langgraph-a2a-mcp-example

# LangGraph A2A MCP Example This repository demonstrates how to build an [A2A Protocol](https://google.github.io/A2A/)-compatible application using [LangGraph](https://github.com/langchain-ai/langgraph) with Multi-Channel Protocol ([MCP](https://github.com/langchain-ai/langchain-mcp-adapters)) capabilities. ## Video Overview [](https://www.youtube.com/watch?v=1LlX45CMc84) ## Architecture ```mermaid graph TD A2AClient[A2A Client] -->|A2A Protocol| A2AAdapter[LangGraph A2A Adapter] A2AAdapter -->|LangGraph Server API| Graph[LangGraph Agent Graph] Graph -->|MCP| Tavily[Tavily Search] subgraph "This Repository" Graph Tavily end style A2AClient fill:#e1e8ed,stroke:#333,stroke-width:1.5px style A2AAdapter fill:#9aadc2,stroke:#333,stroke-width:1.5px style Graph fill:#c2dcf2,stroke:#333,stroke-width:1.5px style Tavily fill:#d3e0ea,stroke:#333,stroke-width:1.5px ``` ## Overview [LangGraph](https://github.com/langchain-ai/langgraph) is a library for building stateful, multi-actor applications with LLMs. This example shows how to create a LangGraph application that is compatible with the A2A (Agent-to-Agent) Protocol, enabling it to communicate with any A2A-compatible client. Key features: - **A2A Protocol Support**: Seamless integration with A2A-compatible clients through the adapter - **Multi-Channel Protocol (MCP)**: Support for structured communication between AI systems using [LangChain MCP Adapters](https://github.com/langchain-ai/langchain-mcp-adapters) - **Stateful Conversation**: Built-in support for persistent state, checkpoints, and multi-step interactions - **Human-in-the-Loop**: Capability for both autonomous operation and human collaboration ## Setup Requirements You'll need to set up two components: 1. **This LangGraph application** - The actual agent implementation 2. **LangGraph A2A Adapter** - Translates between A2A protocol and LangGraph API ([GitHub repo](http://github.com/n-sviridenko/langgraph-a2a-adapter)) ### 1. Setting up the LangGraph Application 1. Clone the repository: ```bash git clone <repository-url> cd langgraph-a2a-mcp-example ``` 2. Create and activate a virtual environment: ```bash python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate ``` 3. Install dependencies: ```bash pip install -r my_agent/requirements.txt ``` 4. Create a `.env` file with your API keys: ```bash cp .env.example .env ``` Then add your API keys for Anthropic, Tavily, and OpenAI. ### 2. Setting up the A2A Adapter 1. Clone the adapter repository: ```bash git clone https://github.com/n-sviridenko/langgraph-a2a-adapter.git cd langgraph-a2a-adapter ``` 2. Follow the installation instructions in the adapter's README. 3. Create a `.env` file with the following configuration: ```sh # LangGraph Connection LANGGRAPH_API_URL=http://localhost:2024 # A2A Server Configuration A2A_PUBLIC_BASE_URL=http://localhost:8000 A2A_PORT=8000 # Agent Card Configuration AGENT_NAME="Weather Assistant" AGENT_DESCRIPTION="An AI assistant that provides weather information, forecasts, and related climate data." AGENT_VERSION=1.0.0 AGENT_SKILLS='[{"id":"weather_info","name":"Weather Information","description":"Get current weather conditions for any location","examples":["What\'s the weather like in New York?","Is it raining in London right now?"]},{"id":"weather_forecast","name":"Weather Forecast","description":"Get weather forecasts for upcoming days","examples":["What\'s the forecast for Tokyo this weekend?","Will it snow in Chicago next week?"]}]' ``` ## Running the System 1. Start the LangGraph application: ```bash langgraph dev ``` 2. In a separate terminal, start the A2A adapter: ```bash cd langgraph-a2a-adapter python main.py ``` 3. Connect any A2A-compatible client to the adapter at `http://localhost:8000`. You can use the [Google A2A Demo Web App](https://github.com/google/A2A/blob/main/demo/README.md) for testing. ## A2A Integration The A2A adapter provides: - Agent discovery through standard A2A agent cards - Message exchange with assistants - Task management - Streaming responses - Push notifications for task updates ## Deployment In order to deploy this agent to LangGraph Cloud you will want to first fork this repo. After that, you can follow the instructions [here](https://langchain-ai.github.io/langgraph/cloud/) to deploy to LangGraph Cloud. For the A2A Adapter, see the deployment instructions in its repository.