faisalman/ua-parser-js

UAParser.js - The Essential Web Development Tool for User-Agent Detection. Detect Browsers, OS, Devices, Bots, Apps, AI Crawlers, and more. Run in Browser...

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Summary Information

Updated 10 minutes ago
Added to GitGenius on September 6th, 2026
Created on February 1st, 2012
Open Issues & Pull Requests: 15 (+0)
GitHub issues: Enabled
Number of forks: 1,217
Total Stargazers: 10,187 (+0)
Total Subscribers: 131 (+0)

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Issue Activity (beta)

Open issues: 11
New in 7 days: 1
Closed in 7 days: 1
Avg open age: 780 days
Stale 30+ days: 10
Stale 90+ days: 10

Recent activity

Opened in 7 days: 0
Closed in 7 days: 1
Comments in 7 days: 0
Events in 7 days: 0

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  • Feature Request (9)
  • Open Discussion (7)
  • Enhancement (4)
  • Waiting for Confirmation (2)
  • Wontfix (2)
  • Out of Scope (1)

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Detailed Description

UAParser.js is a user-agent parser library that detects browsers, operating systems, devices, bots, apps, and AI crawlers in JavaScript environments.

The tool solves the problem of reliably identifying client characteristics from user-agent strings. It works by parsing the user-agent header against an actively maintained detection database, enabling developers to make decisions based on what browser, device, or bot is making a request. The library is designed to be compact and dependency-free, with ES5 compatibility, and runs in both browser and server contexts including Node.js and modern JavaScript runtimes.

Developers should choose this tool if they need accurate user-agent detection without external dependencies. It suits projects requiring bot detection, device classification for responsive behavior, or analytics that depend on identifying browser and OS versions. The tool is particularly valuable for applications that must run detection logic on both client and server sides without adding significant bundle weight.

The project maintains an actively updated detection database that backs its parsing logic, indicating ongoing effort to keep pace with new browsers, devices, and crawlers. Development activity shows consistent engagement with the codebase through regular updates and maintenance of the underlying detection patterns.