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Inc.","license":"MIT","homepage":"https://github.com/mcp-use/mcp-use#readme","keywords":["mcp","model-context-protocol","agent","langchain","ai","typescript"],"repository":{"url":"git+https://github.com/mcp-use/mcp-use.git","type":"git","directory":"libraries/typescript/packages/agent"},"description":"Native cross-platform MCP agent. Inspector → MCPAgent → loop → fetch + @mcp-use/client. LangChain bridge at @mcp-use/agent/langchain.","maintainers":[{"name":"pzullo","email":"dev@mcp-use.io"},{"name":"pederzh","email":"luigipederzani@gmail.com"},{"name":"tonxxd","email":"tonxipad+npm@gmail.com"}],"readme":"<div align=\"center\" style=\"margin: 0 auto; max-width: 80%;\">\n  <picture>\n    <source media=\"(prefers-color-scheme: dark)\" srcset=\"https://raw.githubusercontent.com/mcp-use/mcp-use/main/static/logo_white.svg\">\n    <source media=\"(prefers-color-scheme: light)\" srcset=\"https://raw.githubusercontent.com/mcp-use/mcp-use/main/static/logo_black.svg\">\n    <img alt=\"mcp use logo\" src=\"https://raw.githubusercontent.com/mcp-use/mcp-use/main/static/logo_white.svg\" width=\"80%\" style=\"margin: 20px auto;\">\n  </picture>\n</div>\n\n<h1 align=\"center\">@mcp-use/agent</h1>\n\n<p align=\"center\">\n    <a href=\"https://www.npmjs.com/package/@mcp-use/agent\" alt=\"NPM Downloads\">\n        <img src=\"https://img.shields.io/npm/dw/@mcp-use/agent.svg\"/></a>\n    <a href=\"https://www.npmjs.com/package/@mcp-use/agent\" alt=\"NPM Version\">\n        <img src=\"https://img.shields.io/npm/v/@mcp-use/agent.svg\"/></a>\n    <a href=\"https://github.com/mcp-use/mcp-use/blob/main/LICENSE\" alt=\"License\">\n        <img src=\"https://img.shields.io/github/license/mcp-use/mcp-use\" /></a>\n    <a href=\"https://discord.gg/XkNkSkMz3V\" alt=\"Discord\">\n        <img src=\"https://dcbadge.limes.pink/api/server/XkNkSkMz3V?style=flat\" /></a>\n</p>\n\n**Native cross-platform MCP agent** — connect an LLM to MCP servers, run a tool-calling loop, stream steps, and return answers. Works in **Node and browser** from the same import. Part of the [mcp-use](https://github.com/mcp-use/mcp-use) TypeScript framework.\n\n> **Docs:** [Agent overview](https://mcp-use.com/docs/typescript/agent) · [API reference](https://mcp-use.com/docs/typescript/api-reference/agent/mcp-agent)\n\n---\n\n## Install\n\n```bash\nnpm install @mcp-use/agent\n```\n\nRequires Node.js ≥ 22.22.2. `@mcp-use/client` is included as a dependency.\n\nFor the LangChain bridge (`import { MCPAgent } from \"@mcp-use/agent/langchain\"`), also install the peers you use, for example:\n\n```bash\nnpm install langchain @langchain/openai\n```\n\n---\n\n## Quick start\n\n```typescript\nimport { MCPAgent } from \"@mcp-use/agent\";\n\nconst agent = new MCPAgent({\n  llm: \"openai/gpt-4o\",\n  mcpServers: {\n    filesystem: {\n      command: \"npx\",\n      args: [\"-y\", \"@modelcontextprotocol/server-filesystem\", \"./\"],\n    },\n  },\n});\n\nconst result = await agent.run({ prompt: \"What is in the current folder?\" });\nconsole.log(result);\n\nawait agent.close();\n```\n\n`run({ prompt })` returns the final answer. Raise `maxSteps` for multi-tool tasks. Always call `close()` when the agent owns its client.\n\n---\n\n## LangChain bridge\n\nUse the same `MCPAgent` class name from a separate entry when you need LangChain models, Langfuse tracing, Server Manager, or AI SDK streaming:\n\n```typescript\nimport { ChatOpenAI } from \"@langchain/openai\";\nimport { MCPAgent } from \"@mcp-use/agent/langchain\";\nimport { MCPClient } from \"@mcp-use/client\";\n\nconst mcpServers = {\n  filesystem: {\n    command: \"npx\",\n    args: [\"-y\", \"@modelcontextprotocol/server-filesystem\", \"./\"],\n  },\n};\n\nconst client = new MCPClient({ mcpServers });\nconst agent = new MCPAgent({\n  llm: new ChatOpenAI({ model: \"gpt-4o\" }),\n  client,\n});\n\nconst result = await agent.run({ prompt: \"Summarize the repo README\" });\nawait agent.close();\n```\n\n---\n\n## Features\n\n| Feature | Entry | Description |\n| --- | --- | --- |\n| **Tool-calling loop** | `@mcp-use/agent` | LLM discovers and calls MCP tools until it can answer |\n| **Simplified setup** | `@mcp-use/agent` | `\"provider/model\"` + `mcpServers` — no manual client/LLM wiring |\n| **Streaming** | `@mcp-use/agent` | `stream()` for steps, `streamEvents()` for native LLM events |\n| **Structured output** | `@mcp-use/agent/langchain` | Zod `schema` on `run({ prompt, schema })` |\n| **Memory** | both | Conversation history across `run()` calls |\n| **Server Manager** | `@mcp-use/agent/langchain` | Dynamically add MCP servers during a run |\n| **Observability** | `@mcp-use/agent/langchain` | Langfuse metadata, tags, and trace flush |\n| **Remote agent** | `@mcp-use/agent` | Run against Manufact Cloud by `agentId` |\n\n---\n\n## Package entry points\n\n| Import | Environment | Description |\n| --- | --- | --- |\n| `@mcp-use/agent` | Node + browser | Native `MCPAgent` (fetch-based LLM drivers) |\n| `@mcp-use/agent/langchain` | Node | LangChain `MCPAgent` (optional peer deps) |\n\nIn the browser, pass HTTP MCP URLs or live connections from `@mcp-use/client/react`. Stdio `command`/`args` server configs are Node-only.\n\n---\n\n## Examples\n\nSee [examples/README.md](./examples/README.md). Run from this package:\n\n```bash\nOPENAI_API_KEY=... pnpm exec tsx examples/basic/simplified_agent_example.ts\n```\n\n---\n\n## Related packages\n\n| Package | Description |\n| --- | --- |\n| [@mcp-use/client](https://www.npmjs.com/package/@mcp-use/client) | MCP client (HTTP, stdio, React hooks) |\n| [mcp-use](https://www.npmjs.com/package/mcp-use) | Full framework (server + agent + client) |\n| [@mcp-use/inspector](https://www.npmjs.com/package/@mcp-use/inspector) | Web-based MCP debugger |\n\n---\n\n## License\n\nMIT\n","readmeFilename":""}