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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.

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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

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Minimal config

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"