CoolPrompt

<p align="center"> <picture> <source media="(prefers-color-scheme: light)" srcset="docs/images/logo_light.png"> <source media="(prefers-color-scheme: dark)" srcset="docs/images/logo_dark.png"> <img alt="CoolPrompt Logo" width="40%" height="40%"> </picture> </p>

CoolPrompt is a framework for automative prompting creation and optimization.

Practical cases

• Automatic prompt engineering for solving tasks using LLM
• (Semi-)automatic generation of markup for fine-tuning
• Formalization of response quality assessment using LLM
• Prompt tuning for agent systems

Core features

• Optimize prompts with our autoprompting optimizers: HyPE, ReflectivePrompt, DistillPrompt
• LLM-Agnostic Choice: work with your custom llm (from open-sourced to proprietary) using supported Langchain LLMs
• Generate synthetic evaluation data when no input dataset is provided
• Evaluate prompts incorporating multiple metrics for both classification and generation tasks
• Retrieve feedbacks to interpret prompt optimization results
• Automatic task detecting for scenarios without explicit user-defined task specifications

<p align="center"> <picture> <source srcset="docs/images/coolprompt_scheme.png"> <img alt="CoolPrompt Scheme" width="100%" height="100%"> </picture> </p>

Quick install

• Install with pip:

pip install coolprompt

• Install with git:

git clone https://github.com/CTLab-ITMO/CoolPrompt.git

pip install -r requirements.txt

Quick start

Import and initialize PromptTuner using model qwen3-4b-instruct via HuggingFace

from coolprompt.assistant import PromptTuner

prompt_tuner = PromptTuner()

prompt_tuner.run('Write an essay about autumn')

print(prompt_tuner.final_prompt)

# You are an expert writer and seasonal observer tasked with composing a rich,
# well-structured, and vividly descriptive essay on the theme of autumn...

Examples

See more examples in notebooks to familiarize yourself with our framework

About project

• The framework is developed by Computer Technologies Lab (CT-Lab) of ITMO University.
• <a href="https://github.com/CTLab-ITMO/CoolPrompt/blob/master/docs/API.md">API Reference</a>

Contributing

• We welcome and value any contributions and collaborations, so please contact us. For new code check out <a href="https://github.com/CTLab-ITMO/CoolPrompt/blob/master/docs/CONTRIBUTING.md">CONTRIBUTING.md</a>.

Reference

For technical details and full experimental results, please check our papers.

<a href="https://www.fruct.org/files/publications/volume-38/fruct38/Kul.pdf">CoolPrompt</a>

@INPROCEEDINGS{11239071,
  author={Kulin, Nikita and Zhuravlev, Viktor and Khairullin, Artur and Sitkina, Alena and Muravyov, Sergey},
  booktitle={2025 38th Conference of Open Innovations Association (FRUCT)}, 
  title={CoolPrompt: Automatic Prompt Optimization Framework for Large Language Models}, 
  year={2025},
  volume={},
  number={},
  pages={158-166},
  keywords={Technological innovation;Systematics;Large language models;Pipelines;Manuals;Prediction algorithms;Libraries;Prompt engineering;Optimization;Synthetic data},
  doi={10.23919/FRUCT67853.2025.11239071}
}

<a href="https://ntv.ifmo.ru/file/article/23927.pdf">ReflectivePrompt</a>

@misc{zhuravlev2025reflectivepromptreflectiveevolutionautoprompting,
      title={ReflectivePrompt: Reflective evolution in autoprompting algorithms}, 
      author={Viktor N. Zhuravlev and Artur R. Khairullin and Ernest A. Dyagin and Alena N. Sitkina and Nikita I. Kulin},
      year={2025},
      eprint={2508.18870},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2508.18870}, 
}

<a href="https://arxiv.org/pdf/2508.18992">DistillPrompt</a>

@misc{dyagin2025automaticpromptoptimizationprompt,
      title={Automatic Prompt Optimization with Prompt Distillation}, 
      author={Ernest A. Dyagin and Nikita I. Kulin and Artur R. Khairullin and Viktor N. Zhuravlev and Alena N. Sitkina},
      year={2025},
      eprint={2508.18992},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2508.18992}, 
}