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Prompt logs help you inspect how a prompt ran after you tested it or invoked it through runtime paths. Use the logs view to review individual runs, inspect request details, and check the inputs and response for a selected completion.

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Accessing activity logs

Open the prompt in the editor. Click Activity Logs in the header. Fetch Hive opens the prompt-specific activity log view for that prompt. The logs page includes a paginated table of runs on the left and a detail panel for the selected run on the right. You can also use Refresh logs to reload the table or Editor to go back to the prompt editor.

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What’s tracked

The log table shows summary data for each run, including:
  • Owner
  • Completed time
  • Provider
  • Model
  • Charge type
  • Status
  • Provider charge
  • Total tokens
  • Duration
When you select a run, the detail panel gives you additional views:
  • Response for the generated output
  • Request for model settings and usage details
  • Inputs for the values passed into the prompt
  • User metadata for caller-defined audit fields sent as user_metadata
For Claude runs, the Request panel can also show Prompt Cache, which is the configured Anthropic prompt-cache TTL assigned to that request. Cached read and write token counts remain in the existing token usage breakdowns. For models that emit visible thinking or reasoning content, the Response view shows it in a collapsible thinking section above the assistant output. If the selected run failed, the detail panel shows the error message. Some providers can also add provider-specific metadata. For example, Perplexity runs include a citations view. Perplexity Sonar Deep Research logs can also show separate citation tokens, reasoning tokens, and search query counts when the provider returns those usage fields.

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Traces

The prompt activity log view focuses on run details such as Response, Request, and Inputs. Start with Response, then check Request and Inputs to understand how the prompt ran. The prompt activity log view includes pagination, row selection, and Refresh logs. Use User metadata to filter by a tracked metadata property. Choose a property key to find runs where that key exists, or add an exact value to match scalar values such as cus_123, enterprise, true, or 12. Fetch Hive tracks metadata property names over time from prompt invokes so the filter can suggest keys your workspace has actually sent. Old logs without user_metadata remain visible normally, but they are excluded when a user metadata filter is active. See Invoke metadata for request examples.

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Notes

  • The log detail view includes a share link action for the selected run.
  • A selected run can also include a discussion thread so your team can comment on the run in context.
  • Use the prompt editor for iteration and the logs view for after-the-fact inspection.
See also: Creating and Editing and Run with API