LLM4AD is an open-source Python-based Platform leveraging Large Language Models (LLMs) for Automatic Algorithm Design (AD). Please refer to the paper [LLM4AD] for detailed information, including the overview, methodology, and benchmark results.

LLM4AD is accomplished with Documents and Examples materials to support users and developers to easily test, build and deploy their own LLM4AD applications and conduct research.

LLM4AD was originally developed for optimisation tasks. The framework is versatile enough to be used in other areas, including machine learning, science discovery, game theory and engineering design.

For more information, see the contact list

• 2025.6 🎉🎉 We're excited to share that EoH recently set a New World Record in Circle Packing Problem, achieving a score of 2.63594 for 26 circles ! Results here

• 2025.6 🎉🎉 LLM4AD at CEC 2025 Tutorial !

• 2025.3 🎉🎉 LLM4AD at EMO 2025 Tutorial !

• 2024.12 🎉🎉 LLM4AD paper Released “LLM4AD: A Platform for Algorithm Design with Large Language Model" !

• 2024.11 🎉🎉 LLM4AD v1.0 Released !

• 2024.10 🎉🎉 Survey Paper “A Systematic Survey on Large Language Models for Algorithm Design” is online !

• Please refer to requirements.txt

• Please install numba (if you want to use Numba accelerate)

• Please install tensorboard (if you want to use a Tensorboard logger)

• Please install wandb (if you want to use wandb logger)

• Please install gym (if you want to try GUI, and Machine Learning tasks)

• Please install pandas (if you want to try Science Discovery tasks)

• Please install all required packages in requirements.txt (if you want to use GUI)

We suggest to install and run LLM4AD in conda env with python>=3.9, <3.13

$ cd LLM4AD
$ pip install .

We suggest to install and run LLM4AD in conda env with python>=3.9, <3.13

$ pip install llm4ad

from llm4ad.task.optimization.online_bin_packing import OBPEvaluation
from llm4ad.tools.llm.llm_api_https import HttpsApi
from llm4ad.method.eoh import EoH, EoHProfiler

if __name__ == '__main__':
    llm = HttpsApi(
        host='xxx',   # your host endpoint, e.g., api.openai.com, api.deepseek.com
        key='sk-xxx', # your key, e.g., sk-xxxxxxxxxx
        model='xxx',  # your llm, e.g., gpt-3.5-turbo, deepseek-chat
        timeout=20
    )
    task = OBPEvaluation()
    method = EoH(
        llm=llm,
        profiler=EoHProfiler(log_dir='logs/eoh', log_style='simple'),
        evaluation=task,
        max_sample_nums=20,
        max_generations=10,
        pop_size=4,
        num_samplers=1,
        num_evaluators=1,
        debug_mode=False
    )
    method.run()

• Constructive Heuristics for TSP
• Constructive Heuristics for VRPTW
• ...

Check Documents for more tasks and examples

$ cd GUI
$ python run_gui.py

Check GUI Introduction for more information

There are three approaches on LLM interface implementation, check Tutorial on LLM interface implementation for more information.

• Remote LLM API (e.g., GPT4o, GPT3.5, Gemini Pro, Deepseek ...) (<Recommended !!!>)
• Local HuggingFace LLM Deployment (e.g., Llamacode, Llama, Gemma, Deepseek, ...)
• Your Implementation If you want to use your own GPT API or local LLMs deployment, please create and add your interface in LLM

A Step-by-step Tutorial on using LLM4AD to solve your algorithm design task is provided here

Find responses to some common questions in our Discussion Area

常见问题答疑

This project is licensed under the MIT License - see the LICENSE file for details. Parts of this project use code licensed under the Apache License 2.0.

If you find LLM4AD helpful please cite:

@article{liu2024llm4ad,
    title = {LLM4AD: A Platform for Algorithm Design with Large Language Model},
    author = {Fei Liu and Rui Zhang and Zhuoliang Xie and Rui Sun and Kai Li and Xi Lin and Zhenkun Wang and Zhichao Lu and Qingfu Zhang},
    year = {2024},
    eprint = {2412.17287},
    archivePrefix = {arXiv},
    primaryClass = {cs.AI},
    url = {https://arxiv.org/abs/2412.17287},
}

This platform is developed and maintained by LLM4AD developer group from the City University of Hong Kong (CityUHK) and the Southern University of Science and Technology (SUSTech). We develop LLM4AD platform for research purposes and hope to contribute to the research area by delivering tools for LLM-based algorithm design methods.

• Contribution: We are more than welcome to contribute (see our 📖 contribution guide) including developing code and ideas to improve our platform.
• Collaborations: If you like our platform, and you would like to use it for profit-making purposes? We are always searching for industrial collaborations because they help direct research to meet the industry’s needs.
• Issue: If you find a bug or you have any kind of concern regarding the correctness, please report us an issue.
• Profit Purpose: If you intend to use LLM4AD for any profit-making purposes, please contact us.

If you are interested in LLM4AD or if you encounter any difficulty using the platform, you can:

1. Visit our website LLM4AD Web

2. Visit our collection a collection of resources and research papers on LLM4AD

3. Visit Discussions to connect with other members of our community

4. Join our QQ Group

   

5. Contact us through email [email protected]

6. Submit an issue

Any new ideas, features, and improvements are welcomed!

You can contribute to LLM4AD follow our 📖 Contribution Guide.

Thank you for contributing to LLM4AD and welcome to being part of the LLM4AD community! ✨

Fei Liu

Kai Li

Rui Sun

Julian XIE

Shunyu Yao

Rui Zhang

Weiwei Sun

Zhi Zheng