🛍️ E-commerce AI Agent

Intelligent multi-platform product search agent powered by AI, LangChain, and MCP (Model Context Protocol)

An advanced AI-powered e-commerce search agent that simultaneously searches across multiple online platforms, providing intelligent product comparisons with structured results, price analysis, and smart recommendations.

✨ Features

🎯 Core Capabilities

Multi-Platform Search: Simultaneously search across 11+ e-commerce platforms
AI-Powered Intelligence: GPT-4o with ReAct agents for smart decision making
Structured Responses: Organized results using Pydantic models with type validation
Smart Analysis: Intelligent price comparison, rating analysis, and best deal detection
Professional Web Interface: Modern Bootstrap-based responsive frontend
Multiple MCP Providers: Support for BrightData, FireCrawl, and Tavily

🌍 Supported Platforms

Platform	Region	Type	Availability
🔸 Amazon	Global	General E-commerce	All versions
🔸 eBay	Global	Marketplace	All versions
🔸 Walmart	US	Retail Chain	All versions
🔸 Best Buy	US	Electronics	BrightData/FireCrawl
🔸 Target	US	Retail Chain	BrightData/FireCrawl
🔸 Costco	US	Warehouse Club	BrightData/FireCrawl
🔸 Newegg	Global	Technology	BrightData/FireCrawl
🔸 MercadoLibre Colombia	LATAM	Marketplace	Tavily version
🔸 Falabella	LATAM	Department Store	Tavily version
🔸 Alibaba	Global	B2B	Tavily version
🔸 AliExpress	Global	B2C Retail	Tavily version

🏗️ Architecture

Technology Stack

🐍 Python 3.13+          - Core language
🌐 Flask 3.1+            - Web framework  
🧠 LangChain 0.3+        - AI framework
🤖 LangGraph 0.6+        - ReAct agents
🔗 MCP 1.13+             - Model Context Protocol
📊 Pydantic              - Data validation
🎨 Bootstrap 5           - Frontend styling
⚡ OpenAI GPT-4o         - Language model

Project Structure

Ecommerce_AI_Agent/
├── 📄 app.py                 # Version 1: BrightData MCP (Production-ready)
├── 📄 app_firecrawl.py       # Version 2: FireCrawl MCP (Content extraction)
├── 📄 app_tavily.py          # Version 3: Tavily MCP (Most advanced) ⭐
├── 📁 templates/
│   ├── base.html             # Base template with Bootstrap
│   └── index.html            # Main interface with smart dashboard
├── 📁 static/
│   └── style.css            # Custom styling
├── ⚙️ .env                   # Environment variables (create this)
├── 📋 pyproject.toml         # Python dependencies
├── 📖 CLAUDE.md              # Claude Code integration docs
├── 📚 README.md              # This file
└── 🔐 requirements.txt       # Alternative dependency format

🚀 Quick Start

Prerequisites

Python 3.13+
Node.js 16+ (for MCP servers)
API Keys (OpenAI + chosen provider)

1. Clone & Setup

git clone https://github.com/yourusername/ecommerce-ai-agent.git
cd ecommerce-ai-agent

# Using uv (recommended)
uv sync

# Or using pip
pip install -r requirements.txt

2. Environment Configuration

Create a .env file:

# Core - Required for all versions
OPENAI_API_KEY=your-openai-api-key-here

# Version 1: BrightData (app.py)
WEB_UNLOCKER_ZONE=your-zone
BROWSER_AUTH=your-browser-auth  
API_TOKEN=your-api-token

# Version 2: FireCrawl (app_firecrawl.py)
FIRECRAWL_API_KEY=your-firecrawl-api-key

# Version 3: Tavily (app_tavily.py) ⭐ Recommended
TAVILY_API_KEY=your-tavily-api-key

3. Install MCP Server

# For Tavily version (recommended)
npm install -g tavily-mcp@latest

# For BrightData version
npm install -g @brightdata/mcp

# For FireCrawl version  
npm install -g firecrawl-mcp

4. Run Application

# Recommended version (Advanced features + International platforms)
python app_tavily.py

# Alternative versions
python app.py           # BrightData (Most reliable)
python app_firecrawl.py # FireCrawl (Clean extraction)

Visit: http://localhost:8000

🎮 Usage Guide

Web Interface

📝 Enter Search Query: "iPhone 15 Pro", "Samsung Galaxy S24", "wireless headphones"
🎯 Select Platforms: Choose from available e-commerce sites
🚀 Execute Search: AI agent searches and structures results
📊 View Results: Organized by platform with smart analysis

Example Search Results

{
  "platforms": [
    {
      "platform": "Amazon",
      "results": [
        {
          "title": "Samsung Galaxy Buds2 Pro Wireless Earbuds",
          "url": "https://amazon.com/dp/B0B4RJP5J1",
          "rating": "4.3 out of 5 stars (15,240 reviews)",
          "price": "$149.99"
        }
      ]
    }
  ],
  "summary": {
    "total_products": 42,
    "platforms_count": 8,
    "price_range": "$24.99 - $299.99",
    "best_deal": {...},
    "top_rated": {...}
  }
}

🔧 Version Comparison

Feature	BrightData `app.py`	FireCrawl `app_firecrawl.py`	Tavily `app_tavily.py` ⭐
Platforms	7	7	11
Reliability	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐
International	❌	❌	✅
Price Analysis	❌	❌	✅
Smart Dashboard	❌	❌	✅
Error Handling	Basic	Basic	Advanced
Prompt Engineering	Simple	Hardcoded	Advanced
Best For	Production	Clean Data	Development

🧠 AI Agent Architecture

Workflow Diagram

graph TD
    A[🔍 User Query] --> B[🌐 Flask App]
    B --> C[🔗 MCP Client]
    C --> D[🧰 Load LangChain Tools]
    D --> E[🤖 ReAct Agent Processing]
    E --> F[📊 Multi-Platform Search]
    F --> G[✅ Structured Response]
    G --> H[🎨 Web Template Rendering]
    
    style A fill:#e1f5fe
    style E fill:#f3e5f5
    style G fill:#e8f5e8

Key Components

🌐 Flask Application: Web server with async MCP integration
🔗 MCP Client: Bridge to external scraping tools via stdio
🧠 LangChain Integration: Loads MCP tools into LangChain ecosystem
🤖 LangGraph Agent: ReAct pattern for intelligent decision making
📊 Pydantic Models: Type-safe response structuring and validation
🎨 Jinja2 Templates: Dynamic HTML rendering with Bootstrap styling

🛠️ Advanced Configuration

Custom MCP Server

# Add your own MCP server
server_params = StdioServerParameters(
    command='your-custom-mcp-server',
    args=['--config', 'config.json'],
    env={'API_KEY': os.getenv('YOUR_API_KEY')}
)

Extend Platform Support

# Add new platforms to PLATFORMS list
PLATFORMS = ['Amazon', 'eBay', 'YourPlatform']

# Update system prompt for new platform
SYSTEM_PROMPT += "For YourPlatform: use specific extraction logic..."

🐛 Troubleshooting

Common Issues

❌ MCP Server Not Found

# Verify MCP server installation
npm list -g tavily-mcp
npm install -g tavily-mcp@latest

❌ API Key Errors

# Check environment variables
echo $OPENAI_API_KEY
echo $TAVILY_API_KEY

❌ Node.js Issues

# Update Node.js to latest LTS
nvm install --lts
nvm use --lts

Debug Mode

# Enable debug logging
import logging
logging.basicConfig(level=logging.DEBUG)

🤝 Contributing

We welcome contributions! Here's how to get started:

Development Setup

# Fork and clone
git clone https://github.com/yourusername/ecommerce-ai-agent.git
cd ecommerce-ai-agent

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

# Install development dependencies
uv sync --dev

Contribution Guidelines

🍴 Fork the repository
🌿 Create feature branch (git checkout -b feature/amazing-feature)
✅ Test your changes thoroughly
📝 Commit with clear messages (git commit -m 'Add: amazing feature')
📤 Push to branch (git push origin feature/amazing-feature)
🔄 Open a Pull Request

Code Standards

✅ Type hints for all functions
📚 Docstrings for public methods
🧪 Unit tests for new features
🎨 Black formatting (black .)
🔍 Linting with ruff (ruff check .)

📋 Roadmap

🎯 Version 2.0 (Coming Soon)

🔒 User Authentication: Save searches and preferences
💾 Database Integration: Store search history
📱 Mobile App: React Native companion
🌍 More Platforms: Add 20+ international platforms
📈 Analytics Dashboard: Search trends and insights
🔔 Price Alerts: Notify when prices drop

🚀 Version 3.0 (Future)

🤖 Advanced AI: GPT-4 Vision for product images
💰 Price Prediction: ML models for price forecasting
🛒 Shopping Lists: Collaborative shopping features
📊 Business Intelligence: Enterprise analytics suite

📊 Performance Metrics

Metric	Target	Current
Response Time	< 10s	~8s
Platforms Searched	15+	11
Success Rate	> 95%	~92%
Concurrent Users	100+	50

🔒 Security & Privacy

✅ No Data Storage: Search queries are not logged
✅ Secure API Keys: Environment variable protection
✅ HTTPS Ready: SSL/TLS support for production
✅ Rate Limiting: Built-in request throttling
✅ Input Sanitization: XSS and injection prevention

📄 License

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

MIT License - Feel free to use, modify, and distribute!

👨‍💻 Author

Edwin Quintero

🐙 GitHub: @edwinquintero
💼 LinkedIn: Edwin Quintero
📧 Email: edwin@example.com
🌐 Website: edwinquintero.dev

🙏 Acknowledgments

Special thanks to these amazing projects and services:

🦜 LangChain - AI framework foundation
🧠 OpenAI - GPT-4o language model
🔍 Tavily - Web search API
🌐 BrightData - Enterprise web scraping
🔥 FireCrawl - Clean web extraction
🐍 Flask - Web framework
🎨 Bootstrap - UI framework

⭐ If this project helps you, please give it a star on GitHub! ⭐

Made with ❤️ for the open source community

Name		Name	Last commit message	Last commit date
Latest commit History 1 Commit
static		static
templates		templates
.env.example		.env.example
.gitignore		.gitignore
.python-version		.python-version
CLAUDE.md		CLAUDE.md
LICENSE		LICENSE
README.md		README.md
app.py		app.py
app_firecrawl.py		app_firecrawl.py
app_tavily.py		app_tavily.py
pyproject.toml		pyproject.toml
requirements.txt		requirements.txt
uv.lock		uv.lock

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