adaptive-workflows

Experiment tracking captures each run's configuration, metrics, and outcomes in a searchable history. Create a WorkflowTracker instance, call record_experiment() after each run completes, and build a persistent knowledge base to extract patterns and inform future iterations.

Yes. Pattern recognition identifies effective configurations across your experiment history. Call tracker.suggest_config() to receive recommendations based on past results, enabling systematic optimization without manual analysis of prior runs.

Workflow templates extract best-practices from high-performing experiment runs. Record outcomes systematically, recognize patterns in successful configurations, and convert them into templates stored in your knowledge base for reuse across new tasks.

A/B testing support in adaptive workflows lets you record and compare different configurations systematically. Track metrics for each variant in your experiment history, then use pattern recognition to determine the superior approach based on aggregated results.

No. Initialize an empty WorkflowTracker and begin recording experiments immediately. The lightweight persistence layer accumulates data over time; pattern recognition and template generation improve as your experiment history grows.

Yes. Adaptive workflows apply across research and production contexts—ML experiment pipelines, knowledge-building tasks, and template generation. The Python-based framework with persistence scales to both exploratory and operational workflows.