Content
# 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.
