An evaluation of six prompt-optimization frameworks—DSPy, GEPA, TextGrad, agent-opt, Arize Prompt Learning, and MLflow's optimizer—reveals that they are not interchangeable, ranging from full programming models to single algorithms. The author concludes that the most effective framework is one that optimizes against specific metrics and datasets while allowing for the flexible swapping of search algorithms.