How to integrate Whautomate MCP with LangChain

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Introduction

This guide walks you through connecting Whautomate to LangChain using the Composio tool router. By the end, you'll have a working Whautomate agent that can add a new contact for follow-up, fetch all scheduled broadcasts this week, get chat messages for a specific contact, list all segments with more than 50 members through natural language commands.

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

The Whautomate MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Whautomate account. It provides structured and secure access to customer engagement resources, so your agent can manage contacts, schedule broadcasts, retrieve chat histories, and organize messaging segments automatically on your behalf.

  • Contact management and automation: Quickly add new contacts or retrieve lists of WhatsApp contacts to streamline customer engagement and outreach.
  • Broadcast scheduling and tracking: Instruct your agent to fetch, inspect, or get details on message broadcasts—including status tracking and filtering by date or type.
  • Chat history and message retrieval: Have your agent pull detailed chat messages for individual contacts, so you can review conversations, follow up intelligently, or analyze engagement history.
  • Segment and service organization: Effortlessly manage audience segments and services—fetching, deleting, or organizing them to keep your communication campaigns targeted and up-to-date.
  • Webhook and integration oversight: Retrieve all registered webhooks to monitor and audit external integrations, ensuring your automations stay connected and reliable.

Supported Tools & Triggers

Tools
Add ContactTool to add a new contact.
Delete SegmentTool to delete a specific segment.
Delete Service CategoryTool to delete a service category.
Get All WebhooksTool to retrieve all registered webhooks.
Get Broadcast By IDTool to retrieve a specific broadcast's details.
Get BroadcastsTool to retrieve a list of broadcasts.
Get ContactsTool to retrieve a list of contacts.
Get Messages of ContactTool to retrieve chat messages for a specific contact.
Get SegmentsTool to retrieve a list of segments.
Get Service By IdTool to retrieve details of a specific service by its unique id.
Get Service CategoriesTool to retrieve a list of service categories.
Get ServicesTool to retrieve a list of services with optional filters.
Get Staff Availability BlocksTool to retrieve a staff member's blocked time schedule over a date range.
Get Staff By IDTool to retrieve detailed information about a specific staff member.
Get StaffsTool to retrieve a list of staff members.
Update ServiceTool to update an existing whatsapp service.

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

Create a Tool Router session

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

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

Configure the agent with the MCP URL

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

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

OpenAI Agents SDK

OpenAI Agents SDK

Use Whautomate MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Whautomate MCP with Claude Agent SDK

Google ADK

Google ADK

Use Whautomate MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Whautomate MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Whautomate MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Whautomate MCP with LlamaIndex

CrewAI

CrewAI

Use Whautomate MCP with CrewAI

FAQ

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

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

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

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

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