promptolution: A Unified, Modular Framework for Prompt Optimization

TL;DR

promptolution introduces a modular, open-source framework that unifies multiple discrete prompt optimizers under an LLM-agnostic umbrella. It provides interchangeable LLM interfaces, task definitions, and evaluation workflows, enabling both practitioners and researchers to perform systematic prompt optimization and benchmark studies. The paper demonstrates competitive performance across standard datasets (GSM8K, SST-5) and outlines a roadmap for interoperability, scalable experimentation, and multi-agent prompt optimization. By consolidating tools and enabling rigorous benchmarking, promptolution aims to accelerate progress in prompt optimization and broaden practical adoption.

Abstract

Prompt optimization has become crucial for enhancing the performance of large language models (LLMs) across a broad range of tasks. Although many research papers show its effectiveness, practical adoption is hindered as existing implementations are often tied to unmaintained and isolated research codebases. To address this, we introduce promptolution, a unified and 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 while remaining agnostic to the underlying LLM implementation.

Figures (4)

• Figure 1: Overview of the promptolution framework. promptolution takes a dataset (dev set + few-shot examples), token budget constraints, a description of the task, and optionally initial prompts as input. In an iterative process, a user-selected prompt optimizer refines the prompt(s) by evaluating the LLM's prediction performance on the task's development set and adapting the prompts accordingly (e.g., through another LLM). Detailed logging and callbacks enable tracking the entire process. The optimized prompts are returned and can be evaluated on test data.
• Figure 2: Core components of promptolution. The upper part of each box lists the component implementations, the lower part important functions. Arrows between components indicate conceptual connections.
• Figure 3: Setup of promptolution's core components.
• Figure 4: Running an experiment via the ExperimentConfig abstraction.