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Version](https://img.shields.io/npm/v/@mcpc-tech/acp-ai-provider)](https://www.npmjs.com/package/@mcpc-tech/acp-ai-provider) [![JSR](https://jsr.io/badges/@mcpc/acp-ai-provider)](https://jsr.io/@mcpc/acp-ai-provider)","maintainers":[{"name":"yaonyan","email":"i@mcpc.tech"}],"readme":"# @mcpc/acp-ai-provider\n\n[![NPM Version](https://img.shields.io/npm/v/@mcpc-tech/acp-ai-provider)](https://www.npmjs.com/package/@mcpc-tech/acp-ai-provider)\n[![JSR](https://jsr.io/badges/@mcpc/acp-ai-provider)](https://jsr.io/@mcpc/acp-ai-provider)\n\n> **Version Compatibility**\n>\n> - `v0.2.x` requires **AI SDK v6**\n>   ([`main`](https://github.com/mcpc-tech/mcpc/tree/main/packages/acp-ai-provider)\n>   branch)\n> - For **AI SDK v5**, use the\n>   [`release-v5`](https://github.com/mcpc-tech/mcpc/tree/release-v5/packages/acp-ai-provider)\n>   branch or install `@mcpc-tech/acp-ai-provider@ai-v5`\n\nUse [ACP (Agent Client Protocol)](https://agentclientprotocol.com/) agents with\nthe [AI SDK](https://ai-sdk.dev/).\n\n![acp-demo](./examples/acp-demo.gif)\n\nThis package bridges ACP agents to the AI SDK. It spawns ACP agents (Claude\nCode, Gemini, Codex CLI, and more) as child processes and exposes them through\nthe AI SDK's `LanguageModelV3`/`LanguageModelV2` protocol.\n\n[Try a full stack web ACP example here](https://github.com/mcpc-tech/dev-inspector-mcp)\n\n## Installation\n\n```bash\n# npm\nnpm i @mcpc-tech/acp-ai-provider\n\n# deno\ndeno add jsr:@mcpc/acp-ai-provider\n```\n\nFor **AI SDK v5** users:\n\n```bash\n# npm (v5)\nnpm i @mcpc-tech/acp-ai-provider@ai-v5\n\n# deno (v5)\ndeno add jsr:@mcpc/acp-ai-provider@0.1\n```\n\n## Usage\n\n[See all examples](https://github.com/mcpc-tech/mcpc/tree/main/packages/acp-ai-provider/examples)\n\n### Basic Example\n\n```typescript\nimport { createACPProvider } from \"@mcpc/acp-ai-provider\";\nimport { generateText } from \"ai\";\nimport process from \"node:process\";\n\n// Create provider for an ACP agent\nconst provider = createACPProvider({\n  command: \"gemini\",\n  args: [\"--experimental-acp\"],\n  session: {\n    cwd: process.cwd(),\n    mcpServers: [],\n  },\n});\n\n// Use with AI SDK\nconst result = await generateText({\n  model: provider.languageModel(),\n  prompt: \"Hello, what can you help me with?\",\n  tools: provider.tools,\n});\n\nconsole.log(result.text);\n```\n\n### Streaming Example\n\n```typescript\nimport { createACPProvider } from \"@mcpc/acp-ai-provider\";\nimport { streamText } from \"ai\";\nimport process from \"node:process\";\n\nconst provider = createACPProvider({\n  command: \"claude-agent-acp\",\n  args: [],\n  session: {\n    cwd: process.cwd(),\n    mcpServers: [],\n  },\n});\n\nconst { textStream } = streamText({\n  model: provider.languageModel(),\n  prompt: \"Write a simple Hello World program\",\n  tools: provider.tools,\n});\n\nfor await (const chunk of textStream) {\n  process.stdout.write(chunk);\n}\n```\n\n### Structured JSON Output\n\nUse AI SDK's `Output.object()` to get structured JSON from ACP agents. The\nprovider automatically injects JSON schema instructions into the prompt and\nstrips markdown fences from the response if needed.\n\n```typescript\nimport { createACPProvider } from \"@mcpc/acp-ai-provider\";\nimport { generateText, Output, streamText } from \"ai\";\nimport process from \"node:process\";\nimport { z } from \"zod\";\n\nconst provider = createACPProvider({\n  command: \"gemini\",\n  args: [\"--experimental-acp\"],\n  session: { cwd: process.cwd(), mcpServers: [] },\n});\n\nconst result = await generateText({\n  model: provider.languageModel(),\n  prompt: \"Give me a recipe for chocolate chip cookies.\",\n  output: Output.object({\n    schema: z.object({\n      name: z.string(),\n      ingredients: z.array(z.object({ item: z.string(), amount: z.string() })),\n      steps: z.array(z.string()),\n    }),\n  }),\n});\n\nconsole.log(result.output); // Typed object matching the schema\n```\n\nThis also works with `streamText`:\n\n```typescript\nconst stream = streamText({\n  model: provider.languageModel(),\n  prompt: \"Tell me about Tokyo\",\n  output: Output.object({\n    schema: z.object({\n      name: z.string(),\n      country: z.string(),\n      landmarks: z.array(z.string()),\n    }),\n  }),\n});\n\nconst output = await stream.output; // Parsed object\n```\n\n> **How it works**: When `Output.object()` (or `Output.array()`,\n> `Output.json()`, `Output.choice()`) is used, AI SDK sets\n> `responseFormat.type = \"json\"` with an optional JSON Schema. The ACP provider\n> detects this and:\n>\n> 1. Prepends a structured-output instruction (with the schema) to the prompt\n> 2. Strips markdown code fences from the response (in both generate and stream\n>    modes)\n> 3. Passes clean JSON text to AI SDK for validation and parsing\n\n### Authentication (Lazy by Default)\n\nAuthentication is **lazy** by default: the provider does not authenticate during\n`initialize`. If an ACP request fails with an auth-required error, the provider\nwill:\n\n1. call `authenticate(authMethodId)`\n2. retry the request **once**\n\nBy default, if `authMethodId` is not set and `initialize.authMethods` is\navailable, the provider will use the first method and print a warning.\n\nTo explicitly control this flow, set `authMethodId`:\n\n```typescript\nconst provider = createACPProvider({\n  command: \"gemini\",\n  args: [\"--experimental-acp\"],\n  authMethodId: process.env.AUTH_METHOD_ID,\n  session: { cwd: process.cwd(), mcpServers: [] },\n});\n```\n\nYou can also authenticate manually:\n\n```typescript\nawait provider.authenticate(process.env.AUTH_METHOD_ID);\n```\n\n### With Tools (MCP Servers)\n\nTools are defined through MCP (Model Context Protocol) servers, not AI SDK's\n`tools` parameter:\n\n```typescript\nconst provider = createACPProvider({\n  command: \"gemini\",\n  args: [\"--experimental-acp\"],\n  session: {\n    cwd: process.cwd(),\n    mcpServers: [\n      {\n        type: \"stdio\",\n        name: \"filesystem\",\n        command: \"npx\",\n        args: [\"-y\", \"@modelcontextprotocol/server-filesystem\", \"/tmp\"],\n      },\n    ],\n  },\n});\n\nconst result = await generateText({\n  model: provider.languageModel(),\n  prompt: \"List files in /tmp\",\n  tools: provider.tools,\n});\n```\n\n### Dynamic Host-Side Tools (Experimental)\n\nYou can also define AI SDK-style tools that execute on the host side using\n`acpTools()`:\n\n```typescript\nimport { acpTools, createACPProvider } from \"@mcpc/acp-ai-provider\";\nimport { streamText, tool } from \"ai\";\nimport { z } from \"zod\";\n\nconst provider = createACPProvider({\n  command: \"claude-agent-acp\",\n  session: { cwd: process.cwd(), mcpServers: [] },\n});\n\nconst result = await streamText({\n  model: provider.languageModel(),\n  prompt: \"Please greet Alice\",\n  // acpTools() registers host-side tools for the agent to call\n  tools: acpTools({\n    greet: tool({\n      description: \"Greet a person by name\",\n      inputSchema: z.object({\n        name: z.string().describe(\"The name of the person to greet\"),\n      }),\n      execute: async ({ name }) => `Hello, ${name}!`,\n    }),\n  }),\n});\n```\n\n#### Tool Result Format\n\nBy default, tools can return simple values like strings or objects:\n\n```typescript\nexecute: async ({ name }) => `Hello, ${name}!`,\nexecute: async () => ({ status: \"ok\" }),\n```\n\nThese are automatically wrapped into the MCP `CallToolResult` format with a\n`text` content block.\n\nHowever, for tools that return rich content like images or audio, you should\nreturn the MCP\n[`CallToolResult`](https://spec.modelcontextprotocol.io/specification/2025-03-26/server/tools/#tool-result)\nformat directly:\n\n```typescript\nexecute: async ({ url }) => {\n  const response = await fetch(url);\n  const buffer = Buffer.from(await response.arrayBuffer());\n\n  return {\n    content: [\n      {\n        type: \"text\" as const,\n        text: `Fetched image from ${url}`,\n      },\n      {\n        type: \"image\" as const,\n        data: buffer.toString(\"base64\"),\n        mimeType: response.headers.get(\"content-type\") || \"image/jpeg\",\n      },\n    ],\n  };\n},\n```\n\n**Why?** Under the hood, `acpTools()` are implemented as MCP tools. Simple\nreturns get wrapped as `{ content: [{ type: \"text\", text: ... }] }`. If you\nreturn an object with `image` or `audio` blocks without the MCP wrapper, those\nmedia blocks get JSON-stringified into a text block and lost. By returning the\nMCP `CallToolResult` format explicitly, the media data is preserved and sent to\nthe agent as proper MCP content blocks.\n\nSee [image-tool-result-example.ts](./examples/image-tool-result-example.ts) for\na complete working example.\n\n#### How It Works (TCP Socket Callback)\n\nSince ACP agents spawn their own MCP server subprocesses, we use a TCP socket\nfor the runtime to call back to the host for tool execution:\n\n```\n┌─────────────────────────────────────────────────────────┐\n│  Host Process                                           │\n│    - Starts TCP server (random port)                    │\n│    - Passes TCP port via env vars to ACP                │\n│                         ▲    │                          │\n│                         │    │ TCP (getTools → definitions)\n│                         │    │ TCP (callHandler → execute)\n│                         │    │                          │\n└─────────────────────────┼────┼──────────────────────────┘\n                          │    │\n┌─────────────────────────┼────┼──────────────────────────┐\n│  ACP Agent spawns tool-proxy-runtime                    │\n│    - Reads port from ACP_TOOL_PROXY_PORT env            │\n│    - Connects and requests tools via `getTools`         │\n│    - On MCP tools/call → TCP callHandler → result       │\n└─────────────────────────────────────────────────────────┘\n```\n\n### Session Management\n\n> **Key Difference**: ACP providers maintain **stateful sessions** across\n> multiple requests, while `streamText` itself is **stateless**. Each\n> `streamText` call is independent, but the underlying ACP agent process can\n> persist conversation context when properly managed.\n\n#### Simple Session Persistence (Single Provider)\n\nFor simple use cases, use `persistSession` to keep the same provider alive:\n\n```typescript\nconst provider = createACPProvider({\n  command: \"gemini\",\n  args: [\"--experimental-acp\"],\n  session: { cwd: process.cwd(), mcpServers: [] },\n  persistSession: true, // Keep session alive\n});\n\nconst model = provider.languageModel();\nawait generateText({ model, prompt: \"Hi, my name is Alice\" });\nawait generateText({ model, prompt: \"What's my name?\" }); // Agent remembers\n\nprovider.cleanup(); // Clean up when done\n```\n\nResume a previous session:\n\n```typescript\nconst provider = createACPProvider({\n  command: \"gemini\",\n  args: [\"--experimental-acp\"],\n  session: { cwd: process.cwd(), mcpServers: [] },\n  existingSessionId: \"previous-session-id\",\n  persistSession: true,\n});\n```\n\n#### Multi-Session Management (Server/API Pattern)\n\nFor server applications handling multiple concurrent users, manage sessions in a\nMap:\n\n```typescript\ninterface SessionEntry {\n  provider: ReturnType<typeof createACPProvider>;\n  createdAt: number;\n}\n\nconst sessionProviders = new Map<string, SessionEntry>();\n\n// Initialize a new session\nasync function initSession(agentCommand: string): Promise<string> {\n  const provider = createACPProvider({\n    command: agentCommand,\n    args: [],\n    session: { cwd: process.cwd(), mcpServers: [] },\n    persistSession: true,\n  });\n\n  const session = await provider.initSession();\n  const sessionId = session.sessionId;\n\n  sessionProviders.set(sessionId, { provider, createdAt: Date.now() });\n  return sessionId;\n}\n\n// Use existing session\nasync function chat(sessionId: string, prompt: string) {\n  const entry = sessionProviders.get(sessionId);\n  if (!entry) throw new Error(\"Session not found\");\n\n  const { textStream } = streamText({\n    model: entry.provider.languageModel(),\n    prompt,\n    tools: entry.provider.tools,\n  });\n\n  // Stream response...\n}\n\n// Cleanup session\nfunction cleanupSession(sessionId: string) {\n  const entry = sessionProviders.get(sessionId);\n  if (entry) {\n    entry.provider.cleanup();\n    sessionProviders.delete(sessionId);\n  }\n}\n```\n\nSee [session-management-example.ts](./examples/session-management-example.ts)\nfor a complete working example.\n\n### Selecting Models, Modes, and Session Config Options\n\nSome ACP agents support multiple models, modes, or general session config\noptions. Use `initSession()` to discover what the current agent advertises.\nConfig IDs and values are agent-defined, so do not assume that the same ID is\nused by every agent.\n\n```typescript\nconst provider = createACPProvider({\n  command: \"claude-agent-acp\",\n  args: [],\n  session: { cwd: process.cwd(), mcpServers: [] },\n  persistSession: true,\n});\n\n// Initialize and get available options\nconst session = await provider.initSession();\n\n// Existing model/mode APIs remain supported.\nconsole.log(session.modes?.availableModes);\nconsole.log(session.models?.availableModels);\nawait provider.setMode(\"plan\");\nawait provider.setModel(\"opus\");\n\n// Inspect arbitrary options advertised by this agent.\nconsole.log(session.configOptions);\nconsole.log(provider.getConfigOptions(\"thought_level\"));\n\n// Set an option using its advertised ID and value.\nconst option = session.configOptions?.find((item) =>\n  item.category === \"thought_level\"\n);\nif (option) {\n  await provider.setConfigOption(option.id, \"high\");\n}\n\n// Or resolve the option by semantic category without hard-coding its ID.\nawait provider.setThoughtLevel(\"high\");\nawait provider.setConfigOptionByCategory(\"model_config\", \"balanced\");\n\nconst result = await generateText({\n  model: provider.languageModel(),\n  prompt: \"...\",\n});\n```\n\n`setConfigOption()` returns the ACP `SetSessionConfigOptionResponse`. The\nprovider caches its returned `configOptions`, because changing one option may\nchange the available values or state of other options. `getConfigOptions()`\ntherefore reflects the latest successful update for the provider's active\nsession.\n\n`setConfigOptionByCategory()` and `setThoughtLevel()` only select an option when\nexactly one advertised option has that category. They throw when none or\nmultiple match; in the multiple-match case, inspect `getConfigOptions(category)`\nand call `setConfigOption()` with the intended ID. Unknown custom categories are\nsupported.\n\n## FAQ\n\n### How to stream tool calls\n\nTools are passed to the AI SDK as\n[provider-defined tools](https://ai-sdk.dev/docs/reference/ai-sdk-core/tool#tool.tool.type)\nbecause they are called and executed by the ACP agent (for example, Codex).\n\nSo, to stream tool calls, pass the provider tools to the AI SDK:\n\n```ts\nconst result = await generateText({\n  model: provider.languageModel(),\n  prompt: \"List files in /tmp\",\n  tools: provider.tools,\n});\n```\n\nThe actual tool name and arguments live inside\n`acp.acp_provider_agent_dynamic_tool`'s input and follow this structure:\n\n```ts\nexport const providerAgentDynamicToolSchema = z.object({\n  toolCallId: z.string().describe(\"The unique ID of the tool call.\"),\n  toolName: z.string().describe(\"The name of the tool being called.\"),\n  args: z.record(z.any()).describe(\"The input arguments for the tool call.\"),\n});\n```\n\nYou can import this constant using `ACP_PROVIDER_AGENT_DYNAMIC_TOOL_NAME`.\n\n### Raw stream parts (plan, diffs, terminals)\n\nThe provider emits additional data as `raw` stream parts.\n\n> **Important**: You must set `includeRawChunks: true` to receive raw stream\n> parts, otherwise they will be filtered out by the AI SDK.\n\nYou can handle them directly in the stream:\n\n```ts\nconst { fullStream } = streamText({\n  includeRawChunks: true, // Required to receive raw parts\n  model: provider.languageModel(),\n  prompt: \"...\",\n});\n\nfor await (const chunk of fullStream) {\n  if (chunk.type === \"raw\") {\n    const data = JSON.parse(chunk.rawValue);\n\n    switch (data.type) {\n      case \"plan\":\n        // Plan steps: data.entries\n        break;\n      case \"diff\":\n        // File changes: data.path, data.oldText, data.newText, data.toolCallId\n        break;\n      case \"terminal\":\n        // Terminal output: data.terminalId, data.toolCallId\n        break;\n    }\n  }\n}\n```\n\nOr use `messageMetadata` to attach them to messages when streaming to UI:\n\n```ts\nconst result = streamText({\n  includeRawChunks: true, // Required to receive raw parts\n  model: provider.languageModel(),\n  prompt: \"...\",\n});\n\nconst response = result.toUIMessageStreamResponse({\n  messageMetadata: ({ part }) => {\n    // Convert raw parts to metadata for easier UI access\n    if (part.type === \"raw\" && part.rawValue) {\n      const data = JSON.parse(part.rawValue as string);\n      switch (data.type) {\n        case \"plan\":\n          return { plan: data.entries };\n        case \"diff\":\n          return { diffs: [data] }; // Accumulate multiple diffs\n        case \"terminal\":\n          return { terminals: [data] }; // Accumulate terminal outputs\n      }\n    }\n  },\n});\n\n// In your UI component:\n// message.metadata?.plan → plan entries\n// message.metadata?.diffs → file changes\n// message.metadata?.terminals → terminal outputs\n```\n\n## Performance Optimization\n\n<details>\n<summary><strong>Performance ⚡️</strong></summary>\n\nFor the best user experience, we recommend **pre-initializing the session** with\nyour tools. Benchmarking shows this can reduce the Time to First Token (TTFT) by\nover 60%.\n\n| Strategy            | Connect Time | TTFT (Perceived) |\n| ------------------- | ------------ | ---------------- |\n| Standard (Lazy)     | N/A          | ~7.3s            |\n| **Pre-Initialized** | ~2.3s        | **~2.8s**        |\n\n```typescript\n// 1. Create provider and tools\nconst provider = createACPProvider({/* ... */});\nconst tools = acpTools({/* ... */});\n\ntry {\n  // 2. Pre-initialize to warm up connection (saves ~5s)\n  await provider.initSession(tools);\n\n  // 3. Use in streamText (instant start)\n  await streamText({\n    model: provider.languageModel(\"model-id\"),\n    tools, // Must use same tools instance\n    prompt: \"Hello\",\n  });\n} finally {\n  provider.cleanup();\n}\n```\n\n</details>\n\n## Debugging\n\nSet the `ACP_AI_PROVIDER_DEBUG` environment variable to enable debug logging:\n\n```bash\n# Enable debug logging\nexport ACP_AI_PROVIDER_DEBUG=1\n\n# Run your script\nnpx tsx example.ts\n```\n\nWhen enabled, raw ACP messages are logged to a temporary file:\n\n```\n[acp-ai-provider] Agent message log: /tmp/acp-ai-provider-xxx/agent-messages.ndjson\n```\n\n## Limitations\n\n- **No token counting** — ACP doesn't provide token usage information (it always\n  returns 0).\n- **Dynamic tools are experimental** — The `tools` parameter uses TCP callback\n  which adds some complexity.\n\n## Related\n\n- [AI SDK](https://ai-sdk.dev/)\n- [ACP Specification](https://agentclientprotocol.com/overview/introduction)\n\n## License\n\nMIT\n","readmeFilename":"README.md"}