Scrapy

Features

Web Scraping - Extracts structured information from websites by defining navigation rules and processing content into organized storage formats.
Web Scrapers - Automates the navigation of websites to collect and process structured information at scale.
Structured - Converts unstructured web content into clean, typed, and organized data formats using defined extraction logic.
Distributed Crawling Engines - Powers large-scale data collection through a scalable, asynchronous engine with built-in rate control and memory management.
Event-Driven Engines - Handles non-blocking network requests and concurrent data processing tasks via an asynchronous, event-driven core loop.
Selector-Based Extractors - Maps raw HTML content into structured objects using CSS selectors and XPath expressions.
Distributed Crawling Systems - Coordinates high-volume, asynchronous crawling operations to ensure reliability during long-running data collection tasks.
Modular Pipeline Architectures - Decouples data collection stages into independent, configurable components using modular middleware and signal handlers.
Concurrency-Controlled Schedulers - Regulates request volume through a priority-based queue to balance throughput against target server load constraints.
Crawler Middleware - Customizes data collection flows through specialized middleware and signal handlers for request and response processing.
Crawling Optimization - Optimizes large-scale data collection by dynamically managing memory usage and request rates for efficient performance.
Web Scraping - High-performance Python framework for web scraping.
Developer Tools - Framework for web crawling and data scraping.
Python Crawling Frameworks - High-level framework for screen scraping and web crawling.
Python Projects - Listed in the “Python Projects” section of the Awesome For Beginners awesome list.
Web Scraping - High-level framework for web crawling and scraping.
Item Pipelines - Processes individual data items through a sequential chain of validation, cleaning, and storage handlers before persistence.
Middleware-Based Request Pipelines - Intercepts and modifies network requests and responses as they flow through a chain of pluggable components.
Crawler Health Monitoring - Tracks operational statistics and diagnostic metrics to identify potential bottlenecks during active data collection processes.
Lifecycle Signal Handlers - Enables external components to hook into specific lifecycle events to monitor or alter behavior during execution.
Distributed Tracing and Execution Analysis - Inspects active processes and execution metadata to maintain visibility into performance during long-running extraction jobs.

Open-source alternatives to Scrapy

Similar open-source projects, ranked by how many features they share with Scrapy.

apify/crawlee
apify/crawlee
24,002View on GitHub
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 project distinguishes itself through its resource-aware concurrency controller, which dynamically scales task execution based on real-time CPU and memory usage to prevent host machine exhaustion. It also features a rob
TypeScriptapifyautomationcrawler
View on GitHub24,002
unclecode/crawl4ai
unclecode/crawl4ai
68,644View on GitHub
Crawl4AI is an AI-powered web crawling and data extraction engine designed to transform complex web content into structured formats. It functions as a headless browser orchestrator, enabling the navigation of dynamic websites, the execution of custom scripts, and the capture of visual assets like screenshots and PDFs. By integrating language models directly into the extraction workflow, the system converts raw HTML into clean, structured data or Markdown files optimized for downstream ingestion. The platform distinguishes itself through a distributed, self-hosted infrastructure that manages l
Python
View on GitHub68,644
binux/pyspider
binux/pyspider
16,809View on GitHub
PySpider is a Python web crawling framework designed for automated data extraction. It provides a pipeline for periodically fetching web content, processing HTML, and persisting scraped information into database backends. The system features a web-based management interface for editing scraping scripts, monitoring task progress, and reviewing collected data. It includes a headless browser JavaScript renderer to capture rendered HTML from dynamic web pages and a distributed architecture that uses message queues to scale crawling workloads across multiple nodes. The framework also covers task
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firecrawl/firecrawl
firecrawl/firecrawl
133,479View on GitHub
Firecrawl is a web data extraction platform designed to convert unstructured web content into clean, LLM-ready formats like markdown or JSON. It functions as an autonomous web crawler and scraper, capable of mapping entire domains, performing recursive navigation, and executing complex data gathering tasks. By leveraging headless browser orchestration, the system handles dynamic, JavaScript-heavy pages to ensure comprehensive data capture. The platform distinguishes itself through its focus on agentic workflows, providing a programmatic interface that allows autonomous agents to perform live
TypeScriptaiai-agentsai-crawler
View on GitHub133,479

See all 30 alternatives to Scrapy

scrapyscrapy

Features

Open-source alternatives to Scrapy

apify/crawlee

unclecode/crawl4ai

binux/pyspider

firecrawl/firecrawl

Star history

Open-source alternatives to Scrapy

apify/crawlee

unclecode/crawl4ai

binux/pyspider

firecrawl/firecrawl