Scrapy is an open-source Python framework for large-scale web crawling and scraping — built on an asynchronous networking engine and structured around spiders (the crawl logic), item pipelines (post-processing and storage), and middlewares (hooks for proxies, retries, and headers).
A spider defines a starting list of URLs and a parse() callback that yields either structured Items or new Requests to follow — the engine schedules all of it concurrently through an asynchronous reactor rather than fetching one page at a time, which is what lets a single Scrapy process run hundreds of requests in flight without manual async code. Item pipelines then run each yielded item through a configurable chain of validation, cleaning, and storage steps.
Downloader middlewares are the extension point most production setups actually customize: proxy rotation, user-agent rotation, automatic retry with backoff, and duplicate-URL filtering all hook in at that layer, which is why Scrapy scales past a hand-rolled requests loop without needing a rewrite as request volume grows.
Built-in concurrency is the headline reason — Scrapy runs many requests in parallel out of the box, where a plain script needs manual threading or asyncio to do the same safely. Automatic retry logic, request deduplication, and a CLI that exports straight to JSON, CSV, or XML round out the case for anything beyond a one-off script scraping a handful of pages.
The tradeoffs are real: a steeper learning curve than a simple script, and no JavaScript rendering built in — Scrapy fetches raw HTML, so JavaScript-heavy targets need a separate integration like scrapy-playwright layered on top rather than something Scrapy handles natively.
import scrapy
class ProductSpider(scrapy.Spider):
name = "products"
start_urls = ["https://example.com/products"]
def parse(self, response):
for card in response.css("div.product-card"):
yield {
"name": card.css("h2::text").get(),
"price": card.css("span.price::text").get(),
}How Crawlora handles this
Crawlora replaces the infrastructure Scrapy still leaves to you — proxy rotation, browser rendering, anti-bot handling — behind a single API call, so a Scrapy pipeline (or any other client) can call Crawlora as a data source instead of maintaining its own downloader middleware stack for every target.
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Glossary
FAQ
For a handful of pages, plain requests plus BeautifulSoup is simpler and sufficient. Scrapy earns its complexity at scale — built-in concurrency, retry logic, and pipelines matter once you're crawling thousands of pages across many domains.
Not natively — Scrapy fetches raw HTML over HTTP. JavaScript-heavy targets need a separate integration like scrapy-playwright or scrapy-splash layered on top to render pages before Scrapy's selectors can extract from them.
No — it provides the middleware hooks (proxy rotation, custom headers, retry logic) for you to configure, but you still have to supply and maintain the proxy pool, fingerprint consistency, and anti-bot handling yourself.
Beyond Scrapy, Crawlora's own docs cover the rest of the stack — browse the APIs, test a request in Playground, and move from scraping infrastructure work to production data workflows.