This project demonstrates Agent-to-Agent (A2A) conversations using LangGraph with the A2A protocol. It includes implementations in both Python and TypeScript, allowing you to explore A2A communication patterns across different language ecosystems.

The project showcases how two conversational AI agents can communicate with each other using the standardized A2A protocol. Each agent:

• Uses OpenAI's GPT-4o-mini for generating responses
• Maintains independent conversation state
• Communicates via JSON-RPC 2.0 formatted messages
• Responds to messages from other agents in a conversational loop

agent2agent/
├── python/                    # Python implementation
│   ├── langgraph_agent.py     # Agent definition
│   ├── a2a_conversation.py   # Conversation orchestrator
│   ├── langgraph.json         # LangGraph configuration
│   ├── requirements.txt       # Python dependencies
│   └── README.md              # Python-specific docs
│
├── typescript/                 # TypeScript implementation
│   ├── src/
│   │   ├── langgraph_agent.ts      # Agent definition
│   │   └── a2a_conversation.ts     # Conversation orchestrator
│   ├── langgraph.json              # LangGraph configuration
│   ├── package.json                # Node.js dependencies
│   ├── tsconfig.json               # TypeScript configuration
│   └── README.md                   # TypeScript-specific docs
│
└── README.md                  # This file

1. Setup:

   cd python
   python -m venv venv
   source venv/bin/activate  # On macOS/Linux
   pip install -r requirements.txt

2. Configure environment:

   cp .env.example .env
   # Add your OPENAI_API_KEY to .env

3. Run agents:

   # Terminal 1
   langgraph dev --port 2024
   
   # Terminal 2
   langgraph dev --port 2025

4. Run conversation:

   python a2a_conversation.py

See python/README.md for detailed Python setup instructions.

1. Setup:

   cd typescript
   npm install

2. Configure environment:

   cp .env.example .env
   # Add your OPENAI_API_KEY to .env

3. Run agents:

   # Terminal 1
   npx @langchain/langgraph-cli dev --port 2024
   
   # Terminal 2
   npx @langchain/langgraph-cli dev --port 2025

4. Run conversation:

   npm run conversation

See typescript/README.md for detailed TypeScript setup instructions.

• Python: The local dev server (langgraph dev) fully supports A2A protocol endpoints at /a2a/{assistant_id}
• TypeScript:
  • A2A protocol is supported on LangGraph Cloud deployments
  • The local dev server (langgraph dev) does not expose A2A endpoints
  • For local TypeScript development, use the Threads API instead

Both implementations require:

• OPENAI_API_KEY: Your OpenAI API key
• AGENT_A_ID: Assistant ID from the first server (port 2024)
• AGENT_B_ID: Assistant ID from the second server (port 2025)

Both implementations define a conversational agent using LangGraph's StateGraph:

• State: Maintains a list of messages using LangChain's BaseMessage types
• Node: Processes messages using OpenAI's GPT-4o-mini
• Configuration: Brief responses (max 100 tokens), temperature 0.7

The orchestrator:

• Sends JSON-RPC 2.0 formatted messages between agents
• Uses the A2A protocol endpoint: /a2a/{assistant_id}
• Implements the message/send method
• Extracts responses from the artifacts array
• Simulates 3 rounds of back-and-forth conversation

• Swagger UI: http://localhost:2024/docs or http://localhost:2025/docs
• LangGraph Studio: Available via the server output

• LangGraph Studio: Available via the server output (e.g., https://smith.langchain.com/studio?baseUrl=http://localhost:2025)
• Note: /docs endpoint is not available in TypeScript dev server

• Run conversation: python a2a_conversation.py

• npm run dev - Watch mode for development
• npm run build - Build TypeScript to JavaScript
• npm run conversation - Run the A2A conversation simulation

See LICENSE for details.