promptolution: A Unified, Modular Framework for Prompt Optimization

2025-12-02

AI Summary

Existing LLM prompt optimization methods suffer from fragmented codebases, poor maintenance, and inconsistent interfaces, severely hindering practical reusability. To address this, we propose PromptOpt—the first open-source, modular, model-agnostic prompt optimization framework. Its core innovation lies in decoupling optimizer logic from underlying LLM implementations, enabling plug-and-play componentization, seamless compatibility with mainstream LLM APIs (e.g., OpenAI, Anthropic, Hugging Face), and dual access via both CLI and Python API. PromptOpt integrates multiple discrete prompt optimization algorithms and demonstrates strong stability, scalability, and deployability across diverse tasks and LLMs. Empirical evaluation confirms its effectiveness in improving reproducibility, maintainability, and industrial deployment efficiency of prompt engineering.

Technology Category

• Natural Language Processing: Prompt Engineering / Prompting
• Search and Optimization: Learning to Search
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

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.

Problem

• Unifies modular prompt optimization for LLMs
• Addresses fragmented research codebases hindering adoption
• Integrates discrete optimizers agnostically across LLMs

Innovation

• Unified modular framework for prompt optimization
• Integrates multiple discrete prompt optimizers
• Agnostic to underlying LLM implementation