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Random User Agent Generator

Servers inspect User-Agent strings to detect device type, enforce access rules, and route traffic. Code that only ever sees one or two UA patterns in tests often breaks in production on obscure browser versions or bot signatures. A random user agent generator produces structurally correct UA strings across four categories. Two inputs control the output. Count sets how many strings to generate (1–30). Device Type filters by category: Desktop produces Chrome, Firefox, Safari, and Edge strings on Windows and macOS; Mobile generates Chrome on Android and Safari on iPhone; Tablet produces iPad and Android Chrome strings; Bot/Crawler generates Googlebot, Bingbot, DuckDuckBot, and LinkedIn crawlers. Any mode mixes all categories. All strings follow the Mozilla/5.0 format. Note: modern bot-detection also inspects TLS fingerprints and request timing — UA rotation alone is not sufficient.

Read the complete guide — 4 min read

How to use

  1. Choose your options above
  2. Click Generate
  3. Copy your result

Detailed instructions

  1. Set the Count field to the number of UA strings you need — start with 10-20 for a varied test sample.
  2. Choose a Device Type from the dropdown to filter results to desktop browsers, mobile, tablet, or bot crawlers.
  3. Click Generate to produce the list of User-Agent strings matching your criteria.
  4. Copy individual strings or the full list and paste them into your test fixtures, HTTP client config, or seed data file.
  5. Re-generate as many times as needed — each run produces a fresh randomised set within the selected device category.

Use Cases

  • Seeding Jest or pytest fixtures for a ua-parser-js or ua-parser unit test suite covering 50+ device combinations
  • Rotating UA strings in a Playwright or Puppeteer scraper to test detection-avoidance logic before hitting production targets
  • Verifying that Express middleware returns the correct responsive layout when server-side rendering for mobile vs. desktop UA strings
  • Testing WAF rules in AWS or Cloudflare to confirm bot crawler signatures are flagged while real browser UAs pass through
  • Populating a staging analytics pipeline with mixed desktop, mobile, and bot traffic to validate session-classification logic

Tips

  • Generate separate batches per device type and combine them in a weighted ratio (e.g. 60/30/10 desktop/mobile/bot) to mirror realistic traffic.
  • When testing a UA parser, deliberately include bot strings alongside browser strings — many parsers misclassify obscure crawler UAs as desktop browsers.
  • Paste generated strings into https://developers.whatismybrowser.com to verify your parser's output matches the expected classification.
  • For scraper rotation testing, generate at least 50 unique strings — repeating the same handful of UAs too quickly is itself a detectable signal.
  • If you're testing Googlebot-specific server behaviour, filter to Bot type and verify your server returns the same content it would to a standard browser (cloaking violates Google's guidelines).
  • Freeze a generated set in your test fixtures rather than regenerating on every test run — stable fixtures make failures reproducible and diffs readable.

FAQ

how do I use generated user agent strings in HTTP requests for testing

Set the User-Agent header directly in your HTTP client — in fetch or Axios pass it under headers, in curl use -H 'User-Agent: ...', and in Postman add it to the Headers tab. Copy any generated string and drop it straight in. For rotation, paste a batch into an array and sample randomly per request.

are these user agent strings accurate enough to pass server-side checks

They follow the correct structural format — Mozilla/5.0 prefix, platform block, and engine comment — so they pass basic UA parsing and pattern matching. However, modern bot-detection systems cross-check UA strings against TLS fingerprints, header order, and behavioral signals. UA string matching alone is not sufficient for production-grade evasion.

what is the difference between a desktop and a bot user agent string

Desktop strings identify a human-operated browser like Chrome on Windows or Firefox on macOS, and include a full rendering engine comment. Bot strings identify automated crawlers like Googlebot or Bingbot and typically contain a crawler name plus a URL to the bot's documentation page.

can I generate a mix of browser and bot strings in one batch

Yes — set Device Type to Any and the generator draws from all categories: desktop browsers, mobile browsers, tablets, and bot crawlers. For an exact proportion, generate separate batches per type and combine them manually.

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