How to integrate Mistral ai MCP with LangChain

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Introduction

This guide walks you through connecting Mistral ai to LangChain using the Composio tool router. By the end, you'll have a working Mistral ai agent that can summarize this research paper in simple terms, generate python code for sorting a list, explain the difference between ai and ml through natural language commands.

This guide will help you understand how to give your LangChain agent real control over a Mistral ai account through Composio's Mistral ai MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Mistral ai with

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Mistral ai project to Composio
  • Create a Tool Router MCP session for Mistral ai
  • Initialize an MCP client and retrieve Mistral ai tools
  • Build a LangChain agent that can interact with Mistral ai
  • Set up an interactive chat interface for testing

What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.

Key features include:

  • Agent Framework: Build agents that can use tools and make decisions
  • MCP Integration: Connect to external services through Model Context Protocol adapters
  • Memory Management: Maintain conversation history across interactions
  • Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

What is the Mistral ai MCP server, and what's possible with it?

The Mistral ai MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Mistral ai account. It provides structured and secure access to your Mistral AI models, so your agent can perform actions like generating text, summarizing content, answering questions, extracting structured information, and handling advanced language tasks on your behalf.

  • Text generation and completion: Have your agent produce coherent, context-aware text responses, complete prompts, or generate creative content leveraging Mistral's advanced models.
  • Summarization and paraphrasing: Ask your agent to summarize lengthy documents or rephrase input text for improved clarity or brevity.
  • Question answering and information extraction: Let your agent answer questions, extract key facts, or pull structured data from unstructured content automatically.
  • Content classification and sentiment analysis: Enable your agent to categorize text, detect topics, or analyze sentiment to inform downstream workflows.
  • Conversational AI and dialogue management: Build rich, multi-turn conversations or chatbots that handle context, intent, and user queries seamlessly using Mistral's models.

Supported Tools & Triggers

Tools
Append to conversationTool to append new entries to an existing conversation in Mistral AI.
Create AgentTool to create a new AI agent with custom configuration (Beta).
Create Agents CompletionTool to generate completions using a Mistral AI agent with specific instructions and tools.
Create Audio TranscriptionTranscribe audio files to text using Mistral AI's Voxtral models.
Create Chat CompletionGenerate conversational responses from Mistral AI models.
Create Chat ModerationTool to classify chat content for moderation purposes across 9 categories.
Create EmbeddingsTool to generate vector embeddings for input text using Mistral AI embedding models.
Create FIM CompletionGenerate code completions using fill-in-the-middle functionality.
Create libraryTool to create a new document library.
Create library shareCreate or update sharing permissions for a library.
Create ModerationTool to classify text content for moderation purposes across 9 categories.
Create OCRExtract text and structured data from images and documents using Mistral AI's OCR capabilities.
Create or Update Agent AliasTool to create or update an agent version alias.
Delete agentPermanently deletes an agent by its ID (Beta feature).
Delete ConversationTool to delete a conversation by its ID (Beta).
Delete FileDelete a file by its ID from Mistral AI.
Delete libraryPermanently deletes a library and all of its documents from Mistral AI.
Delete library documentPermanently deletes a document from a Mistral AI library.
Delete library shareRemove sharing permissions for a library from a user, workspace, or organization.
Download FileDownload the content of a previously uploaded file from Mistral AI.
Get AgentTool to retrieve details of a specific Mistral AI agent by its ID.
Get Agent VersionRetrieve a specific version of an agent (Beta).
Get ConversationTool to retrieve details of a specific conversation.
Get Conversation HistoryRetrieve the full history of a conversation in Mistral AI.
Get Conversation MessagesRetrieve all messages from a Mistral AI conversation.
Get document extracted text URLRetrieve a signed URL to download the extracted text from a document in a Mistral AI library.
Get document signed URLGet a signed URL to download a document from a Mistral AI library.
Get Document StatusRetrieve the processing status of a document in a Mistral AI library.
Get Document Text ContentRetrieve the extracted text content of a specific document from a Mistral AI library (Beta).
Get File Signed URLGet a time-limited signed URL for downloading a file from Mistral AI.
List Fine Tuning JobsList fine-tuning jobs with optional filtering and pagination.
Get libraryRetrieve detailed information about a specific library.
Get Library DocumentRetrieve metadata for a specific document in a Mistral AI library.
Get ModelTool to retrieve detailed information about a specific Mistral AI model by its ID.
List agent aliasesRetrieve all aliases for an agent version.
List AgentsTool to list all configured agents (Beta).
List Agent VersionsList all versions of a specific agent.
List Batch JobsTool to retrieve a list of all batch jobs with optional filtering and pagination.
List ConversationsList all created conversations (Beta).
List FilesTool to list all files available to the user.
List librariesList all document libraries accessible to your organization.
List Library DocumentsList all documents in a Mistral AI document library.
List library sharesList all sharing permissions for a document library.
List ModelsTool to retrieve all available Mistral AI models including base models and fine-tuned models.
Reprocess documentReprocess a document in a Mistral AI library (Beta).
Restart ConversationTool to restart a conversation from a specific point (Beta).
Retrieve FileRetrieve metadata of a file uploaded to Mistral AI.
Start ConversationTool to start a new conversation with a Mistral AI agent or base model.
Update AgentTool to update an existing agent's configuration.
Update agent versionTool to update the current version of an agent (Beta).
Update libraryTool to update an existing document library's properties.
Update library documentUpdate the metadata of a document in a Mistral AI library.
Upload FileUpload a file to Mistral AI for use in fine-tuning, batch processing, or OCR.
Upload Library DocumentUpload a document to a Mistral AI library for use with RAG-enabled agents.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Prerequisites

Before starting this tutorial, make sure you have:
  • Python 3.10 or higher installed on your system
  • A Composio account with an API key
  • An OpenAI API key
  • Basic familiarity with Python and async programming

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.

Install dependencies

pip install composio-langchain langchain-mcp-adapters langchain python-dotenv

Install the required packages for LangChain with MCP support.

What's happening:

  • composio-langchain provides Composio integration for LangChain
  • langchain-mcp-adapters enables MCP client connections
  • langchain is the core agent framework
  • python-dotenv loads environment variables

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your requests to Composio's API
  • COMPOSIO_USER_ID identifies the user for session management
  • OPENAI_API_KEY enables access to OpenAI's language models

Import dependencies

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Mistral ai functionality through MCP

Initialize Composio client

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
What's happening:
  • We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
  • Creating a Composio instance that will manage our connection to Mistral ai tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

# Create Tool Router session for Mistral ai
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['mistral_ai']
)

url = session.mcp.url
What's happening:
  • We're creating a Tool Router session that gives your agent access to Mistral ai tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
  • This approach allows the agent to dynamically load and use Mistral ai tools as needed

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "mistral_ai-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

agent = create_agent("gpt-5", tools)
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Mistral ai MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available Mistral ai tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model

Set up interactive chat interface

conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Mistral ai related question or task to the agent.\n")

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ['exit', 'quit', 'bye']:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
What's happening:
  • We initialize an empty conversation_history list to maintain context across interactions
  • A while loop continuously accepts user input from the command line
  • When a user types a message, it's added to the conversation history and sent to the agent
  • The agent processes the request using the ainvoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully

Run the application

if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • We call the main() function using asyncio.run() to start the application

Complete Code

Here's the complete code to get you started with Mistral ai and LangChain:

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['mistral_ai']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "mistral_ai-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Ask any Mistral ai related question or task to the agent.\n")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

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

Conclusion

You've successfully built a LangChain agent that can interact with Mistral ai through Composio's Tool Router.

Key features of this implementation:

  • Dynamic tool loading through Composio's Tool Router
  • Conversation history maintenance for context-aware responses
  • Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.

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FAQ

What are the differences in Tool Router MCP and Mistral ai MCP?

With a standalone Mistral ai MCP server, the agents and LLMs can only access a fixed set of Mistral ai tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Mistral ai and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with LangChain?

Yes, you can. LangChain fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Mistral ai tools.

Can I manage the permissions and scopes for Mistral ai while using Tool Router?

Yes, absolutely. You can configure which Mistral ai scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Mistral ai data and credentials are handled as safely as possible.

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