Perplexity-User

The Citead team, updated

In short. Perplexity-User fetches pages for user actions and generally ignores robots.txt rules, according to Perplexity’s crawler documentation.

Definition: As of 17 October 2026, Perplexity-User is Perplexity’s documented fetcher for retrieving web pages in response to user actions, such as asking a question. Perplexity says this fetcher generally ignores robots.txt, the file used to communicate crawler access rules.

Key facts

What triggers Perplexity-User?

Perplexity’s Perplexity Crawlers documentation connects the fetcher to actions within Perplexity: a user asks a question, and the service may visit a web page to help provide an accurate answer. The documentation describes a possible page visit and a possible link in the response.

The same documentation assigns PerplexityBot the role of surfacing and linking websites in Perplexity search results. For Perplexity-User, it explicitly excludes web crawling and collecting content to train AI foundation models. These separate descriptions matter when interpreting a website’s request logs: the operator associates Perplexity-User with a user action and PerplexityBot with search results.

How does it handle robots.txt?

Perplexity’s documented robots policy says that, because a user requested the fetch, Perplexity-User generally ignores robots.txt rules. The qualifier “generally” is part of the policy. A robots.txt exclusion therefore cannot serve as a dependable access barrier for this fetcher.

RFC 9309 explains the distinction between crawler rules and access security. Its Security Considerations section calls for an application-layer security measure to control access to paths, giving HTTP Authentication as an example. A site that needs restricted content to require credentials can apply that measure independently of its published crawler rules. Listing a path in robots.txt also exposes that path publicly, according to the same specification.

How can a website identify and allow these requests?

A user-agent string identifies the client in a request. Perplexity publishes this full string in its crawler documentation:

Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; Perplexity-User/1.0; +https://perplexity.ai/perplexity-user)

For a web application firewall, or WAF, Perplexity recommends combining user-agent matching with IP address verification. Its configuration guidance pairs the matching user agent with addresses from the corresponding official list and sets the rule action to allow the request.

The Perplexity-User JSON endpoint supplies that address list. Perplexity’s guidance calls for periodically refreshing the ranges because they change, then monitoring logs to check that legitimate requests pass the configured rules. This makes both the client string and the source address relevant to a firewall review.

FAQ

Does Perplexity-User collect content for model training?

Perplexity’s crawler documentation says this fetcher is not used to collect content for training AI foundation models. It describes its purpose as supporting user actions within Perplexity.

Can I rely on robots.txt to stop Perplexity-User?

Perplexity says the fetcher generally ignores those rules because a user requested the fetch. For access restrictions, RFC 9309 points to application-layer security measures such as HTTP Authentication.

Should I allow requests based only on the user-agent string?

Perplexity recommends combining the string with IP address verification in WAF rules. You can use its published Perplexity-User address list and refresh the ranges periodically, as its configuration guidance recommends.

Sources

  1. Perplexity, "Perplexity Crawlers", undated, consulted 17 October 2026
  2. Perplexity, Perplexity-User IP address list, undated, consulted 17 October 2026
  3. IETF, "RFC 9309: Robots Exclusion Protocol", September 2022

Check how a page reads to answer engines. Open the GEO score analyzer

Test a page and see what to fix first

The analysis usually takes less than 10 seconds and needs no account.

400,000+ pages scored in the public benchmark