tavily-a2a-agent

# 🧠 Tavily-A2A-Agent The Tavily-A2A-Agent is an AI agent built with the LangGraph framework, leveraging Agent-to-Agent (A2A) communication and Tavily’s advanced search capabilities via the Tavily MCP Server. It showcases how autonomous agents can collaborate using real-time web search integrated seamlessly into their workflows. ## 🔍 Key Features - LangGraph-powered multi-agent architecture: Compose complex, controllable agent workflows using LangGraph framework. - Agent-to-agent protocol (A2A): Enables agents to collaborate, delegate tasks, and exchange results in a modular and scalable manner. - Tavily MCP integration: Equipped with Tavily search, extract, map, and crawl tools, offering real-time, AI-optimized web search and data extraction. - Dynamic tool use: Agents automatically decide when to search, extract, or analyze based on task requirements, enabling deep and adaptable workflows 🚀 Why It Matters - Tavily’s search API is purpose-built for LLMs and agents, delivering AI-friendly, citation-rich results ideal for research or reasoning tasks. - When combined with LangGraph, which enables structured planning, tool invocation, state management, and agent collaboration, this agent showcases how to build truly intelligent, web-connected systems. ## 📓 Prerequisites - Python 3.12 or higher - UV - Python package manager - Gemini API Key - Tavily API Key ## 🛠️ Installation Instructions To install the project, follow these steps: create a .env in the project directory with the following content ``` GOOGLE_API_KEY="<Your-Gemini-key>" TAVILY_API_KEY="<Your-Tavily-key>" ``` ```bash git clone https://github.com/Major-wagh/tavily-a2a-agent.git cd tavily-a2a-agent # if .env file is not created export GOOGLE_API_KEY="<Your-Gemini-key>" export TAVILY_API_KEY="<Your-Tavily-key>" uv run . ``` ## 🚀 Usage Instructions ### Option 1: Using A2A Host Agent You can test the Tavily agent using the A2A host cli: ```bash # Clone the A2A samples if you haven't already git clone https://github.com/a2aproject/a2a-samples.git cd a2a-samples/samples/python/hosts/cli/ # run cli uv run . --agent http://localhost:10000 ``` This will start an interactive CLI that connects to your Tavily agent server. ### Option 2: Using Direct HTTP Requests You can also test using curl or any HTTP client: ```bash curl -X POST http://localhost:10000/ \ -H "Content-Type: application/json" \ -d '{ "jsonrpc": "2.0", "id": 1, "method": "message/send", "params": { "message": { "role": "user", "parts": [ { "kind": "text", "text": "Who is Cristiano Ronaldo?" } ], "messageId": "728bc428-5769-4dd6-a97a-96ba42b450b3" }, "metadata": {} } }' ``` ## 🤝 Contribution Guidelines To contribute to the project, follow these steps: 1. Fork the repository. 2. Create a new branch. 3. Make your changes. 4. Submit a pull request. ## 📄 License Information This project is licensed under the MIT License.