Smart Product Buyer AI Agent 🚗

An intelligent car buying assistant powered by LangGraph and OpenAI that helps users make informed purchasing decisions through automated web scraping, intelligent filtering, and comprehensive market analysis.

🌟 Features

• Conversational Interface: Natural language interaction to understand user requirements
• Intelligent Filtering: Dynamic filter creation based on user preferences
• Web Scraping: Automated car listing retrieval from AutoTrader
• Market Analysis: Real-time research on car reliability and common issues
• Detailed Insights: Comprehensive summaries and recommendations
• Extensible Design: Easily adaptable to other platforms and product categories

🏗️ Architecture

The agent follows a structured workflow using LangGraph's state management:

1. User Need Assessment - Gathers requirements and preferences
2. Filter Building - Constructs search parameters
3. Listing Retrieval - Scrapes relevant car listings
4. Analysis & Insights - Provides market research and recommendations

🚀 Getting Started

Prerequisites

• Python 3.8+
• OpenAI API key
• Jupyter Notebook or Google Colab

Installation

1. Clone the repository:

git clone https://github.com/Debarjita/smart-car-buyer-agent.git
cd smart-car-buyer-agent

2. Install dependencies:

pip install -r requirements.txt
playwright install
patchright install chromium

3. Set up environment variables:

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

Quick Start

1. Run in Jupyter Notebook:

   • Open car_buyer_agent.ipynb
   • Execute all cells
   • Follow the Gradio interface link

2. Run without GUI:

   • Set USE_GRADIO = False in the notebook
   • Execute cells for command-line interaction

📋 Requirements

langgraph==0.2.28
langchain==0.3.1
langchain-openai==0.2.1
langchain-community==0.3.0
python-dotenv==1.0.0
playwright==1.47.0
patchright==1.16.0
lxml==5.3.0
nest-asyncio==1.6.0
duckduckgo-search==6.2.13
gradio==4.44.0

🛠️ Configuration

Environment Variables

Create a .env file with:

OPENAI_API_KEY=your_openai_api_key_here
LANGCHAIN_API_KEY=your_langchain_api_key_here  # Optional

Usage Settings

• USE_GRADIO = True: Web-based interface
• USE_GRADIO = False: Command-line interface

🎯 How It Works

1. User Interaction

The agent starts by asking about your car preferences:

• Budget range
• Intended usage (commuting, family trips)
• Size preferences
• Special features or constraints

2. Smart Filtering

Based on your requirements, it automatically:

• Builds search filters for AutoTrader
• Constructs optimized query URLs
• Applies intelligent parameter mapping

3. Data Collection

• Scrapes car listings using Playwright
• Extracts detailed information (price, mileage, features)
• Handles dynamic content loading

4. Analysis & Recommendations

• Researches car reliability using web search
• Identifies common issues for specific models
• Provides personalized recommendations

🔧 Extending the Agent

Adding New Platforms

1. Create a new scraper class inheriting from WebsiteInterface:

class NewPlatformInterface(WebsiteInterface):
    def __init__(self):
        self.base_url = "https://newplatform.com"
    
    async def crawl(self):
        # Implement scraping logic
        pass
    
    def get_filters_info(self):
        # Return filter information
        pass

2. Add it to the web interfaces list:

web_interfaces=[AutotraderInterface(), NewPlatformInterface()]

📱 Example Output

🤖 Car Buyer Assistant: Hi! I'm here to help you find the perfect car. 
What are you looking for in terms of:
- Budget range
- Intended use (daily commuting, family trips, etc.)
- Size preferences
- Any specific features?

👤 User: I need a reliable SUV under $25,000 for family trips

🤖 Assistant: Building filters based on your needs...
Successfully set filters for: AutotraderInterface
Updated URL: https://www.autotrader.com/cars-for-sale/all-cars/cars-under-25000?makeCode=HONDA&makeCode=TOYOTA&vehicleStyleCode=SUVCROSS

Found 15 listings matching your criteria:

1. 2018 Honda CR-V EX - $22,995 - 45,000 miles
   Dealer: Metro Honda
   [Additional details and analysis...]

⚠️ Important Notes

• Performance: Best performance on macOS/Linux. Windows users should use WSL
• Rate Limits: Respects website rate limits and robots.txt
• Legal Compliance: Only scrapes publicly available data
• API Usage: Monitor OpenAI API usage for cost management

🤝 Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add 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.

🐛 Troubleshooting

Common Issues

Gradio interface not loading:

• Check if all dependencies are installed
• Ensure ports are not blocked
• Try setting USE_GRADIO = False for debugging

Scraping errors:

• Verify internet connection
• Check if AutoTrader is accessible
• Update Playwright browsers: playwright install

API errors:

• Verify OpenAI API key is valid
• Check API quotas and billing
• Ensure environment variables are set correctly

🙏 Acknowledgments

• Built with LangGraph for state management
• Uses Playwright for web scraping
• Powered by OpenAI for intelligent processing
• Inspired by the GenAI_Agents tutorial collection

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