# How to integrate Whautomate MCP with LangChain

```json
{
  "title": "How to integrate Whautomate MCP with LangChain",
  "toolkit": "Whautomate",
  "toolkit_slug": "whautomate",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/whautomate/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/whautomate/framework/langchain.md",
  "updated_at": "2026-05-12T10:30:20.623Z"
}
```

## 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 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.

## Also integrate Whautomate with

- [OpenAI Agents SDK](https://composio.dev/toolkits/whautomate/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/whautomate/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/whautomate/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/whautomate/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/whautomate/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/whautomate/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/whautomate/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/whautomate/framework/cli)
- [Google ADK](https://composio.dev/toolkits/whautomate/framework/google-adk)
- [Vercel AI SDK](https://composio.dev/toolkits/whautomate/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/whautomate/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/whautomate/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/whautomate/framework/crew-ai)

## 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

| Tool slug | Name | Description |
|---|---|---|
| `WHAUTOMATE_ADD_CONTACT` | Add Contact | Tool to add a new contact. Use when you need to programmatically create contacts via the API. |
| `WHAUTOMATE_DELETE_SEGMENT` | Delete Segment | Tool to delete a specific segment. Use when you need to remove a segment by its ID. Use after confirming you have the correct segmentId. |
| `WHAUTOMATE_DELETE_SERVICE_CATEGORY` | Delete Service Category | Tool to delete a service category. Use when you need to remove a specific service category by its ID. Use after confirming you have the correct serviceCategoryId. |
| `WHAUTOMATE_GET_ACCOUNT_INFO` | Get Account Info | Tool to retrieve account information for the authenticated user. Use when you need to fetch account details like account name and owner email. |
| `WHAUTOMATE_GET_ALL_WEBHOOKS` | Get All Webhooks | Tool to retrieve all registered webhooks. Use after authenticating to audit or inspect existing webhook subscriptions. |
| `WHAUTOMATE_GET_BROADCAST_BY_ID` | Get Broadcast By ID | Tool to retrieve a specific broadcast's details. Use when you have a broadcast ID and need its metadata. Use after confirming the broadcastId is valid, as this returns detailed broadcast data. |
| `WHAUTOMATE_GET_BROADCASTS` | Get Broadcasts | Tool to retrieve a list of broadcasts. Use when you need to fetch broadcasts with optional filters such as status or date ranges. Example: "Get all scheduled broadcasts after 2023-01-01T00:00:00Z." |
| `WHAUTOMATE_GET_CONTACTS` | Get Contacts | Tool to retrieve a list of contacts. Use when you need to fetch contacts from WhatsApp with optional pagination and filters. |
| `WHAUTOMATE_GET_MESSAGES_OF_CONTACT` | Get Messages of Contact | Tool to retrieve chat messages for a specific contact. Use after providing a valid contactId. Supports pagination and optional date filtering. |
| `WHAUTOMATE_GET_SEGMENTS` | Get Segments | Tool to retrieve a list of segments. Use when you need to fetch segments with optional name filtering and pagination. Segments can be used to organize clients and contacts into groups based on various criteria. |
| `WHAUTOMATE_GET_SERVICE_BY_ID` | Get Service By Id | Tool to retrieve details of a specific service by its unique ID. Use when you need comprehensive service information. |
| `WHAUTOMATE_GET_SERVICE_CATEGORIES` | Get Service Categories | Tool to retrieve a list of service categories. Use when you need to list categories with optional pagination. |
| `WHAUTOMATE_GET_SERVICES` | Get Services | Tool to retrieve a list of services with optional filters. Use when you need to fetch available services for display or scheduling. |
| `WHAUTOMATE_GET_STAFF_AVAILABILITY_BLOCKS` | Get Staff Availability Blocks | Tool to retrieve a staff member's blocked time schedule over a date range. Use when you need to view unavailable slots after confirming staffId and desired date range. |
| `WHAUTOMATE_GET_STAFF_BY_ID` | Get Staff By ID | Tool to retrieve detailed information about a specific staff member. Use when you have a valid staff ID and need full profile details. Use after confirming staffId. |
| `WHAUTOMATE_GET_STAFFS` | Get Staffs | Tool to retrieve a list of staff members. Use when you need to list staff with optional pagination or search filters. |
| `WHAUTOMATE_UPDATE_SERVICE` | Update Service | Tool to update an existing Whautomate service. Use when you need to modify service attributes like name, pricing, duration, or active status. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Whautomate MCP server is an implementation of the Model Context Protocol that connects your AI agent to Whautomate. It provides structured and secure access so your agent can perform Whautomate operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

No description provided.

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install dependencies

No description provided.
```python
pip install composio-langchain langchain-mcp-adapters langchain python-dotenv
```

```typescript
npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters dotenv
```

### 3. Set up environment variables

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
```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
```

### 4. Import dependencies

No description provided.
```python
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()
```

```typescript
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();
```

### 5. Initialize Composio client

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
```python
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")
```

```typescript
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()
    });
```

### 6. Create a Tool Router session

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

url = session.mcp.url
```

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

const url = session.mcp.url;
```

### 7. Configure the agent with the MCP URL

No description provided.
```python
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)
```

```typescript
const client = new MultiServerMCPClient({
    "whautomate-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 });
```

### 8. Set up interactive chat interface

No description provided.
```python
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")
```

```typescript
let conversationHistory: any[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log("Ask any Whautomate 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);
    });
```

### 9. Run the application

No description provided.
```python
if __name__ == "__main__":
    asyncio.run(main())
```

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

## Complete Code

```python
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())
```

```typescript
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: ['whautomate']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "whautomate-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 Whautomate 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 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](https://composio.dev/toolkits/whautomate/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/whautomate/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/whautomate/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/whautomate/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/whautomate/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/whautomate/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/whautomate/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/whautomate/framework/cli)
- [Google ADK](https://composio.dev/toolkits/whautomate/framework/google-adk)
- [Vercel AI SDK](https://composio.dev/toolkits/whautomate/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/whautomate/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/whautomate/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/whautomate/framework/crew-ai)

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- [Emelia](https://composio.dev/toolkits/emelia) - Emelia is an all-in-one B2B prospecting platform for cold-email, LinkedIn outreach, and prospect research. It streamlines outbound campaigns so you can find, engage, and warm up leads faster.
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## Frequently Asked Questions

### 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.

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)