Mastra Evals

Mastra Evaluation and Testing guide - built-in scorers, custom scorers, datasets, experiments, and CI integration. Covers createScorer(), built-in scorer factories like createAnswerRelevancyScorer(), runEvals(), dataset management, and experiments that compare models, prompts, or agent variants against the same dataset.

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Mastra Evaluation and Testing

Comprehensive guide for evaluating AI agent quality with Mastra. Covers 17 built-in scorer factories, custom scorer creation with createScorer(), datasets for reproducible benchmarks, experiments for comparison, and CI pipeline integration.

Usage

/mastra-evals

Provides context for:

  • Scorer factory functions (e.g., createAnswerRelevancyScorer())
  • Import paths: @mastra/evals/scorers/llm and @mastra/evals/scorers/code
  • createScorer() from @mastra/core/scores
  • runEvals({ target, scorers, data })
  • Dataset management (create, addItems, experiments)
  • Experiment comparison and CI integration
  • Agent-level scorer configuration with sampling

artsmc/claude-dev-agents/tree/main/skills/mastra-evals commit e490bd805d

Frequently asked questions

npx skillmds@latest add artsmc-claude-dev-agents/mastra-evals