evolve

Automate evaluator-driven evolution loops by defining objectives, metrics, and writable scope in a run spec, then executing iterative learn-design-evaluate-analyze cycles via the bundled CLI. The framework enforces preflight validation and persists experiment data to guide objective alignment.

An evaluator-driven evolution workflow is an automated loop that iteratively learns, designs, experiments, and analyzes code or pipelines against objective metrics. It reduces manual guesswork by validating run specs, persisting cognition stores, and tracking best snapshots across rounds.

Set up a run spec preflight gate by defining clear objectives, evaluation metrics, timeouts, and writable scope before execution. The evolve framework validates this run spec to ensure your automation workflow has explicit alignment and evaluation criteria before running iterative cycles.

Track best snapshots across iterative code evolution rounds by persisting cognition stores and experiment databases during the learn-design-evaluate-analyze cycles. The framework automatically logs experiment data and tracks highest-performing snapshots to guide subsequent optimization rounds.

The evolve framework operates without external dependencies, using bundled scripts and references to execute evaluator-driven evolution loops. It relies on its internal CLI to manage run-spec validation, cognition persistence, and experiment tracking across iterative optimization cycles.

Avoid automated evolution loops for prompt optimization when tasks lack objective alignment, clear evaluation metrics, or a defined writable scope. The framework enforces preflight gating specifically because evolution cycles require quantifiable evaluation criteria to function effectively.