promptolution: A Unified, Modular Framework for Prompt Optimization
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Abstract (arXiv)
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.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 24 of its 51 references that are in the map. First posted on arXiv 2 Dec 2025.
Builds on
24 of 51 references.
References · 24
- 2020Language Models are Few-Shot Learnerscited by 32,419
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- 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,915
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- 2021Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivitycited by 1,135
- 2022Large Language Models Are Human-Level Prompt Engineerscited by 1,045
- 2019How Can We Know What Language Models Know?cited by 959
12 more
- 2023Large Language Models as Optimizerscited by 864
- 2023A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPTcited by 619
- 2025GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learningcited by 542
- 2021Learning How to Ask: Querying LMs with Mixtures of Soft Promptscited by 377
- 2024Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programscited by 291
- 2021OpenPrompt: An Open-source Framework for Prompt-learningcited by 148
- 2023Black-Box Prompt Optimization: Aligning Large Language Models without Model Trainingcited by 122
- 2023TELeR: A General Taxonomy of LLM Prompts for Benchmarking Complex Taskscited by 49
- 2025A Survey of Automatic Prompt Engineering: An Optimization Perspectivecited by 41
- 2024Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Togethercited by 40
- 2024PromptWizard: Task-Aware Prompt Optimization Frameworkcited by 36
- 2025Promptomatix: An Automatic Prompt Optimization Framework for Large Language Modelscited by 12
Built on by
Citing papers per year: 2026: 6.
6 papers.
- 2026Modular Prompt Optimization: Optimizing Structured Prompts with Section-Local Textual Gradientscited by 8
- 2026Synthetic Interaction Data for Scalable Personalization in Large Language Modelscited by 4
- 2026p1: Better Prompt Optimization with Fewer Promptscited by 1
- 2026CANTANTE: Optimizing Agentic Systems via Contrastive Credit Attributioncited by 1
- 2026MO-CAPO: Multi-Objective Cost-Aware Prompt Optimizationcited by 1
- 2026Prompting Policies for Multi-step Reasoning and Tool-Use in Black-box LLMs with Iterative Distillation of Experiencecited by 0
- 2026MO-CAPO: Multi-Objective Cost-Aware Prompt Optimization15 May 2026
- 2026Prompting Policies for Multi-step Reasoning and Tool-Use in Black-box LLMs with Iterative Distillation of Experience14 May 2026
- 2026CANTANTE: Optimizing Agentic Systems via Contrastive Credit Attribution13 May 2026
- 2026p1: Better Prompt Optimization with Fewer Prompts9 Apr 2026
- 2026Synthetic Interaction Data for Scalable Personalization in Large Language Models12 Feb 2026
- 2026Modular Prompt Optimization: Optimizing Structured Prompts with Section-Local Textual Gradients7 Jan 2026