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Metadata-Version: 2.4
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Name: langchain-mcp-adapters
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Version: 0.1.14
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Summary: Make Anthropic Model Context Protocol (MCP) tools compatible with LangChain and LangGraph agents.
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Author-Email: Vadym Barda <19161700+vbarda@users.noreply.github.com>
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License-Expression: MIT
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Project-URL: repository, https://www.github.com/langchain-ai/langchain-mcp-adapters
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Requires-Python: >=3.10
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Requires-Dist: langchain-core<2.0.0,>=0.3.36
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Requires-Dist: mcp>=1.9.2
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Requires-Dist: typing-extensions>=4.14.0
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Description-Content-Type: text/markdown
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# LangChain MCP Adapters
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This library provides a lightweight wrapper that makes [Anthropic Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction) tools compatible with [LangChain](https://github.com/langchain-ai/langchain) and [LangGraph](https://github.com/langchain-ai/langgraph).
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> [!note]
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> A JavaScript/TypeScript version of this library is also available at [langchainjs](https://github.com/langchain-ai/langchainjs/tree/main/libs/langchain-mcp-adapters/).
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## Features
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- 🛠️ Convert MCP tools into [LangChain tools](https://python.langchain.com/docs/concepts/tools/) that can be used with [LangGraph](https://github.com/langchain-ai/langgraph) agents
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- 📦 A client implementation that allows you to connect to multiple MCP servers and load tools from them
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## Installation
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```bash
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pip install langchain-mcp-adapters
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```
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## Quickstart
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Here is a simple example of using the MCP tools with a LangGraph agent.
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```bash
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pip install langchain-mcp-adapters langgraph "langchain[openai]"
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export OPENAI_API_KEY=<your_api_key>
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```
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### Server
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First, let's create an MCP server that can add and multiply numbers.
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```python
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# math_server.py
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from mcp.server.fastmcp import FastMCP
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mcp = FastMCP("Math")
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@mcp.tool()
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def add(a: int, b: int) -> int:
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"""Add two numbers"""
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return a + b
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@mcp.tool()
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def multiply(a: int, b: int) -> int:
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"""Multiply two numbers"""
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return a * b
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if __name__ == "__main__":
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mcp.run(transport="stdio")
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```
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### Client
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```python
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# Create server parameters for stdio connection
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from mcp import ClientSession, StdioServerParameters
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from mcp.client.stdio import stdio_client
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from langchain_mcp_adapters.tools import load_mcp_tools
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from langchain.agents import create_agent
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server_params = StdioServerParameters(
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command="python",
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# Make sure to update to the full absolute path to your math_server.py file
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args=["/path/to/math_server.py"],
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)
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async with stdio_client(server_params) as (read, write):
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async with ClientSession(read, write) as session:
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# Initialize the connection
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await session.initialize()
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# Get tools
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tools = await load_mcp_tools(session)
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# Create and run the agent
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agent = create_agent("openai:gpt-4.1", tools)
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agent_response = await agent.ainvoke({"messages": "what's (3 + 5) x 12?"})
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```
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## Multiple MCP Servers
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The library also allows you to connect to multiple MCP servers and load tools from them:
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### Server
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```python
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# math_server.py
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...
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# weather_server.py
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from typing import List
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from mcp.server.fastmcp import FastMCP
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mcp = FastMCP("Weather")
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@mcp.tool()
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async def get_weather(location: str) -> str:
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"""Get weather for location."""
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return "It's always sunny in New York"
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if __name__ == "__main__":
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mcp.run(transport="streamable-http")
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```
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```bash
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python weather_server.py
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```
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### Client
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```python
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from langchain_mcp_adapters.client import MultiServerMCPClient
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from langchain.agents import create_agent
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client = MultiServerMCPClient(
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{
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"math": {
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"command": "python",
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# Make sure to update to the full absolute path to your math_server.py file
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"args": ["/path/to/math_server.py"],
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"transport": "stdio",
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},
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"weather": {
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# Make sure you start your weather server on port 8000
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"url": "http://localhost:8000/mcp",
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"transport": "streamable_http",
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}
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}
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)
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tools = await client.get_tools()
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agent = create_agent("openai:gpt-4.1", tools)
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math_response = await agent.ainvoke({"messages": "what's (3 + 5) x 12?"})
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weather_response = await agent.ainvoke({"messages": "what is the weather in nyc?"})
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```
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> [!note]
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> Example above will start a new MCP `ClientSession` for each tool invocation. If you would like to explicitly start a session for a given server, you can do:
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>
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> ```python
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> from langchain_mcp_adapters.tools import load_mcp_tools
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>
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> client = MultiServerMCPClient({...})
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> async with client.session("math") as session:
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> tools = await load_mcp_tools(session)
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> ```
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## Streamable HTTP
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MCP now supports [streamable HTTP](https://modelcontextprotocol.io/specification/2025-03-26/basic/transports#streamable-http) transport.
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To start an [example](examples/servers/streamable-http-stateless/) streamable HTTP server, run the following:
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```bash
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cd examples/servers/streamable-http-stateless/
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uv run mcp-simple-streamablehttp-stateless --port 3000
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```
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Alternatively, you can use FastMCP directly (as in the examples above).
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To use it with Python MCP SDK `streamablehttp_client`:
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```python
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# Use server from examples/servers/streamable-http-stateless/
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from mcp import ClientSession
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from mcp.client.streamable_http import streamablehttp_client
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from langchain.agents import create_agent
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from langchain_mcp_adapters.tools import load_mcp_tools
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async with streamablehttp_client("http://localhost:3000/mcp") as (read, write, _):
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async with ClientSession(read, write) as session:
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# Initialize the connection
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await session.initialize()
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# Get tools
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tools = await load_mcp_tools(session)
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agent = create_agent("openai:gpt-4.1", tools)
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math_response = await agent.ainvoke({"messages": "what's (3 + 5) x 12?"})
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```
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Use it with `MultiServerMCPClient`:
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```python
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# Use server from examples/servers/streamable-http-stateless/
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from langchain_mcp_adapters.client import MultiServerMCPClient
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from langchain.agents import create_agent
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client = MultiServerMCPClient(
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{
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"math": {
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"transport": "streamable_http",
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"url": "http://localhost:3000/mcp"
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},
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}
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)
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tools = await client.get_tools()
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agent = create_agent("openai:gpt-4.1", tools)
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math_response = await agent.ainvoke({"messages": "what's (3 + 5) x 12?"})
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```
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## Passing runtime headers
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When connecting to MCP servers, you can include custom headers (e.g., for authentication or tracing) using the `headers` field in the connection configuration. This is supported for the following transports:
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- `sse`
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- `streamable_http`
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### Example: passing headers with `MultiServerMCPClient`
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```python
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from langchain_mcp_adapters.client import MultiServerMCPClient
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from langchain.agents import create_agent
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client = MultiServerMCPClient(
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{
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"weather": {
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"transport": "streamable_http",
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"url": "http://localhost:8000/mcp",
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"headers": {
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"Authorization": "Bearer YOUR_TOKEN",
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"X-Custom-Header": "custom-value"
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},
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}
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}
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)
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tools = await client.get_tools()
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agent = create_agent("openai:gpt-4.1", tools)
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response = await agent.ainvoke({"messages": "what is the weather in nyc?"})
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```
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> Only `sse` and `streamable_http` transports support runtime headers. These headers are passed with every HTTP request to the MCP server.
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## Using with LangGraph StateGraph
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```python
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from langchain_mcp_adapters.client import MultiServerMCPClient
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from langgraph.graph import StateGraph, MessagesState, START
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from langgraph.prebuilt import ToolNode, tools_condition
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from langchain.chat_models import init_chat_model
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model = init_chat_model("openai:gpt-4.1")
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client = MultiServerMCPClient(
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{
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"math": {
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"command": "python",
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# Make sure to update to the full absolute path to your math_server.py file
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"args": ["./examples/math_server.py"],
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"transport": "stdio",
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},
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"weather": {
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# make sure you start your weather server on port 8000
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"url": "http://localhost:8000/mcp",
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"transport": "streamable_http",
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}
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}
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)
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tools = await client.get_tools()
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def call_model(state: MessagesState):
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response = model.bind_tools(tools).invoke(state["messages"])
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return {"messages": response}
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builder = StateGraph(MessagesState)
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builder.add_node(call_model)
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builder.add_node(ToolNode(tools))
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builder.add_edge(START, "call_model")
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builder.add_conditional_edges(
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"call_model",
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tools_condition,
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)
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builder.add_edge("tools", "call_model")
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graph = builder.compile()
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math_response = await graph.ainvoke({"messages": "what's (3 + 5) x 12?"})
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weather_response = await graph.ainvoke({"messages": "what is the weather in nyc?"})
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```
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## Using with LangGraph API Server
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> [!TIP]
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> Check out [this guide](https://langchain-ai.github.io/langgraph/tutorials/langgraph-platform/local-server/) on getting started with LangGraph API server.
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If you want to run a LangGraph agent that uses MCP tools in a LangGraph API server, you can use the following setup:
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```python
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# graph.py
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from contextlib import asynccontextmanager
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from langchain_mcp_adapters.client import MultiServerMCPClient
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from langchain.agents import create_agent
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async def make_graph():
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client = MultiServerMCPClient(
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{
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"weather": {
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# make sure you start your weather server on port 8000
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"url": "http://localhost:8000/mcp",
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"transport": "streamable_http",
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},
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# ATTENTION: MCP's stdio transport was designed primarily to support applications running on a user's machine.
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# Before using stdio in a web server context, evaluate whether there's a more appropriate solution.
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# For example, do you actually need MCP? or can you get away with a simple `@tool`?
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"math": {
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"command": "python",
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# Make sure to update to the full absolute path to your math_server.py file
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"args": ["/path/to/math_server.py"],
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"transport": "stdio",
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},
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}
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)
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tools = await client.get_tools()
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agent = create_agent("openai:gpt-4.1", tools)
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return agent
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```
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In your [`langgraph.json`](https://langchain-ai.github.io/langgraph/cloud/reference/cli/#configuration-file) make sure to specify `make_graph` as your graph entrypoint:
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```json
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{
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"dependencies": ["."],
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"graphs": {
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"agent": "./graph.py:make_graph"
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}
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}
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```
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