How to integrate Monday MCP with LangChain

BOOK A CALLGET STARTED FOR FREE
Framework Integration Gradient
Monday Logo
LangChain Logo
divider

Introduction

This guide walks you through connecting Monday to LangChain using the Composio tool router. By the end, you'll have a working Monday agent that can create a new project board for q3, add users to the design review board, archive completed tasks from last week, change task status to 'in progress' for marketing through natural language commands.

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

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

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Monday project to Composio
  • Create a Tool Router MCP session for Monday
  • Initialize an MCP client and retrieve Monday tools
  • Build a LangChain agent that can interact with Monday
  • 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 Monday MCP server, and what's possible with it?

The Monday MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Monday.com workspace. It provides structured and secure access to your boards, items, and workflows, so your agent can perform actions like creating items, managing boards, updating columns, and organizing groups on your behalf.

  • Automated board and item management: Effortlessly create new boards, add items to existing boards, and archive or delete items as your projects evolve.
  • Dynamic column and group organization: Let your agent create, update, or remove columns and groups to keep your boards tailored to your team's needs.
  • Collaborative user and role administration: Add users to boards and assign roles, ensuring the right people have access and permissions for every project.
  • Streamlined workflow customization: Change column values, assign statuses or dropdowns, and create groups to match your workflow requirements in real time.
  • Efficient cleanup and restructuring: Archive or permanently delete boards, columns, or groups when they're no longer needed, keeping your workspace organized and clutter-free.

Supported Tools & Triggers

Tools
Add users to boardAdds users to a monday.
Archive boardArchives a specified, existing, and unarchived board in monday.
Archive itemArchives an existing monday.
Change simple column valueChanges a specific column's value for a monday.
Create a Monday boardCreates a monday.
Create columnCreates a new column with a specified type and title on a monday.
Create groupCreates a new group with the given `group name` on an existing monday.
Create itemCreates a new item on a monday.
Delete columnDeletes a specified column from a monday.
Delete groupPermanently deletes an existing group (and its items) from an existing board in monday.
Delete itemPermanently deletes an existing monday.
Duplicate itemDuplicates an item on a monday.
Get group detailsRetrieves details (id, title, color, archived/deleted status) for a specific group on an accessible monday.
List board itemsRetrieves id, name, and state for all items on a specified monday.
List Monday boardsRetrieves a list of boards from a monday.
List columnsLists all columns and their properties for a specified monday.
List itemsRetrieves specified subitems from monday.
List usersRetrieves a list of users from monday.
Move item to boardMoves a monday.
Move item to groupMoves an item to a different group on the same monday.
Update boardUpdates a specified attribute of an existing board on monday.

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

What is Tool Router?

Composio's Tool Router 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 Tool Router

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

How the Tool Router works

The Tool Router 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 Monday 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 Monday tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

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

url = session.mcp.url
What's happening:
  • We're creating a Tool Router session that gives your agent access to Monday 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 Monday tools as needed

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "monday-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 Monday MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available Monday 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 Monday 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 Monday 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=['monday']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "monday-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 Monday 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 Monday 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.

How to build Monday MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Monday MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Monday MCP with Claude Agent SDK

Google ADK

Google ADK

Use Monday MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Monday MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Monday MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Monday MCP with LlamaIndex

CrewAI

CrewAI

Use Monday MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Monday MCP?

With a standalone Monday MCP server, the agents and LLMs can only access a fixed set of Monday tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Monday 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 Monday tools.

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

Yes, absolutely. You can configure which Monday 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 Monday data and credentials are handled as safely as possible.

Used by agents from

Context
ASU
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai
Context
ASU
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai
Context
ASU
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai

Never worry about agent reliability

We handle tool reliability, observability, and security so you never have to second-guess an agent action.

Book a call↗Get started for free