Network

User-Agent Parser

Paste a User-Agent string from logs or a client to see the browser, version, operating system, device class, rendering engine, app context, and whether the request comes from a crawler or AI bot. Useful for triaging browser compatibility issues, verifying crawl traffic, and attaching environment notes to tickets and incidents.

  • Breaks a UA into browser, OS, device, rendering engine, and app context fields
  • Separates search crawlers, AI crawlers, social preview fetchers, and monitoring clients
  • Loads the current browser UA so you can compare it field by field with production logs
  • Outputs a readable block you can paste directly into tickets, reports, and incident notes
tools/User-Agent Parser
10 chars
No bot signal

Overview

Browser

-

OS

-

Device

Desktop

Bot

No bot signal

Browser

Browser

-

Browser version

-

Rendering engine

-

Engine version

-

Device

Operating system

-

OS version

-

Device type

Desktop

Device category

Desktop

Device vendor

-

Device model

-

Is mobile

No

Is touch device

No

App context

-

Bot signals

Is bot

No

Bot name

-

Bot type

-

Is AI crawler

No

Technical

Client hints

Unavailable

CPU architecture

-

Command

Overview

Built around the questions teams actually ask in log triage and crawl analysis, so one UA string turns into structured fields you can act on.

  1. 01

    Browser and rendering engine

    Resolve browser name, version, engine type, and engine version so you can narrow regressions or compatibility issues quickly.

  2. 02

    Operating system and device class

    Identify OS, OS version, device type, vendor, and model to separate desktop, mobile, tablet, and TV traffic.

  3. 03

    Search and AI crawler labels

    Cover Googlebot, Bingbot, and other search crawlers alongside GPTBot, ClaudeBot, PerplexityBot, and similar AI sources.

  4. 04

    Social preview and monitoring

    Flag Slack, Discord, and Twitter link-preview fetchers as well as UptimeRobot, Pingdom, and Datadog monitoring probes.

  5. 05

    App context hints

    Detect in-app browsers such as WeChat, DingTalk, Feishu, and Alipay so you can tell webview traffic apart from regular mobile.

  6. 06

    Current browser shortcut

    Load the current browser UA in one click for direct comparison with production logs and incident reports.

How to use

A repeatable UA triage flow covers paste, identify, double-check, and record. The four steps below stay consistent across tickets.

  1. 01

    Paste the User-Agent string into the input area, or click Current browser in the command bar.

  2. 02

    Review browser, operating system, device, and bot fields on the right and compare them against the request you expected.

  3. 03

    Focus on Is bot, Bot type, and Is AI crawler to decide which policy a request should fall under.

  4. 04

    Copy the parsed result into a ticket, SEO log review, or incident note and keep the original UA for later audits.

Details

Field selection is tuned for log triage, crawler analysis, and client reproduction so results drop straight into tickets and reports.

  • Parses browser name, version, rendering engine, and engine version
  • Classifies desktop, mobile, tablet, TV, wearable, console, and embedded clients
  • Labels search crawlers, AI crawlers, social preview fetchers, and monitoring tools
  • Detects common in-app browsers such as WeChat, DingTalk, Feishu, Alipay, and X
  • Reports whether Client Hints metadata is available so it does not get mistaken for UA-only signals
  • Loads the current browser UA so reproduction and log comparison stay one click apart
  • Outputs a readable block ready to paste into tickets, review notes, and incident notes
  • Comfortable fullscreen workspace for long UA strings and dense diagnostic output

Use cases

Built for real log review, crawler analysis, frontend triage, and support handoff so an original UA becomes a usable environment clue.

  1. SEO crawl triage

    Inspect Googlebot, Bingbot, and Baiduspider traffic to confirm crawl was not blocked, redirected, or served the wrong status code.

  2. AI crawler governance

    Detect GPTBot, ClaudeBot, and PerplexityBot traffic, then pair it with robots policy and log segmentation rules.

  3. Frontend compatibility triage

    Use browser version and rendering engine data to scope CSS and JavaScript regressions to specific client profiles.

  4. Traffic quality and abuse review

    Separate likely human sessions from automated traffic and monitoring probes for risk control and analytics.

  5. Landing page conversion diagnostics

    Reconstruct device and app context to check whether a conversion drop on a client segment matches a rendering issue.

  6. Support and incident handoff

    Parse the UA from a user report and attach normalized browser, OS, and device fields to the ticket or incident.

  7. Log normalization pipelines

    Turn original UA strings into browser, OS, device, and bot dimensions so dashboards and BI tooling can rely on stable keys.

  8. Social preview inspection

    Identify Slack, Discord, and Twitter preview fetchers to verify Open Graph metadata across share workflows.

See also

If the same log line packs a long request path, callback URL, or campaign parameters, sort that out first with URL Tools before returning to the UA parser. When the report points to a browser-specific rendering issue, pair the parsed UA with Browser Compatibility Detector to verify the runtime capabilities, so what the UA claims and what the browser actually supports line up. When the same log line or capture also carries a full request, hand the whole message to HTTP Message Parser so the request line, headers, cookies, and body sit next to the UA fields in one workspace.

Best practices

Use the practices below to keep UA-based decisions reliable across teams.

  • Combine UA parsing with runtime feature detection instead of relying on the string alone.
  • Keep the original UA in logs so you can replay parsing after rule updates and audits.
  • Use one shared bot taxonomy across teams to make trend analysis and alerting consistent.
  • Tag unknown UA patterns explicitly and revisit them periodically so new clients do not slip into default segments.
  • Normalize browser, OS, device, and bot fields into a shared field structure so dashboards and BI can reuse them.
  • For SEO workflows, track the share of search crawlers and AI crawlers separately to spot crawl structure changes.
  • When rolling out allow, challenge, or rate-limit policies, observe a staged rollout before hard enforcement.
  • Do not treat mobile classification as proof of app context, combine it with app environment fields.
  • Share both the technical field key and the display label across teams to avoid mismatched glossaries.

Limitations

Knowing the limits prevents overconfident calls and keeps UA results as one signal among many.

  • User-Agent strings can be spoofed and must not be the sole input for identity or security decisions.
  • Different parser libraries may interpret the same UA slightly differently, so confirm assumptions across tools.
  • Newly released browsers and AI crawlers may parse incompletely until rule libraries catch up.
  • Pair UA results with IP, request rate, request path, and behavioral signals before taking action.
  • The same client may report different UA strings across app webviews, so normalize before cross-channel analysis.
  • Some crawlers imitate mainstream browser UAs, so UA-only bot classification has hard limits.
  • Proxy and gateway layers can rewrite request headers, so reconcile UA results with edge logs.
  • Low-sample logs bias UA distributions and should not be used alone for long-horizon strategy.

FAQ

Common questions about usage, accuracy, and the boundaries to keep in mind before relying on results.

Can this parser detect AI crawlers

Yes. The output flags whether the request is a bot, the bot type, and whether it is an AI crawler. It covers GPTBot, ClaudeBot, PerplexityBot, Google-Extended, and similar sources, which is enough for log segmentation and robots policy review.

Why do some UA strings look only partially parsed

Some UA strings only expose limited metadata, or they belong to brand new clients. Keep the original UA, combine it with behavioral data, and replay parsing once your rules are refreshed.

Is this useful for SEO diagnostics

Yes. It supports crawler source checks, AI crawler identification, social preview verification, and the traffic quality work that sits next to indexing.

Should I allow or block traffic based only on the parsed UA

No. UA is a single signal. Combine IP reputation, reverse DNS, request behavior, rate limits, and path patterns before enforcing a decision.

Why does the same UA parse differently in another tool

Parser libraries differ in rules and update cadence. Keep one canonical parser rule set per business so comparisons and reports stay consistent.

Can the tool prove whether a visitor is a real human

No. UA only narrows things down. Combine it with behavior, request rate, IP reputation, cookies, and challenge outcomes for a real judgment.

Can it tell Googlebot from a UA pretending to be Chrome

It detects Googlebot patterns. Spoofed UAs still need reverse DNS, IP range, and behavior checks to confirm identity.

Why are vendor and model fields sometimes empty

Many desktop UA strings do not carry vendor or model information, or the values are stripped. Empty fields are expected in those cases.

Why is an iPad sometimes detected as a desktop

Some iPadOS UA strings include Macintosh-like fragments. The tool applies heuristics, but edge cases may still need a manual check.

Does the page support batch UA parsing

The interface focuses on single-string analysis. For batch workloads, use the same library in your server pipeline and persist fields to a warehouse.

Is my User-Agent input uploaded

No. Parsing, detection, and copy actions run inside the current browser and the UA never leaves the page.

Can the result drive a robots policy directly

Use it as supporting evidence. Validate the proposed grouping against real crawl logs and roll changes out gradually.

How does this fit into SEO log analysis

Parse UA first, then segment by bot type to track crawl share, frequency, and status code mix. Indexing issues often line up with crawl structure changes.

How can I separate social preview fetches from real clicks

Start with bot type and app context, then confirm with referrer, request timing, and follow-up page behavior.

What does Client hints available or unavailable mean

It indicates whether the current environment exposes Client Hints metadata, which can improve browser and platform detection beyond the UA string.

Is this enough for long-term monitoring

It is best as a diagnostics and rule validation surface. For monitoring, embed the same parsing logic into your server log pipeline and hook it into alerts.

Related tools

Keep moving across network and browser triage with URL parsing, subnet math, and runtime capability checks.