evotoolkit 1.0.0rc1

LLM-driven code evolution method library.

EvoToolkit

LLM-driven solution evolutionary optimization toolkit

EvoToolkit is a Python library that leverages Large Language Models (LLMs) to evolve solutions for optimization problems. It combines the power of evolutionary algorithms with LLM-based solution generation and refinement.

Installation

pip install evotoolkit

import evotoolkit  # Note: import name is different from package name

Quick Start

import evotoolkit
from evotoolkit.task.python_task.scientific_regression import ScientificRegressionTask
from evotoolkit.task.python_task import EvoEngineerPythonInterface
from evotoolkit.tools import HttpsApi

# 1. Create a task
task = ScientificRegressionTask(dataset_name="bactgrow")

# 2. Create an interface
interface = EvoEngineerPythonInterface(task)

# 3. Solve with LLM
llm_api = HttpsApi(
    api_url="https://api.openai.com/v1/chat/completions",
    key="your-api-key-here",
    model="gpt-4o"
)
result = evotoolkit.solve(
    interface=interface,
    output_path='./results',
    running_llm=llm_api,
    max_generations=5
)

Features

• 🤖 LLM-Driven Evolution: Use language models to generate and evolve solutions
• 🔬 Multiple Algorithms: EoH, EvoEngineer, and FunSearch
• 🌍 Task-Agnostic: Supports code, text, math expressions, etc.
• 🎯 Extensible: Easy-to-extend task system
• 🔌 Simple API: High-level evotoolkit.solve() function

Documentation

Full documentation: https://evotoolkit.readthedocs.io/

Citation

If you use EvoToolkit in your research, please cite:

@article{guo2025evotoolkit,
  title={evotoolkit: A Unified LLM-Driven Evolutionary Framework for Generalized Solution Search},
  author={Guo, Ping and Zhang, Qingfu},
  journal={arXiv preprint arXiv:XXXX.XXXXX},
  year={2025},
  note={Submitted to arXiv}
}

License

MIT License. For academic use, please cite our paper above.