User Agent Parser
How It Works
- Step 1: Paste user agent string from HTTP headers, server logs, or browser navigator.userAgent containing browser, OS, and device identifiers.
- Step 2: The parser uses regex patterns and UA databases to identify browser name/version (Chrome 120, Firefox 121), rendering engine (Blink, Gecko), and browser capabilities.
- Step 3: Extracts operating system details including OS name (Windows, macOS, Linux, iOS, Android), version numbers, and architecture (x64, ARM64).
- Step 4: Identifies device type (desktop, mobile, tablet), manufacturer (Apple, Samsung), model name, and bot detection for analytics, responsive design, and security monitoring.
User Agent Parsing Examples
Example: Browser UA Parsing
Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36 Browser Information:
Name: Chrome
Version: 120.0.0.0
Major Version: 120
Rendering Engine: Blink (WebKit 537.36)
Operating System:
Name: Windows
Version: 10
Architecture: 64-bit (x64)
Device Information:
Type: Desktop
Form Factor: Computer
Additional Details:
- Compatible with Safari (WebKit-based)
- Supports modern web standards (ES6+, CSS Grid)
- Not a bot or crawler
Use Cases:
✓ Serve desktop-optimized layout
✓ Enable Chrome-specific features
✓ Track Windows 10 usage in analytics
✓ Apply browser-specific CSS fixes if needed Key Changes:
The parser decodes the UA string structure where 'Mozilla/5.0' is a legacy compatibility token, not indicating Firefox. The Windows NT 10.0 maps to Windows 10 (Microsoft's versioning scheme). The Win64 and x64 tokens confirm 64-bit architecture. AppleWebKit/537.36 indicates the rendering engine version, while Chrome/120.0.0.0 provides the actual browser identity and version. The 'Safari/537.36' token is included for compatibility with sites checking for WebKit. This parsing enables responsive design decisions—serving desktop layouts vs mobile, enabling features based on browser capabilities (WebGL, WebRTC), and analytics segmentation by OS/browser. Security teams use UA parsing to detect bots, scrapers, or outdated browsers with known vulnerabilities. The parser distinguishes real Chrome from headless browsers or spoofed UAs used in automated testing.
Frequently Asked Questions
How does the parser identify browsers and their versions accurately?
The parser uses regex patterns and heuristics to identify browsers by matching signature strings in user agent format. For Chrome, it looks for "Chrome/XXX.X" or "CriOS" (iOS Chrome). For Firefox, "Firefox/XXX.X" or "FxiOS". For Safari, "Version/XXX.X Safari" or "Mobile/XXX Safari". The parser handles version extraction including major, minor, and patch numbers (e.g., Chrome 120.0.6099.129 → major: 120, minor: 0, patch: 6099). It distinguishes between similar browsers: Chrome vs Chromium vs Edge (Chromium-based), Safari vs Safari Mobile, Firefox vs Firefox Focus. The parser detects rendering engines: Blink (Chrome/Edge), WebKit (Safari), Gecko (Firefox), Trident (IE). It also identifies browser features like headless mode (HeadlessChrome), WebView contexts (wv), and in-app browsers (FB, Instagram, LinkedIn). This accurate detection is critical for browser compatibility testing, feature detection, and analytics where knowing exact browser versions determines CSS/JavaScript support.
What device and operating system information can be extracted?
The parser extracts comprehensive device information: device type (desktop, mobile, tablet, smart TV, game console, wearable), manufacturer (Apple, Samsung, Google, Huawei, Xiaomi), model (iPhone 15 Pro, Galaxy S24, Pixel 8), and form factor (phone, phablet, tablet). For operating systems, it detects OS name (Windows, macOS, Linux, Android, iOS, ChromeOS), version (Windows 11, macOS Sonoma 14.2, Android 14, iOS 17.2), and architecture (x86, x64, ARM, ARM64). The parser identifies specific OS variants: Windows 10 vs 11, macOS versions by name (Ventura, Sonoma), Linux distributions (Ubuntu, Fedora, Debian), and Android custom ROMs. It also extracts CPU architecture, screen resolution hints, and touch capability indicators. For mobile devices, it distinguishes between phone and tablet based on screen size indicators. This detailed device profiling enables responsive design testing, feature availability checks, and user segmentation in analytics.
How does the parser handle bot and crawler user agents?
The parser identifies bots and crawlers by detecting signature patterns: Googlebot (Google Search crawler), Bingbot (Microsoft Bing), Slurp (Yahoo), DuckDuckBot (DuckDuckGo), Baiduspider (Baidu), YandexBot (Yandex), and social media crawlers (facebookexternalhit, Twitterbot, LinkedInBot, WhatsApp). It distinguishes between search engine bots, SEO tools (Ahrefs, SEMrush, Moz), monitoring services (Pingdom, UptimeRobot), and malicious bots. The parser extracts bot version, purpose (indexing, preview generation, link validation), and contact information when available. It flags suspicious user agents: empty strings, generic strings ("Mozilla/5.0"), or strings claiming to be multiple browsers simultaneously. For legitimate bots, it provides verification methods (reverse DNS lookup for Googlebot). This bot detection is essential for analytics filtering, rate limiting, content serving decisions (serving static HTML to bots vs dynamic content to users), and security (blocking malicious scrapers while allowing legitimate crawlers for SEO).