Scrapy — AI Agent Framework: Live Stats & TrendScore

Live GitHub stats, community sentiment, and trend data for Scrapy. TrendingBots tracks star velocity, fork activity, and what developers are saying — updated from real data sources.

GitHub data synced: May 6, 2026 • Sentiment updated: Apr 24, 2026

GitHub Statistics

Community Sentiment

Community Buzz: The Scrapy case was useful because it's a well-known, actively maintained codebase with real architectural complexity, as mentioned on Reddit. Scrapy (@Scrapy_) has 444 likes on Twitter and users appreciate its functionality.

Pros & Cons

What People Love

Scrapy's functionality, Reddit users praise Scrapy's performance, Dev.to users appreciate Scrapy's ease of use

Common Complaints

Security vulnerabilities, Bot protection issues

Biggest Positive: Scrapy Useful

Biggest Negative: Security Concerns

Why Scrapy Stands Out

Scrapy stands out from other web scraping frameworks due to its asynchronous architecture, which enables fast and efficient data extraction. Its ability to handle different data formats and store data in various formats makes it a versatile tool for a wide range of use cases. Additionally, Scrapy's large community and extensive documentation make it a great choice for both beginners and experienced developers. As stated in the README, Scrapy is 'a web scraping framework to extract structured data from websites' and is maintained by Zyte and many other contributors.

Built With

Build a web scraper that extracts product information from e-commerce sites — Scrapy's asynchronous architecture enables fast and efficient data extraction, Build a data pipeline that integrates with databases and data warehouses — Scrapy's item pipelines allow for easy data processing and storage, Build a monitoring tool that tracks website changes and updates — Scrapy's scheduler and notifier systems enable real-time monitoring and alerts, Build a research tool that analyzes website structure and content — Scrapy's link extractor and content parser enable in-depth analysis of website data, Build a web archiving tool that preserves historical website data — Scrapy's ability to handle different data formats and store data in various formats enables long-term preservation of website data

Getting Started

  1. Install Scrapy using pip: `pip install scrapy`
  2. Create a new Scrapy project using `scrapy startproject myproject`
  3. Define your spider in `myproject/spiders/myspider.py` and configure the settings in `myproject/settings.py`
  4. Run your spider using `scrapy crawl myspider` and store the data in a JSON file using `scrapy crawl myspider -o data.json`
  5. Try scraping a website using `scrapy shell https://example.com` to verify that Scrapy works as expected

About

Scrapy, a fast high-level web crawling & scraping framework for Python.

Official site: https://scrapy.org

Category & Tags

Category: data

Tags: crawler, crawling, framework, hacktoberfest, python, scraping, web-scraping, web-scraping-python

Market Context

Competitive web scraping market with alternatives like Apify and BeautifulSoup