onevcat/kingfisher

A lightweight, pure-Swift library for downloading and caching images from the web.

View on GitHub ↗Jump to charts ↓

Summary Information

Updated 36 minutes ago
Added to GitGenius on September 2nd, 2026
Created on April 6th, 2015
Open Issues & Pull Requests: 169 (+0)
GitHub issues: Enabled
Number of forks: 2,767
Total Stargazers: 24,388 (+0)
Total Subscribers: 357 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 3.9 days
Mean response time: 52.7 days
90th percentile: 86.1 days
Tracked items: 123

How this project is maintained

Around half of the issues opened in the past year never receive a reply. 100% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Only 11% of issues opened in the past year have been closed. Three people close 74% of everything that gets resolved.

Charts & Analytics

Fetching additional details & charts...

Issue Activity (beta)

Open issues: 53
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 939 days
Stale 30+ days: 51
Stale 90+ days: 50

Recent activity

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

Top labels

  • feature in plan (2)
  • v5 (1)

Most active issues this week

No issue events were indexed in the last 7 days.

Detailed Description

Kingfisher is a lightweight image downloading and caching library for Swift.

The library solves the problem of efficiently fetching and storing images from remote URLs in iOS and macOS applications. It handles the common challenges of image loading by providing automatic caching, memory management, and network request optimization. The approach centers on a pure-Swift implementation that integrates directly with native frameworks, avoiding external dependencies while offering a straightforward API for setting images on UI components.

Developers should choose Kingfisher for projects that need reliable image handling without the overhead of heavier frameworks. It suits applications ranging from simple image galleries to complex feeds with many remote images, particularly where memory efficiency matters. The library is especially valuable for iOS and macOS targets where native Swift integration is preferred. The README does not position the tool against named alternatives, so direct comparisons cannot be made.

The project maintains steady activity with regular updates addressing both bug fixes and feature additions. Development includes ongoing refinement of caching strategies and image processing capabilities. The codebase receives consistent attention to performance optimization and compatibility with evolving Swift language features. Community engagement through issue resolution and pull request review remains active.