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Show HN: OpenEvolve – open-source implementation of DeepMind's AlphaEvolve

May 20, 2025 - news.ycombinator.com
OpenEvolve is an open-source implementation of Google DeepMind's AlphaEvolve system, designed to evolve entire codebases using an ensemble of large language models (LLMs) and automated evaluation. It follows an evolutionary approach to discover and optimize algorithms, inspired by the AlphaEvolve paper, but is fully open-source and configurable. The system consists of four main components: a Program Database, a Prompt Sampler, an LLM Ensemble, and an Evaluator Pool, which work together in an evolutionary loop to generate and test code modifications. OpenEvolve allows users to run existing examples, define custom problems, configure LLM backends, and optimize algorithms with multiple objectives.

The project has successfully replicated examples from the AlphaEvolve paper, such as Circle Packing and Function Minimization, achieving results close to DeepMind's reported outcomes. Technical insights highlight the importance of low-latency LLMs, with the best results achieved using Gemini-Flash-2.0-lite and Gemini-Flash-2.0 as the ensemble. The system is easy to set up, requiring Python 3.9+ and an API key for an LLM service, with configuration managed through YAML files. Users can experiment with evolutionary code generation and explore the potential of LLMs in optimizing complex algorithms.

Key takeaways:

  • OpenEvolve is an open-source implementation of Google DeepMind's AlphaEvolve system, designed to evolve entire codebases using LLMs and an evolutionary approach.
  • The system consists of four main components: Program Database, Prompt Sampler, LLM Ensemble, and Evaluator Pool, which work together in an evolutionary loop.
  • OpenEvolve allows users to run existing examples, define custom problems, configure LLM backends, and optimize algorithms with multiple objectives.
  • Technical insights include the importance of low latency LLMs, the effectiveness of a two-phase approach, and the use of specific LLM ensembles for optimal results.
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