Google-GeminiNotebook Replaced Google-NotebookLM: Update Log Parsers Without Losing History
Google renamed the NotebookLM crawler token. Update exact-match rules and analytics without splitting one crawler into two histories.
Confirmed August 10, 2026: Google’s crawling documentation says the NotebookLM crawler product token changed from Google-NotebookLM to Google-GeminiNotebook on July 16, 2026. Google also says the old token remains supported during a transition period.
This is an identity migration, not evidence that a new crawler suddenly appeared. Publishers should update exact-match controls while preserving both strings in one historical reporting series. The goal is to prevent a documentation change from becoming a false traffic trend, a broken firewall rule, or an unexplained access-policy gap.
What Google changed
The documented product token is the recognizable name inside the HTTP user-agent string. Log pipelines frequently use that token to label traffic. Google’s changelog records the new name and explicitly notes transitional support for the prior token, but it does not publish an end date for that transition.
| Field | Before | Current handling |
|---|---|---|
| Product token | Google-NotebookLM | Google-GeminiNotebook |
| Transition | Old token may remain visible | Accept and normalize both |
| Reporting identity | Notebook crawler | Keep one continuous series |
| Removal date | Not published | Do not invent a cutoff |
Do not classify the new string as an AI-search referral, a ranking signal, or a new source of demand. A user-agent identifies an automated fetcher; it does not reveal why a document was selected, whether the fetched material influenced an answer, or whether a human later visited the site.
Find every exact-match dependency
- Search raw WAF rules, allowlists, deny lists, rate limits, and bot-management expressions for the old token.
- Search log-processing code, SQL views, regular expressions, ETL jobs, saved queries, and dashboard filters.
- Check monitoring alerts and anomaly baselines that treat an unknown token as suspicious.
- Review robots-policy documentation and internal crawler registries, even though robots.txt normally addresses user-agent groups rather than an analytics label.
- Record the rule owner and the date each dependency was updated.
Use case-insensitive matching only where the surrounding system and security model permit it. Avoid an overly broad substring such as Notebook; it can group unrelated clients. Prefer two exact known tokens mapped to one stable internal identifier.
Preserve one crawler history
Create a normalized field such as crawler_family=google_gemini_notebook. Keep the original user-agent in immutable raw logs, then map both documented tokens to the normalized family in the reporting layer. That preserves auditability while preventing a discontinuity in charts.
Backfill only the derived label, not the raw request. Version the normalization rule and save a before-and-after count by day, status code, host, path group, and response bytes. If the combined total changes materially, investigate crawl behavior separately; the rename alone cannot explain a real volume change.
For a wider access review, use the search and AI bot access policy framework. For release controls, pair this migration with the technical SEO launch checklist.
Validate the migration
- Replay representative old-token and new-token log lines through the parser.
- Confirm both produce the same crawler family and security action.
- Confirm malformed lookalikes remain unknown rather than trusted.
- Compare dashboard totals before and after deployment.
- Retain an alert for the first day on which the legacy token disappears, but do not remove compatibility immediately.
User-agent text can be spoofed. If a security decision depends on verified Google ownership, follow Google’s documented reverse-DNS or published-IP verification methods rather than trusting the string alone. Reporting normalization and crawler authentication are separate jobs.
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