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HyperAgents: Self-referential self-improving agents

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Why This Matters

HyperAgents introduces self-referential, self-improving agents capable of optimizing for any computable task, marking a significant advancement in autonomous AI systems. This development could lead to more adaptable, efficient, and intelligent AI solutions across various industries, empowering both developers and consumers with highly capable automation tools.

Key Takeaways

HyperAgents Self-referential self-improving agents that can optimize for any computable task

Setup

# 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

Running HyperAgents

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

By default, outputs will be saved in outputs/ directory.

File Structure

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