Here's a comparison of modern web crawling services and GitHub projects:
Title | Description | GitHub Stars | Type |
Crawlee | Complete web scraping and browser automation library with built-in anti-blocking features and support for HTTP/browser crawling | 12.3K | Library |
PySpider | Powerful Python-based web crawling system with web UI for monitoring and control | 16.3K | Library |
Apache Nutch | Extensible crawler for large-scale web crawling with Hadoop integration | 2.8K | Framework |
Reader (Jina AI) | Modern URL-to-markdown converter optimized for LLM input | 4.5K | Service |
LLM Scraper | TypeScript library converting webpages to structured data using LLMs | N/A | Library |
FireCrawl | API service for converting URLs into clean, LLM-friendly markdown | N/A | Service |
ScrapeGraphAI | Python library using LLM and graph logic for web scraping | N/A | Library |
Spider-Flow | Visual spider framework requiring no coding to crawl websites | N/A | Framework |
Crawlee | Fast and reliable web crawler with Node.js support | N/A | Library |
PHPScraper | Simple PHP-based scraper and crawler | N/A | Library |
WebCollector | Multi-threaded web crawler with simple interfaces | N/A | Library |
StormCrawler | Scalable crawler built on Apache Storm | N/A | Framework |
Are there any other GitHub projects similar to D4Vinci/Scrapling
Based on the search results, I'll create a comprehensive table of notable web crawling and scraping frameworks/projects:
Project Name | Description | GitHub Stars | Language |
Scrapy | High-level web crawling & scraping framework with extensive features and async capabilities | 51K | Python |
Crawl4AI | LLM-friendly web crawler optimized for AI applications | 15.8K | Python |
PySpider | Powerful web crawling system with web-based UI and JavaScript support | 16.3K | Python |
Colly | Fast and elegant scraping framework with clean API and high performance | ~10K | Go |
Crawlee | Modern web scraping library with anti-blocking features and browser automation | 12.3K | Node.js |
WebMagic | Flexible Java-based scraping framework for targeted data extraction | 11.3K | Java |
Sublist3r | OSINT-based subdomain enumeration tool | ~8K | Python |
Pholcus | Distributed, high-concurrency web crawler | ~5K | Go |
Fetchbot | Simple and flexible web crawler with robots.txt support | ~3K | Go |
Go-Spider | Concurrent crawler framework with extensive features | ~2K | Go |
Notable Features
Popular Features Across Projects:
Async/parallel crawling capabilities
Proxy support and rotation
JavaScript rendering
Custom middleware support
Data export in multiple formats
Rate limiting and politeness controls
Cookie and session handling
Distributed crawling options
Many of these projects are actively maintained and regularly updated with new features and security patches.
Are there any other GitHub projects similar to Firecrawl
Based on the search results, here are similar GitHub projects that focus on LLM-friendly web crawling and content processing:
Project Name | Description | GitHub Stars | Key Features |
Crawl4AI | Open-source LLM-friendly web crawler & scraper | 15.8K | JavaScript execution, custom hooks, content loading verification |
Firecrawl | Website to LLM-ready markdown converter | 1.4K | Clean markdown conversion, structured data output, built-in LangChain/LlamaIndex loaders |
Crawlee | Web scraping library with LLM optimization | 12.3K | Anti-blocking features, browser automation, proxy rotation |
WebMagic | Targeted web scraping framework | 11.3K | Flexible architecture, efficient data extraction, good for specific scraping tasks |
Unique Features Comparison
Content Processing:
Firecrawl specializes in converting websites into clean markdown with handling for images, videos, and tables
Crawl4AI focuses on making content LLM-friendly with JavaScript rendering support
Crawlee offers human-like browser fingerprints and automatic concurrency management
Integration Capabilities:
Firecrawl provides direct integration with LlamaIndex and LangChain
Most frameworks support custom middleware and export formats
Several options include built-in proxy support and caching mechanisms
The choice between these tools often depends on specific requirements such as scale, content type handling, and integration needs with existing LLM infrastructure.
How to crawl the uncrawlable site
Based on the search results, here's a comprehensive guide on how to crawl challenging websites:
Core Strategies
Browser Emulation
Use headless browsers to handle JavaScript-heavy sites and dynamic content loading
Implement tools like Playwright or Puppeteer for full browser rendering
Enable JavaScript rendering to analyze the rendered version of pages
Request Management
- Rotate User-Agents to mimic different browsers and devices
from fake_useragent import UserAgent
ua = UserAgent()
headers = {
'User-Agent': ua.random,
}
Anti-Detection Measures
Add legitimate referrer headers to appear as organic traffic
Watch for and avoid honeypot traps (hidden links with CSS properties like "display: none")
Implement proper delays between requests to avoid triggering rate limits
Advanced Techniques
IP Management
Use proxy rotation to avoid IP-based blocking
Implement VPNs for testing and debugging purposes
Consider residential proxies for more legitimate-appearing traffic
Content Access
Handle AJAX requests and infinite scrolling by:
Intercepting API calls
Simulating scroll events
Extracting data from XHR requests
Error Handling
Monitor for common blocking indicators:
HTTP status codes (401, 403, 429)
CAPTCHAs
Redirect chains
Implement CAPTCHA solving services when necessary
Best Practices
Run initial test crawls with low limits to identify potential issues
Monitor site performance impact and adjust crawl rates accordingly
Respect robots.txt while implementing custom crawl restrictions
Keep track of website layout changes and update scrapers accordingly
Following these techniques will help you successfully crawl even the most challenging websites while maintaining a legitimate appearance and avoiding blocks.
Browser Emulation crawler github project
Based on the search results, here are some notable browser emulation crawler projects:
Project Name | Description | Key Features | GitHub Stars |
Browsertrix Crawler | High-fidelity browser-based crawling system | Customizable browser crawling, complex site handling | ~1.5K |
Selenium | Browser automation framework | Multi-browser support, extensive ecosystem, comprehensive testing capabilities | 30.7K |
Ulixee Hero | Web browser built specifically for scraping | Built-in DOM emulation, Chrome engine, browser profile emulation | ~8K |
Key Features Comparison
Ulixee Hero
First modern headless browser designed specifically for scraping
Full DOM compliance in NodeJS
Advanced detection avoidance capabilities
Browser profile emulation system
Built-in TCP/TLS fingerprint protection
Browsertrix Crawler
High-fidelity crawling system
Complex site handling
Customizable browser-based crawling
Single container deployment
Selenium
Industry standard for browser automation
Supports multiple browsers
Extensive testing capabilities
Large ecosystem of tools and plugins
Advanced Capabilities
Anti-Detection Features:
Browser fingerprint manipulation
User behavior emulation
Network signature matching
TLS/TCP stack protection
Technical Integration:
Headless browser support
JavaScript rendering
Multi-container testing capabilities
Custom profile management
These tools are particularly useful for crawling JavaScript-heavy sites and those with advanced anti-bot measures.