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crawleevscrawl4ai

Apache-2.0 175 26 22,720
341.9 thousand (month) Apr 22 2022 3.16.0(2026-04-09 07:36:53 ago)
63,373 5 54 Apache-2.0
May 01 2024 1.5 million (month) 0.8.6(2026-03-24 15:07:50 ago)

Crawlee is a modern web scraping and browser automation framework for JavaScript and TypeScript, built by Apify. It is the successor to the Apify SDK and provides a unified interface for building reliable web scrapers and crawlers that can scale from simple scripts to large-scale data extraction projects.

Crawlee supports multiple crawling strategies through different crawler classes:

  • CheerioCrawler For fast, lightweight HTML scraping using Cheerio (no browser needed). Best for static pages.
  • PlaywrightCrawler Uses Playwright for full browser automation. Handles JavaScript-rendered pages, SPAs, and complex interactions.
  • PuppeteerCrawler Similar to PlaywrightCrawler but uses Puppeteer as the browser automation backend.
  • HttpCrawler Minimal crawler for raw HTTP requests without HTML parsing.

Key features include:

  • Automatic request queue management with configurable concurrency and rate limiting
  • Built-in proxy rotation with session management
  • Persistent request queue and dataset storage (local or cloud via Apify)
  • Automatic retry and error handling with configurable strategies
  • TypeScript-first design with full type safety
  • Middleware-like request/response hooks (preNavigationHooks, postNavigationHooks)
  • Output pipelines for storing extracted data
  • Easy deployment to Apify cloud platform

Crawlee is considered the most feature-complete web scraping framework in the JavaScript/TypeScript ecosystem, comparable to Python's Scrapy but with native browser automation support.

Crawl4AI is an open-source AI-powered web crawling and data extraction library for Python. It uses large language models (LLMs) to intelligently extract structured data from web pages with minimal code. Unlike traditional scraping frameworks that rely on CSS selectors or XPath, Crawl4AI can understand page content semantically and extract data based on natural language descriptions of what you want.

Key features include:

  • LLM-based extraction Define what data you want in plain English and Crawl4AI uses LLMs to find and extract it from the page content. Supports multiple LLM providers including OpenAI, Anthropic, and local models.
  • Automatic crawling Built-in crawler with support for JavaScript rendering, parallel crawling, and session management.
  • Structured output Returns data in structured formats (JSON, Pydantic models) making it easy to integrate into data pipelines.
  • Markdown conversion Can convert web pages to clean markdown format, useful for feeding content to LLMs.
  • Chunking strategies Multiple strategies for breaking down large pages into processable chunks for LLM extraction.
  • Async support Built on async Python for efficient concurrent crawling and extraction.

Crawl4AI is particularly useful for scraping unstructured content where writing traditional CSS/XPath selectors would be tedious or fragile. It excels at content extraction, article parsing, and data mining from diverse page layouts.

Highlights


populartypescriptextendiblemiddlewaresoutput-pipelineslarge-scaleproxy
ai-poweredasyncpopular

Example Use


```javascript import { PlaywrightCrawler, Dataset } from 'crawlee'; // Create a crawler with Playwright for JS rendering const crawler = new PlaywrightCrawler({ // Limit concurrency to avoid overwhelming the target maxConcurrency: 5, // This function is called for each URL async requestHandler({ request, page, enqueueLinks }) { const title = await page.title(); // Extract data from the page const products = await page.$$eval('.product', (els) => els.map((el) => ({ name: el.querySelector('.name')?.textContent, price: el.querySelector('.price')?.textContent, })) ); // Store extracted data await Dataset.pushData({ url: request.url, title, products, }); // Follow links to crawl more pages await enqueueLinks({ globs: ['https://example.com/products/**'], }); }, }); // Start crawling await crawler.run(['https://example.com/products']); ```
```python from crawl4ai import AsyncWebCrawler, CrawlerRunConfig from crawl4ai.extraction_strategy import LLMExtractionStrategy import asyncio async def main(): # Basic crawling - get page as markdown async with AsyncWebCrawler() as crawler: result = await crawler.arun(url="https://example.com") print(result.markdown) # clean markdown content # AI-powered extraction with structured output strategy = LLMExtractionStrategy( instruction="Extract all product names and prices from this page", ) config = CrawlerRunConfig(extraction_strategy=strategy) async with AsyncWebCrawler() as crawler: result = await crawler.arun( url="https://example.com/products", config=config, ) print(result.extracted_content) # structured JSON output asyncio.run(main()) ```

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