product-harvest 0.1.0

Extract structured product data from any web page using Schema.org JSON-LD — no AI required.

product-harvest

Extract structured product data from any web page using Schema.org — no AI required.

Features

• Zero config — reads the structured data sites already embed for Google
• Three extraction methods — JSON-LD (preferred), Open Graph, and meta tag fallback
• Rich product data — name, price, currency, brand, SKU, GTIN, availability, rating, reviews, images, categories
• Works with any ecommerce site — Shopify, WooCommerce, BigCommerce, Amazon, and millions of sites using Schema.org
• Batch processing — extract from multiple URLs in one command
• Multiple output formats — plain text, CSV, JSON, JSONL
• URL file input — read URLs from a file or stdin pipe
• No AI/LLM required — pure parsing of existing structured data
• Minimal dependencies — only requests + beautifulsoup4

How It Works

Most ecommerce sites embed Schema.org structured data in their HTML to help Google understand their products. product-harvest reads this existing data — no scraping rules, no CSS selectors, no AI needed.

<!-- This is what sites already have in their HTML for Google: -->
<script type="application/ld+json">
{
  "@type": "Product",
  "name": "Wireless Headphones",
  "offers": { "price": "149.99", "priceCurrency": "USD" }
}
</script>

product-harvest just finds and parses it.

Installation

pip install product-harvest

Requires Python 3.8+.

Quick Start

Extract product data from a URL:

productx https://shop.example.com/product

Output:

ProMax Wireless Headphones    USD 149.99

With full details:

productx https://shop.example.com/product --detail --format json

[
  {
    "name": "ProMax Wireless Headphones",
    "price": "149.99",
    "currency": "USD",
    "brand": "ProMax",
    "availability": "InStock",
    "rating": "4.5",
    "review_count": "328",
    "sku": "WH-PM-2024",
    "category": "Electronics > Audio > Headphones",
    "description": "Premium noise-cancelling wireless headphones...",
    "image": "https://shop.example.com/images/headphones.jpg",
    "url": "https://shop.example.com/headphones",
    "source_url": "https://shop.example.com/headphones",
    "extraction_method": "json-ld"
  }
]

Batch processing from a URL file:

productx --file product-urls.txt --format csv -o products.csv

Pipe URLs from another tool:

cat urls.txt | productx --stdin --format jsonl

CLI Reference

productx [OPTIONS] URL...

Input

Flag	Description
URL...	Product page URLs (positional)
--file, -F FILE	Read URLs from file (one per line)
--stdin	Read URLs from stdin

Output

Flag	Default	Description
--format, -f	plain	Output: plain, csv, json, jsonl
--output, -o FILE	stdout	Write to file
--detail, -d	off	Include all fields

Fields Extracted

Field	Source	Description
name	All methods	Product name
price	All methods	Price value
currency	All methods	Currency code (USD, EUR, etc.)
brand	JSON-LD, OG	Brand name
availability	JSON-LD, OG	InStock, OutOfStock, etc.
rating	JSON-LD	Average rating
review_count	JSON-LD	Number of reviews
sku	JSON-LD	Stock Keeping Unit
gtin	JSON-LD	Global Trade Item Number
category	JSON-LD, OG	Product category
description	All methods	Product description
image	JSON-LD, OG	Product image URL
url	All methods	Canonical product URL

Python API

from product_extractor import ProductExtractor

ex = ProductExtractor()

# From URL
products = ex.from_url("https://shop.example.com/product")
for p in products:
    print(f"{p.name}: {p.currency} {p.price} ({p.brand})")

# From HTML string
products = ex.from_html(html_content, source_url="https://example.com")

# Batch URLs
products = ex.from_urls([
    "https://shop1.com/product-a",
    "https://shop2.com/product-b",
])

# Access all fields
p = products[0]
print(p.name, p.price, p.currency, p.brand, p.sku)
print(p.rating, p.review_count, p.availability)
print(p.to_dict())  # as dictionary

Extraction Priority

product-harvest tries three methods in order and returns the first successful result:

1. JSON-LD (most reliable) — <script type="application/ld+json"> with @type: Product
2. Open Graph — <meta property="og:*"> and <meta property="product:*">
3. Meta tags + price regex (fallback) — page title + price pattern matching

Comparison

Feature	product-harvest	Crawl4AI	Scrapling	oxylabs/amazon-scraper
No AI/LLM needed	Yes	No	Yes	Yes
No API key needed	Yes	Depends	Yes	No
Works on any site	Yes*	Yes	Yes	Amazon only
Zero config	Yes	No	No	No
Structured output	Yes	Yes	No	Yes
pip install	Yes	Yes	Yes	No
Dependencies	2	Many	Many	N/A
Price	Free	Free	Free	Paid proxy

*Requires the site to have Schema.org or Open Graph product data.

Also by Thunderbit

• email-harvest — Extract email addresses from text, files, and web pages
• phone-harvest — Extract and identify phone numbers from text, files, and web pages
• image-harvest — Discover and batch download images from web pages

License

MIT

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