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Works with DeepEval

What DeepEval does well

  • Python-native evaluator ecosystem for LLM quality checks.
  • Broad evaluator set for correctness, relevance, safety, and custom metrics.
  • Natural fit for teams already in pytest/Python pipelines.

Tradeoffs

  • Output schemas vary across versions and custom evaluators.
  • Python dependency footprint can diverge from Node-only CI environments.

Minimal conversion path into eval-report/v1

Use the built-in import adapter.

  • Required: DeepEval JSON export (test_results or results).
  • Output: normalized eval-report/v1 rows with suite/pass metadata.

Concrete command/pattern

eval-dashboards import --from=deepeval --input=./deepeval-results.json --out=.evals_output/import-deepeval.json
eval-dashboards lint --input=.evals_output
eval-dashboards report --input=.evals_output --reporter=html --report-dir=eval-report

Related risks: integration risk register