# How to integrate Tavily MCP with Pydantic AI ```json { "title": "How to integrate Tavily MCP with Pydantic AI", "toolkit": "Tavily", "toolkit_slug": "tavily", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/tavily/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/tavily/framework/pydantic-ai.md", "updated_at": "2026-05-06T08:30:48.574Z" } ``` ## Introduction This guide walks you through connecting Tavily to Pydantic AI using the Composio tool router. By the end, you'll have a working Tavily agent that can find latest news about electric vehicles, search for recent ai research papers, get top articles on remote work trends through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Tavily account through Composio's Tavily MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Tavily with - [ChatGPT](https://composio.dev/toolkits/tavily/framework/chatgpt) - [OpenAI Agents SDK](https://composio.dev/toolkits/tavily/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/tavily/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/tavily/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/tavily/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/tavily/framework/codex) - [Cursor](https://composio.dev/toolkits/tavily/framework/cursor) - [VS Code](https://composio.dev/toolkits/tavily/framework/vscode) - [OpenCode](https://composio.dev/toolkits/tavily/framework/opencode) - [OpenClaw](https://composio.dev/toolkits/tavily/framework/openclaw) - [Hermes](https://composio.dev/toolkits/tavily/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/tavily/framework/cli) - [Google ADK](https://composio.dev/toolkits/tavily/framework/google-adk) - [LangChain](https://composio.dev/toolkits/tavily/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/tavily/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/tavily/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/tavily/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/tavily/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 Tavily - 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 Tavily 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 Tavily MCP server, and what's possible with it? The Tavily MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Tavily account. It provides structured and secure access to advanced web search and data retrieval, so your agent can perform actions like searching the web, filtering results, setting search parameters, and extracting relevant information from online sources on your behalf. - Custom web search with filters: Ask your agent to search the web with custom depth, result count, and domain restrictions for highly targeted results. - Document and content type discovery: Direct your agent to locate specific types of content—like articles, PDFs, or news—from across the internet. - Relevant data extraction: Have your agent pull and summarize key information from search results to save you time and effort. - Domain-specific research: Instruct your agent to confine searches to specific websites or sources for more trustworthy or relevant outcomes. - Efficient knowledge retrieval: Let your agent quickly surface facts, references, or recent developments from the web without manual browsing. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `TAVILY_TAVILY_SEARCH` | Tavily search | Use this to perform a web search via the tavily api; offers controls for search depth, content types, result count, and domain filtering. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Tavily MCP server is an implementation of the Model Context Protocol that connects your AI agent to Tavily. It provides structured and secure access so your agent can perform Tavily 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 Tavily - 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 Tavily - MCPServerStreamableHTTP connects to the Tavily 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 Tavily 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 Tavily composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["tavily"], ) 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 Tavily endpoint - The agent uses GPT-5 to interpret user commands and perform Tavily operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent tavily_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[tavily_mcp], instructions=( "You are a Tavily assistant. Use Tavily 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 - Tavily 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 Tavily.\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 Tavily composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["tavily"], ) 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 tavily_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[tavily_mcp], instructions=( "You are a Tavily assistant. Use Tavily 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 Tavily.\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 Tavily through Composio's Tool Router. With this setup, your agent can perform real Tavily 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 + Tavily 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 Tavily MCP Agent with another framework - [ChatGPT](https://composio.dev/toolkits/tavily/framework/chatgpt) - [OpenAI Agents SDK](https://composio.dev/toolkits/tavily/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/tavily/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/tavily/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/tavily/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/tavily/framework/codex) - [Cursor](https://composio.dev/toolkits/tavily/framework/cursor) - [VS Code](https://composio.dev/toolkits/tavily/framework/vscode) - [OpenCode](https://composio.dev/toolkits/tavily/framework/opencode) - [OpenClaw](https://composio.dev/toolkits/tavily/framework/openclaw) - [Hermes](https://composio.dev/toolkits/tavily/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/tavily/framework/cli) - [Google ADK](https://composio.dev/toolkits/tavily/framework/google-adk) - [LangChain](https://composio.dev/toolkits/tavily/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/tavily/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/tavily/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/tavily/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/tavily/framework/crew-ai) ## Related Toolkits - [Firecrawl](https://composio.dev/toolkits/firecrawl) - Firecrawl automates large-scale web crawling and data extraction. It helps organizations efficiently gather, index, and analyze content from online sources. - [Exa](https://composio.dev/toolkits/exa) - Exa is a data extraction and search platform for gathering and analyzing information from websites, APIs, or databases. It helps teams quickly surface insights and automate data-driven workflows. - [Serpapi](https://composio.dev/toolkits/serpapi) - SerpApi is a real-time API for structured search engine results. It lets you automate SERP data collection, parsing, and analysis for SEO and research. - [Peopledatalabs](https://composio.dev/toolkits/peopledatalabs) - Peopledatalabs delivers B2B data enrichment and identity resolution APIs. Supercharge your apps with accurate, up-to-date business and contact data. - [Snowflake](https://composio.dev/toolkits/snowflake) - Snowflake is a cloud data warehouse built for elastic scaling, secure data sharing, and fast SQL analytics across major clouds. - [Posthog](https://composio.dev/toolkits/posthog) - PostHog is an open-source analytics platform for tracking user interactions and product metrics. It helps teams refine features, analyze funnels, and reduce churn with actionable insights. - [Amplitude](https://composio.dev/toolkits/amplitude) - Amplitude is a digital analytics platform for product and behavioral data insights. It helps teams analyze user journeys and make data-driven decisions quickly. - [Bright Data MCP](https://composio.dev/toolkits/brightdata_mcp) - Bright Data MCP is an AI-powered web scraping and data collection platform. Instantly access public web data in real time with advanced scraping tools. - [Browseai](https://composio.dev/toolkits/browseai) - Browseai is a web automation and data extraction platform that turns any website into an API. It's perfect for monitoring websites and retrieving structured data without manual scraping. - [ClickHouse](https://composio.dev/toolkits/clickhouse) - ClickHouse is an open-source, column-oriented database for real-time analytics and big data processing using SQL. Its lightning-fast query performance makes it ideal for handling large datasets and delivering instant insights. - [Coinmarketcal](https://composio.dev/toolkits/coinmarketcal) - CoinMarketCal is a community-powered crypto calendar for upcoming events, announcements, and releases. It helps traders track market-moving developments and stay ahead in the crypto space. - [Control d](https://composio.dev/toolkits/control_d) - Control d is a customizable DNS filtering and traffic redirection platform. It helps you manage internet access, enforce policies, and monitor usage across devices and networks. - [Databox](https://composio.dev/toolkits/databox) - Databox is a business analytics platform that connects your data from any tool and device. It helps you track KPIs, build dashboards, and discover actionable insights. - [Databricks](https://composio.dev/toolkits/databricks) - Databricks is a unified analytics platform for big data and AI on the lakehouse architecture. It empowers data teams to collaborate, analyze, and build scalable solutions efficiently. - [Datagma](https://composio.dev/toolkits/datagma) - Datagma delivers data intelligence and analytics for business growth and market discovery. Get actionable market insights and track competitors to inform your strategy. - [Delighted](https://composio.dev/toolkits/delighted) - Delighted is a customer feedback platform based on the Net Promoter System®. It helps you quickly gather, track, and act on customer sentiment. - [Dovetail](https://composio.dev/toolkits/dovetail) - Dovetail is a research analysis platform for transcript review and insight generation. It helps teams code interviews, analyze feedback, and create actionable research summaries. - [Dub](https://composio.dev/toolkits/dub) - Dub is a short link management platform with analytics and API access. Use it to easily create, manage, and track branded short links for your business. - [Elasticsearch](https://composio.dev/toolkits/elasticsearch) - Elasticsearch is a distributed, RESTful search and analytics engine for all types of data. It delivers fast, scalable search and powerful analytics across massive datasets. - [Fireflies](https://composio.dev/toolkits/fireflies) - Fireflies.ai is an AI-powered meeting assistant that records, transcribes, and analyzes voice conversations. It helps teams capture call notes automatically and search or summarize meetings effortlessly. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Tavily MCP? With a standalone Tavily MCP server, the agents and LLMs can only access a fixed set of Tavily tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Tavily 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 Tavily tools. ### Can I manage the permissions and scopes for Tavily while using Tool Router? Yes, absolutely. You can configure which Tavily 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 Tavily data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)