boris-code/feapder

🚀🚀🚀feapder is an easy to use, powerful crawler framework |...

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Summary Information

Updated 1 hour ago
Added to GitGenius on September 18th, 2026
Created on February 8th, 2021
Open Issues & Pull Requests: 107 (+0)
GitHub issues: Enabled
Number of forks: 551
Total Stargazers: 3,739 (+0)
Total Subscribers: 37 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 14.0 days
Mean response time: 80.3 days
90th percentile: 299.7 days
Tracked items: 21

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How this project is maintained

100% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Three people close 60% of everything that gets resolved.

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Issue Activity (beta)

Open issues: 30
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 568 days
Stale 30+ days: 30
Stale 90+ days: 28

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

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Detailed Description

Feapder is a Python web scraping framework that provides multiple spider implementations for different crawling scenarios.

The framework addresses the need for flexible web scraping by offering four built-in spider types—AirSpider, Spider, TaskSpider, and BatchSpider—each designed to handle different use cases. It supports breakpoint resumption for interrupted crawls, monitoring and alerting capabilities, browser rendering for JavaScript-heavy sites, and large-scale data deduplication. The tool comes in three installation variants: a minimal version without browser rendering or in-memory deduplication, a browser-rendering version with limited deduplication, and a complete version supporting all features.

Developers should choose this framework if they need straightforward spider creation with built-in support for common crawling challenges. The project suits teams wanting to move beyond basic scraping scripts toward a more structured approach, particularly those dealing with large datasets or complex crawling requirements. The framework integrates with feaplat, a companion management system that handles deployment and scheduling of crawlers, making it suitable for production environments where multiple crawls need coordination.

The project maintains active engagement with its user community through documented contribution guidelines and actively tracks community feedback. The codebase supports multiple Python versions and runs across Linux, Windows, and macOS platforms. The project provides comprehensive documentation including release notes and maintains integration with external tools for proxy management and CAPTCHA solving, indicating ongoing attention to practical deployment needs.