Thanks for considering contributing. This is an experimental research project exploring how genetic algorithms can evolve complete agent architectures. Contributions that improve the evolution system, agent quality, documentation, or reproducibility are welcome.
- Add mutation operators: Extend
agent_forge/meta_evolver.pywith new crossover/mutation strategies - Improve agent evaluation: Extend
agent_forge/full_agent_evaluator.pywith new fitness dimensions - Enhance agent specs: Add new blueprint components to
agent_forge/agent_spec_generator.py - Tune meta-evolution: Adjust adaptation rates and operator weightings in
meta_evolver.py - Add benchmark tasks: Contribute new benchmark definitions to
benchmarks/tasks.json - Improve the dashboard: Extend
dashboard/main.pywith new visualizations
- Improve README clarity and accuracy
- Add architecture decision records
- Document experiment results and observations
- Improve code quality (type hints, self-documenting patterns)
- Fork the repo and create a branch from
main. - Make focused, single-purpose changes.
- Follow the code style (ruff-compatible Python).
- Verify with lint:
ruff check agent_forge/ dashboard/
- Open a pull request against
main.
Open an issue using the templates in .github/ISSUE_TEMPLATE/.
- Python: ruff-compatible. Run
ruff check .before committing. - Type hints on all function signatures and public variables.
- No comments unless absolutely necessary (the code should explain itself).
- No exaggeration in documentation — accurately describe capabilities.
- This is evolutionary software optimization research, not AGI.
- Do not claim sentience, consciousness, or general intelligence.
- Frame contributions as evolutionary computation research.
- Be honest about limitations and failure modes.
Open a discussion.