A growing share of discovery is shifting to AI search and conversational answer engines. Reading server logs for AI crawler user-agents is the same discipline as auditing Googlebot — it tells you how often these models are crawling, and whether a firewall or WAF rule is silently blocking them.
Known AI crawler user-agents
These identifiers are publicly documented by each provider and can change — treat this as a starting point to verify against current documentation, not a permanent reference:
| Crawler | Operator |
|---|---|
| GPTBot | OpenAI |
| ClaudeBot | Anthropic |
| PerplexityBot | Perplexity AI |
| Bytespider | ByteDance/TikTok |
What to actually check
Map AI crawler hit volume against how often your brand shows up as a citation in those tools' answers, where you can observe it. Check robots.txt and any WAF rules for overly broad bot-blocking that catches AI crawlers along with genuinely malicious scrapers — this is a common accidental misconfiguration. And if you've published an llms.txt file, track requests against it specifically; it's a useful signal for whether AI crawlers are actually finding and using the structured context you've given them.
Related articles
Extracting Googlebot Insights From Server Log Files
GSC gives you aggregates. Log files are the only record of what actually happened, request by request.
Debugging Crawl Budget Allocation on Large Sites
Crawl budget is capacity times demand. On large sites, most of the waste comes from a handful of repeatable structural mistakes.
Google Indexing API vs. IndexNow: A Practical Comparison
Passive sitemaps aren't enough for high-velocity publishing. Here's how the two push-based indexing protocols actually differ.
Written by
Paul Lovell
International SEO Consultant & Founder of Always Evolving SEO. 15+ years running technical audits, log-file analysis, and root-cause fixes for multimillion-dollar businesses — speaker at SMX London, Search & Content Summit, and SEMrush events.