# How to integrate Openrouter MCP with Pydantic AI ```json { "title": "How to integrate Openrouter MCP with Pydantic AI", "toolkit": "Openrouter", "toolkit_slug": "openrouter", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/openrouter/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/openrouter/framework/pydantic-ai.md", "updated_at": "2026-05-12T10:20:56.035Z" } ``` ## Introduction This guide walks you through connecting Openrouter to Pydantic AI using the Composio tool router. By the end, you'll have a working Openrouter agent that can generate python code from this prompt, summarize this article using claude-3, list all available llama-3 model endpoints through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Openrouter account through Composio's Openrouter MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Openrouter with - [ChatGPT](https://composio.dev/toolkits/openrouter/framework/chatgpt) - [OpenAI Agents SDK](https://composio.dev/toolkits/openrouter/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/openrouter/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/openrouter/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/openrouter/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/openrouter/framework/codex) - [Cursor](https://composio.dev/toolkits/openrouter/framework/cursor) - [VS Code](https://composio.dev/toolkits/openrouter/framework/vscode) - [OpenCode](https://composio.dev/toolkits/openrouter/framework/opencode) - [OpenClaw](https://composio.dev/toolkits/openrouter/framework/openclaw) - [Hermes](https://composio.dev/toolkits/openrouter/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/openrouter/framework/cli) - [Google ADK](https://composio.dev/toolkits/openrouter/framework/google-adk) - [LangChain](https://composio.dev/toolkits/openrouter/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/openrouter/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/openrouter/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/openrouter/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/openrouter/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 Openrouter - 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 Openrouter 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 Openrouter MCP server, and what's possible with it? The Openrouter MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Openrouter account. It provides structured and secure access to a wide range of large language models, so your agent can generate completions, manage model access, check credits, and retrieve generation details seamlessly on your behalf. - Unified model completions: Let your agent generate chat-based or text completions using any model available through Openrouter, perfect for conversation or content creation tasks. - Model catalog and provider discovery: Ask your agent to list all available AI models and providers, helping you compare capabilities, endpoints, and pricing in real time. - Credit monitoring and usage tracking: Have your agent fetch your current API credit balance, so you always know your usage limits before starting new tasks. - Generation result retrieval: Direct your agent to pull detailed metadata for any previous generation, including token counts, costs, and latency for analysis or auditing. - Endpoint and configuration info: Empower your agent to fetch the latest model endpoints and supported parameters, making it easy to fine-tune routing and optimize performance. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `OPENROUTER_CREATE_CHAT_COMPLETION` | Create Chat Completion | Tool to generate a chat-style completion. Use after assembling messages and selecting a model. Supports streaming and function calls. Response format varies across models; use explicit prompt instructions to standardize output. Provider-level rate limits and moderation policies differ per model. | | `OPENROUTER_CREATE_COINBASE_CHARGE` | Create Coinbase Charge | Tool to create a Coinbase charge for crypto payment to add credits to your OpenRouter account. Use when you need to purchase credits using cryptocurrency. Returns calldata needed to fulfill the transaction on the specified blockchain. | | `OPENROUTER_CREATE_MESSAGE` | Create Message (Anthropic Format) | Tool to create a message using Anthropic Messages API format via OpenRouter. Use when you need Claude-compatible chat completion with support for text, images, PDFs, tools, and extended thinking. | | `OPENROUTER_GET_CREDITS` | Get Credits | Tool to get the current API credit balance for the authenticated user. Use before large or batch jobs to verify sufficient balance. A successful response may return total_credits=0, which confirms authentication but will cause all paid model generations to fail. Avoid polling this endpoint; call only as needed. | | `OPENROUTER_GET_CURRENT_KEY` | Get Current Key | Tool to get information about the currently authenticated API key. Use to check usage limits, spending, and key metadata. | | `OPENROUTER_GET_GENERATION` | Get Generation | Tool to retrieve a generation result by its unique ID. Use after a generation completes to fetch metadata like token counts, cost, and latency. | | `OPENROUTER_GET_MODELS_COUNT` | Get Models Count | Tool to get the total count of available models on OpenRouter. Use when you need to know how many models are available without fetching the full list. | | `OPENROUTER_LIST_AVAILABLE_MODELS` | List Available Models | Tool to list available models via OpenRouter API. Use after confirming authentication to fetch the model catalog. Use exact model IDs returned here in OPENROUTER_CREATE_CHAT_COMPLETION or OPENROUTER_CREATE_COMPLETION calls — hard-coded IDs may break when the catalog changes. Use exact author and slug values from this response as inputs to OPENROUTER_LIST_MODEL_ENDPOINTS. Models have varying capabilities (e.g., tools, reasoning); verify individual model capabilities before downstream use. Pricing and latency metadata may be null or approximate — handle missing values in routing logic. | | `OPENROUTER_LIST_EMBEDDING_MODELS` | List Embedding Models | Tool to list all available embeddings models via OpenRouter API. Returns a list of embeddings models with their properties including architecture, pricing, and capabilities. | | `OPENROUTER_LIST_MODEL_ENDPOINTS` | OpenRouter List Model Endpoints | Tool to list endpoints for a specific model. Use after specifying model author and slug to get endpoint details including pricing, context length, and supported parameters. Some metadata fields (e.g., latency, pricing) may be null or approximate; handle missing values in routing logic. | | `OPENROUTER_LIST_PROVIDERS` | OpenRouter List Providers | Tool to list all AI model providers available through the OpenRouter API. Use after authentication to retrieve available provider options for routing configuration. Providers differ in latency, context window sizes, and rate limits — switching providers affects these constraints. Newly added providers may not appear immediately due to catalog propagation delays. | | `OPENROUTER_LIST_USER_MODELS` | List User Models | Tool to list models filtered by user provider preferences, privacy settings, and guardrails. Use after authenticating to get models tailored to the user's configuration. | | `OPENROUTER_LIST_ZDR_ENDPOINTS` | OpenRouter List ZDR Endpoints | Tool to preview the impact of Zero Data Retention (ZDR) on the available endpoints. Use to see which model endpoints remain accessible when ZDR is enabled. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Openrouter MCP server is an implementation of the Model Context Protocol that connects your AI agent to Openrouter. It provides structured and secure access so your agent can perform Openrouter 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 Openrouter - 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 Openrouter - MCPServerStreamableHTTP connects to the Openrouter 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 Openrouter 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 Openrouter composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["openrouter"], ) 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 Openrouter endpoint - The agent uses GPT-5 to interpret user commands and perform Openrouter operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent openrouter_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[openrouter_mcp], instructions=( "You are a Openrouter assistant. Use Openrouter 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 - Openrouter 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 Openrouter.\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 Openrouter composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["openrouter"], ) 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 openrouter_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[openrouter_mcp], instructions=( "You are a Openrouter assistant. Use Openrouter 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 Openrouter.\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 Openrouter through Composio's Tool Router. With this setup, your agent can perform real Openrouter 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 + Openrouter 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 Openrouter MCP Agent with another framework - [ChatGPT](https://composio.dev/toolkits/openrouter/framework/chatgpt) - [OpenAI Agents SDK](https://composio.dev/toolkits/openrouter/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/openrouter/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/openrouter/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/openrouter/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/openrouter/framework/codex) - [Cursor](https://composio.dev/toolkits/openrouter/framework/cursor) - [VS Code](https://composio.dev/toolkits/openrouter/framework/vscode) - [OpenCode](https://composio.dev/toolkits/openrouter/framework/opencode) - [OpenClaw](https://composio.dev/toolkits/openrouter/framework/openclaw) - [Hermes](https://composio.dev/toolkits/openrouter/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/openrouter/framework/cli) - [Google ADK](https://composio.dev/toolkits/openrouter/framework/google-adk) - [LangChain](https://composio.dev/toolkits/openrouter/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/openrouter/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/openrouter/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/openrouter/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/openrouter/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 Openrouter MCP? With a standalone Openrouter MCP server, the agents and LLMs can only access a fixed set of Openrouter tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Openrouter 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 Openrouter tools. ### Can I manage the permissions and scopes for Openrouter while using Tool Router? Yes, absolutely. You can configure which Openrouter 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 Openrouter data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)