promptolution 2.2.0

A framework for prompt optimization and a zoo of prompt optimization algorithms.

🚀 What is Promptolution?

Promptolution is a unified, modular framework for prompt optimization built for researchers and advanced practitioners who want full control over their experimental setup. Unlike end-to-end application frameworks with high abstraction, promptolution focuses exclusively on the optimization stage, providing a clean, transparent, and extensible API. It allows for simple prompt optimization for one task up to large-scale reproducible benchmark experiments.

Key Features

• Implementation of many current prompt optimizers out of the box.
• Unified LLM backend supporting API-based models, Local LLMs, and vLLM clusters.
• Built-in response caching to save costs and parallelized inference for speed.
• Detailed logging and token usage tracking for granular post-hoc analysis.

Have a look at our Release Notes for the latest updates to promptolution.

📦 Installation

pip install promptolution[api]

Local inference via vLLM or transformers:

pip install promptolution[vllm,transformers]

From source:

git clone https://github.com/automl/promptolution.git
cd promptolution
poetry install

🔧 Quickstart

Start with the Getting Started tutorial: https://github.com/automl/promptolution/blob/main/tutorials/getting_started.ipynb

Full docs: https://automl.github.io/promptolution/

🧠 Featured Optimizers

Name	Paper	Init prompts	Exploration	Costs	Parallelizable	Few-shot
CAPO	Zehle et al., 2025	required	👍	💲	✅	✅
EvoPromptDE	Guo et al., 2023	required	👍	💲💲	✅	❌
EvoPromptGA	Guo et al., 2023	required	👍	💲💲	✅	❌
OPRO	Yang et al., 2023	optional	👎	💲💲	❌	❌

🏗 Components

• Task – Manages the dataset, evaluation metrics, and subsampling.
• Predictor – Defines how to extract the answer from the model's response.
• LLM – A unified interface handling inference, token counting, and concurrency.
• Optimizer – The core component that implements the algorithms that refine prompts.
• ExperimentConfig – A configuration abstraction to streamline and parametrize large-scale scientific experiments.

🤝 Contributing

Open an issue → create a branch → PR → CI → review → merge. Branch naming: feature/..., fix/..., chore/..., refactor/....

Please ensure to use pre-commit, which assists with keeping the code quality high:

pre-commit install
pre-commit run --all-files

We encourage every contributor to also write tests, that automatically check if the implementation works as expected:

poetry run python -m coverage run -m pytest
poetry run python -m coverage report

Developed by Timo Heiß, Moritz Schlager, and Tom Zehle (LMU Munich, MCML, ELLIS, TUM, Uni Freiburg).