# LLM tracing

URL: https://www.getdynamiq.ai/glossary/llm-tracing

> A recorded, node-by-node execution tree of one run, including every prompt, tool call, retrieved document and token count.

A recorded, node-by-node execution tree of one run, including every prompt, tool call, retrieved document and token count.

A trace is the record of one run, not a summary of it: every node that executed, every tool call and its arguments, every document retrieved, every prompt sent to a model and the tokens it used, in order, with timing. Aggregate observability tells you a system is slow or expensive on average; a trace tells you exactly what one specific run did.

When an outcome is challenged, a customer, a regulator, an internal reviewer, someone needs to reconstruct exactly what the system did on that one run, not a statistical picture of typical behavior.

A disputed refund decision is reconstructed by opening the run's trace and reading the exact policy passage the agent retrieved and the exact tool call that issued the refund.

**In Dynamiq**, every run of a deployed App is recorded as a trace, browsable as a graph, a tree or a timeline, with each node's input, output, timing, rendered prompt and token usage available individually, and downloadable as JSON on its own or in bulk. Workflows built with the open source SDK can ship the same traces into the platform with a single callback handler.

## See it in Dynamiq

-   [Observability](https://www.getdynamiq.ai/product/observability)

## Related terms

-   [AI observability](https://www.getdynamiq.ai/glossary/ai-observability)
-   [LLM evaluation](https://www.getdynamiq.ai/glossary/llm-evaluation)
-   [Human in the loop](https://www.getdynamiq.ai/glossary/human-in-the-loop)

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