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

# Full traces

> Work with full trace-level Luna metrics in advanced or label-only workflows.

Use this tutorial when the full trace is the metric input, not just a simplified input/output view. These metrics usually evaluate the complete workflow or full reasoning/action trace.

## Current support

In the current SDK, full traces are advanced workflows:

* use `metric.input_format: "trace"`
* run in `label_only_mode`
* are not part of the standard synthetic data generation path

## Minimal config

```yaml theme={null}
run_steps:
  - data_generation
  - training

pipeline_provider: "local"
metric_name: "custom"

data_generation:
  metric:
    name: "Action Advancement"
    type: "binary"
    input_format: "trace"
    llmaj_source_prompt: "Determine whether the action is advancement or not."
  source_data:
    dataset:
      source_type: "huggingface"
      huggingface:
        name: "action_advancement_dataset"
  labelling:
    label_only_mode: true
  output:
    dataset:
      repo_name: "action-advancement-labelled"
  training:
    dataset:
      name: "action-advancement-labelled"
    prompt_template: |
      Determine whether the action is advancement or not.
      Trace:
      {input}

      Respond with "true" or "false".
    output:
      model_name: "action-advancement-model"
```