https://github.com/langchain-ai/langchain-mcp-adapters
https://github.com/langchain-ai/langchain-mcp-adapters
Last synced: 4 days ago
JSON representation
- Host: GitHub
- URL: https://github.com/langchain-ai/langchain-mcp-adapters
- Owner: langchain-ai
- License: mit
- Created: 2025-02-07T23:08:03.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2025-04-03T17:55:03.000Z (12 days ago)
- Last Synced: 2025-04-03T22:06:23.151Z (12 days ago)
- Language: Python
- Size: 255 KB
- Stars: 935
- Watchers: 14
- Forks: 76
- Open Issues: 13
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-LangGraph - langchain-mcp-adapters
- awesome-generative-ai-data-scientist - LangChain MCP Adapters - ai/langchain-mcp-adapters) | (LangGraph Extensions)
README
# LangChain MCP Adapters
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).
## Features
- 🛠️ 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
- 📦 A client implementation that allows you to connect to multiple MCP servers and load tools from them
## Installation
```bash
pip install langchain-mcp-adapters
```
## Quickstart
Here is a simple example of using the MCP tools with a LangGraph agent.
```bash
pip install langchain-mcp-adapters langgraph langchain-openai
export OPENAI_API_KEY=
```
### Server
First, let's create an MCP server that can add and multiply numbers.
```python
# math_server.py
from mcp.server.fastmcp import FastMCP
mcp = FastMCP("Math")
@mcp.tool()
def add(a: int, b: int) -> int:
"""Add two numbers"""
return a + b
@mcp.tool()
def multiply(a: int, b: int) -> int:
"""Multiply two numbers"""
return a * b
if __name__ == "__main__":
mcp.run(transport="stdio")
```
### Client
```python
# Create server parameters for stdio connection
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
from langchain_mcp_adapters.tools import load_mcp_tools
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI
model = ChatOpenAI(model="gpt-4o")
server_params = StdioServerParameters(
command="python",
# Make sure to update to the full absolute path to your math_server.py file
args=["/path/to/math_server.py"],
)
async with stdio_client(server_params) as (read, write):
async with ClientSession(read, write) as session:
# Initialize the connection
await session.initialize()
# Get tools
tools = await load_mcp_tools(session)
# Create and run the agent
agent = create_react_agent(model, tools)
agent_response = await agent.ainvoke({"messages": "what's (3 + 5) x 12?"})
```
## Multiple MCP Servers
The library also allows you to connect to multiple MCP servers and load tools from them:
### Server
```python
# math_server.py
...
# weather_server.py
from typing import List
from mcp.server.fastmcp import FastMCP
mcp = FastMCP("Weather")
@mcp.tool()
async def get_weather(location: str) -> str:
"""Get weather for location."""
return "It's always sunny in New York"
if __name__ == "__main__":
mcp.run(transport="sse")
```
```bash
python weather_server.py
```
### Client
```python
from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI
model = ChatOpenAI(model="gpt-4o")
async with MultiServerMCPClient(
{
"math": {
"command": "python",
# Make sure to update to the full absolute path to your math_server.py file
"args": ["/path/to/math_server.py"],
"transport": "stdio",
},
"weather": {
# make sure you start your weather server on port 8000
"url": "http://localhost:8000/sse",
"transport": "sse",
}
}
) as client:
agent = create_react_agent(model, client.get_tools())
math_response = await agent.ainvoke({"messages": "what's (3 + 5) x 12?"})
weather_response = await agent.ainvoke({"messages": "what is the weather in nyc?"})
```
## Using with LangGraph API Server
> [!TIP]
> Check out [this guide](https://langchain-ai.github.io/langgraph/tutorials/langgraph-platform/local-server/) on getting started with LangGraph API server.
If you want to run a LangGraph agent that uses MCP tools in a LangGraph API server, you can use the following setup:
```python
# graph.py
from contextlib import asynccontextmanager
from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_anthropic import ChatAnthropic
model = ChatAnthropic(model="claude-3-5-sonnet-latest")
@asynccontextmanager
async def make_graph():
async with MultiServerMCPClient(
{
"math": {
"command": "python",
# Make sure to update to the full absolute path to your math_server.py file
"args": ["/path/to/math_server.py"],
"transport": "stdio",
},
"weather": {
# make sure you start your weather server on port 8000
"url": "http://localhost:8000/sse",
"transport": "sse",
}
}
) as client:
agent = create_react_agent(model, client.get_tools())
yield agent
```
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:
```json
{
"dependencies": ["."],
"graphs": {
"agent": "./graph.py:make_graph"
}
}
```