What is an AI crawler?
An AI crawler is software that requests web resources for an AI system. RFC 9309 identifies crawlers as automated clients that access URI resources through the Robots Exclusion Protocol. An AI crawler may request HTML pages, PDFs, image files, metadata, links, text files, or other public resources.
Crawler purpose depends on the product behind each request. OpenAI separates OAI-SearchBot, GPTBot, and ChatGPT-User into search crawling, training-related crawling, and user-triggered page access. That separation matters because one crawler name can support search visibility, while another crawler name can collect material for model-training systems.
Which AI crawler types matter?
AI crawler type decides which access rule belongs in robots.txt. A publisher may allow AI search discovery while blocking model-training collection. Another publisher may allow user-requested fetches while blocking automated crawling.
| AI crawler type | Website role | Official examples |
|---|---|---|
| AI search crawler | Finds pages for AI search answers or source links | OAI-SearchBot, Claude-SearchBot, PerplexityBot |
| Training crawler | Collects content that may support model training | GPTBot, ClaudeBot, Meta-ExternalAgent |
| User-requested fetcher | Opens pages after a user action inside an AI product | ChatGPT-User, Claude-User, Perplexity-User |
| Product-control token | Controls later product use of crawled content | Google-Extended, Applebot-Extended |
| Web corpus crawler | Collects public pages for a broad crawl corpus | CCBot |
| Knowledge-base crawler | Crawls chosen URLs for retrieval knowledge bases | bedrockbot |
PerplexityBot belongs in the AI search crawler group because Perplexity uses it to surface and link websites in search results. GPTBot belongs in the training crawler group because OpenAI connects GPTBot with content that may support generative AI foundation model training.
Which official AI crawler names should be checked?
OpenAI uses three main crawler names for separate access decisions. OAI-SearchBot supports ChatGPT Search source discovery. GPTBot can collect content connected with OpenAI model training. ChatGPT-User covers certain user-triggered actions inside ChatGPT.
Google uses Googlebot preferences for Google Search surfaces, including Google Search features. Google records that crawler preferences addressed to Googlebot affect Google Search and related Search products. Google-Extended needs separate treatment because it works as a product token for some Gemini and Vertex AI use, not as the same crawler request as Googlebot.
Google Search AI features also follow Google Search controls. Google Search Central covers AI Overviews and AI Mode from a site-owner perspective, which keeps Googlebot access central for Google Search inclusion.
Anthropic separates Claude-User and Claude-SearchBot as different access cases. Claude-User covers user-initiated requests, while Claude-SearchBot crawls for Claude search result quality. ClaudeBot belongs in a training-related review because Anthropic crawler coverage connects it with model-development access.
Perplexity separates PerplexityBot and Perplexity-User. PerplexityBot supports Perplexity search result discovery, while Perplexity-User supports user actions and does not crawl for AI foundation model training.
Apple uses Applebot for Apple search experiences. Applebot powers search technology across Spotlight, Siri, and Safari. Applebot-Extended needs separate handling because Apple uses it as an opt-out control for generative AI training use while Applebot can still support search discovery.
Meta uses Meta-ExternalAgent and Meta-ExternalFetcher for different access purposes. Meta-ExternalFetcher fetches individual links after user requests and supports product functions, while Meta-ExternalAgent belongs in an AI product review.
Common Crawl uses CCBot for open web corpus collection. Common Crawl gives CCBot identity details and crawler verification steps because false CCBot identification can appear in logs.
Amazon Bedrock uses bedrockbot for selected knowledge-base crawling. Amazon Bedrock checks bedrockbot-UUID rules first and then generic bedrockbot rules when it crawls source URLs for a knowledge base.
How does robots.txt control AI crawler access?
robots.txt gives crawler instructions from the host root. A crawler group names a user-agent token and then lists URL paths that the crawler can request or should avoid. RFC 9309 records these rules as the standard method for crawler access control through the Robots Exclusion Protocol.
robots.txt is not access authorization. RFC 9309 treats crawler access rules as protocol instructions, not a security barrier. Google crawler guidance also warns that robots.txt is not a mechanism for keeping a page out of Google.
Private resources need server-side controls. Google recommends password protection or indexing controls when material must stay private or absent from Google Search. Crawler rules should manage cooperative bot access, not protect confidential files.
Crawler-specific groups reduce later mistakes. A GPTBot group does not control OAI-SearchBot. A Googlebot group does not control Google-Extended. A PerplexityBot group does not control Perplexity-User. Each crawler rule should match the official token for one access purpose.
What robots.txt rules match common AI crawler decisions?
A site that wants ChatGPT Search discovery but not OpenAI training collection can separate OAI-SearchBot and GPTBot. OpenAI treats OAI-SearchBot and GPTBot as independent crawler settings.
A site that wants to limit Google-Extended can use a separate Google-Extended group. Google separates Googlebot Search crawling from Google-Extended product control.
A site that wants to block Common Crawl can target CCBot. Common Crawl provides the CCBot token and shows robots.txt control as the expected blocking method.
A site that uses Amazon Bedrock knowledge-base crawling needs the right bedrockbot rule. Amazon Bedrock checks bedrockbot-UUID before generic bedrockbot rules, so account-specific knowledge-base crawls may need more specific groups.
How can server logs verify AI crawler requests?
Server logs show which crawler requests reached the website. Useful fields include timestamp, IP address, requested URL, user-agent string, HTTP status code, response size, referrer, and robots.txt version.
User-agent text cannot verify crawler identity by itself. Common Crawl warns that some crawlers falsely identify as CCBot and provides reverse DNS plus IP range checks for verification. Perplexity also ties crawler checks to current IP address files for PerplexityBot and Perplexity-User.
A log record needs context before interpretation. Record crawler name, requested URL, response code, rule group, official crawler source, and review date. One crawler request only proves server access. It does not prove indexing, model training, AI citation, ranking, or referral traffic.
What can AI crawler access control?
AI crawler access control can allow or block cooperative crawler requests. It can reduce unwanted bot traffic and separate search discovery from training collection when a platform publishes separate crawler names or product tokens.
Access control cannot guarantee AI citations, source links, brand mentions, referral visits, rankings, revenue, or deletion from past crawls. Google records that AI features use Google Search systems and that site owners still need normal Search controls for inclusion management. Google robots.txt guidance also notes that disallowed pages can still appear as URLs in Search when other pages link to them.
robots.txt compliance varies across the web. A 2025 large-scale empirical study found that certain bot categories, including AI search crawlers, rarely checked robots.txt in its test environment. Sensitive resources need authentication, server rules, indexing controls, and log monitoring instead of robots.txt alone.
How should a site owner audit AI crawler access?
Start with an AI crawler inventory. Match each AI product to its official crawler name, crawler purpose, robots.txt token, verification method, and source record. Separate search crawlers, training crawlers, user-requested fetchers, product-control tokens, corpus crawlers, and knowledge-base crawlers.
Map crawler rules to URL groups before publishing changes. Public articles, downloadable files, staging URLs, account pages, private files, and internal documents need different access decisions. Google warns that robots.txt does not protect private files, so private URL groups need authentication or server-side restriction.
Review logs after each rule change. Check important URLs, returned status codes, allowed crawler requests, blocked crawler requests, firewall behavior, and official IP verification where available. Save crawler name, crawler purpose, robots.txt rule, affected URL paths, source record, review date, and log evidence for future audits.
Manish Singh is the Team Lead at IMMWIT, where he brings over 14 years of experience in SEO, UX, and digital marketing. Known for helping businesses rank, scale, and grow smarter online, he blends strategic thinking with AI and NLP-backed insights. His hands-on approach to semantic SEO and UX design turns ideas into real results clients can see and trust.