# How to integrate Wati MCP with CrewAI

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

## Introduction

This guide walks you through connecting Wati to CrewAI 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 CrewAI 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)
- [LangChain](https://composio.dev/toolkits/wati/framework/langchain)
- [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)

## TL;DR

Here's what you'll learn:
- Get a Composio API key and configure your Wati connection
- Set up CrewAI with an MCP enabled agent
- Create a Tool Router session or standalone MCP server for Wati
- Build a conversational loop where your agent can execute Wati operations

## What is CrewAI?

CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews.
Key features include:
- Agent Roles: Define specialized agents with specific goals and backstories
- Task Management: Create tasks with clear descriptions and expected outputs
- Crew Orchestration: Combine agents and tasks into collaborative workflows
- MCP Integration: Connect to external tools through Model Context Protocol

## 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

Before starting, make sure you have:
- Python 3.9 or higher
- A Composio account and API key
- A Wati connection authorized in Composio
- An OpenAI API key for the CrewAI LLM
- Basic familiarity with Python

### 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

**What's happening:**
- composio connects your agent to Wati via MCP
- crewai provides Agent, Task, Crew, and LLM primitives
- crewai-tools[mcp] includes MCP helpers
- python-dotenv loads environment variables from .env
```bash
pip install composio crewai crewai-tools[mcp] python-dotenv
```

### 3. Set up environment variables

Create a .env file in your project root.
What's happening:
- COMPOSIO_API_KEY authenticates with Composio
- USER_ID scopes the session to your account
- OPENAI_API_KEY lets CrewAI use your chosen OpenAI model
```bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here
```

### 4. Import dependencies

**What's happening:**
- CrewAI classes define agents and tasks, and run the workflow
- MCPServerHTTP connects the agent to an MCP endpoint
- Composio will give you a short lived Wati MCP URL
```python
import os
from composio import Composio
from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
import dotenv

dotenv.load_dotenv()

COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")
```

### 5. Create a Composio Tool Router session for Wati

**What's happening:**
- You create a Wati only session through Composio
- Composio returns an MCP HTTP URL that exposes Wati tools
```python
composio_client = Composio(api_key=COMPOSIO_API_KEY)
session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["wati"])

url = session.mcp.url
```

### 6. Initialize the MCP Server

**What's Happening:**
- Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication.
- MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format.
- Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools.
- Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops.
- Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries.
```python
server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users search the internet effectively",
        backstory="You are a helpful assistant with access to search tools.",
        tools=tools,
        verbose=False,
        max_iter=10,
    )
```

### 7. Create a CLI Chatloop and define the Crew

**What's Happening:**
- Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable.
- Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean.
- Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses.
- Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context.
- Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response.
- Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity.
```python
print("Chat started! Type 'exit' or 'quit' to end.\n")

conversation_context = ""

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_context += f"\nUser: {user_input}\n"
    print("\nAgent is thinking...\n")

    task = Task(
        description=(
            f"Conversation history:\n{conversation_context}\n\n"
            f"Current request: {user_input}"
        ),
        expected_output="A helpful response addressing the user's request",
        agent=agent,
    )

    crew = Crew(agents=[agent], tasks=[task], verbose=False)
    result = crew.kickoff()
    response = str(result)

    conversation_context += f"Agent: {response}\n"
    print(f"Agent: {response}\n")
```

## Complete Code

```python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter
from composio import Composio
from dotenv import load_dotenv
import os

load_dotenv()

GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not GOOGLE_API_KEY:
    raise ValueError("GOOGLE_API_KEY is not set in the environment.")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment.")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment.")

# Initialize Composio and create a session
composio = Composio(api_key=COMPOSIO_API_KEY)
session = composio.create(
    user_id=COMPOSIO_USER_ID,
    toolkits=["wati"],
)
url = session.mcp.url

# Configure LLM
llm = LLM(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY"),
)

server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users with internet searches",
        backstory="You are an expert assistant with access to Composio Search tools.",
        tools=tools,
        llm=llm,
        verbose=False,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end.\n")

    conversation_context = ""

    while True:
        user_input = input("You: ").strip()

        if user_input.lower() in ["exit", "quit", "bye"]:
            print("\nGoodbye!")
            break

        if not user_input:
            continue

        conversation_context += f"\nUser: {user_input}\n"
        print("\nAgent is thinking...\n")

        task = Task(
            description=(
                f"Conversation history:\n{conversation_context}\n\n"
                f"Current request: {user_input}"
            ),
            expected_output="A helpful response addressing the user's request",
            agent=agent,
        )

        crew = Crew(agents=[agent], tasks=[task], verbose=False)
        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")
```

## Conclusion

You now have a CrewAI agent connected to Wati through Composio's Tool Router. The agent can perform Wati operations through natural language commands.
Next steps:
- Add role-specific instructions to customize agent behavior
- Plug in more toolkits for multi-app workflows
- Chain tasks for complex multi-step operations

## 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)
- [LangChain](https://composio.dev/toolkits/wati/framework/langchain)
- [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)

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- [Convolo ai](https://composio.dev/toolkits/convolo_ai) - Convolo ai is an AI-powered communications platform for sales teams. It accelerates lead response and improves conversion rates by automating calls and integrating workflows.
- [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.
- [Findymail](https://composio.dev/toolkits/findymail) - Findymail is a B2B data provider offering verified email and phone contacts for sales prospecting. Enhance outreach with automated exports, email verification, and CRM enrichment.
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- [Fullenrich](https://composio.dev/toolkits/fullenrich) - FullEnrich is a B2B contact enrichment platform that aggregates emails and phone numbers from 15+ data vendors. Instantly find and verify lead contact data to boost your outreach.
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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 CrewAI?

Yes, you can. CrewAI 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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