How to integrate Landbot MCP with LangChain

This guide walks you through connecting Landbot to LangChain using the Composio tool router. By the end, you'll have a working Landbot agent that can list all active bots in your account, find customer details by phone number, show all whatsapp message templates through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Landbot account through Composio's Landbot MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Landbot is a no-code chatbot builder for creating interactive conversations on web and messaging apps. It helps businesses automate customer engagement, lead generation, and support without code.

13 Tools

Introduction

This guide walks you through connecting Landbot to LangChain using the Composio tool router. By the end, you'll have a working Landbot agent that can list all active bots in your account, find customer details by phone number, show all whatsapp message templates through natural language commands.

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

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

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

The Landbot MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Landbot account. It provides structured and secure access to your Landbot bots, agents, channels, customers, and WhatsApp templates, so your agent can perform actions like listing bots, retrieving customer details, managing agents, and more on your behalf.

  • Bot management and discovery: Instantly list all your Landbot bots or remove unused ones, making it easy to oversee and streamline your chatbot fleet.
  • Customer insights and lookup: Retrieve customer records or pull up detailed profiles by phone number, letting your agent surface valuable user data for support or engagement.
  • Agent roster access: List all agents in your Landbot account, so your AI can help with team coordination or assign conversations based on up-to-date agent info.
  • Channel integration overview: Get a full inventory of all messaging channels connected to your Landbot account, including WhatsApp, to ensure your bots are reaching the right audiences.
  • WhatsApp template management: Fetch and review all available WhatsApp message templates, making it easy for your agent to suggest or automate template-driven outreach.

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 Landbot 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 Landbot 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: ['landbot']
    }
);

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

Configure the agent with the MCP URL

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

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

Delete Bot

Tool to delete a specific bot from your account.

Get Brand

Tool to retrieve your brand data including contact information and settings.

List Agents

Tool to retrieve a list of agents in your Landbot account.

List Bots

Tool to list all bots in your Landbot account.

List Channels

Tool to list all channels integrated with your account.

List Customers

Tool to list customers who have interacted with your bot.

List WhatsApp Templates

Tool to list all WhatsApp message templates available for the account.

Replace Agent

Tool to replace all data for a specific agent (full update).

Replace Brand

Tool to replace or update brand data with a full update (PUT operation).

Send Message

Tool to send a plain text outbound message to a Landbot customer.

Set Agent Status

Tool to change your agent status to online, offline, or busy.

Update Agent

Tool to update an agent's information in your Landbot account.

Update Brand

Tool to partially update your brand data in Landbot.

FRAMEWORKS

How to build Landbot MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Landbot MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Landbot MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Landbot MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Landbot MCP with Claude Cowork

Codex logo

Codex

Use Landbot MCP with Codex

OpenClaw logo

OpenClaw

Use Landbot MCP with OpenClaw

Hermes logo

Hermes

Use Landbot MCP with Hermes

CLI logo

CLI

Use Landbot MCP with CLI

Google ADK logo

Google ADK

Use Landbot MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Landbot MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Landbot MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Landbot MCP with LlamaIndex

CrewAI logo

CrewAI

Use Landbot MCP with CrewAI

FAQ

Frequently asked questions

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

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

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