Optuna for Agentic AI: A Practical Guide to Hyperparameter Optimization of LLM Pipelines
Optuna is a mature, MIT-licensed Python framework for hyperparameter optimization. This guide explains its core abstractions (studies, trials, samplers, pruning, storage) and ecosystem, then shows how to apply it to agentic AI pipelines: prompt tuning, retry-budget allocation, and multi-objective quality-vs-cost routing. It includes an honest cost guide — a Claude-class study costs $10–$600, not cents — and a framework for when Optuna is and isn't the right tool.