autoscraper vs Scrapling
Learn-by-example Python scraper: give it a URL and sample values you want, it infers the extraction rules and reapplies them to similar pages. Tiny, fast, zero selectors. — versus — Adaptive Python scraping framework: selectors that relearn when sites redesign, stealth fetchers that pass Cloudflare, spiders with proxy rotation and an MCP server — request to full crawl.
Both attack selector fragility by learning: autoscraper infers extraction rules from one example and stops there; Scrapling relearns elements after redesigns inside a full, maintained crawling framework.
| autoscraper | Scrapling | |
|---|---|---|
| Stars | 7.6k | 70k |
| Forks | 778 | 6.9k |
| Language | Python | Python |
| License | MIT | BSD-3-Clause |
| Last activity | 1 years ago | 2 days ago |
| Topics | web | web |
| Curated connections | 2 | 4 |
autoscraper — the curator's take
The cleverest 500 lines in scraping: show it one example of what you want off a page and it figures out the rules — no selectors, no XPath, and the learned model reapplies across similar pages. Perfect for quick structured grabs and prototyping. But check the commit log before adopting: it's been quiet for over a year, so treat it as a finished small tool, NOT a maintained framework — no JS rendering, no anti-bot, no crawling infrastructure. When sites fight back or scale arrives, move to a real framework.
Scrapling — the curator's take
The modern-web answer to scraping's two chronic pains: selectors break (its parser relearns elements after redesigns via auto-save/auto-match) and bots get blocked (StealthyFetcher passes Cloudflare Turnstile out of the box). The MCP server is a quiet killer feature — your coding agent can scrape through it directly. NOT the veteran choice: younger ecosystem than Scrapy with fewer third-party answers when you're deep in the weeds, and the adaptive magic needs its cache warmed — first-run breakage still lands on you.