> ## Documentation Index > Fetch the complete documentation index at: https://www.adaline.ai/docs/llms.txt > Use this file to discover all available pages before exploring further. # Filter, search, export logs > Narrow Monitor logs, inspect the matching traces and spans, and export the result set for review Use this workflow when Monitor shows a signal but the project has too many logs to inspect row by row. Filter or search the log set, confirm the matching traces and spans, then export the exact evidence if it needs to leave Adaline. Traces table filtered by name with filter chips, export, clear, save view, summary metrics, and result rows ## Start from the question Good filters begin with a specific question: | Question | Useful starting point | | ------------------------------------------ | ------------------------------------------------------------------------ | | Which logs belong to this customer report? | Time range, trace name, reference ID, and safe user or account metadata. | | What changed after a release? | Environment, deployment, prompt, release tag, and time range. | | Which requests are slow or expensive? | Latency, cost, tokens, model, prompt, route, and status. | | Which logs failed quality checks? | Evaluator score, evaluator status, Behavior context, prompt, and status. | | Which tool path is causing trouble? | Tool or function name, status, tags, attributes, and trace name. | Filters are only as useful as the metadata you send. If you cannot isolate traffic by environment, release, route, prompt, model, or safe customer segment, improve [instrumentation](/docs/instrument/overview) before using Monitor for incident review. ## Filter and search Use filters for precise fields such as time range, status, name, tags, attributes, cost, latency, tokens, prompt context, and evaluator context. Use search when you are looking for a term or phrase that may appear in names, inputs, outputs, or metadata. Use [Deep search](/docs/monitor/deep-search) when you know the meaning of the evidence you want, but not the exact words or metadata. Deep search is better for exploratory questions such as "users trying to cancel but keep old data" or "assistant gives a confident answer without using the right tool." After applying a filter, check the summary row before opening individual traces. The result count, latency, cost, tokens, and eval score tell you whether the filtered set still matches the question. ## Shape the table Keep only the columns needed for the investigation. For example, a latency review usually needs started time, name, latency, status, tags, and attributes. A cost review usually needs cost, tokens, model or prompt context, and first span input. Traces column control showing visible columns such as Started At, Name, Latency, Cost, Tokens, First span input, Last span output, Tags, Attributes, and Ended At Use the column menu to pin or hide fields while you scan. Open the trace viewer when you need full content, span details, raw payloads, evaluator output, or tool responses. Traces table column menu with pin column and hide column actions ## Save repeatable views Save a view when the same filter set is useful more than once: production errors, high-cost requests, low eval-score traces after deployment, tool failures, release-window logs, or Behavior-linked examples. Name the view after the operational question, not the person debugging it. Traces table with a save-view popover naming a reusable filtered view ## Export logs Export the current result set when you need an incident packet, offline analysis, support handoff, or review by another system. The export follows the current filters, so check the time range, filter chips, result count, and visible evidence before downloading. Trace exports can contain production content, metadata, raw IDs, tool payloads, and model output. Treat exported logs as sensitive data. Traces table after exporting filtered results with an export confirmation toast Open filtered traces and inspect the spans behind them. Search logs by meaning when exact filters are not enough. Start from charts when you need the time window first. Turn filtered evidence into datasets, evaluators, and Improve cycles.