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Model Logs

The Model Logs tab displays detailed logs of individual AI model responses, providing visibility into performance metrics, token usage, execution steps, and generated artifacts. This tab is useful for debugging model behavior, monitoring response quality, and tracking resource consumption.

Filters

The filter panel at the top of the tab provides three filter controls arranged in a grid:

FilterDescription
Date RangeStart and end date pickers with quick-select preset buttons for common ranges (e.g., last 7 days, last 30 days). Defaults to the last 7 days.
ModelDropdown to filter by a specific model (All Models, GPT-4, GPT-3.5 Turbo, Claude 3 Opus, Claude 3 Sonnet)
SearchFree-text search input to filter logs by content

A Refresh button in the header reloads the log data with the current filters applied.

Log Entry Fields

Each log entry is displayed as a card with two rows of information:

Primary Row

FieldDescription
ModelThe name of the AI model used for the response
TimestampWhen the response was generated (formatted as YYYY-MM-DD HH:mm:ss)
Response TimeTotal response time in milliseconds
Token UsageInput and output token counts (e.g., "1,234 in / 567 out")

Secondary Row

FieldDescription
ContextNumber of attached files and message history length
Execution StepsCount of completed and failed execution steps

Artifacts

If the model response produced artifacts, they are listed below the secondary row with:

  • Total artifact count
  • Each artifact's type and a preview of its content (first 100 characters)

Pagination

The log list supports pagination with controls at the bottom:

ControlDescription
Previous / NextNavigate between pages
Page indicatorShows current page number and total pages
Items per pageRadio buttons to select 10, 25, or 50 items per page
Total LogsDisplays the total number of logs matching the current filters

Logs are sorted by timestamp in descending order (newest first).

Empty State

When no logs match the current filter criteria, a message reads "No logs found for the selected filters."

Best Practices

  • Start with a narrow date range (last 7 days) and expand if needed to avoid loading excessive data.
  • Use the model filter to isolate performance comparisons between different AI models.
  • Monitor response times and token usage to identify unusual spikes or degradation.
  • Check execution step failure counts to identify reliability issues with specific model workflows.