What is Multi Agents With Mcp And A2a

Multi-Agents-With-MCP-and-A2A is a comprehensive travel planning system that integrates Agent-to-Agent (A2A) communication with Model Context Protocol (MCP) to provide intelligent services such as flight searches, hotel recommendations, and itinerary generation.

Use cases

Use cases include personalized travel planning, automated flight and hotel searches, itinerary generation for travelers, and multi-agent collaboration for comprehensive travel solutions.

How to use

To use Multi-Agents-With-MCP-and-A2A, clone the repository, install the required dependencies using the Python package manager ‘uv’, and set up your environment variables with necessary API keys. Then, you can interact with the system through its modern web interface.

Key features

Key features include a Flight Search Agent powered by Google ADK, a Hotel Search Agent built with LangGraph, an Itinerary Generation Agent using CrewAI, seamless Agent-to-Agent communication, a user-friendly web interface, and integration with MCP for enhanced communication.

Where to use

Multi-Agents-With-MCP-and-A2A can be used in the travel and tourism industry, particularly for applications requiring intelligent travel planning, such as flight and hotel booking systems.

Flight Recommendation System

A comprehensive travel planning system that combines A2A (Agent-to-Agent) communication with MCP (Model Context Protocol) to provide intelligent flight search, hotel recommendations, and itinerary generation services.

🎥 Demo Video

https://github.com/user-attachments/assets/d2d0b306-f414-4190-9044-b0609ab95c96

🌟 Features

• 🛫 Flight Search Agent: Powered by Google ADK (Agent Development Kit)
• 🏨 Hotel Search Agent: Built with LangGraph for intelligent hotel recommendations
• 📋 Itinerary Generation Agent: Uses CrewAI for comprehensive travel planning
• 🔄 Agent-to-Agent Communication: Seamless multi-agent collaboration
• 🌐 Web Interface: Modern UI for easy interaction
• 🔌 MCP Integration: Model Context Protocol for enhanced agent communication

🏗️ Architecture

This project demonstrates the integration of multiple AI frameworks:

• A2A (Agent-to-Agent): Enables seamless communication between different specialized agents
• MCP (Model Context Protocol): Provides standardized communication protocol for AI agents
• Multi-Agent System: Three specialized agents working together to create comprehensive travel solutions

Agent Overview

🚀 Quick Start

Prerequisites

• Python 3.11+
• uv (Python package manager)
• Required API keys (see Configuration section)

Installation

1. Clone the repository

   git clone https://github.com/YOUR_USERNAME/Flight-Recommendation.git
   cd Flight-Recommendation
   

2. Install dependencies

   uv sync
   

3. Set up environment variables

   cp .env.example .env
   # Edit .env with your API keys
   

4. Run the demo UI

   cd demo/ui
   uv run python main.py
   

🔧 Configuration

Create a .env file in the project root with the following variables:

# Google ADK Configuration (for Flight Search Agent)
GOOGLE_ADK_API_KEY=your_google_adk_api_key

# SerpAPI Configuration (for Hotel Search Agent)
SERP_API_KEY=your_serpapi_key

# Groq Configuration (for LangGraph)
GROQ_API_KEY=your_groq_api_key

# CrewAI Configuration (for Itinerary Agent)
OPENAI_API_KEY=your_openai_api_key

# MCP Server Configuration
MCP_SERVER_HOST=localhost
MCP_SERVER_PORT=8000

📁 Project Structure

Flight-Recommendation/
├── demo/                           # Demo application and UI
│   ├── ui/                        # Web interface
│   └── a2a_demo_arch.png         # Architecture diagram
├── mcp_server/                    # MCP server implementation
│   └── server.py                  # Main MCP server
├── samples/python/                # Agent implementations
│   ├── agents/
│   │   ├── flight_search_agent/   # Google ADK-based flight search
│   │   ├── hotel_search_agent/    # LangGraph-based hotel search
│   │   └── itinerary_generation_agent/ # CrewAI-based itinerary
│   ├── common/                    # Shared utilities and types
│   └── hosts/                     # Agent hosting infrastructure
└── README.md                      # This file

🎯 Usage Examples

Flight Search

from samples.python.agents.flight_search_agent import FlightSearchAgent

agent = FlightSearchAgent()
flights = agent.search_flights(
    origin="NYC",
    destination="LAX",
    departure_date="2024-06-15",
    return_date="2024-06-22"
)

Hotel Search

from samples.python.agents.hotel_search_agent import HotelSearchAgent

agent = HotelSearchAgent()
hotels = agent.search_hotels(
    location="Los Angeles",
    check_in_date="2024-06-15",
    check_out_date="2024-06-22"
)

Itinerary Generation

from samples.python.agents.itinerary_generation_agent import ItineraryAgent

agent = ItineraryAgent()
itinerary = agent.generate_itinerary(
    destination="Los Angeles",
    duration=7,
    interests=["beaches", "entertainment", "dining"]
)

🔗 Agent Communication

The system uses A2A communication to enable agents to work together:

1. Flight Search Agent finds available flights
2. Hotel Search Agent recommends accommodations based on flight details
3. Itinerary Generation Agent creates a comprehensive travel plan using both flight and hotel information

🛠️ Development

Running Individual Agents

# Flight Search Agent
cd samples/python/agents/flight_search_agent
uv run python agent.py

# Hotel Search Agent
cd samples/python/agents/hotel_search_agent
uv run python agent.py

# Itinerary Generation Agent
cd samples/python/agents/itinerary_generation_agent
uv run python agent.py

Running the MCP Server

cd mcp_server
uv run python server.py

Testing

# Run all tests
uv run pytest

# Run specific test suite
uv run pytest demo/ui/tests/

📚 Documentation

• Google ADK Documentation
• LangGraph Documentation
• CrewAI Documentation
• MCP Protocol Specification

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🔮 Future Enhancements

• [ ] Real-time price monitoring
• [ ] Weather integration for travel planning
• [ ] Multi-language support
• [ ] Mobile application
• [ ] Advanced analytics and reporting
• [ ] Integration with more travel APIs

🙏 Acknowledgments

• Google ADK team for the flight search capabilities
• LangGraph community for the hotel search framework
• CrewAI developers for the itinerary generation tools
• MCP protocol contributors for standardized agent communication

📞 Support

If you encounter any issues or have questions:

1. Check the Issues page
2. Create a new issue with detailed information
3. Join our community discussions

Made with ❤️ by [Your Name]