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Metrics unify three categories of measurement into a single system:
  • System metrics — auto-computed per completion (TTFT, latency, token counts). No setup required.
  • Grader metrics — produced by Graders (AI judges, pre-built, custom, external). Scores are written automatically when graders run.
  • User metrics — custom ratings you define and log via SDK. Use these for human evaluation, application-specific scores, or any signal not covered by system or grader metrics.

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Register a user metric

Before logging values, register a metric key:
adaptive.feedback.register_key(
    project="my-project",
    key="quality",
    kind="scalar",  # or "bool"
    scoring_type="higher_is_better",
)
ParameterTypeRequiredDescription
projectstrYesProject to register the metric in
keystrYesUnique identifier
kindstrNo"scalar" (default) or "bool"
scoring_typestrNo"higher_is_better" (default) or "lower_is_better"
The SDK resource is adaptive.feedback — the entity is called “metrics” in the UI but the SDK interface retains the feedback name.

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Log metric feedback

Log a rating for a completion using its completion_id from the inference response:
response = adaptive.chat.create(
    messages=[{"role": "user", "content": "Hello"}]
)
completion_id = response.choices[0].completion_id

adaptive.feedback.log_metric(
    value=5,
    completion_id=completion_id,
    feedback_key="quality",
)
Feedback is associated with the completion’s Interaction record.

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Log preference feedback

Log a pairwise comparison between two completions:
adaptive.feedback.log_preference(
    feedback_key="quality",
    preferred_completion=completion_id_a,
    other_completion=completion_id_b,
)
Use preferences for RLHF/DPO training when you can judge which completion is better but can’t assign absolute scores.

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List and get metrics

keys = adaptive.feedback.list_keys(project="my-project")

key = adaptive.feedback.get_key(
    project="my-project",
    key="quality",
)
See SDK Reference for all feedback methods.