generated from kgod/ai-review-template
343 lines
10 KiB
Plaintext
343 lines
10 KiB
Plaintext
Metadata-Version: 2.4
|
|
Name: langchain-mcp-adapters
|
|
Version: 0.1.14
|
|
Summary: Make Anthropic Model Context Protocol (MCP) tools compatible with LangChain and LangGraph agents.
|
|
Author-Email: Vadym Barda <19161700+vbarda@users.noreply.github.com>
|
|
License-Expression: MIT
|
|
Project-URL: repository, https://www.github.com/langchain-ai/langchain-mcp-adapters
|
|
Requires-Python: >=3.10
|
|
Requires-Dist: langchain-core<2.0.0,>=0.3.36
|
|
Requires-Dist: mcp>=1.9.2
|
|
Requires-Dist: typing-extensions>=4.14.0
|
|
Description-Content-Type: text/markdown
|
|
|
|
# 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).
|
|
|
|
|
|
|
|
> [!note]
|
|
> A JavaScript/TypeScript version of this library is also available at [langchainjs](https://github.com/langchain-ai/langchainjs/tree/main/libs/langchain-mcp-adapters/).
|
|
|
|
## 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=<your_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 langchain.agents import create_agent
|
|
|
|
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_agent("openai:gpt-4.1", 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="streamable-http")
|
|
```
|
|
|
|
```bash
|
|
python weather_server.py
|
|
```
|
|
|
|
### Client
|
|
|
|
```python
|
|
from langchain_mcp_adapters.client import MultiServerMCPClient
|
|
from langchain.agents import create_agent
|
|
|
|
client = 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/mcp",
|
|
"transport": "streamable_http",
|
|
}
|
|
}
|
|
)
|
|
tools = await client.get_tools()
|
|
agent = create_agent("openai:gpt-4.1", 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?"})
|
|
```
|
|
|
|
> [!note]
|
|
> 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:
|
|
>
|
|
> ```python
|
|
> from langchain_mcp_adapters.tools import load_mcp_tools
|
|
>
|
|
> client = MultiServerMCPClient({...})
|
|
> async with client.session("math") as session:
|
|
> tools = await load_mcp_tools(session)
|
|
> ```
|
|
|
|
## Streamable HTTP
|
|
|
|
MCP now supports [streamable HTTP](https://modelcontextprotocol.io/specification/2025-03-26/basic/transports#streamable-http) transport.
|
|
|
|
To start an [example](examples/servers/streamable-http-stateless/) streamable HTTP server, run the following:
|
|
|
|
```bash
|
|
cd examples/servers/streamable-http-stateless/
|
|
uv run mcp-simple-streamablehttp-stateless --port 3000
|
|
```
|
|
|
|
Alternatively, you can use FastMCP directly (as in the examples above).
|
|
|
|
To use it with Python MCP SDK `streamablehttp_client`:
|
|
|
|
```python
|
|
# Use server from examples/servers/streamable-http-stateless/
|
|
|
|
from mcp import ClientSession
|
|
from mcp.client.streamable_http import streamablehttp_client
|
|
|
|
from langchain.agents import create_agent
|
|
from langchain_mcp_adapters.tools import load_mcp_tools
|
|
|
|
async with streamablehttp_client("http://localhost:3000/mcp") as (read, write, _):
|
|
async with ClientSession(read, write) as session:
|
|
# Initialize the connection
|
|
await session.initialize()
|
|
|
|
# Get tools
|
|
tools = await load_mcp_tools(session)
|
|
agent = create_agent("openai:gpt-4.1", tools)
|
|
math_response = await agent.ainvoke({"messages": "what's (3 + 5) x 12?"})
|
|
```
|
|
|
|
Use it with `MultiServerMCPClient`:
|
|
|
|
```python
|
|
# Use server from examples/servers/streamable-http-stateless/
|
|
from langchain_mcp_adapters.client import MultiServerMCPClient
|
|
from langchain.agents import create_agent
|
|
|
|
client = MultiServerMCPClient(
|
|
{
|
|
"math": {
|
|
"transport": "streamable_http",
|
|
"url": "http://localhost:3000/mcp"
|
|
},
|
|
}
|
|
)
|
|
tools = await client.get_tools()
|
|
agent = create_agent("openai:gpt-4.1", tools)
|
|
math_response = await agent.ainvoke({"messages": "what's (3 + 5) x 12?"})
|
|
```
|
|
|
|
## Passing runtime headers
|
|
|
|
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:
|
|
|
|
- `sse`
|
|
- `streamable_http`
|
|
|
|
### Example: passing headers with `MultiServerMCPClient`
|
|
|
|
```python
|
|
from langchain_mcp_adapters.client import MultiServerMCPClient
|
|
from langchain.agents import create_agent
|
|
|
|
client = MultiServerMCPClient(
|
|
{
|
|
"weather": {
|
|
"transport": "streamable_http",
|
|
"url": "http://localhost:8000/mcp",
|
|
"headers": {
|
|
"Authorization": "Bearer YOUR_TOKEN",
|
|
"X-Custom-Header": "custom-value"
|
|
},
|
|
}
|
|
}
|
|
)
|
|
tools = await client.get_tools()
|
|
agent = create_agent("openai:gpt-4.1", tools)
|
|
response = await agent.ainvoke({"messages": "what is the weather in nyc?"})
|
|
```
|
|
|
|
> Only `sse` and `streamable_http` transports support runtime headers. These headers are passed with every HTTP request to the MCP server.
|
|
|
|
## Using with LangGraph StateGraph
|
|
|
|
```python
|
|
from langchain_mcp_adapters.client import MultiServerMCPClient
|
|
from langgraph.graph import StateGraph, MessagesState, START
|
|
from langgraph.prebuilt import ToolNode, tools_condition
|
|
|
|
from langchain.chat_models import init_chat_model
|
|
model = init_chat_model("openai:gpt-4.1")
|
|
|
|
client = MultiServerMCPClient(
|
|
{
|
|
"math": {
|
|
"command": "python",
|
|
# Make sure to update to the full absolute path to your math_server.py file
|
|
"args": ["./examples/math_server.py"],
|
|
"transport": "stdio",
|
|
},
|
|
"weather": {
|
|
# make sure you start your weather server on port 8000
|
|
"url": "http://localhost:8000/mcp",
|
|
"transport": "streamable_http",
|
|
}
|
|
}
|
|
)
|
|
tools = await client.get_tools()
|
|
|
|
def call_model(state: MessagesState):
|
|
response = model.bind_tools(tools).invoke(state["messages"])
|
|
return {"messages": response}
|
|
|
|
builder = StateGraph(MessagesState)
|
|
builder.add_node(call_model)
|
|
builder.add_node(ToolNode(tools))
|
|
builder.add_edge(START, "call_model")
|
|
builder.add_conditional_edges(
|
|
"call_model",
|
|
tools_condition,
|
|
)
|
|
builder.add_edge("tools", "call_model")
|
|
graph = builder.compile()
|
|
math_response = await graph.ainvoke({"messages": "what's (3 + 5) x 12?"})
|
|
weather_response = await graph.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 langchain.agents import create_agent
|
|
|
|
async def make_graph():
|
|
client = MultiServerMCPClient(
|
|
{
|
|
"weather": {
|
|
# make sure you start your weather server on port 8000
|
|
"url": "http://localhost:8000/mcp",
|
|
"transport": "streamable_http",
|
|
},
|
|
# ATTENTION: MCP's stdio transport was designed primarily to support applications running on a user's machine.
|
|
# Before using stdio in a web server context, evaluate whether there's a more appropriate solution.
|
|
# For example, do you actually need MCP? or can you get away with a simple `@tool`?
|
|
"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",
|
|
},
|
|
}
|
|
)
|
|
tools = await client.get_tools()
|
|
agent = create_agent("openai:gpt-4.1", tools)
|
|
return 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"
|
|
}
|
|
}
|
|
```
|