How to integrate Kit MCP with LangChain

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Introduction

This guide walks you through connecting Kit to LangChain using the Composio tool router. By the end, you'll have a working Kit agent that can add new subscriber to my welcome form, create a custom field for subscriber notes, delete an outdated broadcast by its id, create a tag called 'vip' for top customers through natural language commands.

This guide will help you understand how to give your LangChain agent real control over a Kit account through Composio's Kit 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 Kit project to Composio
  • Create a Tool Router MCP session for Kit
  • Initialize an MCP client and retrieve Kit tools
  • Build a LangChain agent that can interact with Kit
  • 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 Kit MCP server, and what's possible with it?

The Kit MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Kit account. It provides structured and secure access to your subscriber lists, tags, forms, and automations, so your agent can perform actions like managing subscribers, creating tags, updating custom fields, and handling broadcasts on your behalf.

  • Subscriber management and automation: Add new subscribers to forms, remove subscribers, or update their details to keep your audience lists accurate and engaged.
  • Custom field and tag creation: Automatically create, update, or delete custom fields and tags, making it easy to segment and personalize your communications.
  • Webhook and event setup: Set up or remove webhooks so your agent can listen for subscriber or purchase events and trigger automations as needed.
  • Broadcast and campaign control: Delete obsolete broadcasts or manage your messaging campaigns directly through your agent for streamlined outreach.
  • Account insights and configuration: Retrieve detailed account information, including plan details and primary contact, to keep your integrations and automations running smoothly.

Supported Tools & Triggers

Tools
Add Subscriber to FormTool to add a subscriber to a specific form by id.
Create Custom FieldTool to create a new custom field for subscriber data.
Create TagTool to create a new tag in the account.
Create WebhookTool to create a new webhook subscription.
Delete BroadcastTool to delete a specific broadcast.
Delete Custom FieldTool to delete a specific custom field.
Delete SubscriberTool to delete (unsubscribe) a subscriber by id.
Delete TagTool to delete a tag by id.
Delete WebhookTool to delete a webhook by id.
Get AccountTool to retrieve current account information.
Get Account ColorsTool to retrieve list of colors associated with the account.
Get BroadcastTool to retrieve details of a specific broadcast by id.
Get Broadcast StatsTool to retrieve statistics for a specific broadcast by id.
Get Creator ProfileTool to retrieve the creator profile information for the account.
Get Email StatsTool to retrieve email statistics for the account.
List BroadcastsTool to retrieve a paginated list of all broadcasts.
List Custom FieldsTool to retrieve a paginated list of custom fields.
List FormsTool to list all forms.
List SegmentsTool to retrieve a paginated list of segments.
List SequencesTool to retrieve a paginated list of all sequences.
List SubscribersTool to retrieve a list of subscribers.
List Subscribers For FormTool to retrieve subscribers for a specific form by id.
List TagsTool to retrieve a list of all tags.
List Tag SubscribersTool to retrieve subscribers for a specific tag.
Tag SubscriberTool to associate a subscriber with a specific tag by id.
Tag Subscriber by EmailTool to associate a subscriber with a tag using an email address.
Update Account ColorsTool to update the list of colors for the account.
Update Custom FieldTool to update a custom field's label.
Update TagTool to update a tag's name by id.

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 Kit 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 Kit tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

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

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

Configure the agent with the MCP URL

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

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "kit-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 Kit 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 Kit 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 Kit MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Kit MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Kit MCP with Claude Agent SDK

Google ADK

Google ADK

Use Kit MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Kit MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Kit MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Kit MCP with LlamaIndex

CrewAI

CrewAI

Use Kit MCP with CrewAI

FAQ

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

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

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

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

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