Content
# 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
| Agent | Framework | Purpose |
|-------|-----------|---------|
| Flight Search | Google ADK | Find and compare flight options |
| Hotel Search | LangGraph | Discover and recommend hotels |
| Itinerary Generation | CrewAI | Create detailed travel itineraries |
## 🚀 Quick Start
### Prerequisites
- Python 3.11+
- uv (Python package manager)
- Required API keys (see Configuration section)
### Installation
1. **Clone the repository**
```bash
git clone https://github.com/YOUR_USERNAME/Flight-Recommendation.git
cd Flight-Recommendation
```
2. **Install dependencies**
```bash
uv sync
```
3. **Set up environment variables**
```bash
cp .env.example .env
# Edit .env with your API keys
```
4. **Run the demo UI**
```bash
cd demo/ui
uv run python main.py
```
## 🔧 Configuration
Create a `.env` file in the project root with the following variables:
```env
# 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
```python
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
```python
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
```python
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
```bash
# 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
```bash
cd mcp_server
uv run python server.py
```
### Testing
```bash
# Run all tests
uv run pytest
# Run specific test suite
uv run pytest demo/ui/tests/
```
## 📚 Documentation
- [Google ADK Documentation](https://developers.google.com/agent-developer-kit)
- [LangGraph Documentation](https://langchain-ai.github.io/langgraph/)
- [CrewAI Documentation](https://docs.crewai.com/)
- [MCP Protocol Specification](https://modelcontextprotocol.io/)
## 🤝 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](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](https://github.com/YOUR_USERNAME/Flight-Recommendation/issues) page
2. Create a new issue with detailed information
3. Join our community discussions
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