Scrapling

Overview

Scrapling handles the hardest part of web scraping: sites that don’t want to be scraped. It adapts to anti-bot measures in real-time — rotating fingerprints, handling CAPTCHAs, managing cookies, and mimicking human browsing.

How It Works

1. Configure — Set target URL and extraction rules
2. Adapt — Auto-detects and bypasses anti-bot measures
3. Extract — CSS/XPath selectors or AI-based extraction
4. Rotate — Automatic fingerprint and proxy rotation

Use Cases

• Competitive intelligence — Scrape protected competitor sites
• Price monitoring — Track prices across e-commerce
• Content aggregation — Gather content from protected sources
• Market research — Collect data from anti-scraping sites

Getting Started

from scrapling import Fetcher
fetcher = Fetcher(auto_match=True)
page = fetcher.get("https://protected-site.com/products")
for product in page.css("div.product"):
    print(product.css_first("h2").text)

Example

from scrapling import StealthyFetcher
fetcher = StealthyFetcher()
page = fetcher.get("https://example.com/pricing")
# auto_match finds elements even when HTML structure changes
prices = page.css("span.price", auto_match=True)

Alternatives

• Firecrawl — Managed API (faster, paid)
• Crawl4AI — LLM-friendly crawler
• Playwright — Browser automation (manual anti-bot)