How to integrate Chatforma MCP with LangChain

This guide walks you through connecting Chatforma to LangChain using the Composio tool router. By the end, you'll have a working Chatforma agent that can list your bots, review dialog messages, find user variables, browse segments, and send messages through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Chatforma account through Composio's Chatforma MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Chatforma is a chatbot automation platform for building and managing bots. It helps teams run dialogs, segments, broadcasts, forms, and user updates from one place.

10 Tools

Introduction

This guide walks you through connecting Chatforma to LangChain using the Composio tool router. By the end, you'll have a working Chatforma agent that can list your bots, review dialog messages, find user variables, browse segments, and send messages through natural language commands.

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

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

Also integrate Chatforma 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 Chatforma project to Composio
  • Create a Tool Router MCP session for Chatforma
  • Initialize an MCP client and retrieve Chatforma tools
  • Build a LangChain agent that can interact with Chatforma
  • 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 Chatforma MCP server, and what's possible with it?

The Chatforma MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Chatforma account. It provides structured and secure access to your bots, users, segments, dialogs, messages, and variables, so your agent can find audiences, review conversations, inspect user details, and send targeted messages on your behalf.

  • Bot and audience discovery: Have your agent list your bots, browse their users, and find people within a segment or with open dialogs.
  • Dialog review and replies: Let the agent read messages from a user's bot dialog and send a direct text reply into the conversation.
  • Targeted message dispatches: Direct your agent to send a text dispatch to one user or a selected audience segment.
  • Predefined message delivery: Instruct your agent to review configured bot messages and send the right one to an individual user or segment.
  • User variable lookup: Have your agent review available bot variables and retrieve the current value of a chosen variable for a specific user.

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 Chatforma 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 Chatforma 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: ['chatforma']
    }
);

const url = session.mcp.url;
What's happening:
  • We're creating a Tool Router session that gives your agent access to Chatforma 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 Chatforma tools as needed
8

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "chatforma-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 Chatforma MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • getTools() retrieves all available Chatforma 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 Chatforma 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 Chatforma 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: ['chatforma']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "chatforma-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 Chatforma 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 Chatforma 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 Chatforma action and event your agent gets out of the box.

Get User Variable

Get the current value of one bot variable for one bot user.

List Bots

List bots belonging to the connected Chatforma account so their IDs can be used in bot-scoped tools.

List Dialog Messages

Return one page of messages exchanged in a specific user's bot dialog, with a cursor for continuing the history.

List Predefined Messages

List predefined messages configured in a bot so a message ID can be selected for sending.

List Segments

List the user segments configured for a bot, including segment IDs needed for targeted sends.

List Users

List a bot's users, members of one segment, or users with open dialogs; choose the view that matches the task.

List Variables

List variables configured for a bot, including IDs and types needed to read user-specific values.

Send Dialog Message

Immediately send a user-visible text message into a specific user's bot dialog.

Send Predefined Message

Send a configured bot message to one user or dispatch it to a segment.

Send Text Dispatch

Create a user-visible text dispatch for one bot user or a segment.

FRAMEWORKS

How to build Chatforma MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Chatforma MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Chatforma MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Chatforma MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Chatforma MCP with Claude Cowork

Codex logo

Codex

Use Chatforma MCP with Codex

OpenClaw logo

OpenClaw

Use Chatforma MCP with OpenClaw

Hermes logo

Hermes

Use Chatforma MCP with Hermes

CLI logo

CLI

Use Chatforma MCP with CLI

Google ADK logo

Google ADK

Use Chatforma MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Chatforma MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Chatforma MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Chatforma MCP with LlamaIndex

CrewAI logo

CrewAI

Use Chatforma MCP with CrewAI

FAQ

Frequently asked questions

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

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

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