# How to integrate Wati MCP with Autogen ```json { "title": "How to integrate Wati MCP with Autogen", "toolkit": "Wati", "toolkit_slug": "wati", "framework": "AutoGen", "framework_slug": "autogen", "url": "https://composio.dev/toolkits/wati/framework/autogen", "markdown_url": "https://composio.dev/toolkits/wati/framework/autogen.md", "updated_at": "2026-05-12T10:30:01.458Z" } ``` ## Introduction This guide walks you through connecting Wati to AutoGen 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 AutoGen 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) - [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 - Install the required dependencies for Autogen and Composio - Initialize Composio and create a Tool Router session for Wati - Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams - Configure an Autogen AssistantAgent that can call Wati tools - Run a live chat loop where you ask the agent to perform Wati operations ## What is AutoGen? Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows. Key features include: - Multi-Agent Systems: Build collaborative agent workflows - MCP Workbench: Native support for Model Context Protocol tools - Streaming HTTP: Connect to external services through streamable HTTP - AssistantAgent: Pre-built agent class for tool-using assistants ## 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 agents and assistants directly to Wati. Instead of manually wiring Wati APIs, OAuth, and scopes yourself, you get a structured, tool-based interface that an LLM can call safely. 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 You will need: - A Composio API key - An OpenAI API key (used by Autogen's OpenAIChatCompletionClient) - A Wati account you can connect to Composio - Some basic familiarity with Autogen and Python async ### 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 Install Composio, Autogen extensions, and dotenv. What's happening: - composio connects your agent to Wati via MCP - autogen-agentchat provides the AssistantAgent class - autogen-ext-openai provides the OpenAI model client - autogen-ext-tools provides MCP workbench support ```bash pip install composio python-dotenv pip install autogen-agentchat autogen-ext-openai autogen-ext-tools ``` ### 3. Set up environment variables Create a .env file in your project folder. What's happening: - COMPOSIO_API_KEY is required to talk to Composio - OPENAI_API_KEY is used by Autogen's OpenAI client - USER_ID is how Composio identifies which user's Wati connections to use ```bash COMPOSIO_API_KEY=your-composio-api-key OPENAI_API_KEY=your-openai-api-key USER_ID=your-user-identifier@example.com ``` ### 4. Import dependencies and create Tool Router session What's happening: - load_dotenv() reads your .env file - Composio(api_key=...) initializes the SDK - create(...) creates a Tool Router session that exposes Wati tools - session.mcp.url is the MCP endpoint that Autogen will connect to ```python import asyncio import os from dotenv import load_dotenv from composio import Composio from autogen_agentchat.agents import AssistantAgent from autogen_ext.models.openai import OpenAIChatCompletionClient from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams load_dotenv() async def main(): # Initialize Composio and create a Wati session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["wati"] ) url = session.mcp.url ``` ### 5. Configure MCP parameters for Autogen Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for. What's happening: - url points to the Tool Router MCP endpoint from Composio - timeout is the HTTP timeout for requests - sse_read_timeout controls how long to wait when streaming responses - terminate_on_close=True cleans up the MCP server process when the workbench is closed ```python # Configure MCP server parameters for Streamable HTTP server_params = StreamableHttpServerParams( url=url, timeout=30.0, sse_read_timeout=300.0, terminate_on_close=True, headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")} ) ``` ### 6. Create the model client and agent What's happening: - OpenAIChatCompletionClient wraps the OpenAI model for Autogen - McpWorkbench connects the agent to the MCP tools - AssistantAgent is configured with the Wati tools from the workbench ```python # Create model client model_client = OpenAIChatCompletionClient( model="gpt-5", api_key=os.getenv("OPENAI_API_KEY") ) # Use McpWorkbench as context manager async with McpWorkbench(server_params) as workbench: # Create Wati assistant agent with MCP tools agent = AssistantAgent( name="wati_assistant", description="An AI assistant that helps with Wati operations.", model_client=model_client, workbench=workbench, model_client_stream=True, max_tool_iterations=10 ) ``` ### 7. Run the interactive chat loop What's happening: - The script prompts you in a loop with You: - Autogen passes your input to the model, which decides which Wati tools to call via MCP - agent.run_stream(...) yields streaming messages as the agent thinks and calls tools - Typing exit, quit, or bye ends the loop ```python print("Chat started! Type 'exit' or 'quit' to end the conversation.\n") print("Ask any Wati related question or task to the agent.\n") # Conversation loop while True: user_input = input("You: ").strip() if user_input.lower() in ["exit", "quit", "bye"]: print("\nGoodbye!") break if not user_input: continue print("\nAgent is thinking...\n") # Run the agent with streaming try: response_text = "" async for message in agent.run_stream(task=user_input): if hasattr(message, "content") and message.content: response_text = message.content # Print the final response if response_text: print(f"Agent: {response_text}\n") else: print("Agent: I encountered an issue processing your request.\n") except Exception as e: print(f"Agent: Sorry, I encountered an error: {str(e)}\n") ``` ## Complete Code ```python import asyncio import os from dotenv import load_dotenv from composio import Composio from autogen_agentchat.agents import AssistantAgent from autogen_ext.models.openai import OpenAIChatCompletionClient from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams load_dotenv() async def main(): # Initialize Composio and create a Wati session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["wati"] ) url = session.mcp.url # Configure MCP server parameters for Streamable HTTP server_params = StreamableHttpServerParams( url=url, timeout=30.0, sse_read_timeout=300.0, terminate_on_close=True, headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")} ) # Create model client model_client = OpenAIChatCompletionClient( model="gpt-5", api_key=os.getenv("OPENAI_API_KEY") ) # Use McpWorkbench as context manager async with McpWorkbench(server_params) as workbench: # Create Wati assistant agent with MCP tools agent = AssistantAgent( name="wati_assistant", description="An AI assistant that helps with Wati operations.", model_client=model_client, workbench=workbench, model_client_stream=True, max_tool_iterations=10 ) print("Chat started! Type 'exit' or 'quit' to end the conversation.\n") print("Ask any Wati related question or task to the agent.\n") # Conversation loop while True: user_input = input("You: ").strip() if user_input.lower() in ['exit', 'quit', 'bye']: print("\nGoodbye!") break if not user_input: continue print("\nAgent is thinking...\n") # Run the agent with streaming try: response_text = "" async for message in agent.run_stream(task=user_input): if hasattr(message, 'content') and message.content: response_text = message.content # Print the final response if response_text: print(f"Agent: {response_text}\n") else: print("Agent: I encountered an issue processing your request.\n") except Exception as e: print(f"Agent: Sorry, I encountered an error: {str(e)}\n") if __name__ == "__main__": asyncio.run(main()) ``` ## Conclusion You now have an Autogen assistant wired into Wati through Composio's Tool Router and MCP. From here you can: - Add more toolkits to the toolkits list, for example notion or hubspot - Refine the agent description to point it at specific workflows - Wrap this script behind a UI, Slack bot, or internal tool Once the pattern is clear for Wati, you can reuse the same structure for other MCP-enabled apps with minimal code changes. ## 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) - [CrewAI](https://composio.dev/toolkits/wati/framework/crew-ai) ## Related Toolkits - [Aeroleads](https://composio.dev/toolkits/aeroleads) - Aeroleads is a B2B lead generation platform for finding business emails and phone numbers. Grow your sales pipeline faster with powerful prospecting tools. - [Autobound](https://composio.dev/toolkits/autobound) - Autobound is an AI-powered sales engagement platform that crafts hyper-personalized outreach and insights. It helps sales teams boost response rates and close more deals through tailored content and recommendations. - [Better proposals](https://composio.dev/toolkits/better_proposals) - Better Proposals is a web-based tool for crafting and sending professional proposals. It helps teams impress clients and close deals faster with slick, easy-to-use templates. - [Bidsketch](https://composio.dev/toolkits/bidsketch) - Bidsketch is a proposal software that helps businesses create professional proposals quickly and efficiently. It streamlines the proposal process, saving time while boosting client win rates. - [Bolna](https://composio.dev/toolkits/bolna) - Bolna is an AI platform for building conversational voice agents. 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Enhance outreach with automated exports, email verification, and CRM enrichment. - [Freshdesk](https://composio.dev/toolkits/freshdesk) - Freshdesk is customer support software with ticketing and automation tools. It helps teams streamline helpdesk operations for faster, better customer support. - [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. - [Gatherup](https://composio.dev/toolkits/gatherup) - GatherUp is a customer feedback and online review management platform. It helps businesses boost their reputation by streamlining how they collect and manage customer feedback. - [Getprospect](https://composio.dev/toolkits/getprospect) - Getprospect is a business email discovery tool with LinkedIn integration. Use it to quickly find and verify professional email addresses. ## 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 Autogen? Yes, you can. Autogen 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. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)