Prompt optimization has become crucial for enhancing the performance of large language models (LLMs) across a broad range of tasks. Although many research papers demonstrate its effectiveness, practical adoption is hindered because existing implementations are often tied to unmaintained, isolated research codebases or require invasive integration into application frameworks. To address this, we introduce promptolution, a unified, modular open-source framework that provides all components required for prompt optimization within a single extensible system for both practitioners and researchers. It integrates multiple contemporary discrete prompt optimizers, supports systematic and reproducible benchmarking, and returns framework-agnostic prompt strings, enabling seamless integration into existing LLM pipelines while remaining agnostic to the underlying model implementation.

翻译：提示优化已成为提升大语言模型（LLM）在广泛任务中性能的关键技术。尽管众多研究论文已证明其有效性，但由于现有实现通常依赖于无人维护、孤立的研究代码库，或需要侵入式地集成到应用框架中，其实际应用仍受到阻碍。为此，我们提出了 promptolution，一个统一、模块化的开源框架。它为从业者和研究者提供了一个单一、可扩展的系统，其中包含了提示优化所需的所有组件。该框架集成了多种主流的离散提示优化器，支持系统化且可复现的基准测试，并返回与框架无关的提示字符串。这使得 promptolution 能够无缝集成到现有的 LLM 流程中，同时保持对底层模型实现的无关性。