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Rent Hunting Agentic AI

An intelligent AI-powered system designed to streamline the apartment hunting process by automating property searches, scheduling viewings, and managing client communications.

🚀 Features

  • Intelligent Property Search: Automated extraction of rental listings from various sources
  • Calendar Management: Smart scheduling system for apartment viewings
  • Email Communication: Automated email notifications and appointment confirmations
  • Database Integration: TiDB integration for efficient data management
  • Multi-city Support: Configurable city and country data extraction
  • Client Management: Comprehensive client profile and preference tracking

📁 Project Structure

├── app.py                     # Main application entry point
├── calendar_utils.py          # Calendar and scheduling utilities
├── city_data_extractor.py     # City and location data extraction
├── email_server.py           # Email handling and SMTP server
├── tidb_customer_tool.py     # TiDB database operations
├── test_email_template.py    # Email template testing
├── data/                     # Data storage
│   └── country_and_city_urls.yaml
├── email_templates/          # HTML email templates
│   └── apartment_appointment.html
├── mcp_tools/               # MCP (Model Context Protocol) tools
│   ├── cli.py
│   └── framework.py
├── models/                  # Data models and configuration
│   ├── client_model.py
│   └── config.yaml
└── tmp/                     # Temporary files
    └── agent.db

🛠️ Installation

  1. Clone the repository

    git clone https://github.com/minhtrantrong/rent_hunting_agentic_AI.git
cd rent_hunting_agentic_AI

  2. Set up Python environment

    python -m venv venv
# On Windows
venv\Scripts\activate
# On macOS/Linux
source venv/bin/activate

  3. Install dependencies

    pip install -r requirements.txt

  4. Environment Configuration

    cp .env.example .env

    Edit the .env file with your configuration:

    • Database credentials (TiDB)
    • Email server settings
    • API keys for property search services
    • Other service configurations

⚙️ Configuration

Database Setup

The application uses TiDB for data storage. Configure your database connection in the .env file:

TIDB_HOST=your_tidb_host
TIDB_PORT=4000
TIDB_USER=your_username
TIDB_PASSWORD=your_password
TIDB_DATABASE=rent_hunting

Import the prepared data to the TiDB database 'rent_hunting', at the table name 'rents'

From the TiDB web application, select Data > Import Select 'Upload a local file', then select the 'rents.csv' in the data directory Data types: low_price and high_price columns are BIGINT(), others columns are VARCHAR(255) Database name: rent_hunting Table name: rents

City Data Configuration

Modify data/country_and_city_urls.yaml to add or update supported cities and their data sources.

🚀 Usage

Running the Main Application

python app.py

Testing Email Templates

python test_email_template.py

Testing Database Connection

python quick_db_test.py

Using MCP Tools

python -m mcp_tools.cli

📧 Email Templates

The system includes customizable HTML email templates for:

  • Apartment viewing appointments
  • Property match notifications
  • Booking confirmations

Templates are located in the email_templates/ directory and can be customized to match your branding.

🗃️ Database Schema

The application uses the following key models:

  • Client Model: Stores client information and preferences
  • Property Data: Rental listing information
  • Appointments: Scheduled viewing data
  • Communications: Email and message logs

🔧 API Integration

The system supports integration with various property listing APIs and services. Configure API endpoints and authentication in the respective configuration files.

🤖 AI Capabilities

  • Natural Language Processing: Understanding client preferences and requirements
  • Smart Matching: AI-powered property recommendation engine
  • Automated Scheduling: Intelligent calendar management
  • Communication Automation: Context-aware email generation

📊 Monitoring and Logging

The application includes comprehensive logging for:

  • Property search activities
  • Client interactions
  • Email communications
  • System performance metrics

🧪 Testing

Run the test suite:

python -m pytest tests/

For email template testing:

python test_email_template.py

For database connection testing:

python quick_db_test.py

🤝 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.

🆘 Support

For support and questions:

  • Create an issue in the GitHub repository
  • Contact the development team
  • Check the documentation in the docs/ directory

🙏 Acknowledgments

  • TiDB for database infrastructure
  • OpenAI for AI capabilities
  • Contributors and beta testers

Note: This is a hackathon project focused on demonstrating AI-powered automation in the real estate rental market. The system is designed to be extensible and can be adapted for various rental markets and use cases.