> ## Documentation Index
> Fetch the complete documentation index at: https://traceroot.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Reading Results

> Read the summary and per-case results an eval run returns

`evaluate()` returns an `EvalRunResult` — a summary per metric, one result per case, and where the run reported.

## Summary

`summary()` prints one line per metric:

```
matches_expected: mean=0.75 pass=3/4 count=4
```

A boolean score passes or fails on its own value. A numeric score needs a `threshold` to be judged — without one it's averaged, and `pass=k/n` is left off.

<Tabs>
  <Tab title="Python">
    ```python theme={null}
    result = evaluate(name="weather", dataset=ds, task=task, scorers=[reports_temp])
    print(result.summary())
    ```
  </Tab>

  <Tab title="TypeScript">
    ```typescript theme={null}
    const result = await evaluate({ name: "weather", dataset: ds, task, scorers: [reportsTemp] });
    console.log(result.summary());
    ```
  </Tab>
</Tabs>

Token counts and cost aren't in the local summary — the platform prices them from the run's spans and shows them in the UI.

## Per-case results

<Tabs>
  <Tab title="Python">
    ```python theme={null}
    for item in result.results:
        print(item.case_id, item.output, item.scores)
    ```
  </Tab>

  <Tab title="TypeScript">
    ```typescript theme={null}
    for (const item of result.itemResults) {
      console.log(item.caseId, item.output, item.scores);
    }
    ```
  </Tab>
</Tabs>

Each item carries `case_id`, `input`, `output`, `expected`, `scores`, `trace_id`, `duration_ms`, and any `error` or `scorer_errors`. A case that hit an error is `errored`; use `case_status(item)` / `caseStatus(item)` to check.

The run also exposes counts (`case_count`, `errored`, `not_scored`), the `candidate_version` it was labelled with, `upload_state` with the run's `dashboard_url`, and `dataset` — the exact dataset version the run measured.

## Upload later

Save a run and upload it afterwards, for when the run happens somewhere without credentials.

<Tabs>
  <Tab title="Python">
    ```python theme={null}
    from traceroot import EvalRunResult

    result.save("run.json")

    later = EvalRunResult.load("run.json")
    later.upload()
    ```
  </Tab>

  <Tab title="TypeScript">
    ```typescript theme={null}
    import { EvalRunResult } from "@traceroot-ai/traceroot";

    result.save("run.json");

    const later = EvalRunResult.load("run.json");
    await later.upload();
    ```
  </Tab>
</Tabs>

`upload()` rebuilds the reporting destination from the saved run, and keeps its scorers' thresholds — so a later upload records the same pass and fail results as the original run.

## Next steps

<CardGroup cols={2}>
  <Card title="Running Evals" icon="play" href="/docs/evals/running-evals">
    Every option of <code>evaluate()</code>, and where a run reports.
  </Card>

  <Card title="Datasets" icon="table" href="/docs/evals/datasets">
    Pull a dataset version to reproduce a past run.
  </Card>
</CardGroup>