GPTBot is the specific user-agent token string that identifies OpenAI's proprietary web crawler when it makes HTTP requests to web servers. Its disclosed purpose is to scan publicly accessible web pages to collect data that may be used to train, improve, and ground the capabilities of OpenAI's foundation models, including GPT-4 and future iterations. The crawler's full user-agent string is Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko); compatible; GPTBot/1.0; +https://openai.com/gptbot.
Glossary
GPTBot

What is GPTBot?
GPTBot is OpenAI's official web crawler user-agent token, used to identify its bot when it accesses websites to gather data for improving its AI models and grounding generative responses.
Website operators can manage GPTBot's access through the Robots Exclusion Protocol by specifying GPTBot as the user-agent token in their robots.txt file. OpenAI also respects standard directives like the noindex meta tag and provides a published IP range for the crawler to facilitate firewall allow-listing. Crucially, GPTBot is distinct from OAI-SearchBot, a separate token used for real-time search indexing in ChatGPT; blocking GPTBot prevents training data ingestion but does not affect a site's visibility in ChatGPT's browsing feature.
Key Characteristics of GPTBot
A technical breakdown of the operational parameters, identification tokens, and access control mechanisms specific to OpenAI's GPTBot web crawler.
Full User-Agent Token String
The complete identification string for the training crawler is:
Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko); compatible; GPTBot/1.0; +https://openai.com/gptbot
This token must be used in the User-agent field of a robots.txt file to apply directives specifically to this bot. The token includes a link to OpenAI's official documentation page, which serves as a verifiable identifier for the crawler.
Published IP Ranges
OpenAI publishes the IP ranges from which GPTBot makes requests to allow for network-layer verification and firewall configuration.
- Documentation URL:
https://openai.com/gptbot - Verification Method: OpenAI recommends performing reverse DNS lookups on connecting IPs to confirm they resolve back to an
openai.comdomain. - Purpose: This allows infrastructure teams to implement defense-in-depth by verifying the bot's identity at both the application layer (User-Agent) and the network layer (IP range).
Disallow Directive for Training Data
To prevent GPTBot from using your site's content for training OpenAI's foundation models, add the following to your robots.txt:
codeUser-agent: GPTBot Disallow: /
- Effect: This blocks GPTBot from crawling any path on the domain.
- Granularity: You can restrict access to specific directories by replacing
/with a path like/private-data/. - Note: This directive only controls the training crawler. It does not affect the separate OAI-SearchBot used for real-time search features in ChatGPT.
Allow Directive for Selective Access
To permit GPTBot to crawl only specific sections of a site while blocking everything else:
codeUser-agent: GPTBot Allow: /public-docs/ Disallow: /
- Order Matters: The
Allowdirective must precede theDisallowdirective for the specific path to be accessible. - Use Case: This is ideal for organizations that want to contribute public documentation or research to the training corpus while protecting proprietary application logic and user data.
Separation from OAI-SearchBot
OpenAI uses two distinct crawler tokens for different purposes:
- GPTBot: Crawls data to train and improve OpenAI's foundation models. Controlled by the
GPTBottoken. - OAI-SearchBot: Crawls data to provide real-time, cited search results within ChatGPT. Controlled by the
OAI-SearchBottoken.
Disallowing GPTBot does not prevent your content from appearing in ChatGPT's browsing results. You must manage directives for both tokens independently to achieve your desired governance posture.
Crawl Frequency and Politeness
GPTBot adheres to standard crawl politeness conventions:
- robots.txt Compliance: The bot checks and respects
robots.txtdirectives before crawling. - Crawl-Delay: GPTBot respects the non-standard
Crawl-Delaydirective if specified inrobots.txt. - Rate Limiting: OpenAI implements internal rate limiting to avoid overwhelming servers.
- Caching: The bot caches
robots.txtfiles, so changes may not take effect immediately. OpenAI recommends allowing up to 24 hours for directive updates to propagate.
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Frequently Asked Questions
Clear, technical answers to the most common questions about OpenAI's GPTBot crawler, its identification, and how to manage its access to your web content.
GPTBot is the official user-agent token for OpenAI's web crawler, a specialized bot that systematically accesses public websites to collect data used for training and improving OpenAI's foundation models, including the GPT series. It works by making standard HTTP requests to web servers, parsing the HTML content, and extracting text and structured data. The bot identifies itself in the User-Agent request header with the full token string GPTBot/1.0, allowing website administrators to write targeted rules in their robots.txt file. GPTBot respects the Robots Exclusion Protocol, meaning it will check a site's robots.txt before crawling and will obey Disallow directives. It also filters out paywalled content, personally identifiable information (PII), and text that violates OpenAI's usage policies. The data it ingests is processed to help models understand language, improve factual accuracy, and enhance generative capabilities. GPTBot is distinct from OAI-SearchBot, which is used specifically for real-time search grounding in ChatGPT, not for foundation model training.
Related Terms
Understanding GPTBot requires familiarity with the broader landscape of AI crawler directives, competing crawlers, and the technical mechanisms used to control how autonomous agents access and ingest web content.
Robots Exclusion Protocol
The foundational standard that GPTBot respects. A website's robots.txt file is the primary mechanism for declaring crawl permissions. GPTBot obeys the User-agent: GPTBot directive block.
- Standard defined in RFC 9309
- Supports
AllowandDisallowdirectives - GPTBot also respects
*wildcard rules if no specific block exists
Google-Extended
Google's standalone product token for controlling generative AI training access. Unlike GPTBot's single-purpose token, Google-Extended works alongside Googlebot to separate search indexing from Bard/Vertex AI training.
- Token:
Google-Extended - Controls: Bard, Vertex AI, and Cloud AI training
- Does not affect: Google Search indexing
Anthropic ClaudeBot
Anthropic's equivalent to GPTBot, used to gather training data for the Claude family of AI assistants. Comparing directives across providers reveals each lab's approach to transparency and publisher control.
- User-agent:
ClaudeBot - Full string:
Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko); compatible; ClaudeBot/1.0; +https://www.anthropic.com/claudebot - Respects standard robots.txt directives
AI Training Opt-Out
The technical and policy umbrella for signaling that content should not be used for foundation model training. GPTBot is the mechanism; the opt-out is the intent expressed through robots.txt directives.
- Primary method:
User-agent: GPTBot+Disallow: / - Secondary:
X-Robots-Tag: noai(proposed, not universally adopted) - Limitation: Opt-out is a signal, not a legally binding contract
Crawl Transparency Report
A governance document detailing interactions with AI crawlers like GPTBot. Tracks access frequency, data ingested, and directive compliance for auditing purposes.
- Logs GPTBot IP ranges: Published by OpenAI
- Monitors
robots.txtcompliance - Essential for enterprise AI governance and data sovereignty

About the author
Prasad Kumkar
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
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