# How to integrate Piloterr MCP with Autogen ```json { "title": "How to integrate Piloterr MCP with Autogen", "toolkit": "Piloterr", "toolkit_slug": "piloterr", "framework": "AutoGen", "framework_slug": "autogen", "url": "https://composio.dev/toolkits/piloterr/framework/autogen", "markdown_url": "https://composio.dev/toolkits/piloterr/framework/autogen.md", "updated_at": "2026-05-12T10:21:58.411Z" } ``` ## Introduction This guide walks you through connecting Piloterr to AutoGen using the Composio tool router. By the end, you'll have a working Piloterr agent that can find trending laptops on bestbuy today, get full details for auchan product id 12345, search for organic snacks on auchan through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Piloterr account through Composio's Piloterr MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Piloterr with - [OpenAI Agents SDK](https://composio.dev/toolkits/piloterr/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/piloterr/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/piloterr/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/piloterr/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/piloterr/framework/codex) - [OpenClaw](https://composio.dev/toolkits/piloterr/framework/openclaw) - [Hermes](https://composio.dev/toolkits/piloterr/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/piloterr/framework/cli) - [Google ADK](https://composio.dev/toolkits/piloterr/framework/google-adk) - [LangChain](https://composio.dev/toolkits/piloterr/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/piloterr/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/piloterr/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/piloterr/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/piloterr/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 Piloterr - Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams - Configure an Autogen AssistantAgent that can call Piloterr tools - Run a live chat loop where you ask the agent to perform Piloterr 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 Piloterr MCP server, and what's possible with it? The Piloterr MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Piloterr account. It provides structured and secure access to a suite of powerful web data extraction APIs, so your agent can perform actions like searching products, retrieving detailed product info, and monitoring account usage on your behalf. - Product search on retail platforms: Direct your agent to search for products on sites like Auchan or Bestbuy by keyword, category, or filters, and receive curated product listings. - Retrieve detailed product information: Ask your agent to fetch comprehensive product details—including company info—using specific product IDs from supported platforms. - Monitor account usage and credits: Let your agent check your Piloterr account usage and remaining credits, so you always know how much data access you have left. - Automated product data workflows: Enable your agent to seamlessly combine product search and retrieval, powering advanced e-commerce, analytics, or market research tasks. - Multi-platform product integration: Effortlessly access and aggregate product data from multiple online stores to inform business decisions or drive automation. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `PILOTERR_AUCHAN_PRODUCT` | Auchan Product | Tool to retrieve detailed Auchan product information. Use when you have an Auchan product URL. | | `PILOTERR_AUCHAN_SEARCH` | Auchan Product Search | Tool to search for products on Auchan by query or search URL. Use when you need product listings and details from Auchan. | | `PILOTERR_GET_PRODUCT_INFO` | Piloterr Get Product Info | Tool to retrieve detailed product information from G2. Use when you need full product details including ratings, reviews, pricing, and company info. Accepts a product name or G2 URL as query. | | `PILOTERR_LIST_CHEWY_PRODUCTS` | List Chewy Products | Tool to find Chewy product categories by search query. Use when you need to retrieve Chewy product URLs and details based on product ID or title. | | `PILOTERR_LIST_ELECLERC_STORES` | List E.Leclerc Stores | Tool to find E.Leclerc store locations by search query. Use when you need to locate E.Leclerc stores in a specific area or city. | | `PILOTERR_LIST_GOOGLE_COUNTRIES` | List Google Countries | Tool to list available Google search countries. Use when you need to find country codes for Google search localization. | | `PILOTERR_LIST_GOOGLE_LANGUAGES` | List Google Languages | Tool to list available Google search languages filtered by query. Use when you need to find specific language codes for Google searches. | | `PILOTERR_LIST_GOOGLE_LOCATIONS` | List Google Locations | Tool to list available Google search locations via Piloterr API. Use when you need to find location identifiers for geographically-targeted searches. | | `PILOTERR_LIST_LEROY_MERLIN_PRODUCTS` | List Leroy Merlin Products | Tool to list Leroy Merlin product categories. Use when you need to search for Leroy Merlin products by name or identifier. | | `PILOTERR_LIST_LINKED_IN_INDUSTRIES` | List LinkedIn Industries | Tool to list LinkedIn industry codes from Piloterr API. Use when you need to find LinkedIn industry identifiers for targeting or classification. | | `PILOTERR_SEARCH` | Piloterr Google Search | Tool to perform Google web search via Piloterr API. Use when you need to search the web for information using Google. | | `PILOTERR_USAGE_GET` | Get Usage | Tool to get usage information and remaining credits for your Piloterr account. Use when you need to check account usage and credit details. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Piloterr MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Piloterr. Instead of manually wiring Piloterr 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 Piloterr 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 Piloterr 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 Piloterr 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 Piloterr 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 Piloterr session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["piloterr"] ) 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 Piloterr 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 Piloterr assistant agent with MCP tools agent = AssistantAgent( name="piloterr_assistant", description="An AI assistant that helps with Piloterr 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 Piloterr 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 Piloterr 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 Piloterr session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["piloterr"] ) 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 Piloterr assistant agent with MCP tools agent = AssistantAgent( name="piloterr_assistant", description="An AI assistant that helps with Piloterr 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 Piloterr 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 Piloterr 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 Piloterr, you can reuse the same structure for other MCP-enabled apps with minimal code changes. ## How to build Piloterr MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/piloterr/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/piloterr/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/piloterr/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/piloterr/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/piloterr/framework/codex) - [OpenClaw](https://composio.dev/toolkits/piloterr/framework/openclaw) - [Hermes](https://composio.dev/toolkits/piloterr/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/piloterr/framework/cli) - [Google ADK](https://composio.dev/toolkits/piloterr/framework/google-adk) - [LangChain](https://composio.dev/toolkits/piloterr/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/piloterr/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/piloterr/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/piloterr/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/piloterr/framework/crew-ai) ## Related Toolkits - [Excel](https://composio.dev/toolkits/excel) - Microsoft Excel is a robust spreadsheet application for organizing, analyzing, and visualizing data. 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It helps streamline how you gather, organize, and analyze responses. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Piloterr MCP? With a standalone Piloterr MCP server, the agents and LLMs can only access a fixed set of Piloterr tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Piloterr 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 Piloterr tools. ### Can I manage the permissions and scopes for Piloterr while using Tool Router? Yes, absolutely. You can configure which Piloterr 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 Piloterr data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)