# How to integrate Chatfai MCP with Autogen ```json { "title": "How to integrate Chatfai MCP with Autogen", "toolkit": "Chatfai", "toolkit_slug": "chatfai", "framework": "AutoGen", "framework_slug": "autogen", "url": "https://composio.dev/toolkits/chatfai/framework/autogen", "markdown_url": "https://composio.dev/toolkits/chatfai/framework/autogen.md", "updated_at": "2026-05-12T10:06:06.306Z" } ``` ## Introduction This guide walks you through connecting Chatfai to AutoGen using the Composio tool router. By the end, you'll have a working Chatfai agent that can get details for harry potter character, show info about sherlock holmes character, fetch profile for spider-man character through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Chatfai account through Composio's Chatfai MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Chatfai with - [OpenAI Agents SDK](https://composio.dev/toolkits/chatfai/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/chatfai/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/chatfai/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/chatfai/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/chatfai/framework/codex) - [OpenClaw](https://composio.dev/toolkits/chatfai/framework/openclaw) - [Hermes](https://composio.dev/toolkits/chatfai/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/chatfai/framework/cli) - [Google ADK](https://composio.dev/toolkits/chatfai/framework/google-adk) - [LangChain](https://composio.dev/toolkits/chatfai/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/chatfai/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/chatfai/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/chatfai/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/chatfai/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 Chatfai - Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams - Configure an Autogen AssistantAgent that can call Chatfai tools - Run a live chat loop where you ask the agent to perform Chatfai 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 Chatfai MCP server, and what's possible with it? The Chatfai MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Chatfai account. It provides structured and secure access to the Chatfai platform, so your agent can browse, retrieve, and interact with AI-generated fictional characters from across media on your behalf. - Fetch character details by ID: Instantly retrieve comprehensive information on any public AI character by providing its unique identifier. - Access character backgrounds and attributes: Let your agent pull background stories, personality traits, and media origins for specific characters. - Enable character discovery: Use your agent to surface information about new or trending public characters in the Chatfai ecosystem. - Integrate character data into chat experiences: Seamlessly incorporate retrieved character info into custom conversational flows or creative projects. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `CHATFAI_CHATFAI_GET_PUBLIC_CHARACTER_BY_ID` | Get Public Character By ID | Tool to retrieve a public character by its ID. Use when you need to fetch details of a single public character by providing its unique ID. | | `CHATFAI_LIST_CHATFAI_CONVERSATIONS` | List Conversations | Tool to list conversations for the authenticated user. Use when you need to retrieve the user's chat conversations or verify authentication status. | | `CHATFAI_SEARCH_CHARACTERS` | Search Characters | Tool to search for public characters on ChatFAI by name or keyword. Use when you need to find characters matching a specific search query. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Chatfai MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Chatfai. Instead of manually wiring Chatfai 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 Chatfai 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 Chatfai 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 Chatfai 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 Chatfai 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 Chatfai session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["chatfai"] ) 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 Chatfai 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 Chatfai assistant agent with MCP tools agent = AssistantAgent( name="chatfai_assistant", description="An AI assistant that helps with Chatfai 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 Chatfai 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 Chatfai 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 Chatfai session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["chatfai"] ) 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 Chatfai assistant agent with MCP tools agent = AssistantAgent( name="chatfai_assistant", description="An AI assistant that helps with Chatfai 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 Chatfai 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 Chatfai 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 Chatfai, you can reuse the same structure for other MCP-enabled apps with minimal code changes. ## How to build Chatfai MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/chatfai/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/chatfai/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/chatfai/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/chatfai/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/chatfai/framework/codex) - [OpenClaw](https://composio.dev/toolkits/chatfai/framework/openclaw) - [Hermes](https://composio.dev/toolkits/chatfai/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/chatfai/framework/cli) - [Google ADK](https://composio.dev/toolkits/chatfai/framework/google-adk) - [LangChain](https://composio.dev/toolkits/chatfai/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/chatfai/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/chatfai/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/chatfai/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/chatfai/framework/crew-ai) ## Related Toolkits - [Youtube](https://composio.dev/toolkits/youtube) - YouTube is a leading video-sharing platform for uploading, streaming, and discovering content. It empowers creators and businesses to reach global audiences and monetize their work. - [Amara](https://composio.dev/toolkits/amara) - Amara is a collaborative platform for creating and managing subtitles and captions for videos. It helps make content accessible and multilingual for global audiences. - [Cats](https://composio.dev/toolkits/cats) - Cats is an API with a huge library of cat images, breed data, and cat facts. It makes finding adorable cat photos and trivia effortless for your apps and users. - [Cincopa](https://composio.dev/toolkits/cincopa) - Cincopa is a multimedia platform for uploading, managing, and customizing videos, images, and audio. It helps you deliver engaging media experiences with robust APIs and flexible integrations. - [Dungeon fighter online](https://composio.dev/toolkits/dungeon_fighter_online) - Dungeon Fighter Online (DFO) is an arcade-style, side-scrolling action RPG packed with dynamic combat and progression. Play solo or with friends to battle monsters, complete quests, and upgrade your characters. - [Elevenlabs](https://composio.dev/toolkits/elevenlabs) - Elevenlabs is an advanced AI voice generation platform for lifelike, multilingual speech synthesis. Perfect for creating natural voices for videos, apps, and business content in seconds. - [Elevenreader](https://composio.dev/toolkits/elevenreader) - Elevenreader is an AI-powered text-to-speech service by ElevenLabs that converts written content into lifelike audio. It enables fast, natural audio generation from any text. - [Epic games](https://composio.dev/toolkits/epic_games) - Epic Games is a leading video game publisher and digital storefront, known for Fortnite and Unreal Engine. It lets gamers access, manage, and purchase games all in one place. - [Fal.ai](https://composio.dev/toolkits/fal_ai) - Fal.ai is a generative media platform offering 600+ AI models for images, video, voice, and audio. Developers use Fal.ai for fast, scalable access to cutting-edge generative AI tools. - [Giphy](https://composio.dev/toolkits/giphy) - Giphy is the largest online library for searching and sharing GIFs and stickers. Instantly add vibrant animated content to your apps, chats, and workflows. - [Headout](https://composio.dev/toolkits/headout) - Headout is a global platform for booking travel experiences, tours, and entertainment. It helps users discover and secure activities at top destinations, all in one place. - [Imagekit io](https://composio.dev/toolkits/imagekit_io) - ImageKit.io is a cloud-based media management platform for image and video delivery. Instantly optimize, transform, and deliver visuals globally via a lightning-fast CDN. - [Listennotes](https://composio.dev/toolkits/listennotes) - Listennotes is a powerful podcast search engine with a massive global database. Discover, search, and curate podcasts from around the world in seconds. - [News api](https://composio.dev/toolkits/news_api) - News api is a REST API for searching and retrieving live news articles from across the web. Instantly access headlines, coverage, and breaking stories from thousands of sources. - [RAWG Video Games Database](https://composio.dev/toolkits/rawg_video_games_database) - RAWG Video Games Database is the largest video game discovery and info service. Instantly access comprehensive details, ratings, and release dates for thousands of games. - [Seat geek](https://composio.dev/toolkits/seat_geek) - SeatGeek is a live event platform offering APIs for concerts, sports, and theater data. Instantly access events, venues, and performers info for smarter ticketing and discovery. - [Shotstack](https://composio.dev/toolkits/shotstack) - Shotstack is a cloud platform for programmatically generating videos, images, and audio. Automate creative content production at scale with flexible RESTful APIs. - [Spotify](https://composio.dev/toolkits/spotify) - Spotify is a streaming service for music and podcasts with millions of tracks from artists worldwide. Enjoy personalized playlists, recommendations, and seamless listening across all your devices. - [Ticketmaster](https://composio.dev/toolkits/ticketmaster) - Ticketmaster is a global platform for event discovery, ticket sales, and live entertainment management. Get real-time access to events and streamline ticketing for fans and organizers. - [Gmail](https://composio.dev/toolkits/gmail) - Gmail is Google's email service with powerful spam protection, search, and G Suite integration. It keeps your inbox organized and makes communication fast and reliable. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Chatfai MCP? With a standalone Chatfai MCP server, the agents and LLMs can only access a fixed set of Chatfai tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Chatfai 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 Chatfai tools. ### Can I manage the permissions and scopes for Chatfai while using Tool Router? Yes, absolutely. You can configure which Chatfai 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 Chatfai data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) ยท [Composio docs](https://docs.composio.dev/llms.txt)