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Fake User-Agent String Generator

Used by developers, writers, and creators worldwide.

A fake user-agent string generator gives developers and scrapers instant access to realistic HTTP headers that mimic browsers, mobile devices, and known crawlers. Every HTTP request sends a User-Agent header that servers inspect to serve content, enforce rate limits, or trigger bot detection. Getting that string right matters. This tool lets you control the count and agent type — desktop, mobile, or crawler bots like Googlebot. Generate five strings or fifty, drop them into a Python rotation list, paste one into curl, or feed the full set into a load-testing config. Version numbers reflect real browser release ranges, so they pass server-side validation without raising flags.

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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 User-Agent strings you need — use 5 for quick testing, 50 for a scraper rotation pool.
  2. Select an Agent Type from the dropdown: Any, Desktop Browser, Mobile Browser, or Bot to match your use case.
  3. Click Generate to produce the list of realistic User-Agent strings.
  4. Copy individual strings by clicking on them, or select all to paste the full list into your scraper config, test fixture, or load-testing tool.

Use Cases

  • •Rotating desktop and mobile User-Agent headers across Scrapy or httpx requests to reduce fingerprint consistency
  • •Testing server-side browser-detection logic in Express or Django to confirm correct content-type responses
  • •Populating Postman collection variables with realistic browser identities for API header testing
  • •Simulating Googlebot visits to verify robots.txt rules and dynamic rendering behavior in Next.js
  • •Seeding a k6 or Locust load-test config with mixed desktop and mobile client identities

Tips

  • →When building a scraper rotation pool, generate separate batches for desktop and mobile, then merge them — a mixed pool looks more like organic traffic.
  • →If you are testing a WAF rule, generate only Bot-type strings and confirm your server returns 403 or redirects as expected before going live.
  • →Pair each generated User-Agent with a matching Accept-Language header (e.g., en-US for Chrome on Windows) to reduce fingerprinting surface area.
  • →For load testing, generate a count equal to your simulated concurrent users so each virtual user gets a unique identity rather than sharing strings.
  • →Avoid reusing the same User-Agent string across multiple IPs in the same session — bot-detection systems correlate agent consistency with IP patterns.
  • →After generating strings, paste one into a User-Agent parser like ua-parser.github.io to confirm it decodes correctly before embedding it in production code.

FAQ

how do I use a fake user-agent string in Python requests

Pass it in the headers dictionary: `requests.get(url, headers={'User-Agent': 'your_string_here'})`. For rotation, store the generated list in a Python list and pick one per request with `random.choice()`. This gives you full control over which agent types appear in the pool.

will rotating user-agent strings actually prevent bot detection

User-Agent rotation is a useful first layer but not sufficient on its own. Modern detection systems also inspect TLS fingerprints, request timing, and cookie behavior. Pair rotation with realistic request delays, session cookies, and referrer headers for more durable scraper behavior.

what's the difference between desktop, mobile, and bot user-agents

Desktop strings mimic Chrome, Firefox, or Edge on Windows or macOS. Mobile strings replicate Safari on iOS or Chrome on Android, including device identifiers. Bot strings match known crawlers like Googlebot or Bingbot — useful for testing how your robots.txt or server middleware responds to those specific identities.