autoscraper vs Scrapegraph-ai
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 — LLM-driven Python scraping: describe what you want and graph pipelines extract structured data from websites or local docs (HTML, XML, JSON, Markdown). 29k stars; cloud API upsell.
Two ways to skip writing selectors: AutoScraper infers reusable rules from one example page (train once, scrape free); ScrapeGraphAI re-extracts with an LLM every time. AutoScraper wins on cost for uniform pages, loses on messy or varied layouts.
| autoscraper | Scrapegraph-ai | |
|---|---|---|
| Stars | 7.8k | 29k |
| Forks | 798 | 2.8k |
| Language | Python | Python |
| License | MIT | MIT |
| Last activity | 5 days ago | 14 days ago |
| Topics | web | web |
| Curated connections | 3 | 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.
Scrapegraph-ai — the curator's take
The prompt-first scraper: say what you want, SmartScraperGraph builds the extraction pipeline — the fastest route to a structured-extraction prototype, and it eats local HTML/XML/JSON/Markdown too. The catch: an LLM call per page, and the repo increasingly funnels you toward the paid cloud API. For bulk RAG ingestion crawl4ai is cheaper per page; for stable high-volume production pipelines a deterministic scraper (scrapling, scrapy) beats re-asking an LLM the same question about every page.