# How to integrate Respond io MCP with Autogen ```json { "title": "How to integrate Respond io MCP with Autogen", "toolkit": "Respond io", "toolkit_slug": "respond_io", "framework": "AutoGen", "framework_slug": "autogen", "url": "https://composio.dev/toolkits/respond_io/framework/autogen", "markdown_url": "https://composio.dev/toolkits/respond_io/framework/autogen.md", "updated_at": "2026-05-12T10:24:00.473Z" } ``` ## Introduction This guide walks you through connecting Respond io to AutoGen using the Composio tool router. By the end, you'll have a working Respond io agent that can add internal note to latest conversation, create a new contact named alex kim, list all channels connected to workspace through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Respond io account through Composio's Respond io MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Respond io with - [OpenAI Agents SDK](https://composio.dev/toolkits/respond_io/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/respond_io/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/respond_io/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/respond_io/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/respond_io/framework/codex) - [OpenClaw](https://composio.dev/toolkits/respond_io/framework/openclaw) - [Hermes](https://composio.dev/toolkits/respond_io/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/respond_io/framework/cli) - [Google ADK](https://composio.dev/toolkits/respond_io/framework/google-adk) - [LangChain](https://composio.dev/toolkits/respond_io/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/respond_io/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/respond_io/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/respond_io/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/respond_io/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 Respond io - Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams - Configure an Autogen AssistantAgent that can call Respond io tools - Run a live chat loop where you ask the agent to perform Respond io 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 Respond io MCP server, and what's possible with it? The Respond io MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Respond io account. It provides structured and secure access to your customer conversation management platform, so your agent can perform actions like managing contacts, adding internal comments, creating and updating tags, and retrieving messages on your behalf. - Create and manage contacts: Easily have your agent add new customer contacts to your workspace, ensuring your CRM is always up to date. - Add internal comments to conversations: Let your agent insert internal notes into customer conversations, keeping your team informed and collaborating seamlessly. - Retrieve and organize channels: Direct your agent to list all messaging channels connected to your workspace, making it simple to audit or assign channels for support. - Tag and categorize conversations: Enable your agent to create new tags or update existing ones, helping you organize contacts and conversations for efficient follow-up. - Fetch specific messages: Ask your agent to pull up particular messages for review or context, streamlining support and follow-up actions. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `RESPOND_IO_CREATE_COMMENT` | Add internal comment to conversation | Tool to add a comment (internal note) to a contact's conversation. Use after verifying the contact identifier. | | `RESPOND_IO_CREATE_CONTACT` | Create Contact | Creates a new contact in the respond.io workspace with the specified details. The contact is identified by email, phone number, or contact ID. Supports adding profile information, language preferences, and custom fields that have been pre-configured in the workspace. | | `RESPOND_IO_CREATE_SPACE_TAG` | Create Space Tag | Creates a new tag in the Respond.io workspace for organizing and categorizing contacts and conversations. Tags help with segmentation, filtering, and workflow automation. Each tag must have a unique name within the workspace. | | `RESPOND_IO_GET_MESSAGE` | Get Message | Tool to retrieve a specific message. Use when you need the details of a message sent to or received from a contact. | | `RESPOND_IO_LIST_CHANNELS` | List channels | Tool to retrieve a list of channels connected to the workspace. Use when you need to enumerate all messaging channels with pagination support. | | `RESPOND_IO_LIST_USERS` | List users | Tool to retrieve a list of users in the workspace. Use when you need to fetch all workspace users for auditing or assignment. | | `RESPOND_IO_UPDATE_SPACE_TAG` | Update Space Tag | Updates an existing workspace tag by its current name. You can modify the tag's name, description, or emoji. Note: Color codes are not currently supported by the API and will be rejected if provided. At least one field besides currentName must be provided to update. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Respond io MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Respond io. Instead of manually wiring Respond io 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 Respond io 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 Respond io 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 Respond io 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 Respond io 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 Respond io session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["respond_io"] ) 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 Respond io 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 Respond io assistant agent with MCP tools agent = AssistantAgent( name="respond_io_assistant", description="An AI assistant that helps with Respond io 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 Respond io 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 Respond io 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 Respond io session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["respond_io"] ) 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 Respond io assistant agent with MCP tools agent = AssistantAgent( name="respond_io_assistant", description="An AI assistant that helps with Respond io 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 Respond io 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 Respond io 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 Respond io, you can reuse the same structure for other MCP-enabled apps with minimal code changes. ## How to build Respond io MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/respond_io/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/respond_io/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/respond_io/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/respond_io/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/respond_io/framework/codex) - [OpenClaw](https://composio.dev/toolkits/respond_io/framework/openclaw) - [Hermes](https://composio.dev/toolkits/respond_io/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/respond_io/framework/cli) - [Google ADK](https://composio.dev/toolkits/respond_io/framework/google-adk) - [LangChain](https://composio.dev/toolkits/respond_io/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/respond_io/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/respond_io/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/respond_io/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/respond_io/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. 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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 Respond io MCP? With a standalone Respond io MCP server, the agents and LLMs can only access a fixed set of Respond io tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Respond io 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 Respond io tools. ### Can I manage the permissions and scopes for Respond io while using Tool Router? Yes, absolutely. You can configure which Respond io 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 Respond io data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)