# How to integrate Wati MCP with LangChain

```json
{
  "title": "How to integrate Wati MCP with LangChain",
  "toolkit": "Wati",
  "toolkit_slug": "wati",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/wati/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/wati/framework/langchain.md",
  "updated_at": "2026-05-12T10:30:01.458Z"
}
```

## Introduction

This guide walks you through connecting Wati to LangChain using the Composio tool router. By the end, you'll have a working Wati agent that can send session message to new lead, add customer contact from webform submission, update contact attributes after support chat through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Wati account through Composio's Wati MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Wati with

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

## TL;DR

Here's what you'll learn:
- Get and set up your OpenAI and Composio API keys
- Connect your Wati project to Composio
- Create a Tool Router MCP session for Wati
- Initialize an MCP client and retrieve Wati tools
- Build a LangChain agent that can interact with Wati
- 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 Wati MCP server, and what's possible with it?

The Wati MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Wati WhatsApp Business account. It provides structured and secure access to your business messaging, so your agent can add contacts, send WhatsApp messages, update contact details, and retrieve team information automatically on your behalf.
- Automated contact management: Have your agent add new WhatsApp contacts, ensuring customers are registered before any communication starts.
- Proactive messaging via WhatsApp: Empower your agent to send session messages to customers for support, marketing, or updates within the active session window.
- Contact attribute updates: Let your agent update existing contact information or custom attributes, so your customer data stays fresh and relevant.
- Team coordination and retrieval: Quickly fetch lists of your Wati teams, making it easy for your agent to understand and act on organizational structure.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `WATI_ADD_CONTACT` | Add Contact | Tool to add a new contact in WATI. Use when registering a customer's WhatsApp number before sending messages. |
| `WATI_GET_TEAMS` | Get Teams | Tool to retrieve a list of teams from WATI. Use after authenticating when you need to enumerate all available teams. |
| `WATI_SEND_SESSION_MESSAGE` | Send Session Message | Tool to send a session message to a specified WhatsApp number. Use when you need to deliver a free-form text within an active 24-hour session window. |
| `WATI_UPDATE_CHAT_STATUS` | Wati update chat status | Update the status of a chat/conversation in WATI Team Inbox. This action allows you to change the status of a customer's chat to help manage ongoing conversations. Available statuses: - OPEN: Active, two-way conversation - PENDING: Waiting for customer's response - SOLVED: Issue has been resolved - BLOCK: Prevent further communication with the contact |
| `WATI_UPDATE_CONTACT_ATTRIBUTES` | Update Contact Attributes | Tool to update attributes of an existing contact. Use after confirming the contact exists and you need to modify its custom attributes. |

## Supported Triggers

None listed.

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

The Wati MCP server is an implementation of the Model Context Protocol that connects your AI agent to Wati. It provides structured and secure access so your agent can perform Wati 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 Wati 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 Wati 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 Wati tools as needed
```python
# Create Tool Router session for Wati
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['wati']
)

url = session.mcp.url
```

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

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

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

No description provided.
```python
client = MultiServerMCPClient({
    "wati-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({
    "wati-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 Wati 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 Wati 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=['wati']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "wati-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 Wati 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: ['wati']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "wati-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 Wati 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 Wati 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 Wati MCP Agent with another framework

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

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- [Delighted](https://composio.dev/toolkits/delighted) - Delighted is a customer feedback platform based on the Net Promoter System®. It helps you quickly gather, track, and act on customer sentiment.
- [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 Wati MCP?

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

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

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

---
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