User-Agent Parser

Parse any User-Agent string to identify browser, OS, device type, and bot signals.

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User-Agent Parser

Decode a UA string into browser, OS, device, engine, and bot hints.

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Settings Prefilled example
0 chars
Tip: copy the UA from request headers or server logs without trimming.
Adds a simple bot flag and a short reason when applicable.
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Result Copy-ready
No output yet
Paste a User-Agent string and click Generate.
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About User-Agent Parser

User-Agent Parser online tool for parsing User-Agent strings

A User-Agent string can look like random noise, but it contains valuable hints about the browser, operating system, rendering engine, and device type behind a request. User-Agent Parser turns that long header into a readable summary or clean JSON so you can debug analytics, build conditional logic, and validate server-side detection without guesswork. Paste any UA from your logs or headers and get an output that is easy to share with teammates, support, or QA.

How User-Agent Parser Works

This tool scans your input User-Agent and matches well-known tokens and patterns used by major browsers, operating systems, and crawlers. It prioritizes the most specific matches first (for example, distinguishing Edge from Chrome, or iOS Safari from desktop Safari) and then compiles the findings into a structured result you can copy, download, or paste into logs and documentation.

Under the hood, the parser applies a layered approach. First it identifies obvious “families” such as iOS devices, Android devices, desktop platforms, and common engines. Then it extracts the best candidate version token for the detected browser brand. Finally it adds practical notes that explain why a decision was made, which helps when a UA includes multiple compatibility tokens or legacy markers.

Step-by-step

  • 1) Paste a User-Agent: Enter the full header value exactly as you see it in developer tools, server logs, or request headers.
  • 2) Choose an output format: Pick a human-readable summary, JSON, or a combined output depending on what you want to do next.
  • 3) Optional toggles: Enable bot hints and key normalization if you prefer lowercase keys for scripting or consistent downstream storage.
  • 4) Generate the parsed result: The tool detects browser family and version, OS family and version cues, device category, and the likely engine token.
  • 5) Copy or download: Use one click to copy the output or download a TXT file for tickets, bug reports, monitoring notes, or test fixtures.

Key Features

Browser and version detection

The parser recognizes common browser families such as Chrome, Edge, Firefox, Safari, Opera, and legacy Internet Explorer style tokens. It extracts the most relevant version token (for example, Edg/ vs Chrome/) so you can differentiate Chromium-based browsers correctly. This matters when you troubleshoot issues reported as “Chrome” that are actually Edge, Brave, or another Chromium variant.

When possible, the output includes a browser “label” and a “version” field. If the UA does not provide a clear version token, the tool still returns the best browser family match so you can group traffic consistently.

Operating system identification

Operating systems leave distinctive markers in the UA string. The tool highlights Windows, macOS, Linux, Android, and iOS cues and surfaces the most useful version fragments when they are present. For Windows UAs, you may see NT-version markers; for iOS, the tool looks for the iPhone/iPad pattern and iOS-style underscores; for Android, it reads common “Android X” signatures.

Because OS strings vary widely, the parser focuses on practical OS family detection and “best effort” version hints rather than claiming perfect precision for every niche device.

Device type and form factor hints

Many product decisions depend on whether a request comes from a phone, tablet, desktop, or a bot. The output includes a practical device category based on widely used keywords like Mobile, Tablet, iPhone, iPad, and Android device hints. You can use this to quickly validate server-side device bucketing, responsive breakpoints, or “mobile-only” issues.

Device detection is intentionally conservative. If the UA does not clearly indicate a mobile or tablet form factor, the tool falls back to “Desktop” to avoid over-classifying ambiguous clients.

Engine token summary

User-Agent strings often include engine identifiers such as AppleWebKit, Gecko, Trident, and Chromium signatures associated with Blink. The engine field helps explain rendering differences when you see layout issues, font problems, or JavaScript feature gaps. It also helps when you want to quickly differentiate legacy IE/Trident traffic from modern evergreen browsers.

Bot and crawler signal flags

Crawlers frequently announce themselves using tokens like bot, spider, crawl, or recognizable names. With bot hints enabled, the tool adds a simple boolean and a “why” note so you can quickly judge whether traffic is likely human or automated. This is useful for analytics hygiene, rate-limit tuning, and verifying whether an unexpected spike might be caused by indexing activity.

Keep in mind that sophisticated automation can spoof UAs. Treat the bot flag as a quick triage hint rather than a complete security or fraud prevention mechanism.

Use Cases

  • Analytics validation: Confirm whether your “browser” and “OS” reports align with the raw headers your tracking pipeline receives.
  • Debugging login and checkout issues: When a user reports a problem, parse their UA to reproduce the environment and spot outdated or unusual clients.
  • Customer support workflows: Convert a UA from an email into a short, clear environment summary your support team can understand.
  • Server-side feature flags: Decide when to serve alternative assets, polyfills, or reduced experiences based on platform constraints.
  • Bot traffic review: Quickly triage suspicious spikes by checking whether requests resemble known crawlers or headless tooling.
  • WAF and rate-limit tuning: Use parsed fields as part of rule debugging when investigating false positives or missed blocks.
  • API and webhook troubleshooting: Parse UAs from third-party integrations to understand what library or client is hitting your endpoints.
  • Test fixture creation: Generate JSON outputs you can store as baseline fixtures for unit tests or regression checks.

Whether you work on a marketing site, a SaaS app, or an API gateway, having a quick UA decoder helps you move from “it breaks on my device” to an actionable environment profile. The output is designed to be easy to paste into Slack, GitHub issues, incident notes, and bug reports.

Teams that handle high-volume traffic also use UA parsing as part of monitoring: sudden shifts in device mix, spikes of odd browser strings, or unexpected bot-like tokens can indicate outages, scraping, or a misconfigured client rollout.

Optimization Tips

Prefer complete, unmodified header values

Copy the UA exactly as it appears in the HTTP header. Truncation, line breaks, or extra quotes can hide critical version tokens and make detection less accurate. If you capture UAs from logs, ensure the logging format preserves the full string and does not cut off long values.

Use JSON output for automation and comparisons

If you are building scripts, dashboards, or pipelines, choose JSON. It is easier to store, compare, and diff over time, and it works well for test fixtures. Lowercase keys can be useful when you want consistent naming conventions across multiple internal tools and codebases.

Combine UA parsing with capability detection

Modern browsers increasingly reduce fingerprinting surface, and some environments deliberately spoof their UA. Use UA parsing as a practical indicator, then combine it with capability detection (feature flags, progressive enhancement, responsive design) when making important product decisions. In other words, use the UA to guide debugging, but use real runtime features to drive user experiences.

FAQ

A User-Agent is an HTTP header that identifies the client making a request. It typically includes browser and engine tokens, operating system hints, and sometimes device or application identifiers. It is commonly used for diagnostics, analytics grouping, and compatibility checks.

Many browsers include compatibility tokens (for example, Chromium browsers often include “Chrome/…”) even when the actual browser brand is Edge or Opera. The parser prioritizes the most specific token so you can identify the real browser family. This avoids misclassifying Edge traffic as plain Chrome.

Yes. Some apps and privacy tools modify UA strings, and modern browsers may reduce detail to limit fingerprinting. Use UA parsing for diagnostics and analytics, but rely on capability detection and server-side security signals for critical decisions.

Bot detection usually combines multiple signals: UA tokens, request rates, IP reputation, and behavioral checks. The bot flag in this tool is a quick hint based on common keywords and known crawler markers, not a complete security solution. For enforcement, pair it with rate-limits and verification mechanisms.

The parser processes the text you submit to generate the output for this page. If you are handling sensitive data, avoid including additional identifiers in the UA and consider redacting any internal app tokens before sharing outputs outside your organization.

Why Choose User-Agent Parser?

Instead of manually reading long header strings, you get a consistent, copy-ready summary with the fields engineers care about: browser, version, OS, device category, engine, and optional bot hints. That makes it easier to diagnose real-world issues quickly, especially when you only have logs and a single header value to work with.

The interface is built for fast iteration: realistic defaults so you can test instantly, a clean output area for copying into tickets, and a downloadable text file for sharing results across teams. Use it as a UA decoder during development, QA, incident response, or whenever you need to translate a User-Agent string into something meaningful.