Does DeepResearch provide any metrics or logs of its process (such as number of pages visited or sources consulted) to assess its performance?

DeepResearch does not currently provide built-in metrics or logs detailing its internal processes, such as the number of pages visited or sources consulted during a query. The system is designed to prioritize delivering answers based on aggregated information without exposing intermediate steps. This means developers or users cannot directly access a detailed audit trail of the tool’s research steps, such as URLs crawled, API calls made, or time spent analyzing specific sources. For example, if you ask DeepResearch to summarize a technical concept, it won’t return a list of the articles or domains it referenced to generate that summary. This lack of transparency is a trade-off to streamline output delivery and reduce computational overhead.

However, developers can implement custom logging or integrate third-party tools to approximate some of this visibility. For instance, if DeepResearch is part of a larger application, you could wrap its API calls in middleware that tracks timestamps, input queries, and response times. While this wouldn’t reveal the internal research steps, it could help measure performance metrics like latency or error rates. Additionally, browser-based implementations might use tools like Puppeteer or Selenium to automate and log interactions with external resources, though this would require significant customization. For example, a developer building a research assistant tool could layer a proxy service between DeepResearch and the web to capture HTTP requests, effectively logging domains accessed during a session.

The absence of native metrics in DeepResearch means teams needing granular process data would need to invest in custom instrumentation. This could involve combining network monitoring, application performance management (APM) tools, or even modifying open-source LLM frameworks to add logging hooks. While this adds complexity, it allows flexibility in tailoring metrics to specific use cases—such as tracking source credibility by logging domain reputations or measuring efficiency via time-to-answer benchmarks. Until DeepResearch introduces built-in telemetry, developers must weigh the effort of external tooling against the value of process visibility for their projects.