Agent-to-Agent (A2A) Communication Example

This directory contains examples of Agent-to-Agent (A2A) communication using the MCP (Model Context Protocol) framework.

Components

The system consists of three main components:

1. MCP Server (server.py): Provides employee data and skills information
2. Employee Agent (employee-agent.py): An agent that can answer questions about employees
3. HR Agent (hr-agent.py): An agent that can answer HR-related questions by communicating with the Employee Agent

Setup and Running

A2A System Runner: Runs the MCP Server, Employee Agent, and HR Agent in parallel

0. Install Dependencies:

   pip install -r requirements.txt

1. Start the system:

   python3 lab-5/run_a2a_system.py

4. Make Requests to the HR Agent

Once all three components are running, you can make requests to the HR Agent:

curl -X POST --location "http://0.0.0.0:8000/inquire" \
-H "Content-Type: application/json" \
-d '{"question": "list employees that have skills related to AI programming"}'

Architecture

+-------------+         +----------------+         +------------+
|  HR Agent   |  <--->  | Employee Agent |  <--->  | MCP Server |
| (Port 8000) |         |  (Port 8001)   |         | (Port 8002)|
+-------------+         +----------------+         +------------+
       ^
       |
    User Requests

The HR Agent receives questions from users, communicates with the Employee Agent to get information, and the Employee Agent in turn communicates with the MCP Server to retrieve the actual data.

Notes

• The MCP Server uses the FastMCP framework to expose tools (functions) that can be called remotely
• The Employee Agent uses the Strands framework to create an AI agent that can use these tools
• The HR Agent provides a REST API endpoint for users to ask questions 1