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crawl4ai vs Scrapling

The 74k-star LLM-native crawler: turns any site into clean, RAG-ready Markdown — adaptive crawling, JS rendering, extraction strategies, Docker deploy. Python, Apache-2.0. — 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.

The curated verdict

Same Python scraping job, different bets: Scrapling bets on resilience (self-healing selectors, Cloudflare-passing stealth); Crawl4AI bets on LLM-ready output and crawl orchestration. Hostile targets → Scrapling; RAG pipelines → Crawl4AI.

crawl4aiScrapling
Stars75k71k
Forks7.7k7.0k
LanguagePythonPython
LicenseApache-2.0BSD-3-Clause
Last activityyesterday3 days ago
Topicsweb, ragweb
Curated connections25

crawl4ai — the curator's take

The default answer when the deliverable is Markdown for a model rather than structured data for a database: heuristic content filtering, LLM and CSS extraction strategies, deep-crawl dispatchers and a Dockerized API — battle-tested by the largest community in the category. NOT the stealth pick: for hostile anti-bot targets Scrapling's fetchers earn their keep, and for classic item-pipeline scraping at scale Crawlee's queue/proxy machinery is more mature. Watch the cloud beta — the open core is healthy, but the cost-effective-cloud pitch tells you where the roadmap's gravity is.

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.