# How to integrate Strava MCP with Autogen ```json { "title": "How to integrate Strava MCP with Autogen", "toolkit": "Strava", "toolkit_slug": "strava", "framework": "AutoGen", "framework_slug": "autogen", "url": "https://composio.dev/toolkits/strava/framework/autogen", "markdown_url": "https://composio.dev/toolkits/strava/framework/autogen.md", "updated_at": "2026-05-06T08:30:02.740Z" } ``` ## Introduction This guide walks you through connecting Strava to AutoGen using the Composio tool router. By the end, you'll have a working Strava agent that can get your latest cycling activity stats, list all runs i logged this week, show your longest ride from last month through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Strava account through Composio's Strava MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Strava with - [ChatGPT](https://composio.dev/toolkits/strava/framework/chatgpt) - [OpenAI Agents SDK](https://composio.dev/toolkits/strava/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/strava/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/strava/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/strava/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/strava/framework/codex) - [Cursor](https://composio.dev/toolkits/strava/framework/cursor) - [VS Code](https://composio.dev/toolkits/strava/framework/vscode) - [OpenCode](https://composio.dev/toolkits/strava/framework/opencode) - [OpenClaw](https://composio.dev/toolkits/strava/framework/openclaw) - [Hermes](https://composio.dev/toolkits/strava/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/strava/framework/cli) - [Google ADK](https://composio.dev/toolkits/strava/framework/google-adk) - [LangChain](https://composio.dev/toolkits/strava/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/strava/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/strava/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/strava/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/strava/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 Strava - Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams - Configure an Autogen AssistantAgent that can call Strava tools - Run a live chat loop where you ask the agent to perform Strava 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 Strava MCP server, and what's possible with it? The Strava MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Strava account. It provides structured and secure access to your fitness data, so your agent can perform actions like fetching activities, analyzing stats, logging workouts, managing routes, and exploring your social fitness feed on your behalf. - Workout tracking and retrieval: Let your agent pull detailed records of your recent runs, rides, and other logged activities, complete with stats, maps, and performance data. - Fitness analytics and progress insights: Have your agent analyze your weekly or monthly trends, highlight PRs, and summarize progress toward your training goals. - Route exploration and management: Ask your agent to list, suggest, or organize your favorite routes and segments for upcoming workouts or challenges. - Social engagement automation: Enable your agent to fetch kudos, summarize comments, or surface activity highlights from friends and clubs in your Strava network. - Activity creation and editing: Allow your agent to log new activities, edit workout details, or update activity metadata for accurate record keeping. ## Supported Tools None listed. ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Strava MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Strava. Instead of manually wiring Strava 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 Strava 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 Strava 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 Strava 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 Strava 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 Strava session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["strava"] ) 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 Strava 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 Strava assistant agent with MCP tools agent = AssistantAgent( name="strava_assistant", description="An AI assistant that helps with Strava 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 Strava 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 Strava 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 Strava session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["strava"] ) 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 Strava assistant agent with MCP tools agent = AssistantAgent( name="strava_assistant", description="An AI assistant that helps with Strava 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 Strava 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 Strava 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 Strava, you can reuse the same structure for other MCP-enabled apps with minimal code changes. ## How to build Strava MCP Agent with another framework - [ChatGPT](https://composio.dev/toolkits/strava/framework/chatgpt) - [OpenAI Agents SDK](https://composio.dev/toolkits/strava/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/strava/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/strava/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/strava/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/strava/framework/codex) - [Cursor](https://composio.dev/toolkits/strava/framework/cursor) - [VS Code](https://composio.dev/toolkits/strava/framework/vscode) - [OpenCode](https://composio.dev/toolkits/strava/framework/opencode) - [OpenClaw](https://composio.dev/toolkits/strava/framework/openclaw) - [Hermes](https://composio.dev/toolkits/strava/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/strava/framework/cli) - [Google ADK](https://composio.dev/toolkits/strava/framework/google-adk) - [LangChain](https://composio.dev/toolkits/strava/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/strava/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/strava/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/strava/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/strava/framework/crew-ai) ## Related Toolkits - [Twitter](https://composio.dev/toolkits/twitter) - Twitter is a social media platform for sharing real-time updates, conversations, and news. Stay connected, informed, and engaged with communities worldwide. - [Instagram](https://composio.dev/toolkits/instagram) - Instagram is a social platform for sharing photos, videos, and stories with your audience. It helps brands and creators engage, grow, and analyze their online presence. - [Ayrshare](https://composio.dev/toolkits/ayrshare) - Ayrshare is a Social Media API for managing, automating, and analyzing posts across multiple platforms. It helps you streamline social media workflows and centralize analytics. - [Dotsimple](https://composio.dev/toolkits/dotsimple) - Dotsimple is a social media management platform for planning, creating, and publishing content. It helps teams boost their reach with AI-powered content generation and actionable analytics. - [Tiktok](https://composio.dev/toolkits/tiktok) - Tiktok is a short-form video platform for creating, sharing, and discovering viral content. It helps creators and brands reach massive audiences with creative tools and global social features. - [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. - [Google Calendar](https://composio.dev/toolkits/googlecalendar) - Google Calendar is a time management service for scheduling meetings, events, and reminders. It streamlines personal and team organization with integrated notifications and sharing options. - [Google Drive](https://composio.dev/toolkits/googledrive) - Google Drive is a cloud storage platform for uploading, sharing, and collaborating on files. It's perfect for keeping your documents accessible and organized across devices. - [Outlook](https://composio.dev/toolkits/outlook) - Outlook is Microsoft's email and calendaring platform for unified communications and scheduling. It helps users stay organized with powerful email, contacts, and calendar management. - [Google Sheets](https://composio.dev/toolkits/googlesheets) - Google Sheets is a cloud-based spreadsheet tool for real-time collaboration and data analysis. It lets teams work together from anywhere, updating information instantly. - [Supabase](https://composio.dev/toolkits/supabase) - Supabase is an open-source backend platform offering scalable Postgres databases, authentication, storage, and real-time APIs. It lets developers build modern apps without managing infrastructure. - [Composio](https://composio.dev/toolkits/composio) - Composio is an integration platform that connects AI agents with hundreds of business tools. It streamlines authentication and lets you trigger actions across services—no custom code needed. - [Notion](https://composio.dev/toolkits/notion) - Notion is a collaborative workspace for notes, docs, wikis, and tasks. It streamlines team knowledge, project tracking, and workflow customization in one place. - [Slack](https://composio.dev/toolkits/slack) - Slack is a channel-based messaging platform for teams and organizations. It helps people collaborate in real time, share files, and connect all their tools in one place. - [Airtable](https://composio.dev/toolkits/airtable) - Airtable combines the flexibility of spreadsheets with the power of a database for easy project and data management. Teams use Airtable to organize, track, and collaborate with custom views and automations. - [Google Docs](https://composio.dev/toolkits/googledocs) - Google Docs is a cloud-based word processor that enables document creation and real-time collaboration. Its seamless sharing and version history make team editing and content management a breeze. - [Google Super](https://composio.dev/toolkits/googlesuper) - Google Super is an all-in-one suite combining Gmail, Drive, Calendar, Sheets, Analytics, and more. It gives you a unified platform to manage your digital life, boosting productivity and organization. - [Hubspot](https://composio.dev/toolkits/hubspot) - HubSpot is an all-in-one marketing, sales, and customer service platform. It lets teams nurture leads, automate outreach, and track every customer interaction in one place. - [Codeinterpreter](https://composio.dev/toolkits/codeinterpreter) - Codeinterpreter is a Python-based coding environment with built-in data analysis and visualization. It lets you instantly run scripts, plot results, and prototype solutions inside supported platforms. - [Gong](https://composio.dev/toolkits/gong) - Gong is a platform for video meetings, call recording, and team collaboration. It helps teams capture conversations, analyze calls, and turn insights into action. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Strava MCP? With a standalone Strava MCP server, the agents and LLMs can only access a fixed set of Strava tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Strava 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 Strava tools. ### Can I manage the permissions and scopes for Strava while using Tool Router? Yes, absolutely. You can configure which Strava 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 Strava data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)