User Agent Parser
Parse and analyze browser user agent strings
Current browser detection
Custom UA parsing
Sample UA library
JSON export
Device type detection
Bot detection
Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36
That string is hiding in every HTTP request your server receives. Is it a real user or a bot? Chrome or Safari? Desktop or mobile? The answers matter for analytics, security, and feature decisions—but good luck extracting them manually.
This parser decodes user agent strings instantly, revealing the browser, operating system, device type, and whether that visitor is a human or a crawler.
What is a User Agent Parser?
A user agent string is a text identifier that browsers and applications send with every HTTP request. It contains information about the client software, operating system, and device. Parsing extracts this structured information from the raw string.
Extraction example:
Input: Mozilla/5.0 (iPhone; CPU iPhone OS 17_0 like Mac OS X)...
Output:
- Browser: Safari 17.0
- OS: iOS 17.0
- Device: iPhone (Mobile)
- Engine: WebKit
Every browser claims to be "Mozilla" for historical compatibility reasons. Real identification requires pattern matching against known browser signatures.
Why People Actually Need This Tool
Every analytics platform starts with user agent parsing. Understanding your audience—their browsers, devices, and operating systems—drives product decisions.
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Analytics verification — Understand what browsers and devices visit your site.
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Bot detection — Identify crawlers, scrapers, and automated traffic.
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Device-specific debugging — Reproduce issues on the right browser/OS combination.
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Feature targeting — Decide which features to support based on actual usage.
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Security monitoring — Detect unusual user agents that might indicate attacks.
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A/B test segmentation — Analyze test results by browser or device type.
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Browser compatibility — Identify which browsers cause the most issues.
How to Use the User Agent Parser
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Paste a user agent string — From logs, analytics, or request headers.
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Or detect current browser — See your own browser's user agent parsed.
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View parsed results — Browser, OS, device type, rendering engine.
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Test sample user agents — Try different browsers and devices.
| Parsed Field | Information Extracted | Use Case |
|---|---|---|
| Browser | Name and version (Chrome 120.0) | Compatibility decisions |
| Operating System | Name and version (macOS 14.0) | Platform-specific features |
| Device Type | Desktop, Mobile, Tablet | Responsive design priority |
| Device Model | iPhone 15, Samsung Galaxy | Device-specific debugging |
| Engine | WebKit, Blink, Gecko | Rendering compatibility |
| Bot Detection | Known crawler identification | Traffic analysis |
Any client can send any user agent string. Don't rely on it for security decisions. It's useful for analytics and debugging, not authentication.
Real-World Use Cases
1. The Analytics Anomaly
Context: Analytics show 40% of traffic from "unknown browser."
Problem: Marketing can't optimize for a browser they can't identify.
Solution: Parse sample user agents from logs. Discover it's a misconfigured app hitting the API.
Outcome: Filter out API traffic from website analytics. Real browser distribution revealed.
2. The Mobile Bug Report
Context: Customer reports crash on "my phone."
Problem: Need to know exact device, OS version, and browser to reproduce.
Solution: Ask customer to visit a page that displays and parses their user agent.
Outcome: Identify iOS 16.2 Safari bug. Targeted fix deployed.
3. The Bot Traffic Investigation
Context: Server costs spiking unexpectedly.
Problem: Need to identify if traffic is legitimate users or scrapers.
Solution: Parse user agents from access logs. 60% identified as known crawlers.
Outcome: Implement rate limiting for bot user agents. Server costs normalize.
4. The Browser Support Decision
Context: Team debating whether to support Internet Explorer.
Problem: Anecdotal evidence on both sides. Need data.
Solution: Parse one week of user agents. IE represents 0.3% of traffic.
Outcome: Data-driven decision to drop IE support. Development time saved.
5. The Responsive Design Priority
Context: Redesigning e-commerce checkout flow.
Problem: Should mobile or desktop be primary design target?
Solution: Parse traffic user agents. 72% mobile, 28% desktop.
Outcome: Mobile-first redesign. Conversion rates improve 15%.
6. The Security Audit
Context: Security team reviewing access patterns for suspicious activity.
Problem: Unusual user agents in logs might indicate attack tools.
Solution: Parse and categorize all unique user agents. Flag non-standard ones.
Outcome: Identify several requests with curl and scripted user agents probing for vulnerabilities.
7. The QA Test Matrix
Context: QA needs to define browser/device testing matrix.
Problem: Can't test everything. Need to prioritize by real usage.
Solution: Parse 30 days of user agents to get actual browser/OS distribution.
Outcome: Test matrix covers 95% of real user configurations.
Common Mistakes and How to Avoid Them
New browser versions, new devices, and user agent changes can break detection. Update parsing logic regularly.
Privacy and Data Handling
This User Agent Parser operates entirely in your browser.
- No user agents are sent to any server.
- No parsing results are logged or stored.
- No account required.
- Works completely offline.
Parse user agents from your logs safely—nothing leaves your device.
Conclusion
User agent strings are information-dense but human-unreadable. They're essential for understanding your audience: what browsers they use, what devices they carry, whether they're human or bot.
This parser extracts that information instantly. Whether you're debugging a mobile issue, analyzing traffic patterns, or deciding what browsers to support, get the answers you need from any user agent string.
Stop guessing. Start parsing.