The most advanced open-source evolutionary coding agent

Turn your LLMs into autonomous code optimizers that discover breakthrough algorithms

Quick Start • Used In Research • Examples • Docs • Discussions

OpenEvolve is an open-source implementation of Google DeepMind's AlphaEvolve. It uses LLMs to mutate programs and evolves them with MAP-Elites and island populations, so you can point it at any problem you can score. It has become a standard tool and baseline for LLM-driven algorithm discovery: cited in hundreds of research papers, with 7,600+ GitHub stars, 1,200+ forks, 80+ contributors and 17,000+ monthly PyPI downloads.

Producing new results

Security research

Building on OpenEvolve

New evolutionary agents also routinely benchmark against OpenEvolve, including HarnessTTT, FrugalEvo, SEDIMA, EvoDuet, LoongFlow and AscendOptimizer.

Using OpenEvolve in your work? Open a pull request to add it here, and see Citation.

Generation 1	Generation 190	Generation 460 (final)
Random placement	Learning structure	State-of-the-art result

Compared with manual optimization:

Requires Python 3.10+ and access to an LLM.

git clone https://github.com/algorithmicsuperintelligence/openevolve.git
cd openevolve
pip install -e .

# The example uses Google Gemini by default (free tier available)
# Get your API key from: https://aistudio.google.com/apikey
export OPENAI_API_KEY="your-gemini-api-key"  # Yes, use OPENAI_API_KEY

python openevolve-run.py examples/function_minimization/initial_program.py \
  examples/function_minimization/evaluator.py \
  --config examples/function_minimization/config.yaml \
  --iterations 50

To use another provider, change api_base and the model in config.yaml. See LLM Providers.

Or use it as a library (pip install openevolve):

from openevolve import evolve_function

def bubble_sort(arr):
    for i in range(len(arr)):
        for j in range(len(arr) - 1):
            if arr[j] > arr[j + 1]:
                arr[j], arr[j + 1] = arr[j + 1], arr[j]
    return arr

if __name__ == "__main__":
    result = evolve_function(
        bubble_sort,
        test_cases=[([3, 1, 2], [1, 2, 3]), ([5, 2, 8], [2, 5, 8])],
        iterations=50,
    )
    print(result.best_code)

run_evolution also takes inline code and an evaluator function. The __main__ guard is required on macOS and Windows, where multiprocessing uses spawn.

A Docker image is available too:

docker pull ghcr.io/algorithmicsuperintelligence/openevolve:latest

docker run --rm -v $(pwd):/app ghcr.io/algorithmicsuperintelligence/openevolve:latest \
  examples/function_minimization/initial_program.py \
  examples/function_minimization/evaluator.py --iterations 100

• Quality-diversity evolution: MAP-Elites keeps diverse programs across feature dimensions.
• Island populations: separate populations with periodic migration prevent premature convergence.
• LLM ensembles: weighted combinations of models, from any OpenAI-compatible API or the Claude Code, GitHub Copilot and Codex CLIs.
• Artifact feedback: errors and evaluator output feed the next generation's prompt.
• Reproducibility: every component is seeded.

Mark the code to evolve with EVOLVE-BLOCK-START and EVOLVE-BLOCK-END comments. More in How It Works.

Good fits are performance optimization, algorithm discovery, scientific computing, competitive programming and multi-objective problems.

Running Google's AlphaEvolve examples? See the interoperability walkthrough.

• How It Works: the evolution pipeline and its features
• LLM Providers: provider setup and cost
• Configuration: options, evaluator arguments, prompt templates, island and population strategies, artifacts, and the visualizer
• System Messages: the guidance that matters most for good results
• Showcase: worked before-and-after examples
• FAQ and Roadmap

Articles:

• Towards Open Evolutionary Agents
• OpenEvolve: GPU Kernel Discovery
• OpenEvolve: Evolutionary Coding with LLMs

Contributions are welcome. See the Contributing Guide and good first issue labels.

If you use OpenEvolve in your research, please cite:

@software{openevolve,
  title = {OpenEvolve: an open-source evolutionary coding agent},
  author = {Asankhaya Sharma},
  year = {2025},
  publisher = {GitHub},
  url = {https://github.com/algorithmicsuperintelligence/openevolve}
}