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Use @m4trix/evals to define datasets, test cases, and evaluators for repeatable AI evaluation runs.

Location

How It Works

  1. Dataset — Groups test cases by tags and/or file paths
  2. Test Case — Defines input/output pairs (e.g. prompt + expected score threshold)
  3. Evaluator — Applies scoring logic to each test case
  4. RunConfig + CLI — Group dataset/evaluator jobs and execute with eval-agents-simple run --run-config "..."

Setup

Create files with suffixes:
  • *.dataset.ts — Dataset definitions
  • *.evaluator.ts — Evaluator definitions
  • *.test-case.ts — Test case definitions
  • *.run-config.ts — Named multi-job configs

Run Evals

Repeat --run-config to queue multiple configs; they share one --concurrency cap. RunConfig names allow kebab-case, snake_case, camelCase, etc.: only letters, digits, _, and - (no spaces). The CLI matches names case-insensitively.

Key Files in evals-example

  • src/evals/demo.dataset.ts — Dataset with includedTags: ['demo']
  • src/evals/demo.evaluator.ts — Evaluators (score, length, multi-score, diff)
  • src/evals/demo.test-case.ts — Test cases with prompts and expected outputs
  • src/evals/example-name.run-config.ts — Example RunConfig
  • m4trix-eval.config.ts — Discovery and artifact paths

Config

Optional m4trix-eval.config.ts at project root:

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