How to integrate Kit MCP with LangChain

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Kit agent that can add new subscriber to your welcome form, create a custom field for subscriber notes, delete an outdated broadcast by its id through natural language commands.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.

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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Kit agent that can add new subscriber to your welcome form, create a custom field for subscriber notes, delete an outdated broadcast by its id through natural language commands.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.

Also integrate Kit with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKVercel AI SDKMastra AILlamaIndexCrewAI

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.

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

Step by step10 STEPS
1

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
2

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.
3

Install dependencies

npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters 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/core is the core agent framework
  • dotenv/config loads environment variables
4

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
5

Import dependencies

import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

dotenv.config();
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv/config import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Kit functionality through MCP
6

Initialize Composio client

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });
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
7

Create a Tool Router session

const session = await composio.create(
    userId as string,
    {
        toolkits: ['kit']
    }
);

const 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
8

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "kit-agent": {
        transport: "http",
        url: url,
        headers: {
            "x-api-key": process.env.COMPOSIO_API_KEY
        }
    }
});

const tools = await client.getTools();

const agent = createAgent({ model: "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
  • getTools() retrieves all available Kit tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model
9

Set up interactive chat interface

let conversationHistory: any[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log("Ask any Kit related question or task to the agent.\n");

const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: 'You: '
});

rl.prompt();

rl.on('line', async (userInput: string) => {
    const trimmedInput = userInput.trim();

    if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
        console.log("\nGoodbye!");
        rl.close();
        process.exit(0);
    }

    if (!trimmedInput) {
        rl.prompt();
        return;
    }

    conversationHistory.push({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    const response = await agent.invoke({ messages: conversationHistory });
    conversationHistory = response.messages;

    const finalResponse = response.messages[response.messages.length - 1]?.content;
    console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\n👋 Session ended.');
        process.exit(0);
    });
What's happening:
  • We initialize an empty conversationHistory list to maintain context across interactions
  • A readline interface is used to continuously accept 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 invoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully
10

Run the application

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
What's happening:
  • We call the main() function to start the application

Complete Code

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

import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";  
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });

    const session = await composio.create(
        userId as string,
        {
            toolkits: ['kit']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "kit-agent": {
            transport: "http",
            url: url,
            headers: {
                "x-api-key": process.env.COMPOSIO_API_KEY
            }
        }
    });
    
    const tools = await client.getTools();
  
    const agent = createAgent({ model: "gpt-5", tools });
    
    let conversationHistory: any[] = [];
    
    console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
    console.log("Ask any Kit related question or task to the agent.\n");
    
    const rl = readline.createInterface({
        input: process.stdin,
        output: process.stdout,
        prompt: 'You: '
    });

    rl.prompt();

    rl.on('line', async (userInput: string) => {
        const trimmedInput = userInput.trim();
        
        if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
            console.log("\nGoodbye!");
            rl.close();
            process.exit(0);
        }
        
        if (!trimmedInput) {
            rl.prompt();
            return;
        }
        
        conversationHistory.push({ role: "user", content: trimmedInput });
        console.log("\nAgent is thinking...\n");
        
        const response = await agent.invoke({ messages: conversationHistory });
        conversationHistory = response.messages;
        
        const finalResponse = response.messages[response.messages.length - 1]?.content;
        console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\nSession ended.');
        process.exit(0);
    });
}

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});

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.
TOOLS

Supported Tools

Every Kit action and event your agent gets out of the box.

Add Subscriber to Form

Add an existing subscriber to a form by their IDs.

Add Subscriber to Form by Email

Tool to add an existing subscriber to a form using their email address.

Create Broadcast

Tool to create a new broadcast (email campaign) to send to subscribers.

Create Custom Field

Tool to create a new custom field for subscriber data.

Create Subscriber

Tool to create a new subscriber or update an existing one (upsert).

Create Tag

Tool to create a new tag in the account.

Create Webhook

Creates a webhook subscription for real-time event notifications.

Delete Broadcast

Tool to delete a specific broadcast.

Delete Custom Field

Tool to delete a specific custom field.

Delete Subscriber

Unsubscribe a subscriber from all email communications by their ID.

Delete Tag

Tool to delete a tag by ID.

Delete Webhook

Tool to delete a webhook by ID.

Filter Subscribers

Tool to filter subscribers based on engagement criteria such as email opens, clicks, or delivery status.

Get Account

Tool to retrieve current account information.

Get Account Colors

Tool to retrieve list of colors associated with the account.

Get Broadcast

Tool to retrieve details of a specific broadcast by ID.

Get Broadcast Clicks

Tool to retrieve link click data for a specific broadcast by ID.

Get Broadcast Stats

Tool to retrieve statistics for a specific broadcast by ID.

Get Creator Profile

Tool to retrieve the creator profile information for the account.

Get Email Stats

Tool to retrieve email statistics for the account.

Get Growth Stats

Tool to retrieve growth statistics for the account over a date range.

Get Subscriber

Tool to retrieve a specific subscriber by their ID.

Get Subscriber Stats

Tool to retrieve email stats for a specific subscriber.

List Broadcasts

Tool to retrieve a paginated list of all broadcasts.

List Custom Fields

Tool to retrieve a paginated list of custom fields.

List Email Templates

Retrieve a paginated list of all email templates in the Kit account.

List Forms

Lists all forms in your Kit account with optional filtering and cursor-based pagination.

List Segments

Tool to retrieve a paginated list of segments.

List Sequences

Tool to retrieve a paginated list of all sequences.

List Subscribers

Tool to retrieve a list of subscribers.

List Subscribers For Form

Retrieves subscribers for a specific form by ID with optional filtering and cursor-based pagination.

List Tags

Retrieve a paginated list of all tags in the Kit account.

List Tag Subscribers

Tool to retrieve subscribers for a specific tag.

List Webhooks

Retrieve a paginated list of all webhooks configured in the Kit account.

Remove Tag From Subscriber

Tool to remove a tag from a subscriber using their subscriber ID.

Tag Subscriber

Tool to associate a subscriber with a specific tag by ID.

Tag Subscriber by Email

Assigns a tag to an existing subscriber using their email address.

Untag Subscriber by Email

Tool to remove a tag from a subscriber using their email address.

Update Account Colors

Tool to update the list of colors for the account.

Update Custom Field

Tool to update a custom field's label.

Update Subscriber

Tool to update an existing subscriber's information.

Update Tag

Tool to update a tag's name by ID.

FRAMEWORKS

How to build Kit MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Kit MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Kit MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Kit MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Kit MCP with Claude Cowork

Codex logo

Codex

Use Kit MCP with Codex

OpenClaw logo

OpenClaw

Use Kit MCP with OpenClaw

Hermes logo

Hermes

Use Kit MCP with Hermes

CLI logo

CLI

Use Kit MCP with CLI

Google ADK logo

Google ADK

Use Kit MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Kit MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Kit MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Kit MCP with LlamaIndex

CrewAI logo

CrewAI

Use Kit MCP with CrewAI

FAQ

Frequently asked questions

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.

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.

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.

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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Karan skipped his own birthday party to fix our critical issue. It was 10pm and he diverted his Waymo to help us instead. This really sets the bar — it shows the commitment you need when users rely on your software.

Harsha Gaddipati
Harsha Gaddipati
Co-founder, Slashy

A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

Abhi Arya
Abhi Arya
Co-founder, Opennote

We chose Composio over Pipedream because it delivered depth where it mattered — niche tools and tricky edge cases other platforms simply ignored. That gave us confidence to scale without compromising.

Nirman Dave
Nirman Dave
CEO, Zams

As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down managing agent auth would have been a death sentence.

Ryan Yu
Ryan Yu
Founder, Extra Thursday

Before Composio, adding tool integrations was slow and resource-intensive. Each one could take weeks of engineering time, and maintaining them meant constantly keeping up with API changes.

Tomisin Jenrola
Tomisin Jenrola
Founder & CEO, SwarmZero

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

Jerome Leclanche
Jerome Leclanche
Co-Founder, Ingram Technologies