promptolution: A Unified, Modular Framework for Prompt Optimization

Tom Zehle, Timo Heiß, Moritz Schlager, Matthias Aßenmacher, Matthias Feurer


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 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.
Anthology ID:
2026.eacl-demo.21
Volume:
Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 3: System Demonstrations)
Month:
March
Year:
2026
Address:
Rabat, Marocco
Editors:
Danilo Croce, Jochen Leidner, Nafise Sadat Moosavi
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
282–296
Language:
URL:
https://preview.aclanthology.org/ingest-eacl/2026.eacl-demo.21/
DOI:
Bibkey:
Cite (ACL):
Tom Zehle, Timo Heiß, Moritz Schlager, Matthias Aßenmacher, and Matthias Feurer. 2026. promptolution: A Unified, Modular Framework for Prompt Optimization. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 282–296, Rabat, Marocco. Association for Computational Linguistics.
Cite (Informal):
promptolution: A Unified, Modular Framework for Prompt Optimization (Zehle et al., EACL 2026)
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PDF:
https://preview.aclanthology.org/ingest-eacl/2026.eacl-demo.21.pdf