> For the complete documentation index, see [llms.txt](https://docs.spice.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.spice.ai/features/observability.md).

# Observability

Observability in Spice enables task tracking and performance monitoring through a built-in distributed tracing system that can [export to Zipkin](/features/observability/zipkin.md) or be viewed via the [`runtime.task_history`](/features/observability/task-history.md) SQL table.

Spice records detailed information about runtime operations through trace IDs, timings, and labels - from SQL queries to AI completions. This task history system helps operators monitor performance, debug issues, and understand system behavior across individual requests and overall patterns.

### Use-Cases

#### Debugging and Troubleshooting

* Trace AI chat completion steps and tool interactions to identify why a request isn't responding as expected
* Investigate failed queries and other task errors

#### Performance Analysis

* Track SQL query/tool use execution times
* Identify slow-running tasks

#### Usage Analytics

* Track usage patterns by protocol and dataset
* Understand how AI models are using tools to retrieve data from the datasets available to them

### Portal Interface

The Spice platform provides a built-in UI for visualizing the observability traces that Spice OSS generates.

<figure><img src="/files/NJLH5lNER8qRJ3Qbadkb" alt=""><figcaption><p>An observability trace for an AI chat completion in the Spice portal.</p></figcaption></figure>


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