Retail Price Comparison with Browser-Use 🤖

Compare products across Amazon and Walmart automatically using AI-driven browser automation. Made with Browser-Use.

Features

• Automatically searches Amazon.com and Walmart.com for your product query.

• Picks the best organic match (avoiding sponsored listings).

• Extracts key product details:

  • Title
  • Price (numeric)
  • Currency
  • URL
  • Rating (stars)
  • Number of reviews
  • Availability text
  • Shipped and sold by retailer (inferred)

• Compares prices and identifies the cheaper option with the price difference.

• Returns strict JSON output suitable for downstream processing.

Installation

Clone the repository:

git clone <repo-url> # update this
cd <folder>

Create a virtual environment and install dependencies:

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

pip install -r requirements.txt
uvx playwright install chromium --with-deps --no-shell

Add your API keys to a .env file:

GOOGLE_API_KEY=<your_google_api_key>

Usage

Run from command line:

python compare_prices.py "ipad air m3 11 inch 128 gb"

Sample Output

{
  "query": "ipad air m3 11 inch 128 gb",
  "products": [
    {
      "source": "amazon",
      "title": "Apple iPad Air 11-inch with M3 chip Built for Apple Intelligence, Liquid Retina Display, 128GB, 12MP Front/Back Camera, Wi-Fi 6E, Touch ID, All-Day Battery Life — Space Gray",
      "price": 559.0,
      "currency": "USD",
      "url": "https://www.amazon.com/Apple-11-inch-Intelligence-Display-All-Day/dp/B0DZ76BN5D",
      "rating": 4.8,
      "reviews": 1553,
      "availability": "In Stock",
      "shipped_and_sold_by_retailer": true
    },
    {
      "source": "walmart",
      "title": "2025 Apple 11-inch iPad Air M3, Built for Apple Intelligence, Wi-Fi 128GB - Space Gray",
      "price": 557.5,
      "currency": "USD",
      "url": "https://www.walmart.com/ip/2025-Apple-11-inch-iPad-Air-M3-Built-for-Apple-Intelligence-Wi-Fi-128GB-Space-Gray/15450254481?classType=VARIANT&from=%2Fsearch",
      "rating": 4.5,
      "reviews": 297,
      "availability": "Available for shipping (Only 9 left)",
      "shipped_and_sold_by_retailer": false
    }
  ],
  "cheaper_source": "walmart",
  "price_diff": 1.5
}

How It Works

• Launches a stealth-enabled browser session via Browser-Use.

• Visits Amazon and Walmart, searches the product query, and opens the top product page.

• Extracts all required fields and normalizes prices.

• Returns a structured JSON response highlighting the cheaper source.

Customization

• Modify build_task(query) to add more product sources or additional fields.

• Adjust LLM settings in ChatGoogle() or switch to another provider.

• Can integrate with MCP servers to extend agent capabilities.

References & Examples

• Browser-Use GitHub: https://github.com/gregpr07/browser-use

• Documentation: https://docs.browser-use.com

• Example Prompts: Check the examples/ folder for tasks like shopping, job applications, and web automation.