# How to integrate Codeinterpreter MCP with Pydantic AI ```json { "title": "How to integrate Codeinterpreter MCP with Pydantic AI", "toolkit": "Codeinterpreter", "toolkit_slug": "codeinterpreter", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/codeinterpreter/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/codeinterpreter/framework/pydantic-ai.md", "updated_at": "2026-05-06T08:06:43.629Z" } ``` ## Introduction This guide walks you through connecting Codeinterpreter to Pydantic AI 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 Pydantic AI 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: - How to set up your Composio API key and User ID - How to create a Composio Tool Router session for Codeinterpreter - How to attach an MCP Server to a Pydantic AI agent - How to stream responses and maintain chat history - How to build a simple REPL-style chat interface to test your Codeinterpreter workflows ## What is Pydantic AI? Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications. Key features include: - Type Safety: Built on Pydantic for automatic data validation - MCP Support: Native support for Model Context Protocol servers - Streaming: Built-in support for streaming responses - Async First: Designed for async/await patterns ## 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 agent to Codeinterpreter. It provides structured and secure access so your agent can perform Codeinterpreter operations on your behalf through a secure, permission-based interface. 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 Before starting, make sure you have: - Python 3.9 or higher - A Composio account with an active API key - Basic familiarity with Python and async programming ### 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 the required libraries. What's happening: - composio connects your agent to external SaaS tools like Codeinterpreter - pydantic-ai lets you create structured AI agents with tool support - python-dotenv loads your environment variables securely from a .env file ```bash pip install composio pydantic-ai python-dotenv ``` ### 3. Set up environment variables Create a .env file in your project root. What's happening: - COMPOSIO_API_KEY authenticates your agent to Composio's API - USER_ID associates your session with your account for secure tool access - OPENAI_API_KEY to access OpenAI LLMs ```bash COMPOSIO_API_KEY=your_composio_api_key_here USER_ID=your_user_id_here OPENAI_API_KEY=your_openai_api_key ``` ### 4. Import dependencies What's happening: - We load environment variables and import required modules - Composio manages connections to Codeinterpreter - MCPServerStreamableHTTP connects to the Codeinterpreter MCP server endpoint - Agent from Pydantic AI lets you define and run the AI assistant ```python import asyncio import os from dotenv import load_dotenv from composio import Composio from pydantic_ai import Agent from pydantic_ai.mcp import MCPServerStreamableHTTP load_dotenv() ``` ### 5. Create a Tool Router Session What's happening: - We're creating a Tool Router session that gives your agent access to Codeinterpreter tools - The create method takes the user ID and specifies which toolkits should be available - The returned session.mcp.url is the MCP server URL that your agent will use ```python async def main(): api_key = os.getenv("COMPOSIO_API_KEY") user_id = os.getenv("USER_ID") if not api_key or not user_id: raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment") # Create a Composio Tool Router session for Codeinterpreter composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["codeinterpreter"], ) url = session.mcp.url if not url: raise ValueError("Composio session did not return an MCP URL") ``` ### 6. Initialize the Pydantic AI Agent What's happening: - The MCP client connects to the Codeinterpreter endpoint - The agent uses GPT-5 to interpret user commands and perform Codeinterpreter operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent codeinterpreter_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[codeinterpreter_mcp], instructions=( "You are a Codeinterpreter assistant. Use Codeinterpreter tools to help users " "with their requests. Ask clarifying questions when needed." ), ) ``` ### 7. Build the chat interface What's happening: - The agent reads input from the terminal and streams its response - Codeinterpreter API calls happen automatically under the hood - The model keeps conversation history to maintain context across turns ```python # Simple REPL with message history history = [] print("Chat started! Type 'exit' or 'quit' to end.\n") print("Try asking the agent to help you with Codeinterpreter.\n") 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", flush=True) async with agent.run_stream(user_input, message_history=history) as stream_result: collected_text = "" async for chunk in stream_result.stream_output(): text_piece = None if isinstance(chunk, str): text_piece = chunk elif hasattr(chunk, "delta") and isinstance(chunk.delta, str): text_piece = chunk.delta elif hasattr(chunk, "text"): text_piece = chunk.text if text_piece: collected_text += text_piece result = stream_result print(f"Agent: {collected_text}\n") history = result.all_messages() ``` ### 8. Run the application What's happening: - The asyncio loop launches the agent and keeps it running until you exit ```python if __name__ == "__main__": asyncio.run(main()) ``` ## Complete Code ```python import asyncio import os from dotenv import load_dotenv from composio import Composio from pydantic_ai import Agent from pydantic_ai.mcp import MCPServerStreamableHTTP load_dotenv() async def main(): api_key = os.getenv("COMPOSIO_API_KEY") user_id = os.getenv("USER_ID") if not api_key or not user_id: raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment") # Create a Composio Tool Router session for Codeinterpreter composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["codeinterpreter"], ) url = session.mcp.url if not url: raise ValueError("Composio session did not return an MCP URL") # Attach the MCP server to a Pydantic AI Agent codeinterpreter_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[codeinterpreter_mcp], instructions=( "You are a Codeinterpreter assistant. Use Codeinterpreter tools to help users " "with their requests. Ask clarifying questions when needed." ), ) # Simple REPL with message history history = [] print("Chat started! Type 'exit' or 'quit' to end.\n") print("Try asking the agent to help you with Codeinterpreter.\n") 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", flush=True) async with agent.run_stream(user_input, message_history=history) as stream_result: collected_text = "" async for chunk in stream_result.stream_output(): text_piece = None if isinstance(chunk, str): text_piece = chunk elif hasattr(chunk, "delta") and isinstance(chunk.delta, str): text_piece = chunk.delta elif hasattr(chunk, "text"): text_piece = chunk.text if text_piece: collected_text += text_piece result = stream_result print(f"Agent: {collected_text}\n") history = result.all_messages() if __name__ == "__main__": asyncio.run(main()) ``` ## Conclusion You've built a Pydantic AI agent that can interact with Codeinterpreter through Composio's Tool Router. With this setup, your agent can perform real Codeinterpreter actions through natural language. You can extend this further by: - Adding other toolkits like Gmail, HubSpot, or Salesforce - Building a web-based chat interface around this agent - Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Codeinterpreter for workflow automation) This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations. ## 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 Pydantic AI? Yes, you can. Pydantic AI 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)