User Agent Parser - Identify Browser, OS & Device

How the User Agent Parser Works

1. 01Your own browser's user agent loads into the input automatically when the page opens.
2. 02Paste any other user agent string to analyze it instead - from logs, bug reports, or analytics.
3. 03An ordered ruleset checks bot signatures first, then browser markers from most to least specific.
4. 04Read the results as labeled cards and copy the parsed structure as JSON.

Reading User Agent Strings Correctly

Paste the string exactly as it appears in your server log or bug report; the parser is whitespace tolerant and case aware where it matters. The detection order is the key to accuracy. Bots are checked first, because crawlers like Googlebot embed Chrome-like tokens that would otherwise be misread as real browsers. Then come the browsers that wrap Chromium and must be identified before Chrome itself: Edge by its Edg token, Opera by OPR, and Samsung Internet by SamsungBrowser. Firefox and Chrome follow, and Safari is checked last because nearly every WebKit browser includes the word Safari - genuine Safari is recognized by its Version token. Operating system detection maps Windows NT numbers to marketing names (NT 10.0 covers both Windows 10 and 11, which share the token), converts iOS and macOS underscore versions to dotted form, and recognizes Android, ChromeOS, and Linux. Device classification distinguishes tablets (iPad, or Android without the Mobile token) from phones and desktops. The JSON export gives you the same structure programmatically, ready for test fixtures or documentation.

When to Use the User Agent Parser

Use it when a bug report includes a user agent and you need to know what the reporter was actually running, when triaging server logs to separate crawler traffic from human visitors, or when verifying that your device-detection or analytics code classifies clients correctly. It is also useful for checking what your own browser advertises about itself. For runtime feature decisions in production code, prefer feature detection over user agent sniffing.

Common Use Cases

• ·Decoding the user agent attached to a customer bug report
• ·Separating search engine and SEO crawler hits from human traffic in server logs
• ·Verifying device-detection logic against real-world user agent samples
• ·Exporting parsed results as JSON for test fixtures
• ·Inspecting auth tokens from the same request logs → JWT Decoder

Expert Tips

• ✻For feature decisions in production code, use feature detection or User-Agent Client Hints; string parsing is for diagnostics, not branching logic.
• ✻When log entries claim to be Googlebot, verify with a reverse DNS lookup - fake Googlebot user agents are a common scraper disguise.
• ✻Keep a few real user agent samples from your analytics as regression fixtures whenever you change detection code.

Frequently Asked Questions