Should I block LLM crawlers?

Blocking LLM crawlers reduces your content's visibility in AI systems. Consider your goals: if AI visibility is important, allow crawlers. If content protection is priority, blocking may be appropriate. Many sites allow crawlers for public content while protecting premium content.

How do I identify LLM crawler traffic?

Identify LLM crawlers through server log analysis looking for known user agents (GPTBot, ClaudeBot, etc.), unusual traffic patterns, and requests from known AI company IP ranges. Web analytics tools may also provide crawler identification.

Do LLM crawlers respect robots.txt?

Most major LLM crawlers respect robots.txt directives, though compliance varies. OpenAI's GPTBot and Anthropic's ClaudeBot follow robots.txt. Always verify crawler behavior and stay updated on AI company policies.

How often do LLM crawlers visit websites?

Crawl frequency varies by site authority, content freshness, and AI company policies. High-authority sites with frequently updated content may be crawled daily or more often. Less prominent sites may be crawled weekly or monthly.

LLM Crawlers: Definition & Examples | AEO/GEO Glossary - Reaudit

Definition

LLM Crawlers are automated web crawling bots deployed by AI companies to discover, access, and index web content for use in training large language models or enabling real-time information retrieval. These crawlers are essential infrastructure for keeping AI systems informed about current information.

Major LLM crawlers include GPTBot (OpenAI), ClaudeBot (Anthropic), PerplexityBot, Google-Extended, and various other AI company crawlers. Each crawler has different behaviors, respect for robots.txt directives, and data usage policies.

Understanding LLM crawlers is crucial for GEO because they determine what content AI systems can access and potentially cite. Website owners can control crawler access through robots.txt files, though blocking crawlers may reduce AI visibility. The decision involves balancing content protection with AI discoverability.

LLM crawlers differ from traditional search engine crawlers in their purpose (training/retrieval vs. indexing for search), frequency patterns, and data usage. Some crawlers access content for model training, while others enable real-time retrieval for up-to-date responses.

Key Factors

1

Crawler identification

2

Access control

3

robots.txt configuration

4

Content accessibility

5

Data usage policies

Real-World Examples

1
A publisher analyzing server logs to understand which LLM crawlers access their content
2
A website owner configuring robots.txt to allow specific LLM crawlers while blocking others
3
A content strategist ensuring important pages are accessible to LLM crawlers for AI visibility

Frequently Asked Questions about LLM Crawlers

Learn more about this concept and how it applies to AI search optimization.

LLM Crawlers

Definition

Key Factors

Real-World Examples

Frequently Asked Questions about LLM Crawlers

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