DeepResearch does not provide built-in metrics or logs detailing its internal processes, such as the number of pages visited, sources consulted, or time spent on specific tasks. The tool prioritizes delivering summarized or synthesized outputs (like answers, reports, or data extracts) without exposing granular operational details. This design choice reflects a focus on usability for end-users who care primarily about results rather than the steps taken to generate them. Developers integrating DeepResearch would need to rely on its output quality (accuracy, relevance) as the primary performance indicator, rather than process-level telemetry.
For developers seeking visibility into the research process, workarounds may be necessary. For example, if DeepResearch is used via an API, you could implement custom logging layers to track input queries, output responses, and response times. Some implementations might expose limited metadata (e.g., confidence scores for answers, or timestamps for API calls), but these are not equivalent to detailed process logs. If the tool uses browser automation under the hood, third-party tools like Puppeteer or Playwright could theoretically capture navigation events, but this would require custom instrumentation not natively supported by DeepResearch itself.
The lack of process metrics aligns with common trade-offs in AI/automation tools, where exposing internal mechanics could complicate the interface or reveal proprietary methods. For auditability, developers might instead validate performance through sample-based testing (e.g., comparing DeepResearch's outputs against ground-truth datasets for specific queries) or monitoring user feedback on result usefulness. If process-level metrics are critical, alternative tools with explicit logging features (like custom web scrapers or research pipelines built with frameworks like LangChain) might offer more transparency at the cost of increased setup complexity.