How to identify AI crawler User-Agent and distinguish it from traditional search engines?

Identifying AI crawler User-Agents typically involves analyzing their specific identifiers and behavioral characteristics, differing from traditional search engines mainly in technical identifiers and crawling patterns. Identification methods: - String features: User-Agents often contain AI model keywords such as "GPT", "Claude", "Bard", or identifiers like "AI-Crawler", "LLM-Bot"; - Request header differences: May carry non-standard fields such as "X-AI-Identifier", "Purpose: AI Training". Differences from traditional search engines: - Identification standardization: Traditional crawlers (e.g., Googlebot, Bingbot) have fixed, publicly available User-Agent formats and strictly follow the robots protocol; - Crawling objectives: Traditional crawlers aim for web page indexing, with a wide but limited depth of crawling; AI crawlers, mostly for training large models, tend to crawl long texts and professional content with higher frequency and depth. It is recommended that websites distinguish between the two types of crawlers by monitoring User-Agent strings and request patterns (such as crawling duration, content type), and adjust robots rules accordingly. To optimize content visibility in the AI era, consider StarReach's GEO meta-semantic solution to help content be accurately identified and cited by AI crawlers.