See what your AI is doing. Make it better.

Use the Datool SDK to record workflows, model calls, and tool calls, or add Datool's span processor to your existing OpenTelemetry setup. You can also connect an application for dataset evaluations without the tracing SDK. Instrumenting the steps inside it gives you the detailed trace.

Follow a request through its recorded steps, with inputs, outputs, timing, errors, and scores in context. For instrumented model calls, Datool also shows reported token usage and cost estimates when usage and pricing information are available. Related traces can be grouped into sessions.

Yes. Save useful examples from recorded traces into a dataset, add reference answers where needed, and use those cases in evaluation runs. That unexpected answer becomes a repeatable check for the next version of your workflow.

Use recorded traces or dataset cases to evaluate your AI workflows. Combine code-based and LLM scorers with human reviews, then compare results across evaluation runs.

Choose a built-in evaluator, write a JavaScript or Python scorer for a precise rule, or use an LLM scorer with your own rubric. Test the scorer on representative examples before running it across a dataset. Human reviews can add ratings and comments alongside automated judgments.

Yes. Run the same cases against different application versions or prompt and model settings supported by your app. Compare scores and individual outputs to see which cases improved or regressed. You can also re-score saved outputs with a different scorer without calling the application again.

Yes. Create a review session from selected traces, define the ratings that matter to your team, and inspect each answer in context. Reviewers can save scores, leave comments, and annotate specific parts of the input or output. Those judgments complement your automated evaluations.