HyperAgents: Self-referential self-improving agents

# API keys, put these into .env file
OPENAI_API_KEY=...
ANTHROPIC_API_KEY=...
GEMINI_API_KEY=...

# Install things
sudo dnf install -y python3.12-devel
sudo dnf install -y graphviz graphviz-devel cmake ninja-build bzip2-devel zlib-devel ncurses-devel libffi-devel

# Create virtual environment
python3.12 -m venv venv_nat
source venv_nat/bin/activate
pip install -r requirements.txt
pip install -r requirements_dev.txt
# To build the docker container
docker build --network=host -t hyperagents .

# Setup initial agents
bash ./setup_initial.sh

# See the script for args, and baseline selections
python generate_loop.py --domains <domain>

By default, outputs will be saved in outputs/

directory.

agent/

code for using foundation modelsanalysis/

scripts used for plotting and analysisdomains/

code for each domainutils/

common code used in the reporun_meta_agent.py

script to help run the meta agent and get the diffsmeta_agent.py

main implementation of the meta agenttask_agent.py

main implementation of the task agentgenerate_loop.py

entry point for running the algorithm

The experiment logs are stored as a multi-part ZIP archive. To extract them, ensure all .z01, .z02, etc., files are in the same directory as the .zip file, then run:

zip -s 0 outputs_os_parts.zip --out unsplit_logs.zip
unzip unsplit_outputs.zip

Warning

This repository involves executing untrusted, model-generated code. We strongly advise users to be aware of the associated safety risks. While it is highly unlikely that such code will perform overtly malicious actions under our current settings and with the models we use, it may still behave destructively due to limitations in model capability or alignment. By using this repository, you acknowledge and accept these risks.

If you find this project useful, please consider citing:

@misc{zhang2026hyperagents,
title={Hyperagents},
author={Jenny Zhang and Bingchen Zhao and Wannan Yang and Jakob Foerster and Jeff Clune and Minqi Jiang and Sam Devlin and Tatiana Shavrina},
year={2026},
eprint={2603.19461},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2603.19461},
}