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

# Syncing local runs to Cloud

> Sync a local evaluation run to the UI

This page shows how you can sync an evaluation that was run on your machine using `calibrate-agent` to the app for you to see it in the UI and share it easily with others. We currently support syncing your [speech-to-text evaluations](/cli/speech-to-text) and [LLM benchmarking results](/cli/text-to-text).

You need to use the right API for importing the evaluation results, depending on the type of evaluation.

| If you ran                                  | Use                                                     |
| ------------------------------------------- | ------------------------------------------------------- |
| `calibrate-agent stt`                       | `POST /stt/evaluate/import`                             |
| `calibrate-agent llm` across several models | `POST /agent-tests/agent/{agent_uuid}/benchmark/import` |

## Pre-requisites

Depending on the evaluation type, you need to prepare the app for import.

<Accordion title="Upload the speech evaluation dataset on Calibrate">
  Make sure you **add the audio files in the same order in which they were kept
  locally**. Nothing in a calibrate run records which uploaded file a row came
  from, so a row is paired with the dataset item in the same position. Copy the
  dataset ID from the URL as it will be used later.
</Accordion>

<Accordion title="Upload the LLM benchmark tests on Calibrate">
  Create an agent on Calibrate if you haven't already and upload the tests against it. Make sure to **keep the same name for each test case on Calibrate as the `test_case_id` in the dataset used for the offline evaluation run**. Any row that matches no test refuses the
  whole import.

  Create the LLM judges used for the run on Calibrate **with the same name**. Calibrate maps each score to the evaluator that produced it.
</Accordion>

## Prepare the output folder

Only three kinds of file are read: each result folder's `results.csv` or
`results.json`, its `metrics.json`, and the leaderboard.

```bash theme={null}
env COPYFILE_DISABLE=1 tar czf run.tgz -C /path/to/parent --exclude=logs --exclude=results.log output
```

## Authentication

Both the endpoints use the JWT key of your signed-in account on Calibrate to authenticate your request.

Open Calibrate in your browser, open the network tab, and copy the
`Authorization` header from any request made to Calibrate's backend API. It starts with `Bearer`.

Send `X-Org-UUID` as well, which indicates the ID of the workspace where you want to import the evaluations. It is the first part of the Calibrate URL you are looking at,
`.../a9f1d0d4-1afa-4adc-b93b-e6a395208f0e/datasets/...`.

## Run the import

### Speech to text

```bash theme={null}
curl -X POST https://api.calibrate.artpark.ai/stt/evaluate/import \
  -H "Authorization: Bearer $TOKEN" \
  -F dataset_id=$DATASET_UUID \
  -F language=hindi \
  -F archive=@run.tgz
```

### LLM benchmark

```bash theme={null}
curl -X POST https://api.calibrate.artpark.ai/agent-tests/agent/$AGENT_UUID/benchmark/import \
  -H "Authorization: Bearer $TOKEN" \
  -F archive=@run.tgz
```

## What comes back

### Speech to text

The evaluation it stored, the providers it found in the archive, and how many
rows each of them carries.

```json theme={null}
{
  "task_id": "a3b2c1d0-e5f4-3210-abcd-ef1234567890",
  "status": "done",
  "dataset_id": "f47ac10b-58cc-4372-a567-0e02b2c3d479",
  "dataset_name": "ARMMAN hindi v2",
  "providers": ["deepgram", "elevenlabs", "google", "openai", "sarvam"],
  "row_count": 380
}
```

### LLM benchmark

The benchmark it stored, the models it found, and how many tests each was scored
on. `unresolved_evaluators` lists evaluators the results score against that are
no longer in the workspace. Their scores and reasoning are stored either way,
they just render without a name or rubric.

```json theme={null}
{
  "task_id": "a3b2c1d0-e5f4-3210-abcd-ef1234567890",
  "status": "done",
  "models": ["openai/gpt-4.1", "anthropic/claude-sonnet-4.6"],
  "test_count": 470,
  "unresolved_evaluators": []
}
```

## When an import is refused

Nothing is stored when any of these is true, so it is safe to try.

| Refused because                                                                   | Fix                                                              |
| --------------------------------------------------------------------------------- | ---------------------------------------------------------------- |
| A provider's row count differs from the dataset's item count                      | Send the run that matches the dataset                            |
| A row's ground truth differs from the dataset item beside it                      | Add the audio in the same order as the rows                      |
| A row's `test_case_id` matches no test linked to the agent                        | Name each test after that ID and link it                         |
| No `results.csv` or `results.json` in the archive                                 | Send the calibrate output folder, not a folder above or below it |
| Result folders sit under more than one run folder                                 | Send one run at a time                                           |
| The archive is over 200 MB for speech, 500 MB for a benchmark, packed or unpacked | Leave the `logs` and `results.log` files out                     |