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Use server, CDN, or WAF request logs—not referral reports—to find AI crawlers. Filter requests for documented User-Agent tokens such as GPTBot and OAI-SearchBot, then verify the client IP against the operator’s published ranges or documented reverse-DNS procedure. Report User-Agent matches that are not verified as candidates, not confirmed bots.
This distinction matters because a crawler request, a person arriving from an AI service, and a user-directed fetch are different events. The workflow below shows how to classify each one, measure it without inflating counts, and document the confidence of your result.
What “AI bot traffic” means in a log
An AI bot request is an HTTP request made by software associated with an AI company or service. In an access log, it is represented by a timestamp, client address, requested URL, status code, and User-Agent string. The request may be fetching an article for search discovery, model training, a user-directed answer, or another automated task.
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“AI traffic” is not one uniform category. A request from an indexing crawler has a different purpose from a fetch triggered by someone asking an AI product to open a page. A referral in an analytics platform is different again: it indicates that a browser session was attributed to a link or visit from an AI service. None of these measurements proves the others occurred.
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The reliable approach therefore has two stages:
- Classify candidates: filter request logs for documented User-Agent tokens.
- Verify identity: check the actual client IP against a current published list or follow the operator’s reverse-DNS and forward-DNS instructions.
Keep candidate and verified counts separate, and label the source and time window for every report.
Choose the log source before counting requests
Use the system that records the request closest to the public edge. An origin access log is suitable when traffic reaches your server directly. If a CDN, reverse proxy, load balancer, or WAF terminates the connection first, its request log may be the authoritative record.
Fields to capture
| Field | Why it is needed |
|---|---|
| Timestamp with timezone | Defines the reporting window and reveals bursts, schedules, and repeat intervals. |
| Original client IP | Allows comparison with published crawler ranges. Do not substitute the proxy’s address. |
| Requested method and path | Shows whether the agent is fetching HTML, feeds, APIs, assets, or administrative paths. |
| Response status | Separates successful retrievals, redirects, client errors, and server failures. |
| User-Agent | Provides the first-pass documented token match. |
| Host, protocol, and port | Distinguishes sites or environments when several domains share logging infrastructure. |
| Bytes, latency, and cache status | Helps explain load and whether a CDN served the response without reaching origin. |
A reverse proxy commonly records its own source address as the connection peer. Use the platform’s trusted original-client-IP field, or a forwarding header that your infrastructure strips and rewrites safely. Never accept an arbitrary client-supplied forwarding header as proof of identity.
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Confirm retention and time zones
Before filtering, check how long logs are retained, whether timestamps are UTC or local time, and whether fields are sampled. A seven-day CDN export and a 30-day origin archive cannot be compared as if they represented the same population. Write the source, retention period, timezone, and any sampling rule beside each result.
Build a dated list of documented identifiers
User-Agent names and formats can change. Maintain a list with the token, operator, stated purpose, first-seen date, documentation URL, and last verification date. Start with the operator’s own documentation rather than a static list copied from a forum.
OpenAI-related agents
| Token | Documented role | How to interpret a match |
|---|---|---|
GPTBot |
Crawls content that may be used in training foundation models. | Count as a GPTBot candidate until its IP is verified. |
OAI-SearchBot |
Surfaces websites in ChatGPT search. | Keep separate from GPTBot because purpose and reporting may differ. |
ChatGPT-User |
Can visit a page in response to a user action; it is not an automatic web crawler. | Do not merge it with scheduled crawler traffic. |
See the current descriptions and linked IP information in OpenAI’s bot documentation.
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Other commonly seen tokens
Cloudflare’s detection guidance lists examples including Meta-ExternalAgent, GPTBot, GoogleOther, Amazonbot, and PetalBot. Treat that list as a starting point, not a permanent taxonomy; add or remove tokens when the relevant operator changes its documentation. A token that merely contains “AI” is not evidence of an operator or purpose.
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Normalize User-Agent values for case-insensitive matching, but preserve the original value for audit. Search only the User-Agent field; searching the entire raw line can match a URL, referrer, or query parameter that has nothing to do with the client identity.
Command-line example
The following example assumes a combined access log in which the User-Agent is the final quoted field. Adapt the field extraction to your server’s format before using it in production:
grep -Ei 'GPTBot|OAI-SearchBot|ChatGPT-User|Meta-ExternalAgent|GoogleOther|Amazonbot|PetalBot' access.logFor a durable report, parse structured JSON or CSV logs instead of relying on a positional field. Store both user_agent_raw and a normalized agent_token, then assign a state such as candidate, verified, or unverified.
Do not treat a User-Agent as authentication
User-Agent is a string supplied by the requester and can be copied by any script. A match is useful for discovery and triage, but it cannot establish that the named company made the request. A spoofed token, a security scanner, or a misconfigured internal job can all produce the same text. Keep those matches visible, but mark them unverified until the network check succeeds.
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Verification combines the User-Agent match with network evidence. Cloudflare describes two common methods: compare the client IP with a current public IP list, or validate the operator’s reverse-DNS identity and, where required, perform a forward lookup. Follow the operator’s instructions because not every service publishes ranges or uses the same DNS policy. The general procedure is documented in Cloudflare’s IP validation guidance.
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IP-list verification
- Extract the original client IP from the trusted edge-log field.
- Download the operator’s current published ranges from its official documentation.
- Test whether the address falls within one of those ranges.
- Record the list version or retrieval date with the request classification.
- Reclassify the request as verified only when the User-Agent token and IP-range test agree.
Published ranges can change. A request that was verified last month may need to be rechecked against today’s list when you rebuild historical reports.
Reverse-DNS verification
- Perform a PTR lookup for the client IP and record the returned hostname.
- Check that the hostname ends in the operator’s documented domain, using an exact domain-boundary test rather than a loose substring.
- Perform a forward lookup of that hostname if the operator’s instructions require it.
- Confirm that the forward result includes the original IP.
- Combine the DNS result with the documented User-Agent match and store the lookup time.
Reverse DNS alone is not sufficient if the operator requires a forward-confirmation step. DNS records and published ranges are operational data, so retain the evidence used for the decision rather than only the final label.
Why verification can fail
- Proxy masking: you tested the CDN’s address instead of the original client IP.
- Stale ranges: your allow-list predates the operator’s current publication.
- DNS mismatch: the PTR hostname is generic, missing, or does not forward to the same address.
- Token spoofing: the string matches, but neither the range nor DNS test does.
- IPv6 omission: your parser handles IPv4 but drops IPv6 values before testing.
When evidence conflicts, keep the record as an unverified candidate. Do not silently promote it because the request “looks” like a known bot.
Measure activity in a way others can reproduce
After classification, aggregate verified and unverified requests separately. At minimum, group by agent, day, path, status code, and request count. Add response bytes, latency, cache result, and unique client IP when capacity planning or abuse investigation requires them.
| Dimension | Question it answers |
|---|---|
| Agent and verification state | Which claimed agents are present, and how much of the total is verified? |
| Path or route | Which pages, feeds, APIs, or blocked areas are being requested? |
| Status code | Did the server return content, redirect, reject, or fail? |
| Time bucket | Is activity steady, bursty, or tied to a deployment or publication? |
| Client IP | Is one address responsible, or is the activity distributed across published ranges? |
| Cache and origin reach | Did requests consume origin resources or terminate at the edge? |
Use a report with explicit definitions
A useful report states: “Between 2026-10-01 00:00 and 2026-10-07 23:59 UTC, the CDN request logs contained 12,400 User-Agent matches for GPTBot; 11,980 matched the operator’s published ranges retrieved on 2026-10-08; 420 remained unverified.” The example numbers are illustrative; your report should use your own measured values.
Keep the following columns in an export:
- Reporting window and timezone
- Log source and whether traffic was sampled
- Raw User-Agent and normalized token
- Original client IP and verification method
- Request count, unique paths, and status-code distribution
- IP-list or DNS evidence date
Interpret a named trend with the correct scope
Cloudflare reported that GPTBot’s share rose from 5% in May 2024 to 30% in May 2025. That comparison covered an AI-only crawler set identified by matching crawler names in User-Agent strings. It is a historical, vendor-specific share—not a percentage of all Internet traffic, and not a current ranking. The original report is Cloudflare’s 2025 crawler analysis.
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Keep crawler requests separate from referral analytics
Server and CDN logs measure requests arriving at the network or application edge. Web analytics tools generally depend on client-side scripts, cookies, session rules, and referrer attribution. A crawler may never execute your analytics script, while a human clicking a link in an AI product may appear as a referral without any crawler request from the same service.
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|---|---|---|---|
| Crawler request | Origin, CDN, or WAF log | A client requested a URL, with a User-Agent, IP, time, and response. | That the request produced a human visit or recommendation. |
| AI referral/session | Web analytics instrumentation | A tracked browser session was attributed to an AI service or referrer. | That the service’s crawler fetched your page beforehand. |
| User-directed fetch | Request log, if exposed as a distinct agent | A page may have been fetched in response to a user action. | How many people saw or acted on the resulting answer. |
Do not infer crawler volume from a referral report, and do not infer referral volume from crawler logs. If you compare the datasets, publish their definitions, collection windows, and missing-data limitations beside the comparison.
Cloudflare describes combining website logs with log analytics for larger volumes in its AI crawler detection guidance. Its overview of auditing and controlling AI crawlers is available at Cloudflare’s AI crawler analytics article.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use robots.txt as a policy signal, not proof
Robots.txt communicates crawl preferences to compliant crawlers. It does not authenticate a requester, prove that a User-Agent is genuine, or prevent a non-compliant client from requesting a public URL. A request that ignores your robots.txt policy is evidence of policy non-compliance, not automatically evidence of a malicious or fake identity.
Use access controls, authentication, rate limits, or WAF rules when a resource must actually be protected. Treat robots.txt separately as the place where you publish crawl preferences. Cloudflare explains this distinction in its detection guidance and its documentation on verified bots.
A repeatable operating procedure
- Define the question. Decide whether you need crawler volume, pages fetched, origin load, referral sessions, or an identity investigation. These are different reports.
- Select the authoritative log. Use origin logs for direct traffic or CDN/WAF logs when the edge terminates connections. Document retention, timezone, and sampling.
- Normalize fields. Parse original client IP, timestamp, path, status, and raw User-Agent. Preserve the raw record for review.
- Apply documented-token filters. Match the current list case-insensitively and map each token to a stated purpose.
- Verify identity. Compare IPs with current published ranges or perform the operator’s required reverse- and forward-DNS checks.
- Aggregate. Group verified and unverified candidates by agent, path, time, status, and resource impact.
- Compare only like with like. Put request-log totals beside referral/session totals without combining them.
- Archive evidence. Save the token list version, IP-list retrieval date, DNS results, query definition, and reporting window.
- Recheck on a schedule. Review operator documentation and ranges before recurring reports because names, formats, and addresses can change.
Troubleshooting common results
“The analytics dashboard shows no AI traffic.”
Check the request logs first. Crawlers often do not execute client-side analytics code, and CDN-served responses may never reach the page’s analytics script. A zero in the dashboard is therefore not evidence of zero crawler requests.
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“The log shows thousands of GPTBot requests from one IP.”
Confirm that the address is the original client address and that the count is not a proxy artifact. Check status codes, requested paths, and the current published range. If the User-Agent matches but network verification fails, report the activity as unverified rather than confirmed.
“The same agent appears under several spellings.”
Retain the exact raw strings, normalize case for matching, and group only when the operator’s documentation establishes that the variants represent the same agent. Do not collapse unrelated tokens merely because they contain a shared product name.
“DNS verification is inconclusive.”
Record the PTR result, forward lookup result, resolver time, and tested IP. Check the operator’s stated DNS procedure and retry against a current record. Until the required checks pass, leave the request in the candidate or unverified category.
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Use path, status, cache, bytes, and latency breakdowns to identify expensive routes. Apply documented crawl preferences where appropriate, and use rate limiting or authentication for resources that must be protected. Do not rely on robots.txt as an enforcement mechanism.
What a defensible conclusion looks like
A defensible statement identifies the evidence and its limits: “Our October 2026 CDN logs contain requests whose User-Agent matches OAI-SearchBot. Most matched the operator’s published IP data retrieved on the report date, so they are classified as verified for this window. The count measures HTTP requests, not people reached through ChatGPT search, and it does not show whether any request generated a referral session.”
This format prevents the three most common errors: treating a string as authentication, treating a crawler as a visitor, and treating a vendor’s historical share as a universal current statistic.
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