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Every agent invocation produces a hierarchical OpenTelemetry trace. Each LLM call, tool call, sub-agent invocation, memory retrieval, and persistence operation is a span — with duration, status, attributes, and parent/child links you can drill into.
Agent traces

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What’s traced

A typical agent run produces spans like:
Each span captures:

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Browsing traces

  1. Open Agent Framework → Traces.
  2. Use the filters at the top:
    • Time range — last hour, 6h, 24h, 7d, 30d, or custom.
    • Service — filter to a specific agent or workflow.
    • Trace ID — jump straight to a single run.
  3. The list shows aggregate metrics for the current filter (total spans, error rate, avg duration, active services).
  4. Click a trace to open the span tree. Each span can be expanded for its attributes, resource attributes, and any associated errors.

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Jumping in from a chat

In the Chat UI, each assistant message has a View trace link in its footer. Click it to jump to the exact trace for that run — no filter juggling needed.

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What you can answer with traces

  • Why was that response slow? — see which span took the time (LLM vs tool).
  • Why did the agent loop? — count LLM-call spans, see which tool calls failed.
  • What did the model see? — inspect the prompt that went to the LLM, including retrieved knowledge chunks and summarized history.
  • What did this tool actually return? — click the tool span for the raw result.
  • Which version executed? — agent_version attribute disambiguates aliased runs.

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Workflows

Workflow runs produce traces with the same structure — each node is a span, and AI Agent nodes contribute their full agent trace as a sub-tree. So a workflow that runs a sub-agent gives you one trace covering both layers.

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Gateway-side traces

If you’re calling models directly through the LLM Gateway (without the agent framework), see LLM Gateway Tracing — same OpenTelemetry foundation, gateway-scoped view.