Using mcp-use with LangChain

The LangChain adapter allows you to seamlessly integrate tools, resources, and prompts from any MCP server with LangChain agents. This enables you to use mcp-use as a comprehensive tool provider for your LangChain-powered agents.

How it Works

The LangChainAdapter converts not only tools but also resources and prompts from your active MCP servers into a format compatible with LangChain’s tool-calling feature. It maps each of these MCP constructs to a callable function that the LangChain agent can request.

Tools are converted directly to LangChain tools.
Resources are converted into functions that take no arguments and read the resource’s content.
Prompts are converted into functions that accept the prompt’s arguments.

The adapter maintains a mapping of these generated functions to their actual execution logic, allowing you to easily call them when requested by the agent.

Step-by-Step Guide

Here’s how to use the adapter to provide MCP tools, resources, and prompts to a LangChain agent.

Before starting, install the LangChain SDK:

uv pip install langchain

First, set up your MCPClient with the desired MCP servers. This part of the process is the same as any other mcp-use application.

from mcp_use import MCPClient

config = {
    "mcpServers": {
        "airbnb": {"command": "npx", "args": ["-y", "@openbnb/mcp-server-airbnb", "--ignore-robots-txt"]},
    }
}

client = MCPClient(config=config)

Next, instantiate the LangChainAdapter. This adapter will be responsible for converting MCP constructs into a format LangChain can understand.

from mcp_use.agents.adapters import LangChainAdapter

# Creates the adapter for LangChain's format
adapter = LangChainAdapter()

You can pass a disallowed_tools list to the adapter’s constructor to prevent specific tools, resources, or prompts from being exposed to the model.

Use the create_all method on the adapter to inspect all connected MCP servers and generate a list of tools, resources and prompts in the LangChain tool format.

# Convert tools from active connectors to the LangChain's format
# this will populates the list of tools, resources and prompts
await adapter.create_all(client)

# If you decided to create all tools (list concatenation)
langchain_tools = adapter.tools + adapter.resources + adapter.prompts

This list will include tools generated from your MCP tools, resources, and prompts.

If you don’t want to create all tools, you can call single functions. For example, if you only want to use tools and resources, you can do the following:

await adapter.create_tools(client)
await adapter.create_resources(client)

# Then, you can decide which ones to use:
langchain_tools = adapter.tools + adapter.resources

Now, you can use the generated langchain_tools to create a LangChain agent. The agent will use the descriptions of these tools to decide if it needs to call any of them to answer the user’s query.

from langchain.agents import create_agent
from langchain.chat_models import init_chat_model

# Create chat model
model = init_chat_model(
    "gpt-5.5-mini", temperature=0.5, timeout=10, max_tokens=1000
)
# Create the LangChain agent
agent = create_agent(
    model=model,
    tools=langchain_tools,
    system_prompt="You are a helpful assistant",
)

# Run the agent
result = await agent.ainvoke(
    {
        "messages": [
            {
                "role": "user",
                "content": "Please tell me the cheapest hotel for two people in Trapani.",
            }
        ]
    }
)

print(result)

Complete Example

For reference, here is the complete, runnable code for integrating mcp-use with LangChain.

import asyncio
from dataclasses import dataclass

from dotenv import load_dotenv
from langchain.agents import create_agent
from langchain.chat_models import init_chat_model

from mcp_use import MCPClient
from mcp_use.agents.adapters import LangChainAdapter

# This example demonstrates how to use our integration
# adapters to use MCP tools and convert to the right format.
# In particularly, this example uses the LangChainAdapter.

load_dotenv()


# We use a dataclass here, but Pydantic models are also supported.
@dataclass
class ResponseFormat:
    """Response schema for the agent."""

    # AirBnb response (available dates, prices, and relevant information)
    relevant_response: str


async def main():
    config = {
        "mcpServers": {
            "airbnb": {
                "command": "npx",
                "args": ["-y", "@openbnb/mcp-server-airbnb", "--ignore-robots-txt"],
            },
        }
    }

    try:
        client = MCPClient(config=config)

        # Creates the adapter for LangChain's format
        adapter = LangChainAdapter()

        # Convert tools from active connectors to the LangChain's format
        await adapter.create_all(client)

        # List concatenation (if you loaded all tools)
        langchain_tools = adapter.tools + adapter.resources + adapter.prompts

        # Create chat model
        model = init_chat_model(
            "gpt-5.5-mini", temperature=0.5, timeout=10, max_tokens=1000
        )
        # Create the LangChain agent
        agent = create_agent(
            model=model,
            tools=langchain_tools,
            system_prompt="You are a helpful assistant",
            response_format=ResponseFormat,
        )

        # Run the agent
        result = await agent.ainvoke(
            {
                "messages": [
                    {
                        "role": "user",
                        "content": "Please tell me the cheapest hotel for two people in Trapani.",
                    }
                ]
            }
        )

        print(result["structured_response"])
    except Exception as e:
        print(f"Error: {e}")
        raise e


if __name__ == "__main__":
    asyncio.run(main())

Get Started

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LangChain

Using mcp-use with LangChain

How it Works

Step-by-Step Guide

Complete Example

​Using mcp-use with LangChain

​How it Works

​Step-by-Step Guide

​Complete Example

Using mcp-use with LangChain

How it Works

Step-by-Step Guide

Complete Example