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# A2A LangGraph Agent
**Empower your applications with Agent-to-Agent (A2A) protocol support.**
A production-ready agent server built with [LangGraph](https://github.com/langgraph/langgraph) that implements the Agent-to-Agent (A2A) protocol. Perfect for building advanced, real-time AI workflows and integrating AI capabilities into your applications.
## 🚀 Features
- 🔄 A2A Protocol Support: Full implementation of the Agent-to-Agent protocol.
- 🧠 LangGraph-powered Agent: Flexible agent architecture built on LangGraph's React pattern.
- 🔍 Extensible Tools: Easy integration of custom tools like search, database queries, and more.
- ⚡ Real-Time Streaming: Stream responses for live updates and interactive flows.
- 🗄️ Database-backed State Management: Maintain conversation state across sessions.
- 🌐 FastAPI HTTP Server: Robust HTTP server with proper A2A protocol endpoints.
## 🔧 Quick Start
1. **Clone the repo**
```bash
git clone https://github.com/dmi3coder/a2a_template_langgraph.git
cd a2a_template_langgraph
```
2. **Install dependencies**
```bash
pip install -e .
# or with uv
uv install
```
3. **Set up PostgreSQL**
Use the included Docker Compose file to start a PostgreSQL instance:
```bash
docker-compose up -d
```
4. **Configure**
Create a `.env` file:
```env
OPENAI_API_KEY=your_openai_api_key
HOST=0.0.0.0 # Optional, defaults to 0.0.0.0
PORT=10000 # Optional, defaults to 10000
OPENAI_MODEL=o4-mini # Optional, defaults to o4-mini
```
5. **Run the server**
```bash
python main.py
```
Your agent server will be live on `http://{HOST}:{PORT}`.
## 🛠️ API Endpoints
- **GET /** or **GET /.well-known/agent.json**
Returns the agent card information following the A2A protocol.
- **POST /**
Send a one-off task to the agent.
- **POST /send_task_subscribe**
Stream a task to receive real-time responses.
## 📂 Project Structure
```
.
├── main.py # Entry point
├── a2a_service/ # Core service package
│ ├── agent.py # LangGraph agent implementation
│ ├── server.py # A2A HTTP server
│ ├── database.py # Database connection setup
│ ├── models/ # Data models and schemas
│ ├── task_managers/ # Task management modules
│ │ ├── __init__.py # Base task manager interface
│ │ ├── db_task_manager.py # DB-backed task manager
│ │ └── async_inmem_task_manager.py # In-memory task manager
│ └── tools/ # Agent tools
│ └── search.py # Web search tool
├── alembic/ # Database migration scripts
├── alembic.ini # Alembic configuration
├── pyproject.toml # Project configuration & dependencies
├── docker-compose.yaml # Docker setup for PostgreSQL
└── README.md # This file
```
## 🛠️ Tech Stack
- **Python 3.13+**
- **[LangGraph](https://github.com/langgraph/langgraph)** for agent orchestration
- **[LangChain](https://github.com/langchain-ai/langchain)** for LLM components
- **FastAPI** & **Uvicorn** for HTTP server
- **PostgreSQL** via **SQLAlchemy** & **Alembic** migrations
- **OpenAI** for LLM capabilities
## 🔌 Extending the Agent
To add custom tools:
1. Create a new Python file in the `a2a_service/tools/` directory
2. Define your tool using the `@tool` decorator from LangChain
3. Import and add your tool to the Agent's tools list in `main.py`
Example:
```python
# a2a_service/tools/my_tool.py
from langchain_core.tools import tool
@tool
def my_custom_tool(param: str) -> str:
"""Description of what this tool does.
Args:
param: Description of the parameter
Returns:
Description of the return value
"""
# Your implementation here
return f"Processed: {param}"
```
## 💬 Get in touch
- [Website](https://llmx.de)
- [LinkedIn](https://www.linkedin.com/in/dmytro--ch/)
- [X](https://x.com/dmytro__ch)
- [GitHub](https://github.com/llmx-de)
- [Email](mailto:dmytro.de.ch@gmail.com)
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Feel free to contribute or report issues!
