# How to integrate DeepWiki MCP MCP with Pydantic AI ```json { "title": "How to integrate DeepWiki MCP MCP with Pydantic AI", "toolkit": "DeepWiki MCP", "toolkit_slug": "deepwiki_mcp", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/deepwiki_mcp/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/deepwiki_mcp/framework/pydantic-ai.md", "updated_at": "2026-06-18T09:24:42.747Z" } ``` ## Introduction This guide walks you through connecting DeepWiki MCP to Pydantic AI using the Composio tool router. By the end, you'll have a working DeepWiki MCP agent that can explain this repository's authentication flow, summarize react source code structure, find api endpoints in fastapi repo through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a DeepWiki MCP account through Composio's DeepWiki MCP MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate DeepWiki MCP with - [OpenAI Agents SDK](https://composio.dev/toolkits/deepwiki_mcp/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/deepwiki_mcp/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/deepwiki_mcp/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/deepwiki_mcp/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/deepwiki_mcp/framework/codex) - [OpenClaw](https://composio.dev/toolkits/deepwiki_mcp/framework/openclaw) - [Hermes](https://composio.dev/toolkits/deepwiki_mcp/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/deepwiki_mcp/framework/cli) - [Google ADK](https://composio.dev/toolkits/deepwiki_mcp/framework/google-adk) - [LangChain](https://composio.dev/toolkits/deepwiki_mcp/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/deepwiki_mcp/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/deepwiki_mcp/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/deepwiki_mcp/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/deepwiki_mcp/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 DeepWiki MCP - 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 DeepWiki MCP 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 DeepWiki MCP MCP server, and what's possible with it? The DeepWiki MCP MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your DeepWiki MCP account. It provides structured and secure access so your agent can perform DeepWiki MCP operations on your behalf. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `DEEPWIKI_MCP_ASK_QUESTION` | Ask question | Ask any question about a GitHub repository and get an AI-powered, context-grounded response. Args: repoName: GitHub repository or list of repositories (max 10) in owner/repo format question: The question to ask about the repository | | `DEEPWIKI_MCP_READ_WIKI_CONTENTS` | Read wiki contents | View documentation about a GitHub repository. Args: repoName: GitHub repository in owner/repo format (e.g. "facebook/react") | | `DEEPWIKI_MCP_READ_WIKI_STRUCTURE` | Read wiki structure | Get a list of documentation topics for a GitHub repository. Args: repoName: GitHub repository in owner/repo format (e.g. "facebook/react") | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The DeepWiki MCP MCP server is an implementation of the Model Context Protocol that connects your AI agent to DeepWiki MCP. It provides structured and secure access so your agent can perform DeepWiki MCP 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 DeepWiki MCP - 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 DeepWiki MCP - MCPServerStreamableHTTP connects to the DeepWiki MCP 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 DeepWiki MCP 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 DeepWiki MCP composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["deepwiki_mcp"], ) 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 DeepWiki MCP endpoint - The agent uses GPT-5 to interpret user commands and perform DeepWiki MCP operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent deepwiki_mcp_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[deepwiki_mcp_mcp], instructions=( "You are a DeepWiki MCP assistant. Use DeepWiki MCP 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 - DeepWiki MCP 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 DeepWiki MCP.\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 DeepWiki MCP composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["deepwiki_mcp"], ) 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 deepwiki_mcp_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[deepwiki_mcp_mcp], instructions=( "You are a DeepWiki MCP assistant. Use DeepWiki MCP 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 DeepWiki MCP.\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 DeepWiki MCP through Composio's Tool Router. With this setup, your agent can perform real DeepWiki MCP 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 + DeepWiki MCP 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 DeepWiki MCP MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/deepwiki_mcp/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/deepwiki_mcp/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/deepwiki_mcp/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/deepwiki_mcp/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/deepwiki_mcp/framework/codex) - [OpenClaw](https://composio.dev/toolkits/deepwiki_mcp/framework/openclaw) - [Hermes](https://composio.dev/toolkits/deepwiki_mcp/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/deepwiki_mcp/framework/cli) - [Google ADK](https://composio.dev/toolkits/deepwiki_mcp/framework/google-adk) - [LangChain](https://composio.dev/toolkits/deepwiki_mcp/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/deepwiki_mcp/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/deepwiki_mcp/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/deepwiki_mcp/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/deepwiki_mcp/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. - [Codeinterpreter](https://composio.dev/toolkits/codeinterpreter) - Codeinterpreter is a Python-based coding environment with built-in data analysis and visualization. It lets you instantly run scripts, plot results, and prototype solutions inside supported platforms. - [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. - [1password](https://composio.dev/toolkits/_1password) - 1Password is a password manager and digital vault for storing logins, secrets, notes, and secure documents. It helps individuals and teams protect credentials, share access safely, and reduce password risk. - [Ably](https://composio.dev/toolkits/ably) - Ably is a real-time messaging platform for live chat and data sync in modern apps. 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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. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and DeepWiki MCP MCP? With a standalone DeepWiki MCP MCP server, the agents and LLMs can only access a fixed set of DeepWiki MCP tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from DeepWiki MCP 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 DeepWiki MCP tools. ### Can I manage the permissions and scopes for DeepWiki MCP while using Tool Router? Yes, absolutely. You can configure which DeepWiki MCP 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 DeepWiki MCP data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) ยท [Composio docs](https://docs.composio.dev/llms.txt)