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Openlayer generates AI summaries on three surfaces, so you can understand what happened without reading raw spans or row-level results:
  • Sessions — the session detail view opens with a summary of how the session actually went across its traces: what the user was trying to do, what the agent did, and where it struggled.
  • Traces — the trace details view summarizes the trace instead of requiring line-by-line span reading.
  • Test results — results include a summary of the dominant failure modes.

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Where to find them

  • Session summary: open a project with tracing → Sessions → open a session. The summary appears at the top of the session detail view.
  • Trace summary: open a trace from the Traces view. The trace details view has an AI summary button — select it to summarize, analyze, and get up to speed with the trace.
  • Test-result summary: open a test result — the summary sits above the row-level results.
Summaries are generated on demand and cached, so opening the same session or result again is instant.

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Configure AI features

AI features are configured at the workspace level, and each project can override the workspace default.

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Workspace settings

Go to Settings → AI features to control which AI-powered features are enabled in the workspace. There is one section per feature:
  • Trace summaries — summaries for individual traces.
  • Session summaries — summaries for user sessions.
  • Test summaries — summaries for test result sets.
  • Intake assistant — draft project details, intake answers, and risk scores during project registration or from the project’s Lifecycle page. See Project lifecycle for context file limits and draft behavior.
Each section carries the same controls: Test summaries adds Generate automatically, which summarizes test results as they finish computing. The same page holds two more sections:
  • Semantic search — matches test subpopulations by meaning instead of exact text, and requires a ClickHouse-backed pipeline. You can lock it for all projects; the Embedding model is set per deployment via EMBEDDING_MODEL, and changing it requires re-embedding stored data, because existing vectors are not comparable across models. See semantic search filters for how the filters themselves work.
  • AI usage — spend and volume across every project in the workspace, broken down by feature, including Intake assistant, over the time range you pick. Spend is estimated with the token prices in Workspace settings → LLM costs, covered in LLM cost estimation.

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Project settings

Open a project and go to Settings → AI features to control which AI features run for that project. It mirrors the workspace page — Trace summaries, Session summaries, Test summaries, and Intake assistant — and project settings override the workspace default. When a feature is locked at the workspace level, its Enable toggle is unavailable in the project and only the Model remains editable. For Intake assistant, add the selected model provider’s API key under the workspace or project Settings → Environment. Openlayer does not run the assistant with its own provider keys.