# How to integrate Codeinterpreter MCP with Autogen ```json { "title": "How to integrate Codeinterpreter MCP with Autogen", "toolkit": "Codeinterpreter", "toolkit_slug": "codeinterpreter", "framework": "AutoGen", "framework_slug": "autogen", "url": "https://composio.dev/toolkits/codeinterpreter/framework/autogen", "markdown_url": "https://composio.dev/toolkits/codeinterpreter/framework/autogen.md", "updated_at": "2026-05-06T08:06:43.629Z" } ``` ## Introduction This guide walks you through connecting Codeinterpreter to AutoGen using the Composio tool router. By the end, you'll have a working Codeinterpreter agent that can run this python script and show output, generate a line chart from your csv file, upload a dataset and summarize key stats through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Codeinterpreter account through Composio's Codeinterpreter MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Codeinterpreter with - [OpenAI Agents SDK](https://composio.dev/toolkits/codeinterpreter/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/codeinterpreter/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/codeinterpreter/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/codeinterpreter/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/codeinterpreter/framework/codex) - [OpenClaw](https://composio.dev/toolkits/codeinterpreter/framework/openclaw) - [Hermes](https://composio.dev/toolkits/codeinterpreter/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/codeinterpreter/framework/cli) - [Google ADK](https://composio.dev/toolkits/codeinterpreter/framework/google-adk) - [LangChain](https://composio.dev/toolkits/codeinterpreter/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/codeinterpreter/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/codeinterpreter/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/codeinterpreter/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/codeinterpreter/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 Codeinterpreter - Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams - Configure an Autogen AssistantAgent that can call Codeinterpreter tools - Run a live chat loop where you ask the agent to perform Codeinterpreter 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 Codeinterpreter MCP server, and what's possible with it? The Codeinterpreter MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Codeinterpreter environment. It provides structured and secure access to interactive Python sandboxes, so your agent can run scripts, analyze data, visualize results, and manage files on your behalf. - On-demand code execution: Instantly execute Python code snippets, scripts, or notebooks and receive real-time output, including errors and logs. - Sandbox creation and management: Have your agent spin up isolated coding environments for running experiments, testing ideas, or working with data securely. - File upload and retrieval: Seamlessly upload datasets, scripts, or assets to the sandbox and fetch generated files, reports, or images for further analysis. - Terminal command automation: Direct your agent to run Linux shell commands inside the sandbox, enabling advanced automation and environment setup. - Data visualization and reporting: Generate charts, plots, and visual reports by executing code that saves outputs as files—perfect for data-driven tasks. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `CODEINTERPRETER_CREATE_SANDBOX` | Create Sandbox | Create a sandbox to execute python code in a jupyter notebook cell. this is useful for agents to communicate, execute code, see output, read files, write files, etc. it's like you own personal computer, but in the cloud. use /home/user folder to write/read files. | | `CODEINTERPRETER_EXECUTE_CODE` | Execute Code | Execute python code in a sandbox and return any result, stdout, stderr, and error. use /home/user folder to write/read files. try to not use plt.show() as the code is executed remotely. use files for image/chart output instead. | | `CODEINTERPRETER_GET_FILE_CMD` | Get File | Get a file from the sandbox and returns the file. the files should be read from /home/user folder. | | `CODEINTERPRETER_RUN_TERMINAL_CMD` | Run Terminal Command | Run a command in the terminal and returns the stdout, stderr, and error code. use /home/user folder to write/read files. | | `CODEINTERPRETER_UPLOAD_FILE_CMD` | Upload File | Upload a file to the sandbox environment. the files should be uploaded to the /home/user folder. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Codeinterpreter MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Codeinterpreter. Instead of manually wiring Codeinterpreter 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 Codeinterpreter 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 Codeinterpreter 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 Codeinterpreter 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 Codeinterpreter 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 Codeinterpreter session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["codeinterpreter"] ) 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 Codeinterpreter 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 Codeinterpreter assistant agent with MCP tools agent = AssistantAgent( name="codeinterpreter_assistant", description="An AI assistant that helps with Codeinterpreter 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 Codeinterpreter 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 Codeinterpreter 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 Codeinterpreter session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["codeinterpreter"] ) 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 Codeinterpreter assistant agent with MCP tools agent = AssistantAgent( name="codeinterpreter_assistant", description="An AI assistant that helps with Codeinterpreter 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 Codeinterpreter 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 Codeinterpreter 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 Codeinterpreter, you can reuse the same structure for other MCP-enabled apps with minimal code changes. ## How to build Codeinterpreter MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/codeinterpreter/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/codeinterpreter/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/codeinterpreter/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/codeinterpreter/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/codeinterpreter/framework/codex) - [OpenClaw](https://composio.dev/toolkits/codeinterpreter/framework/openclaw) - [Hermes](https://composio.dev/toolkits/codeinterpreter/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/codeinterpreter/framework/cli) - [Google ADK](https://composio.dev/toolkits/codeinterpreter/framework/google-adk) - [LangChain](https://composio.dev/toolkits/codeinterpreter/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/codeinterpreter/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/codeinterpreter/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/codeinterpreter/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/codeinterpreter/framework/crew-ai) ## Related Toolkits - [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. - [GitHub](https://composio.dev/toolkits/github) - GitHub is a code hosting platform for version control and collaborative software development. It streamlines project management, code review, and team workflows in one place. - [Ably](https://composio.dev/toolkits/ably) - Ably is a real-time messaging platform for live chat and data sync in modern apps. It offers global scale and rock-solid reliability for seamless, instant experiences. - [Abuselpdb](https://composio.dev/toolkits/abuselpdb) - Abuselpdb is a central database for reporting and checking IPs linked to malicious online activity. Use it to quickly identify and report suspicious or abusive IP addresses. - [Alchemy](https://composio.dev/toolkits/alchemy) - Alchemy is a blockchain development platform offering APIs and tools for Ethereum apps. 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It's designed for reliability and scalability so you can build faster, smarter applications without the integration headache. - [Appcircle](https://composio.dev/toolkits/appcircle) - Appcircle is an enterprise-grade mobile CI/CD platform for building, testing, and publishing mobile apps. It streamlines mobile DevOps so teams ship faster and with more confidence. - [Appdrag](https://composio.dev/toolkits/appdrag) - Appdrag is a cloud platform for building websites, APIs, and databases with drag-and-drop tools and code editing. It accelerates development and iteration by combining hosting, database management, and low-code features in one place. - [Appveyor](https://composio.dev/toolkits/appveyor) - AppVeyor is a cloud-based continuous integration service for building, testing, and deploying applications. It helps developers automate and streamline their software delivery pipelines. - [Backendless](https://composio.dev/toolkits/backendless) - Backendless is a backend-as-a-service platform for mobile and web apps, offering database, file storage, user authentication, and APIs. It helps developers ship scalable applications faster without managing server infrastructure. - [Baserow](https://composio.dev/toolkits/baserow) - Baserow is an open-source no-code database platform for building collaborative data apps. It makes it easy for teams to organize data and automate workflows without writing code. - [Bench](https://composio.dev/toolkits/bench) - Bench is a benchmarking tool for automated performance measurement and analysis. It helps you quickly evaluate, compare, and track your systems or workflows. - [Better stack](https://composio.dev/toolkits/better_stack) - Better Stack is a monitoring, logging, and incident management solution for apps and services. It helps teams ensure application reliability and performance with real-time insights. - [Bitbucket](https://composio.dev/toolkits/bitbucket) - Bitbucket is a Git-based code hosting and collaboration platform for teams. It enables secure repository management and streamlined code reviews. - [Blazemeter](https://composio.dev/toolkits/blazemeter) - Blazemeter is a continuous testing platform for web and mobile app performance. It empowers teams to automate and analyze large-scale tests with ease. - [Blocknative](https://composio.dev/toolkits/blocknative) - Blocknative delivers real-time mempool monitoring and transaction management for public blockchains. Instantly track pending transactions and optimize blockchain interactions with live data. - [Bolt iot](https://composio.dev/toolkits/bolt_iot) - Bolt IoT is a platform for building and managing IoT projects with cloud-based device control and monitoring. It makes connecting sensors and actuators to the internet seamless for automation and data insights. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Codeinterpreter MCP? With a standalone Codeinterpreter MCP server, the agents and LLMs can only access a fixed set of Codeinterpreter tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Codeinterpreter 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 Codeinterpreter tools. ### Can I manage the permissions and scopes for Codeinterpreter while using Tool Router? Yes, absolutely. You can configure which Codeinterpreter 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 Codeinterpreter data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)