User Agent Parser & Decoder

What is a User-Agent (UA) string?

A User-Agent (UA) string is a text identifier that browsers and HTTP clients send to web servers with every request. It typically contains information about the browser name and version, rendering engine, operating system, device type, and sometimes the architecture of the client device. Servers use this information to customize content, track analytics, and ensure compatibility.

How accurate is the user-agent parser?

The parser uses a regex-based detection engine with tested patterns for major browser families, operating systems, and device types. It achieves high accuracy for well-known, standards-compliant UA strings from mainstream browsers. Accuracy may be lower for obscure, modified, or deliberately obfuscated UA strings. The confidence score gives you a quick indication of how reliable each parse result is.

Can I compare two User-Agent strings?

Yes. You can enable Side-by-Side Comparison mode to paste two UA strings and see their parsed results next to each other. This is particularly useful for debugging browser-specific issues, verifying that a UA change has the intended effect, or comparing how different devices present themselves to your server.

How does device categorization work?

The parser classifies devices by examining the UA string for OS keywords (Windows NT, Macintosh, iPhone, Android) and device keywords (Mobile, Tablet, iPad, PlayBook). If the string matches known bot or crawler patterns (Googlebot, Bingbot, curl, wget), it is categorized as a Bot. Desktop is the default for full-size OS signatures without mobile or tablet indicators.

What does the confidence score mean?

The confidence score (0-100) reflects how reliably the parser was able to identify the browser, OS, engine, device type, and architecture. A high score (80+) indicates all major fields were detected with good signal clarity. A mid-range score (45-79) suggests one or more fields returned "Unknown" or had weak signal. A low score (<45) indicates significant ambiguity - the UA may be malformed, spoofed, or too short to parse reliably.

Can this tool detect bots and crawlers?

Yes. The parser has a built-in bot detection engine that recognizes signatures from Googlebot, Bingbot, AppleBot, DuckDuckBot, YandexBot, Baiduspider, FacebookExternalHit, Twitterbot, Discordbot, ChatGPT, and many others. It also flags headless browsers (HeadlessChrome, PhantomJS), scripting clients (curl, wget, Python-requests), and HTTP library clients.

Is my data sent to a server?

No. All parsing and analysis happens entirely in your browser using JavaScript. The UA strings you paste never leave your device, are not uploaded to any server, and are not stored or logged. Results are only saved locally in your browser's localStorage if you choose to enable history, and can be cleared at any time.

Does the parser detect spoofing or security issues?

Yes. The parser checks for conflicting platform tokens (e.g., Windows NT + iPhone OS in the same string), mixed browser identities (Chrome + Firefox tokens together), suspiciously short strings, and known automation signatures. These are flagged with security hints and contribute to the spoofing risk assessment and confidence score.

What are some common User-Agent strings?

Common examples include: Chrome on Windows - "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/124.0.0.0 Safari/537.36"; Safari on iPhone - "Mozilla/5.0 (iPhone; CPU iPhone OS 17_2 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/17.2 Mobile/15E148 Safari/604.1"; Firefox on macOS - "Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:123.0) Gecko/20100101 Firefox/123.0"; Googlebot - "Mozilla/5.0 (compatible; Googlebot/2.1; +http://www.google.com/bot.html)".

Why does browser market share matter?

Browser market share helps prioritize development and testing efforts. If your analytics show the majority of your visitors use Chrome and Safari, those platforms deserve the most careful testing. Knowing where Edge, Firefox, and niche browsers fall in market share helps you decide what level of compatibility investment each browser warrants. The parser provides estimated market share context for the detected browser family.