# How to integrate RunPod MCP with Pydantic AI ```json { "title": "How to integrate RunPod MCP with Pydantic AI", "toolkit": "RunPod", "toolkit_slug": "runpod", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/runpod/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/runpod/framework/pydantic-ai.md", "updated_at": "2026-03-29T06:48:32.592Z" } ``` ## Introduction This guide walks you through connecting RunPod to Pydantic AI using the Composio tool router. By the end, you'll have a working RunPod agent that can launch a new gpu pod for inference, get status of all active pods, stop a running pod with id 12345 through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a RunPod account through Composio's RunPod MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate RunPod with - [ChatGPT](https://composio.dev/toolkits/runpod/framework/chatgpt) - [Antigravity](https://composio.dev/toolkits/runpod/framework/antigravity) - [OpenAI Agents SDK](https://composio.dev/toolkits/runpod/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/runpod/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/runpod/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/runpod/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/runpod/framework/codex) - [Cursor](https://composio.dev/toolkits/runpod/framework/cursor) - [VS Code](https://composio.dev/toolkits/runpod/framework/vscode) - [OpenCode](https://composio.dev/toolkits/runpod/framework/opencode) - [OpenClaw](https://composio.dev/toolkits/runpod/framework/openclaw) - [Hermes](https://composio.dev/toolkits/runpod/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/runpod/framework/cli) - [Google ADK](https://composio.dev/toolkits/runpod/framework/google-adk) - [LangChain](https://composio.dev/toolkits/runpod/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/runpod/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/runpod/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/runpod/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/runpod/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 RunPod - 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 RunPod 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 RunPod MCP server, and what's possible with it? The RunPod MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your RunPod account. It provides structured and secure access so your agent can perform RunPod operations on your behalf. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `RUNPOD_CREATE_CLUSTER` | Create RunPod Cluster | Tool to create a new GPU cluster for multi-node distributed computing workloads on RunPod. Use when you need to deploy multiple pods with shared configuration for parallel processing, ML training, or HPC workloads. | | `RUNPOD_CREATE_SECRET` | Create Secret | Tool to create a new secure secret in RunPod for credential management. Use when you need to store sensitive values like API keys, passwords, or tokens that will be accessible in pods and endpoints via environment variables (RUNPOD_SECRET_). | | `RUNPOD_DELETE_REGISTRY_AUTH` | Delete Container Registry Authentication | Tool to delete container registry authentication from RunPod. Use when you need to remove stored registry credentials. | | `RUNPOD_DELETE_TEMPLATE` | Delete Template | Tool to remove a RunPod template via GraphQL mutation. Use when you need to delete a template that is no longer needed. The template must not be in use by any pods or assigned to any serverless endpoints, otherwise the operation will fail. | | `RUNPOD_GET_GPU_TYPES` | Get GPU Types | Tool to retrieve available GPU types and their specifications, pricing, and availability from RunPod. Use when you need to find GPU options for deployment. | | `RUNPOD_GET_AUTHENTICATED_USER_INFO` | Get authenticated user info | Retrieve basic information about the authenticated user including ID, email, and security settings. Use this to get the current user's ID, email address, terms of service status, and MFA settings. Note: Access to financial fields (balance, spending, etc.) requires elevated API key permissions. | | `RUNPOD_GET_POD_DETAILS` | Get Pod Details | Retrieve details of a specific RunPod pod by its unique pod ID. Returns pod configuration including GPU count, memory, cost, and status. Use when you need to check the current state or configuration of an existing pod. | | `RUNPOD_LIST_CPU_TYPES` | List CPU Types | Tool to retrieve available CPU types and their specifications from RunPod. Use when you need to view CPU options for provisioning pods or selecting hardware configurations. | | `RUNPOD_SAVE_SERVERLESS_ENDPOINT` | Save Serverless Endpoint | Tool to create or update a RunPod serverless endpoint with GPU configuration and scaling settings. Use when configuring new GPU-accelerated serverless endpoints or modifying existing endpoint parameters. Include 'id' parameter to update an existing endpoint, omit it to create a new one. | | `RUNPOD_SAVE_CONTAINER_REGISTRY_AUTHENTICATION` | Save Container Registry Authentication | Tool to save container registry authentication credentials for accessing private Docker images in RunPod. Use when you need to store credentials for a private container registry. | | `RUNPOD_SAVE_TEMPLATE` | Save Template | Tool to create a new RunPod template or update an existing one with container configuration. Use when you need to define reusable pod/serverless configurations with specific images, environment variables, and resource allocations. For serverless templates, always set volumeInGb to 0. | | `RUNPOD_UPDATE_REGISTRY_AUTH` | Update Registry Auth | Tool to update existing container registry authentication credentials in RunPod. Use when you need to modify the username or password for an existing registry authentication. | | `RUNPOD_UPDATE_USER_SETTINGS` | Update User Settings | Tool to update current user settings (e.g., SSH public key) in RunPod. Use when you need to configure SSH access to pods by setting the user's SSH public key. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The RunPod MCP server is an implementation of the Model Context Protocol that connects your AI agent to RunPod. It provides structured and secure access so your agent can perform RunPod 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 RunPod - 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 RunPod - MCPServerStreamableHTTP connects to the RunPod 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 RunPod 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 RunPod composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["runpod"], ) 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 RunPod endpoint - The agent uses GPT-5 to interpret user commands and perform RunPod operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent runpod_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[runpod_mcp], instructions=( "You are a RunPod assistant. Use RunPod 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 - RunPod 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 RunPod.\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 RunPod composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["runpod"], ) 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 runpod_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[runpod_mcp], instructions=( "You are a RunPod assistant. Use RunPod 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 RunPod.\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 RunPod through Composio's Tool Router. With this setup, your agent can perform real RunPod 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 + RunPod 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 RunPod MCP Agent with another framework - [ChatGPT](https://composio.dev/toolkits/runpod/framework/chatgpt) - [Antigravity](https://composio.dev/toolkits/runpod/framework/antigravity) - [OpenAI Agents SDK](https://composio.dev/toolkits/runpod/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/runpod/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/runpod/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/runpod/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/runpod/framework/codex) - [Cursor](https://composio.dev/toolkits/runpod/framework/cursor) - [VS Code](https://composio.dev/toolkits/runpod/framework/vscode) - [OpenCode](https://composio.dev/toolkits/runpod/framework/opencode) - [OpenClaw](https://composio.dev/toolkits/runpod/framework/openclaw) - [Hermes](https://composio.dev/toolkits/runpod/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/runpod/framework/cli) - [Google ADK](https://composio.dev/toolkits/runpod/framework/google-adk) - [LangChain](https://composio.dev/toolkits/runpod/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/runpod/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/runpod/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/runpod/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/runpod/framework/crew-ai) ## Related Toolkits - [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. - [Composio search](https://composio.dev/toolkits/composio_search) - Composio search is a unified web search toolkit spanning travel, e-commerce, news, financial markets, images, and more. It lets you and your apps tap into up-to-date web data from a single, easy-to-integrate service. - [Perplexityai](https://composio.dev/toolkits/perplexityai) - Perplexityai delivers natural, conversational AI models for generating human-like text. Instantly get context-aware, high-quality responses for chat, search, or complex workflows. - [Browser tool](https://composio.dev/toolkits/browser_tool) - Browser tool is a virtual browser integration that lets AI agents interact with the web programmatically. It enables automated browsing, scraping, and action-taking from any AI workflow. - [Ai ml api](https://composio.dev/toolkits/ai_ml_api) - Ai ml api is a suite of AI/ML models for natural language and image tasks. It provides fast, scalable access to advanced AI capabilities for your apps and workflows. - [Aivoov](https://composio.dev/toolkits/aivoov) - Aivoov is an AI-powered text-to-speech platform offering 1,000+ voices in over 150 languages. Instantly turn written content into natural, human-like audio for any application. - [All images ai](https://composio.dev/toolkits/all_images_ai) - All-Images.ai is an AI-powered image generation and management platform. It helps you create, search, and organize images effortlessly with advanced AI capabilities. - [Anthropic administrator](https://composio.dev/toolkits/anthropic_administrator) - Anthropic administrator is an API for managing Anthropic organizational resources like members, workspaces, and API keys. It helps you automate admin tasks and streamline resource management across your Anthropic organization. - [Api labz](https://composio.dev/toolkits/api_labz) - Api labz is a platform offering a suite of AI-driven APIs and workflow tools. It helps developers automate tasks and build smarter, more efficient applications. - [Apipie ai](https://composio.dev/toolkits/apipie_ai) - Apipie ai is an AI model aggregator offering a single API for accessing top AI models from multiple providers. It helps developers build cost-efficient, latency-optimized AI solutions without juggling multiple integrations. - [Astica ai](https://composio.dev/toolkits/astica_ai) - Astica ai provides APIs for computer vision, NLP, and voice synthesis. Integrate advanced AI features into your app with a single API key. - [Bigml](https://composio.dev/toolkits/bigml) - BigML is a machine learning platform that lets you build, train, and deploy predictive models from your data. Its intuitive interface and robust API make machine learning accessible and efficient. - [Botbaba](https://composio.dev/toolkits/botbaba) - Botbaba is a platform for building, managing, and deploying conversational AI chatbots across messaging channels. It streamlines chatbot automation, making it easier to integrate AI into customer interactions. - [Botpress](https://composio.dev/toolkits/botpress) - Botpress is an open-source platform for building, deploying, and managing chatbots. It helps teams automate conversations and deliver rich, interactive messaging experiences. - [Chatbotkit](https://composio.dev/toolkits/chatbotkit) - Chatbotkit is a platform for building and managing AI-powered chatbots using robust APIs and SDKs. It lets you easily add conversational AI to your apps for better user engagement. - [Cody](https://composio.dev/toolkits/cody) - Cody is an AI assistant built for businesses, trained on your company's knowledge and data. It delivers instant answers and insights, tailored for your team. - [Context7 MCP](https://composio.dev/toolkits/context7_mcp) - Context7 MCP delivers live, version-specific code docs and examples right from the source. It helps developers and AI agents instantly retrieve authoritative programming info—no more out-of-date docs. - [Customgpt](https://composio.dev/toolkits/customgpt) - CustomGPT.ai lets you build and deploy chatbots tailored to your own data and business needs. Get precise and context-aware AI conversations without writing code. - [Datarobot](https://composio.dev/toolkits/datarobot) - Datarobot is a machine learning platform that automates model development, deployment, and monitoring. It empowers organizations to quickly gain predictive insights from large datasets. - [Deepgram](https://composio.dev/toolkits/deepgram) - Deepgram is an AI-powered speech recognition platform for accurate audio transcription and understanding. It enables fast, scalable speech-to-text with advanced audio intelligence features. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and RunPod MCP? With a standalone RunPod MCP server, the agents and LLMs can only access a fixed set of RunPod tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from RunPod 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 RunPod tools. ### Can I manage the permissions and scopes for RunPod while using Tool Router? Yes, absolutely. You can configure which RunPod 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 RunPod data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)