Scrapegraph-ai vs Scrapling
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. — 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.
Opposite bets on brittleness: Scrapling keeps deterministic selectors that self-relearn on redesigns; ScrapeGraphAI drops selectors entirely and pays an LLM call per page. Scrapling for cheap repeatable production runs, ScrapeGraphAI for schema-by-prompt flexibility.
| Scrapegraph-ai | Scrapling | |
|---|---|---|
| Stars | 29k | 72k |
| Forks | 2.8k | 7.1k |
| Language | Python | Python |
| License | MIT | BSD-3-Clause |
| Last activity | 14 days ago | 4 days ago |
| Topics | web | web |
| Curated connections | 4 | 6 |
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.
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.