Scrapegraph-ai vs scrapy
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 — The veteran Python web crawling framework: spiders, middlewares, pipelines and battle-tested scheduling — 60k+ stars and still the reference architecture for structured scraping.
Scrapy is the industrial crawling framework — spiders, middlewares, scheduling — where you write the extraction logic; ScrapeGraphAI replaces that logic with an LLM prompt. Large disciplined crawls → Scrapy; fast prompt-defined extraction → ScrapeGraphAI.
| Scrapegraph-ai | scrapy | |
|---|---|---|
| Stars | 29k | 64k |
| Forks | 2.8k | 12k |
| Language | Python | Python |
| License | MIT | BSD-3-Clause |
| Last activity | 14 days ago | 4 days ago |
| Topics | web | web |
| Curated connections | 4 | 3 |
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.
scrapy — the curator's take
Fifteen-plus years of production hardening in one framework: spiders declare what to extract, middlewares/pipelines handle retries, throttling, dedup and export, and the ecosystem has an answer for everything. For large structured crawls in Python it's still the default. NOT a browser — JS-heavy or anti-bot-protected sites need Playwright bolted on or a different tool (Scrapling's stealth fetchers, Crawlee's browser mode), and the framework's inversion of control feels heavy when you just need one page: for that, requests + a parser beats a Scrapy project.