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participant runtime for Beach: respond() discipline, scoped tools, approval interception.","maintainers":[{"name":"johncandrew","email":"johncandrew@yahoo.co.uk"}],"readme":"# @cool-ai/beach-llm\n\nOwns the LLM-shaped participant runtime — the `respond()` discipline, scoped tools, and approval interception. An LLM-backed handler is an `EventHandler` like any other; this package builds it. The rules here (structured `respond()` calls, no free text, tool-scoped capability) are what make LLMs composable with deterministic handlers and durable processes.\n\n> **Home:** [cool-ai.org](https://cool-ai.org) · **Documentation:** [cool-ai.org/docs](https://cool-ai.org/docs)\n\n## Install\n\n```sh\nnpm install @cool-ai/beach-llm @anthropic-ai/sdk\n```\n\n## What this package provides\n\n- **`createAnthropicProvider(opts?)`** — factory that creates an `AnthropicProvider` without exposing `@anthropic-ai/sdk` to consumer code. Preferred over constructing `AnthropicProvider` directly.\n- **`AnthropicProvider`** — `LLMProvider` wrapping `@anthropic-ai/sdk`. Full extended-thinking support.\n- **`VercelAIProvider`** — `LLMProvider` wrapping the Vercel AI SDK (`ai`). 100+ models across OpenAI, Google, Mistral, Meta, Cohere, and others. Does not support Anthropic extended thinking — use `AnthropicProvider` for that.\n- **`ToolRegistry`** — declares every tool available in the system. Handler configs select by name.\n- **`createLLMHandler(opts)`** — factory that returns an `EventHandler` whose body runs the respond() loop. Register it exactly like a deterministic handler: `router.register(id, handler)`.\n- **`runRespondLoop(options)`** — drives an LLM through its full tool-use loop until `respond()` is called. Used inside `createLLMHandler`; call directly only when you don't need the handler wrapper.\n- **`LLMHandlerConfig`** — the configuration shape for an LLM-backed handler.\n- **System-prompt snippets** — importable text that teaches the LLM the `respond()` tool and turn states.\n- **`LLMProvider` interface** — implement to add other model providers.\n\n## LLMHandlerConfig\n\n```typescript\nimport type { LLMHandlerConfig } from '@cool-ai/beach-llm';\nimport { respondToolSnippet, conversationStatesSnippet } from '@cool-ai/beach-llm';\n\nconst handlerConfig: LLMHandlerConfig = {\n id: 'baxter',\n model: 'claude-haiku-4-5',\n systemPrompt: [\n respondToolSnippet, // required — teaches respond() shape\n conversationStatesSnippet, // required — teaches valid conversationState values\n 'You are Baxter, a personal productivity assistant.',\n ].join('\\n\\n'),\n tools: ['task_list', 'task_create'], // names from ToolRegistry; respond() is injected automatically\n maxTokens: 4096, // optional; defaults apply per provider\n temperature: 0, // optional\n\n // Domain-data enforcement:\n domainDataSchema: { // optional — embedded in respond() input_schema; LLM must produce conforming output\n type: 'object',\n properties: {\n tasks: { type: 'array', items: { type: 'object' } },\n },\n required: ['tasks'],\n },\n domainDataMergeStrategy: 'replace', // optional — 'replace' | 'append' | 'deep-merge'; default 'replace'\n};\n```\n\n`respond()` is injected automatically — do not list it in `tools`.\n\n### Typed state via `stateSchema`\n\n`LLMHandlerConfig` is generic over a `TState extends Record` parameter, defaulting to `Record`. Declare `stateSchema` to type the handler's `context.session.state` with typed coordinates:\n\n```typescript\nimport type { LLMHandlerConfig, StateSchema } from '@cool-ai/beach-llm';\n\ntype ConciergeState = {\n mode: 'discover' | 'search' | 'build';\n subMode: 'initial' | 'results_landed' | 'basket_added';\n};\n\nconst conciergeStateSchema: StateSchema = {\n coordinates: {\n mode: { values: ['discover', 'search', 'build'], default: 'discover' },\n subMode: { values: ['initial', 'results_landed', 'basket_added'], default: 'initial' },\n },\n};\n\nconst concierge: LLMHandlerConfig = {\n id: 'concierge',\n model: 'anthropic/claude-opus-4-7',\n systemPrompt: '...',\n tools: ['search', 'package_results'],\n stateSchema: conciergeStateSchema,\n};\n```\n\nThe handler reads `context.session.state` for the `concierge` coordinates — typed, no runtime undefined check.\n\nThe schema must match the same component's declaration in the router's `state-machine.json` (Beach does not cross-validate the two; the schema is the compile-time view of the same coordinates declared in the runtime config). See the [state machines guide](../../documentation/guides/state-machines.md) for the full picture.\n\n## ToolRegistry\n\n```typescript\nimport { ToolRegistry } from '@cool-ai/beach-llm';\n\nconst registry = new ToolRegistry();\n\nregistry.register({\n name: 'task_list',\n description: 'List open tasks for the current user.',\n scope: 'generalist',\n inputSchema: {\n type: 'object',\n properties: {\n limit: { type: 'number', description: 'Maximum number of tasks to return.' },\n },\n },\n handler: async (args, context) => {\n // context.sessionId, context.eventId, context.slotKey, context.signal,\n // context.routeEvent are available.\n return { tasks: await db.tasks.list({ limit: (args as { limit: number }).limit }) };\n },\n});\n```\n\nThe tool handler returns any JSON-serialisable value. The framework owns the dispatch through the event router (audit, gating, capability scoping); the tool owns the result computation. The result is passed back to the LLM as a tool result and the loop continues until `respond()` is called.\n\nRegistering a name that is already registered throws immediately. Unknown names in `LLMHandlerConfig.tools` throw at invocation time.\n\n```typescript\nregistry.unregister('task_list'); // remove a single tool\nregistry.clear(); // remove all tools (useful in tests)\n```\n\n### Tool scope and routing — the two-axis design\n\n`scope` describes ownership; `routing` describes infrastructure. They are independent.\n\n| Scope | Default routing | Bypass allowed |\n|---|---|---|\n| `generalist` | `routed` | **No** — generalist is the trust-gate scope; bypass would defeat the invariant |\n| `specialist` | `routed` | **Yes**, with an articulated `bypassRouting.reason` |\n\nA specialist tool is one that operates on a private substrate the consumer team owns; the framework still wraps the call for audit and gating unless the tool elects bypass. A specialist tool that wants framework routing (the default) only needs `justification`. A specialist tool that elects bypass needs both `justification` (why specialist) and `bypassRouting.reason` (why bypass).\n\n```typescript\n// Generalist — the recommended default for any tool that touches shared data.\nregistry.register({\n name: 'task_list',\n scope: 'generalist',\n description: 'List open tasks',\n inputSchema: { /* ... */ },\n handler: async (args) => db.tasks.list(args),\n});\n\n// Specialist with default routing — articulated justification required.\nregistry.register({\n name: 'imap_fetch',\n scope: 'specialist',\n justification: 'Operates on the researcher\\'s private IMAP cache; not part of the user-visible capability surface',\n description: 'Fetch raw IMAP message bytes',\n inputSchema: { /* ... */ },\n handler: async (args) => fetchImapBytes(args),\n});\n\n// Specialist electing bypass for inner-loop latency — both reasons required.\nregistry.register({\n name: 'cache_lookup',\n scope: 'specialist',\n routing: 'bypass',\n justification: 'Operates on a process-local cache substrate not part of the consumer\\'s public surface',\n bypassRouting: {\n reason: 'Sub-millisecond latency required for the researcher inner loop; routing overhead exceeds the per-call budget',\n },\n description: 'Look up a value in the in-process cache',\n inputSchema: { /* ... */ },\n handler: async (args) => cache.get((args as { key: string }).key),\n});\n```\n\nThe framework rejects ill-formed declarations at app startup, not at first invocation:\n\n- `scope: 'generalist'` with `routing: 'bypass'` → registration error.\n- Specialist without `justification`, or with a placeholder string (`'TODO'`, `'tbd'`, `'fix me'`, etc.) → registration error.\n- `routing: 'bypass'` without `bypassRouting.reason` → registration error.\n\nGeneralist tools may set `peerExposed: true` to publish on the consumer's Surface Card when that infrastructure ships. Specialist tools cannot set `peerExposed: true` — specialist scope means private substrate, which is by construction not federation-shaped.\n\n### Async tools — `ctx.routeEvent`\n\nA tool that needs to dispatch async work (research, multi-hop fetches, anything taking seconds-to-minutes) calls `ctx.routeEvent` and returns an ack. The handler's turn proceeds; the actual result lands later as a routed event triggering a new handler invocation.\n\n```typescript\nregistry.register({\n name: 'email_research',\n scope: 'generalist',\n description: 'Search the user\\'s email accounts for a topic',\n inputSchema: { /* ... */ },\n handler: async (args, ctx) => {\n const searchId = randomUUID();\n await ctx.routeEvent!({\n source: 'assistant',\n eventType: 'email_research_started',\n data: { ...(args as object), searchId },\n });\n return { searchId };\n },\n});\n```\n\nThe framework does not auto-attach destinations to the eventual result event — that's the routing config's job. See `documentation/changePlans/cr-155-framework-enforced-routing.md` for the locked design and `documentation/migrations/cr-155-framework-enforced-routing.md` for the migration walkthrough.\n\n## AnthropicProvider\n\n```typescript\nimport { createAnthropicProvider } from '@cool-ai/beach-llm';\n\nconst provider = await createAnthropicProvider({ apiKey: process.env.ANTHROPIC_API_KEY });\n```\n\n`createAnthropicProvider` wraps the SDK constructor so consumer code never imports `@anthropic-ai/sdk` directly. Options: `apiKey`, `baseURL`, `maxRetries`, `timeout`, `defaultHeaders`, `defaultQuery` — all optional (the SDK defaults `apiKey` to `process.env.ANTHROPIC_API_KEY`).\n\nUse `AnthropicProvider` (and therefore `createAnthropicProvider`) for all Anthropic models, including those with extended thinking enabled. It preserves thinking block signatures across multi-turn tool-use loops.\n\n## VercelAIProvider\n\n```typescript\nimport { generateText, jsonSchema } from 'ai';\nimport { createOpenAI } from '@ai-sdk/openai';\nimport { VercelAIProvider } from '@cool-ai/beach-llm';\n\nconst provider = new VercelAIProvider(\n createOpenAI()('gpt-4o'),\n { generateText, jsonSchema },\n);\n```\n\n```typescript\nimport { generateText, jsonSchema } from 'ai';\nimport { createGoogleGenerativeAI } from '@ai-sdk/google';\nimport { VercelAIProvider } from '@cool-ai/beach-llm';\n\nconst provider = new VercelAIProvider(\n createGoogleGenerativeAI()('gemini-2.0-flash'),\n { generateText, jsonSchema },\n);\n```\n\n`VercelAIProvider` takes the model instance and the two Vercel AI SDK functions it needs (`generateText` and `jsonSchema`). Beach does not import `ai` directly — only the consumer does, meaning `ai` is not a required install for users of `AnthropicProvider`.\n\nInstall the Vercel AI SDK and the relevant provider package:\n\n```sh\nnpm install ai @ai-sdk/openai # OpenAI / Azure\nnpm install ai @ai-sdk/google # Gemini\nnpm install ai @ai-sdk/mistral # Mistral\n# etc.\n```\n\n## LLMProvider interface\n\nTo add other model providers, implement:\n\n```typescript\ninterface LLMProvider {\n complete(options: CompletionOptions): Promise;\n}\n```\n\n`CompletionOptions` carries the model, system prompt, messages, and tool schemas. `CompletionResult` carries stop reason, tool calls, text blocks, reasoning blocks, and token usage. Pass your implementation to `createLLMHandler()` or `runRespondLoop()`.\n\n## LLM round-trip observer (CAIB-280)\n\n`AnthropicProvider`, `VercelAIProvider`, and `@cool-ai/beach-llm-mastra`'s `MastraProvider` accept an optional `observer` at construction. The observer fires `onRequest` before the SDK call and `onResponse` after it settles, paired by a generated `requestId`. Provider errors propagate; `onResponse` does not fire for failed calls.\n\n```typescript\nimport { AnthropicProvider, type LLMObserver } from '@cool-ai/beach-llm';\n\nconst observer: LLMObserver = {\n onRequest: (e) => console.log(`→ ${e.providerName}/${e.model} (${e.requestSummary.messageCount} msgs)`),\n onResponse: (e) => console.log(`← ${e.providerName} ${e.durationMs}ms · ${e.usage.outputTokens} tokens out`),\n};\n\nconst provider = new AnthropicProvider(sdk, { observer });\n```\n\n`@cool-ai/beach-inspect`'s `wireInspect(store)` exposes a ready-to-pass `llmObserver` that writes `inspect:llm-request` / `inspect:llm-response` missives — these land under the `externalApi` detail group in the side panel:\n\n```typescript\nimport { wireInspect } from '@cool-ai/beach-inspect';\nimport { AnthropicProvider } from '@cool-ai/beach-llm';\n\nconst { routerOptions, llmObserver } = wireInspect(missiveStore);\nconst provider = new AnthropicProvider(sdk, { observer: llmObserver! });\n```\n\n`requestId` correlation lets observers pair `onRequest` and `onResponse` for the same call when a single provider instance serves concurrent requests. The constructor-bound shape (rather than per-call observation) keeps the provider's call signature unchanged.\n\n## System-prompt snippets\n\n```typescript\nimport { respondToolSnippet, conversationStatesSnippet } from '@cool-ai/beach-llm';\n```\n\n`respondToolSnippet` explains the `respond()` tool structure. `conversationStatesSnippet` explains valid `conversationState` values. Both belong in every LLM handler's system prompt — without them the LLM does not know it must call `respond()` instead of replying with free text.\n\n## createLLMHandler()\n\nBuilds the `EventHandler` for an LLM-backed handler. Register it exactly like a deterministic handler. The handler renders the inbound `session:request` parts into a message, runs its context-builders, drives the respond() loop, and replies by routing a `session:reply` event — it never reads `session.destinations`.\n\n```typescript\nimport { createLLMHandler } from '@cool-ai/beach-llm';\n\nconst concierge = createLLMHandler({\n config: handlerConfig,\n provider,\n registry,\n contextBuilders: [async ({ sessionId }) => historyStore.load(sessionId)], // optional — context injected ahead of the inbound parts\n timeoutMs: 60_000, // optional — wall-clock budget per invocation\n onToolExecution: async (record) => auditLog.write(record), // optional — fires after every tool call\n});\n\nrouter.register('concierge', concierge);\n```\n\nThe handler reads `context.session`, drives its own timeout / durable checkpointing / cancellation, and replies by routing a `session:reply` event. The event-router delivers the reply to each of the session's destinations.\n\n## runRespondLoop()\n\nDrives an LLM through its tool loop until it calls `respond()`. `createLLMHandler` uses it internally; call it directly only when you don't need the handler wrapper.\n\n```typescript\nimport { runRespondLoop } from '@cool-ai/beach-llm';\nimport type { SpecialistExecutionRecord } from '@cool-ai/beach-llm';\n\nconst result = await runRespondLoop({\n config: handlerConfig,\n messages: [{ role: 'user', content: 'Hello' }],\n sessionId: 'my-session',\n registry,\n provider,\n signal: abortController.signal, // optional — passed to tool handlers\n onTextBlock: (text) => { ... }, // optional — fires for interim text before respond()\n onToolExecution: async (record: SpecialistExecutionRecord) => {\n // Fires after every tool execution — use for audit/replay log entries.\n // record.toolName, record.toolInput, record.toolOutput, record.durationMs,\n // record.handlerId, record.sessionId, record.eventId, record.iteration\n\n // record.error is set (string) when the tool threw.\n //\n // Routing-audit fields: record.scope ('generalist' | 'specialist'),\n // record.routing ('routed' | 'bypass'), record.bypass (boolean),\n // record.bypassReason (the articulated reason when bypass is true),\n // record.registrationSite (best-effort 'file:line' from the registration\n // site), record.tags (the tool's declared tags).\n await auditLog.write(record);\n },\n});\n\n// result.respond — the RespondCall from the LLM\n// result.messages — full message thread after the tool loop\n// result.usage — { inputTokens, outputTokens }\n// result.latencyMs\n// result.eventId — resolved (generated if not supplied)\n```\n\n## HITL approval\n\nTools declare `requiresApproval` to gate execution on human approval before the handler runs.\n\n```typescript\nregistry.register({\n name: 'book_flight',\n // ...\n requiresApproval: true, // always requires user-level approval\n});\n```\n\nFor context-dependent requirements, pass an `ApprovalPolicy` function instead:\n\n```typescript\nimport type { ApprovalPolicy } from '@cool-ai/beach-llm';\n\nconst bookingPolicy: ApprovalPolicy = async ({ args }, context) => {\n if ((args as { totalValue: number }).totalValue > 500) return 'user';\n return 'auto';\n};\n\nregistry.register({\n name: 'book_flight',\n // ...\n requiresApproval: bookingPolicy,\n});\n```\n\n`ApprovalLevel`:\n- `'auto'` — no approval needed; handler executes immediately\n- `'user'` — requires user approval\n- `'admin'` — requires admin approval\n\n`true` is shorthand for always `'user'`. Absent or `false`-y is always `'auto'`.\n\nWire the intercept with `withApprovalIntercept()`, providing a callback that emits the `approval-request` part and waits for the user's response:\n\n```typescript\nimport { withApprovalIntercept } from '@cool-ai/beach-llm';\n\nconst intercepted = withApprovalIntercept(tool, {\n onApprovalRequired: async (request) => {\n // request.level — 'user' | 'admin'\n // request.toolName, request.toolInput, request.approvalId\n // Emit approval-request part, wait for decision, return:\n return { approvalId: request.approvalId, decision: 'approved' };\n },\n});\n```\n\n## Not in this package\n\n- Event routing and session lifecycle (`@cool-ai/beach-core`).\n- Channel rendering and delivery (the `@cool-ai/beach-channel-*` adaptors and `@cool-ai/beach-a2ui-renderer-*`).\n\n## Related\n\n- [https://cool-ai.org/docs/respond-tool](https://cool-ai.org/docs/respond-tool) — the `respond()` tool schema.\n- [https://cool-ai.org/docs/design-principles](https://cool-ai.org/docs/design-principles) — principles 2.3, 2.4 (LLMs never emit free text; tools constrain and prompts guide).\n","readmeFilename":"README.md"}