span-style observability

```java
@ActionSpec(type = ActionType.LOCAL, description = "Charge a customer's account.")
@Traced(operationName = "payment.charge", includeArgs = true)
public String charge(
    @Param(name = "requestId", description = "Idempotency token") String requestId,
    @Param(name = "amount",    description = "Amount in minor units (cents)") int amount
) {
    // ...
}
```

What happens at runtime:

- A trace id (UUID) is pushed onto the SLF4J `MDC` under the key `traceId`. Every log line emitted from this dispatch (and any nested logger calls) carries the same id, making post-hoc grep correlation trivial.
- Two structured log lines bracket the dispatch:
  - `span.start op=payment.charge traceId=... args={requestId=req-1, amount=100}`
  - `span.end op=payment.charge traceId=... duration_ms=42 status=success`
- On failure: `span.end ... status=failure error=RuntimeException`
- Nested traced calls within the same dispatch reuse the outer span's trace id (no inner-evicted-by-outer issue).

`includeArgs` and `includeResult` default to `false` — opt in only when you need the data in the log (PII risk).

## Pattern 2 — `@Metered` for counter + latency histogram

```java
@Metered(name = "payment.charge", histogram = true)
public String charge(...) { ... }
```

The framework records each invocation in an in-memory `ResilienceMetrics` registry:

- **Counters**: `total`, `success`, `failure` (LongAdder under the hood for hot-path performance).
- **Histogram** (when `histogram = true`): powers-of-2 latency buckets — 1ms, 5ms, 10ms, 25ms, 50ms, 100ms, 250ms, 500ms, 1s, 2.5s, 5s, 10s, 30s, +∞.
- **Default name**: `ClassName.methodName` when `name` is empty.
- **Class-level inheritance**: `@Metered` on the Role class applies to every action that doesn't declare its own (method-level wins entirely — no merging).

Tests can snapshot the registry to assert metrics fired:

```java
ResilienceMetrics.MetricSnapshot snap = ResilienceMetrics.getInstance()
    .snapshot("payment.charge").orElseThrow();
assertEquals(50, snap.total());
assertTrue(snap.successRate() > 0.4);
```

Phase 2 will add a `MetricsSink` SPI for routing the same emissions to Micrometer / Prometheus / OpenTelemetry. Phase 1 stays in-memory — same boundary as the other Phase-1 batches in this sprint.

## Pattern 3 — `@Fallback` for automatic recovery

`@Fallback` works differently from the other two decorators: it doesn't wrap the action — it marks a **separate method** on the same Role as the recovery handler.

```java
@ActionSpec(type = ActionType.LOCAL, description = "Charge a customer's account.")
@Traced(operationName = "payment.charge", includeArgs = true)
@Metered(name = "payment.charge", histogram = true)
public String charge(
    @Param(name = "requestId", description = "Idempotency token") String requestId,
    @Param(name = "amount",    description = "Amount in minor units (cents)") int amount
) {
    // 50% chance of failure (simulated flaky external API)
    if (ThreadLocalRandom.current().nextBoolean()) {
        throw new RuntimeException("Payment API unavailable");
    }
    return "CHARGED:" + requestId + ":" + amount;
}

@Fallback(forAction = "charge", maxRetries = 2)
public String chargeFallback(String requestId, int amount, Throwable failure) {
    return "QUEUED:" + requestId + ":" + amount + " (reason: " + failure.getMessage() + ")";
}
```

When `charge` throws, the framework:

1. **Catches the exception** at the dispatcher's edge (no propagation to caller).
2. **Retries** by re-invoking `charge` up to `maxRetries = 2` additional times.
3. **If all retries also fail**, invokes `chargeFallback` exactly once with `(requestId, amount, lastFailure)`.
4. **Returns** the fallback's value as if `charge` had returned it.

So a single dispatch gets **3 chances** at the primary (1 + maxRetries=2) before falling back. With 50% failure rate, only \~12% of dispatches end up in the fallback (`0.5³ = 0.125`).

### Fallback method conventions

- **Same Role class** as the action it covers (the resolver only scans the role's own methods).
- **Method name** is irrelevant — `@Fallback.forAction` is the binding key.
- **Argument list**: original action's `@Param`s in declaration order, followed by a trailing `Throwable` or `Exception` parameter for the captured failure.
- **Multiple fallbacks**: a Role can declare any number of `@Fallback(forAction = ...)` for different actions. Action-named fallbacks beat the global `@Fallback` (no `forAction`) catch-all.
- **Method-name conflicts**: multiple `@Fallback(forAction = "X")` on the same role logs a warning and keeps the first one for stability (Phase 2 will fail-fast at action discovery).

## Wiring the role into an Agent

```java
Agent agent = AgentBuilder.create()
    .role(new PaymentRole())
    .llm(new OpenAIClient("gpt-4o-mini"))
    .build();

Object result = agent.executeAction(
    "charge",
    Map.of("requestId", "req-001", "amount", 100));

// result is either "CHARGED:req-001:100" (primary or retry success)
// or "QUEUED:req-001:100 (reason: Payment API unavailable)" (fallback recovery)
// — caller never sees the underlying RuntimeException.
```

No imperative wiring needed — the decorator stack is auto-applied based on the action's annotations.

## What's enforced today

| Annotation field                                     | Phase 1                                                  | Phase 2+                     |
| ---------------------------------------------------- | -------------------------------------------------------- | ---------------------------- |
| `@Traced.operationName`                              | ✓                                                        | —                            |
| `@Traced.includeArgs` / `includeResult`              | ✓                                                        | —                            |
| `@Metered.name`                                      | ✓                                                        | —                            |
| `@Metered.tags` (static k=v)                         | Captured but Phase-1 sink doesn't use them               | Routed via Sink SPI          |
| `@Metered.histogram`                                 | ✓ (powers-of-2 buckets)                                  | Configurable bucket layout   |
| `@Fallback.forAction`                                | ✓                                                        | —                            |
| `@Fallback.maxRetries`                               | ✓ (immediate retry)                                      | Exponential backoff + jitter |
| `@RateLimited` (token bucket)                        | Phase 2 (needs distributed-state SPI)                    |                              |
| `@Resilience(circuitBreaker = ...)` (sliding window) | Phase 2 (already in `ResilienceExecutor` at agent level) |                              |
| `@Idempotent` (keyed cache)                          | Phase 2 (coordinates with #91)                           |                              |

`@Metered` registry is in-memory (singleton); `@Traced` emits via SLF4J only. Phase 2 ships:

- `MetricsSink` SPI → Micrometer / Prometheus / OpenTelemetry
- OpenTelemetry SPI handoff for `@Traced`

## Where it sits in the pipeline

Inside `ActionExecutor.executeInternal`:

```
1. Approval check
2. Security access control + parameter encryption
3. @BeforeAction transformation
4. ActionValidator.validateParameters
5. ActionContract.validateInvariants + preconditions
6. Invariant precondition checks
7. @InputGuardrail enforcement (#171 Phase 1)
8. @Retrieval enforcement (#172 Phase 1, optional SPI)
9. ► open @Traced span + start @Metered timer    ← #173 Phase 1
10. Primary action execution (LOCAL / WEB_SERVICE / LLM / MCP_TOOL)
    └─ on failure: @Fallback.tryRecover (with retries)         ← #173 Phase 1
11. @OutputGuardrail enforcement (#171 Phase 1)
12. Postcondition + post-execution invariants
13. record outcome on @Metered + @Traced
14. close @Metered + @Traced in finally
```

## Reference

- `@Traced` annotation — `tnsai-core/src/main/java/com/tnsai/annotations/Traced.java`
- `@Metered` annotation — `tnsai-core/src/main/java/com/tnsai/annotations/Metered.java`
- `@Fallback` annotation — `tnsai-core/src/main/java/com/tnsai/annotations/Fallback.java`
- `TracedResolver` / `MeteredResolver` / `FallbackResolver` — `tnsai-core/src/main/java/com/tnsai/resilience/`
- `ResilienceMetrics` (in-memory registry) — `tnsai-core/src/main/java/com/tnsai/resilience/ResilienceMetrics.java`
- `PaymentRole` cookbook — `tnsai-integration/src/main/java/com/tnsai/integration/scop/examples/PaymentRole.java`
- `PaymentRoleResilienceIntegrationTest` — `tnsai-integration/src/test/java/com/tnsai/integration/scop/examples/`

---

# Tutorials

URL: https://tnsai.dev/docs/tutorials
Description: End-to-end walkthroughs. Each tutorial takes a real scenario and builds a working agent from scratch.

import { Callout } from 'fumadocs-ui/components/callout'

## Tutorials

### Complete, runnable walkthroughs

- [Reusable Capabilities](/docs/tutorials/capability-pattern) — Compose Summarizer, Translator, and SentimentClassifier onto an editorial role with zero method bodies.
- [REST API actions with @WebService](/docs/tutorials/web-service-action) — Bind any REST endpoint to an LLM-callable action through annotations alone. GET + query, path templating, POST + body + custom headers, retry.
- [Declarative Guardrails](/docs/tutorials/declarative-guardrails) — Validate, sanitise, and bound action parameters and return values through `@InputGuardrail` / `@OutputGuardrail` annotations, no per-action plumbing.
- [Declarative RAG with @KnowledgeSource and @Retrieval](/docs/tutorials/declarative-rag) — Wire knowledge sources to a Role and retrieve from them on every action call through annotations alone, no manual vector-store plumbing.
- [Declarative Resilience](/docs/tutorials/declarative-resilience) — Wrap action dispatch with structured tracing, latency + counter metrics, and automatic recovery via `@Traced` / `@Metered` / `@Fallback`.
- [Research Agent](/docs/tutorials/research-agent) — Agent that searches the web, reads PDFs, and produces cited summaries.

### Architecture sketches + building blocks

Each of these pages describes the shape of a non-trivial system and links to the framework pieces you'd compose to build it. The full code walkthrough is still being written — start the implementation today and open a discussion if anything's missing.

- [Customer Support Bot](/docs/tutorials/customer-support-bot) — RAG over a knowledge base, with sentiment-aware escalation handoff.
- [Multi-Agent Research Team](/docs/tutorials/multi-agent-research-team) — Coordinator + specialists + judge.
- [Agent with Tool Approval](/docs/tutorials/agent-with-tool-approval) — Destructive tools gated through a human approval UI.

---

# Tutorial: Multi-Agent Research Team

URL: https://tnsai.dev/docs/tutorials/multi-agent-research-team
Description: A full code walkthrough is still being written. In the meantime this page describes the topology and points at the framework pieces you can wire up today. Open a discussion if you start the implementation and hit something missing.

import { Callout } from 'fumadocs-ui/components/callout'

## Goal

Three agents collaborate on a single research question:

- **Coordinator** — splits the question, delegates each sub-question to a specialist, composes the final answer.
- **Specialists** — a researcher (gathers evidence) and a writer (drafts prose).
- **Judge** — reviews the composed answer for quality before it's returned.

## Topology

```text
                       Coordinator
                            │
            ┌───────────────┼───────────────┐
            ▼               ▼               ▼
        Researcher        Writer        (delegate as needed)
            │               │
            └───────┬───────┘
                    │
                    ▼
                  Judge
                    │
                    ▼
              Final answer
```

This is the **hierarchical** topology with a final **judge** review step — two patterns the `tnsai-coordination` module ships out of the box.

## Building blocks you'd compose

- **Per-specialist roles** — define `ResearcherRole` and `WriterRole` with their own action sets (`@ActionSpec`). Each becomes an `Agent` instance with its own LLM client (you can mix providers per role).
- **`AgentGroup`** — `tnsai-coordination`'s composition primitive. Pick the **hierarchical** topology so the coordinator decides delegation order; alternatives include peer round-robin and pipeline.
- **`JudgeAgent`** — a `tnsai-coordination` primitive that runs as a post-step against the group's composed output. Returns approve / reject / revise with feedback.
- **Streaming progress** — wire `AgentEventPublisher` so the coordinator can stream partial results from each specialist back to the caller while the workflow is still in flight.

## Related

- [Multi-agent guides](/docs/multi-agent)
- [Coordination module](https://github.com/TnsAI-Framework/TnsAI/tree/main/tnsai-coordination)

---

# Tutorial: Research Agent

URL: https://tnsai.dev/docs/tutorials/research-agent
Description: Build an agent that takes a research question, searches the web, reads PDF sources, and produces a cited summary.

import { Callout } from 'fumadocs-ui/components/callout'

## Prerequisites

- [Installation](/docs/start/installation)
- `BRAVE_API_KEY` or `TAVILY_API_KEY` (web search)
- `ANTHROPIC_API_KEY` or `OPENAI_API_KEY` (LLM)

## 1. Define the role

```java
public class ResearchRole extends Role {
    @Override public RoleIdentity getIdentity() {
        return new RoleIdentity("researcher",
            "Find sources and produce cited summaries", "research");
    }
    @Override public List<Responsibility> getResponsibilities() {
        return List.of(
            new CoreDuty("search",  "Find authoritative sources"),
            new CoreDuty("extract", "Read and quote source material"),
            new CoreDuty("cite",    "Always attribute claims")
        );
    }
}
```

## 2. Build the agent

```java
import com.tnsai.enums.BuiltInTool;

Agent agent = AgentBuilder.create()
    .role(new ResearchRole())
    .llm(new AnthropicClient("claude-sonnet-4-20250514"))
    .builtInTools(
        BuiltInTool.WEB_SEARCH_TOOLS,   // brave_search, tavily, wikipedia, …
        BuiltInTool.PDF_TOOLS,          // pdf_extract_text, pdf_metadata, …
        BuiltInTool.MARKDOWN_TOOLS      // markitdown
    )
    .build();

agent.start();
```

Each `BuiltInTool` enum constant registers every `@Tool` method on the
backing POJO in `tnsai-tools` — see the
[built-in tool catalog](/docs/capabilities/tools/catalog) for the full
per-toolkit method list. The LLM picks one method per call (e.g.
`brave_search`, then `pdf_extract_text`).

## 3. Stream the response

```java
agent.streamChatWithTools(
    "What are the latest results on RAG hallucination mitigation?",
    chunk -> {
        if (chunk.isContent()) System.out.print(chunk.getContent());
    }
);
```

## 4. Validate citations (optional)

Wrap the agent call in an [Evaluator](/docs/evaluation) that
checks the response contains at least one URL or DOI per factual
claim. Fail the eval if citation density falls below the threshold.

## Related

- [Agents: Fundamentals](/docs/agents/fundamentals)
- [Tools: Catalog](/docs/capabilities/tools/catalog)
- [Capabilities: RAG](/docs/capabilities/rag) — full RAG pipeline if you want to embed corpora rather than search the web

---

# Tutorial: REST API actions with @WebService

URL: https://tnsai.dev/docs/tutorials/web-service-action
Description: Bind any REST endpoint to an LLM-callable action through annotations alone — no OkHttpClient plumbing, no manual JSON parsing, no per-call retry loops in your Role. The framework's WebServiceExecutor handles the wire-format work; your Role just declares what to call.

import { Callout } from 'fumadocs-ui/components/callout'

## Prerequisites

- [Installation](/docs/start/installation)
- `OPENAI_API_KEY` (LLM)
- A bearer token in `OPENWEATHER_API_KEY` and `ALERTS_API_TOKEN` for the example endpoints (or substitute your own)

## The role

Full source lives at `tnsai-integration/src/main/java/com/tnsai/integration/scop/examples/WeatherRole.java` and is exercised by `WeatherRoleAnnotationRoundTripTest`. The shape:

```java
@RoleSpec(
    name = "WeatherAgent",
    description = "Looks up current weather and forecasts via OpenWeatherMap.",
    responsibilities = {
        @Responsibility(
            name = "WeatherLookup",
            actions = {"getCurrentWeather", "getForecastByCityId"}),
        @Responsibility(
            name = "AlertSubscription",
            actions = {"subscribeToAlerts"})
    },
    llm = @LLMSpec(
        provider = LLMSpec.Provider.OPENAI,
        model = "gpt-4o-mini",
        temperature = 0.2f)
)
public class WeatherRole {
    // ... actions below
}
```

## Pattern 1 — GET with query params + bearer auth

The canonical "fetch from a public API" shape. Each `@Param` value is URL-encoded and appended as a query parameter; the bearer token is read from the env var at request time.

```java
@ActionSpec(
    type = ActionType.WEB_SERVICE,
    description = "Current weather conditions for a city, by name.",
    webService = @WebService(
        endpoint = "https://api.openweathermap.org/data/2.5/weather",
        method = HttpMethod.GET,
        paramType = ParamType.QUERY,
        auth = AuthType.BEARER,
        authTokenEnv = "OPENWEATHER_API_KEY",
        timeout = 5000,
        retryCount = 2,
        retryBackoffMs = 500
    )
)
public String getCurrentWeather(
    @Param(name = "q")     String q,
    @Param(name = "units") String units
) {
    return null; // body unused — WebServiceExecutor calls the API
}
```

The framework's `WebServiceExecutor` resolves this annotation at dispatch time, builds the URL (`?q=Istanbul&units=metric`), injects `Authorization: Bearer $OPENWEATHER_API_KEY`, and parses the JSON response back into the action's return type.

## Pattern 2 — GET with path templating

Single-brace `{paramName}` syntax substitutes path variables before the request leaves the JVM. Remaining params still go to the query string when `paramType = QUERY`.

```java
@ActionSpec(
    type = ActionType.WEB_SERVICE,
    description = "N-day forecast for a city by OpenWeatherMap city id.",
    webService = @WebService(
        endpoint = "https://api.openweathermap.org/data/2.5/forecast/{cityId}",
        method = HttpMethod.GET,
        paramType = ParamType.QUERY,
        auth = AuthType.BEARER,
        authTokenEnv = "OPENWEATHER_API_KEY",
        timeout = 7500
    )
)
public String getForecastByCityId(
    @Param(name = "cityId") String cityId,
    @Param(name = "cnt")    int cnt
) {
    return null;
}
```

Note: the framework uses **single-brace `{cityId}`** syntax, not `${cityId}`. The Javadoc on `@WebService.endpoint()` is the authority.

## Pattern 3 — POST with body, custom headers, separate auth token

`paramType = BODY` serialises `@Param` values as a JSON request body. Custom `@Header` entries pass through verbatim (useful for upstream-required identifiers). Each action gets its own `authTokenEnv`, so different services can use different bearer tokens within the same role.

```java
@ActionSpec(
    type = ActionType.WEB_SERVICE,
    description = "Subscribe a user to weather-threshold alerts.",
    webService = @WebService(
        endpoint = "https://api.example.com/alerts/subscribe",
        method = HttpMethod.POST,
        paramType = ParamType.BODY,
        contentType = "application/json",
        accept = "application/json",
        auth = AuthType.BEARER,
        authTokenEnv = "ALERTS_API_TOKEN",
        headers = {
            @Header(key = "X-Source",        value = "tnsai-weather-agent"),
            @Header(key = "Accept-Language", value = "en-US")
        },
        timeout = 10000
    )
)
public String subscribeToAlerts(
    @Param(name = "city")        String city,
    @Param(name = "thresholdC")  int thresholdC
) {
    return null;
}
```

## Method bodies are deliberately stubs

`return null;` (or any trivial return) is a documented framework convention. `WebServiceExecutor` never invokes the method body — the Java signature exists purely so `@Param` annotations and the LLM-tool schema can be generated from it. The actual return value comes from JSON deserialization of the HTTP response.

## How SCOP routes these

Every `@ActionSpec(type = WEB_SERVICE)` on a registered Role is dispatched by `WebServiceExecutor` (in `tnsai-core/actions/executors/`). The framework's `ActionExecutor` routing table wires `WEB_SERVICE → WebServiceExecutor` at construction (`ActionExecutor.java:177`); no consumer code needs to register it. `SCOPBridge.executeAction(role, name, params)` is the entry point for SCOP-driven invocations.

## Wiring the role into an Agent

```java
Agent agent = AgentBuilder.create()
    .role(new WeatherRole())
    .llm(new OpenAIClient("gpt-4o-mini"))
    .build();

Object current = agent.executeAction(
    "getCurrentWeather",
    Map.of("q", "Istanbul", "units", "metric"));
```

No tool registration call needed — the framework reads `@WebService` reflectively from the role's methods, populates `ActionMetadata.webServiceConfig()`, and the executor pulls every field at dispatch time.

## Authentication today

| `AuthType` | What it sends                                        | Required env var(s)                                                            |
| ---------- | ---------------------------------------------------- | ------------------------------------------------------------------------------ |
| `NO_AUTH`  | Nothing                                              | —                                                                              |
| `BEARER`   | `Authorization: Bearer <token>`                      | `authTokenEnv`                                                                 |
| `BASIC`    | `Authorization: Basic <base64(user:pass)>`           | `authUsernameEnv` + `authPasswordEnv`                                          |
| `API_KEY`  | `<apiKeyHeader>: <key>` (default header `X-API-Key`) | `authTokenEnv` (+ optional `apiKeyHeader` to override the default header name) |

`API_KEY` example — upstream expects `X-Goog-Api-Key: …` instead of a standard `Authorization` header:

```java
@ActionSpec(
    type = ActionType.WEB_SERVICE,
    description = "Search Google Books",
    webService = @WebService(
        endpoint = "https://www.googleapis.com/books/v1/volumes",
        method = HttpMethod.GET,
        paramType = ParamType.QUERY,
        auth = AuthType.API_KEY,
        authTokenEnv = "GOOGLE_BOOKS_API_KEY",
        apiKeyHeader = "X-Goog-Api-Key"
    )
)
public BookSearchResult searchBooks(@Param(name = "q") String query) {
    return null;
}
```

## Retry, timeout, and headers

| Annotation field  | Default            | When to override                                                        |
| ----------------- | ------------------ | ----------------------------------------------------------------------- |
| `timeout`         | 30000 ms           | Slow upstreams, real-time dashboards                                    |
| `retryCount`      | 0                  | Idempotent reads on flaky networks                                      |
| `retryBackoffMs`  | 1000 ms            | Adjust backoff cadence                                                  |
| `followRedirects` | true               | Set `false` for strict single-hop POSTs                                 |
| `contentType`     | `application/json` | Use `application/x-www-form-urlencoded` etc.                            |
| `accept`          | `application/json` | When the upstream returns XML / CSV                                     |
| `headers`         | `{}`               | Per-request static headers (auth tokens go in `authTokenEnv`, not here) |

## When to use `@WebService` vs an LLM action with tools

Both surface as LLM-callable actions, but they solve different problems:

|                 | `@ActionSpec(type = WEB_SERVICE)`                                | `@ActionSpec(type = LLM)` + agent toolkits                                        |
| --------------- | ---------------------------------------------------------------- | --------------------------------------------------------------------------------- |
| **Caller**      | LLM picks the action via tool-call; framework calls the endpoint | LLM picks the action, then picks one of the agent's `@Tool` methods to call       |
| **Wire format** | HTTP endpoint declared in annotation                             | Method-level `@Tool` schema on the registered POJO                                |
| **Auth**        | Per-action env-var injection                                     | Per-toolkit env var (read by the POJO)                                            |
| **Best for**    | Wrapping a known REST endpoint with typed parameters             | Open-ended action where the LLM should choose among several tools                 |
| **Setup cost**  | Just the annotation                                              | Register a POJO toolkit via `AgentBuilder.builtInTools(...)` or `.toolPojos(...)` |

If you have one specific REST endpoint to call with typed parameters, use `@WebService`. If the LLM should pick *which* tool to call from a set you've registered with the agent, use an LLM action and let the dispatcher handle tool routing.

## Reference

- [SCOP Bridge](/docs/integrate/scop) — the integration layer reference
- `@WebService` annotation — `tnsai-core/src/main/java/com/tnsai/annotations/WebService.java`
- `WebServiceExecutor` — `tnsai-core/src/main/java/com/tnsai/actions/executors/WebServiceExecutor.java`
- `WebServiceConfig` record — `tnsai-core/src/main/java/com/tnsai/metadata/WebServiceConfig.java` (the grouped accessor used by executors)
- `WeatherRoleAnnotationRoundTripTest` — `tnsai-integration/src/test/java/com/tnsai/integration/scop/examples/` (annotation-contract sentinel)

---

# Validation and Invariants

URL: https://tnsai.dev/docs/validation
Description: The Quality module provides multi-agent parallel validation (ParallelValidationExecutor), runtime invariant checking (InvariantChecker), conditional rule evaluation (RuleEngine), and input validation (ValidationService).

import { Callout } from 'fumadocs-ui/components/callout'

## ParallelValidationExecutor

The `ParallelValidationExecutor` sends the same prompt to multiple agents (or LLM providers) simultaneously and compares their responses. This is useful for high-stakes tasks where you want cross-validation: if multiple independent models agree on an answer, you can be more confident it is correct.

```java
ParallelValidationExecutor executor = ParallelValidationExecutor.builder()
    .addAgent(claudeAgent)
    .addAgent(geminiAgent)
    .addAgent(ollamaAgent)
    .aggregationStrategy(AggregationStrategy.CONSENSUS)
    .timeout(Duration.ofSeconds(60))
    .synthesisLLM(claudeClient)       // Required for CONSENSUS and ENSEMBLE
    .similarityThreshold(0.5)         // Jaccard similarity for response comparison
    .build();

ValidationResult result = executor.execute("Review this code for security issues");

result.getBestSolution();       // Best response based on strategy
result.getConsensusPoints();    // Points all agents agree on
result.getDivergences();        // Points where agents disagree
result.getMetrics();            // Execution metrics (timing, agreement ratio)
result.getResponses();          // Individual agent responses with confidence scores

// Async execution
CompletableFuture<ValidationResult> future = executor.executeAsync(prompt);
```

You can also add raw `LLMClient` instances instead of full agents:

```java
ParallelValidationExecutor executor = ParallelValidationExecutor.builder()
    .addLLM(openaiClient)
    .addLLM(anthropicClient)
    .aggregationStrategy(AggregationStrategy.BEST_OF)
    .build();
```

## AggregationStrategy Enum

After collecting responses from all agents, the executor combines them using one of four strategies. Choose based on your use case -- voting for simple decisions, consensus for nuanced analysis.

| Strategy    | Description                                            | Requires Synthesis LLM | Best For                              |
| ----------- | ------------------------------------------------------ | ---------------------- | ------------------------------------- |
| `VOTING`    | Selects the most common response (Jaccard similarity)  | No                     | Classification, yes/no decisions      |
| `BEST_OF`   | Selects the response with highest confidence score     | No                     | Quality-critical tasks                |
| `CONSENSUS` | Extracts and combines common points from all responses | Yes                    | Code review, fact extraction          |
| `ENSEMBLE`  | Combines all unique insights from every response       | Yes                    | Brainstorming, comprehensive analysis |

Auto-select a strategy based on task type:

```java
AggregationStrategy strategy = AggregationStrategy.forTaskType("code review");
// Returns CONSENSUS

AggregationStrategy strategy = AggregationStrategy.forTaskType("critical security audit");
// Returns BEST_OF
```

### Confidence Estimation

Each agent response receives an automatically estimated confidence score (0.0-1.0). The score is based on heuristics about the response content, not a self-reported confidence from the LLM.

- Base: 0.5
- Length bonus: +0.1 for \\>500 chars, +0.1 for \\>1000 chars
- Uncertainty penalty: -0.1 for phrases like "I think", "possibly"
- Certainty bonus: +0.1 for "definitely", "certainly"
- Structure bonus: +0.1 for bulleted/numbered lists

## InvariantChecker

The `InvariantChecker` enforces correctness rules on your agents at runtime. You define preconditions, postconditions, and invariants using annotations on your role classes, and the checker verifies them before and after each action. This catches bugs early by ensuring your agent's state remains valid throughout execution.

### Supported Annotations

Invariants are defined through these annotations on your action methods and state fields.

- `@ActionSpec(precondition = "health > 0")` -- checked before action execution
- `@ActionSpec(postcondition = "state == COMPLETED")` -- checked after execution
- `@ActionSpec(invariants = {"energy >= 0", "DEAD if health <= 0"})` -- checked before and after
- `@State(invariants = {"value >= 0"})` -- checked after any state change

### Usage

You can check invariants manually around action execution, or use the batch convenience methods that handle all checks in a single call.

```java
InvariantChecker checker = new InvariantChecker(roleObject);

// Before action
Method method = role.getClass().getMethod("attack");
checker.checkPrecondition(method);
checker.checkActionInvariants(method);

// Execute the action...

// After action
checker.checkPostcondition(method);
checker.checkActionInvariants(method);
checker.checkStateInvariants();

// Or use batch methods
checker.checkBeforeAction(method);  // precondition + invariants
checker.checkAfterAction(method);   // postcondition + invariants + state invariants

// Non-strict mode (logs warnings instead of throwing)
InvariantChecker lenient = new InvariantChecker(roleObject, false);

// Collect all violations without throwing
List<String> violations = checker.collectViolations(method);

// Static convenience methods
InvariantChecker.check(roleObject, "attack");       // check before
InvariantChecker.checkAfter(roleObject, "attack");   // check after
```

### Expression Support

The invariant parser understands comparison operators, boolean logic, and conditional rules. These are written as plain strings in annotation values.

- **Comparisons**: `health > 0`, `state == ALIVE`, `energy != 0`
- **Boolean operators**: `health > 0 && energy > 0`, `state == DEAD || health <= 0`
- **Conditional rules**: `DEAD if neighbors < 2 (underpopulation)` -- see RuleEngine below

Natural language invariants (more than 3 words with no operators) are silently skipped.

## RuleEngine

The `RuleEngine` evaluates conditional state transition rules at runtime. It is useful for game-like scenarios or state machines where you want to verify that an object's state matches what the rules predict. For example, in Conway's Game of Life, you can define rules like "a cell should be DEAD if it has fewer than 2 neighbors" and check whether the actual state matches.

```java
RuleEngine engine = new RuleEngine(cellRole);
engine.setStateField("state");  // Which field to check effects against

engine.addRule("DEAD if neighbors < 2 (underpopulation)");
engine.addRule("ALIVE if neighbors == 3 (reproduction)");
engine.addRule("unchanged if neighbors == 2 (survival)");

// Evaluate all rules
List<RuleEngine.RuleViolation> violations = engine.evaluateAllRules();

for (RuleEngine.RuleViolation v : violations) {
    System.out.printf("Expected %s=%s when %s, but actual=%s%n",
        "state", v.getExpectedValue(), v.getRule().getCondition(), v.getActualValue());
}
```

### Rule Evaluation Semantics

Each rule follows the format "EXPECTED\_STATE if CONDITION". Here is how the engine evaluates a rule.

1. Evaluate the condition: `neighbors < 2`
2. If condition is false, the rule does not apply -- skip
3. If condition is true, the expected effect is `DEAD`
4. Read the actual value of the state field via reflection
5. If actual value does not match `DEAD`, report a violation

Rules with `unchanged` as the effect are documentation-only and never produce violations.

### Static Factory Methods

For quick setup, you can create a rule engine and populate it from an array of invariant strings in one call. The `enforce` method additionally throws if any violations are found, making it convenient for test assertions.

```java
// Create and populate from an array of invariant strings
RuleEngine engine = RuleEngine.from(targetObject, new String[]{
    "DEAD if health <= 0",
    "ALIVE if health > 0"
});

// Evaluate and throw if any violations
RuleEngine.enforce(targetObject, invariants, "state");
// Throws RuleViolationException with all violations
```

## ValidationService

See the [Security documentation](/docs/security) for the full `ValidationService` reference, including:

- `maxStringLength` / `maxNestingDepth` / `maxParameterCount` configuration
- SQL injection, command injection, path traversal, and XSS pattern detection
- `blockDangerousPatterns` toggle
- `sanitize()` for HTML entity escaping and null byte removal
- `validateOrThrow()` for fail-fast validation

---

# Advanced: Content Guardrails

The guardrail system (`com.tnsai.quality.tracing.guardrail`) provides pre-response and post-response content checking.

### Guardrail Interface

```java
public interface Guardrail {
    String name();
    GuardrailResult evaluate(String input, String output);
}
```

### GuardrailResult

```java
// Passing
GuardrailResult.pass();
GuardrailResult.pass("No issues detected");

// Failing
GuardrailResult.fail("Toxic content detected");
GuardrailResult.fail(0.3, "PII detected: email, phone");
```

Fields: `passed` (boolean), `score` (0.0-1.0 confidence), `reason` (human-readable).

### GuardrailRegistry

Thread-safe registry supporting pre-response and post-response guardrails.

```java
GuardrailRegistry registry = new GuardrailRegistry();

// Register guardrails
registry.registerPreResponse(new PiiGuardrail());
registry.registerPostResponse(new ToxicityGuardrail());

// Evaluate pre-response (input only)
List<GuardrailResult> preResults = registry.evaluatePreResponse(userInput);
boolean allPass = registry.allPreResponsePass(userInput);

// Evaluate post-response (input + output)
List<GuardrailResult> postResults = registry.evaluatePostResponse(input, output);
boolean allPostPass = registry.allPostResponsePass(input, output);

// Lookup by name
Guardrail pii = registry.get("pii-detection");
```

### PiiGuardrail

Built-in regex-based PII detection guardrail. Detects email addresses, phone numbers, and SSN-like numbers in both input and output.

```java
PiiGuardrail pii = new PiiGuardrail();

GuardrailResult result = pii.evaluate(null, "Contact me at john@example.com");
// result.passed() -> false
// result.reason() -> "PII detected: email"
// result.score()  -> 0.75 (score decreases with more PII types found)
```

### Implementing a Custom Guardrail

```java
public class ToxicityGuardrail implements Guardrail {
    @Override
    public String name() {
        return "toxicity-check";
    }

    @Override
    public GuardrailResult evaluate(String input, String output) {
        String textToCheck = output != null ? output : input;
        if (textToCheck == null) {
            return GuardrailResult.pass();
        }
        // Your toxicity detection logic here
        boolean isToxic = detectToxicity(textToCheck);
        return isToxic
            ? GuardrailResult.fail("Toxic content detected")
            : GuardrailResult.pass("Content is safe");
    }
}
```

---