> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mcp-use.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Overview

> Set up LangChainMCPAgent with a LangChain chat model and MCP tools.

`LangChainMCPAgent` connects LangChain chat models and callbacks to MCP tools. Import it from `@mcp-use/agent/langchain`.

## When to use LangChainMCPAgent

Use `LangChainMCPAgent` when you need at least one of these capabilities:

* an existing LangChain chat model or custom LangChain tool;
* Zod structured-output conversion after an agent run;
* LangChain `StreamEvent` output or `prettyStreamEvents()`;
* Server Manager for on-demand access to one MCP server's tools; or
* LangChain callbacks, Langfuse tracing, metadata, and tags.

## Install LangChainMCPAgent

Install the agent package, LangChain core packages, and the provider package for your model.

<CodeGroup>
  ```bash npm theme={null}
  npm install @mcp-use/agent @mcp-use/client langchain @langchain/core @langchain/openai
  ```

  ```bash pnpm theme={null}
  pnpm add @mcp-use/agent @mcp-use/client langchain @langchain/core @langchain/openai
  ```

  ```bash bun theme={null}
  bun add @mcp-use/agent @mcp-use/client langchain @langchain/core @langchain/openai
  ```
</CodeGroup>

Add `zod` for structured output. Add `langfuse` and `langfuse-langchain` for built-in Langfuse tracing.

## Run a LangChain agent

Set `OPENAI_API_KEY`, then run:

```typescript theme={null}
import { ChatOpenAI } from "@langchain/openai";
import { MCPClient } from "@mcp-use/client";
import { LangChainMCPAgent } from "@mcp-use/agent/langchain";

async function main() {
  const client = new MCPClient({
    mcpServers: {
      filesystem: {
        command: "npx",
        args: ["-y", "@modelcontextprotocol/server-filesystem", "./"],
      },
    },
  });

  const agent = new LangChainMCPAgent({
    llm: new ChatOpenAI({ model: "gpt-4o", temperature: 0 }),
    client,
  });

  try {
    await agent.initialize();
    const result = await agent.run({
      prompt: "Read package.json and summarize the project scripts.",
    });
    console.log(result);
  } finally {
    await agent.close();
  }
}

main().catch(console.error);
```

A successful run prints a text answer based on `package.json`. `LangChainMCPAgent` defaults to `5` model-call steps.

<Warning>
  `LangChainMCPAgent.close()` closes its MCP client even when you supplied that
  client. Do not share the client with work that must continue after the agent
  closes.
</Warning>

## Next steps

<CardGroup cols={2}>
  <Card title="Structured output" icon="braces" href="/v2/typescript/agent/langchain/structured-output">
    Convert final text into validated Zod data.
  </Card>

  <Card title="Server Manager" icon="server" href="/v2/typescript/agent/langchain/server-manager">
    Load one server's tools on demand.
  </Card>

  <Card title="Observability" icon="eye" href="/v2/typescript/agent/langchain/observability">
    Trace runs with Langfuse or custom callbacks.
  </Card>
</CardGroup>