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Scrapling vs scrapy

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. — versus — The veteran Python web crawling framework: spiders, middlewares, pipelines and battle-tested scheduling — 60k+ stars and still the reference architecture for structured scraping.

The curated verdict

Both are Python scraping frameworks; Scrapling rebuilds the stack for the hostile modern web — adaptive selectors that survive redesigns, Cloudflare-passing stealth fetchers — where Scrapy relies on its ecosystem for both.

Scraplingscrapy
Stars70k63k
Forks6.9k12k
LanguagePythonPython
LicenseBSD-3-ClauseBSD-3-Clause
Last activity2 days ago4 days ago
Topicswebweb
Curated connections42

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.

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.