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Crawlee is a web scraping framework designed for building scalable, reliable, and distributed data extraction pipelines. It provides a unified interface for managing headless browser automation and lightweight HTTP requests, allowing developers to handle complex web navigation, dynamic content rendering, and large-scale data collection within a single, modular architecture.
The main features of apify/crawlee are: Web Crawling, Web Scraping Frameworks, Resource-Aware Scaling Controllers, Web Data Extraction, Headless Browser Automation, Asynchronous Crawl Queues, Distributed Crawling Engines, Browser Automation.
Projects with overlapping indexed features include: apify/crawlee-python — Crawlee-python is a web crawling framework for building scalable scrapers using Python. It serves as a comprehensive… nanmicoder/crawlertutorial — CrawlerTutorial is a comprehensive Python web scraping tutorial and framework designed for extracting data from static… camel-ai/camel — This project is a comprehensive framework for building and managing autonomous agent systems. It provides a unified… andeya/pholcus — Pholcus is a distributed web crawling system designed for large-scale data scraping. It employs a master-worker… getmaxun/maxun — Maxun is an open-source web scraping and automation platform designed to transform dynamic website content into… omkarcloud/botasaurus — Botasaurus is a Python web scraping framework and headless browser automation system used to build scalable data…
Crawlee-python is a web crawling framework for building scalable scrapers using Python. It serves as a comprehensive tool for web scraping automation, providing a system to extract structured data from websites using both lightweight HTTP requests and headless browser automation. The framework is distinguished by its anti-bot evasion capabilities, which include browser fingerprint impersonation and tiered proxy rotation to bypass detection systems and solve challenges such as Cloudflare. It also incorporates artificial intelligence for autonomous website navigation and schema-based data extra
CrawlerTutorial is a comprehensive Python web scraping tutorial and framework designed for extracting data from static and dynamic websites. It functions as a web data extraction pipeline and an HTTP request orchestrator, covering the full lifecycle of scraping applications from initial fetching to final data storage. The project provides specialized guidance on anti-bot bypass techniques and web API reverse engineering. It includes methods for evading browser detection through identity masking and proxy rotation, as well as techniques for identifying hidden API endpoints by analyzing network
This project is a comprehensive framework for building and managing autonomous agent systems. It provides a unified architecture for orchestrating multi-agent societies, where specialized agents collaborate through roleplay to decompose and solve complex tasks. The system integrates language models with external environments, enabling agents to perform real-world actions through a standardized tool-calling abstraction layer. The framework distinguishes itself through its focus on iterative reasoning and data reliability. It employs automated feedback loops to refine agent outputs and self-eva
Pholcus is a distributed web crawling system designed for large-scale data scraping. It employs a master-worker distribution model to coordinate high-concurrency scraping tasks across a network of remote client nodes, enabling both horizontal and vertical data collection. The system features a hot-loadable rule engine that allows extraction and navigation logic to be updated at runtime without restarting the process. It handles dynamic content through headless browser integration and bypasses bot detection using proxy rotation, automated user authentication, and simulated human behavior. The