统一模块化框架，让大模型提示词优化更易用、可复现。
promptolution: A Unified, Modular Framework for Prompt Optimization
- 模块化设计，整合多种离散提示优化方法。
- 支持可复现的基准测试，输出通用提示字符串。
- 适合研究人员和工程师快速集成到现有大模型流程中。
提示词优化已成为提升大语言模型在各类任务中表现的关键技术。尽管诸多研究已证明其有效性，但实际应用仍受限于现有实现多为孤立、难以维护的研究代码，或需深度侵入应用框架。为此，我们提出 promptolution，一个统一、模块化的开源框架，将提示词优化所需全部组件集成于单一可扩展系统中，服务于研究者与实践者。该框架整合了多种主流离散提示优化器，支持系统化、可复现的基准测试，并输出不依赖框架的提示字符串，可无缝嵌入现有LLM流程，且对底层模型实现保持无感。
原文摘要 · Abstract (English)
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.
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