Openevolve — AI Agent Framework: Live Stats & TrendScore

Live GitHub stats, community sentiment, and trend data for Openevolve. TrendingBots tracks star velocity, fork activity, and what developers are saying — updated from real data sources.

GitHub data synced: Mar 18, 2026 • Sentiment updated: Jun 15, 2026

GitHub Statistics

Community Sentiment

Community Buzz: LEVI reaches scores within 100 evaluations that neither OpenEvolve nor GEPA hit at any point, so the gains come from the search architecture, not just throwing...

Pros & Cons

What People Love

effective optimization, Reddit users praise OpenEvolve's performance

Common Complaints

evaluation script crashes, asset bundle missing

Biggest Positive: effective optimization

Biggest Negative: evaluation script crashes

Why Openevolve Stands Out

OpenEvolve is valuable because it provides an autonomous discovery capability that allows users to discover entirely new algorithms without human guidance. Its proven results, including 2-3x speedups on real hardware and state-of-the-art circle packing, demonstrate its effectiveness. Additionally, its research-grade features, such as full reproducibility and extensive evaluation pipelines, make it a reliable choice for researchers and developers.

Built With

Build a GPU-optimized kernel discovery system — OpenEvolve's evolutionary algorithms enable the discovery of optimized kernels, Build a state-of-the-art circle packing algorithm — OpenEvolve's iterative refinement process allows for the discovery of optimal packing solutions, Build an adaptive sorting algorithm — OpenEvolve's autonomous discovery capabilities enable the creation of efficient sorting algorithms, Build a multi-language code optimization platform — OpenEvolve's support for multiple programming languages allows for the optimization of code across different languages, Build a scientific computing automation platform — OpenEvolve's automated filter design and optimization capabilities enable the automation of scientific computing tasks

Getting Started

  1. Install OpenEvolve using pip: `pip install openevolve`
  2. Set up your OpenAI API key: `export OPENAI_API_KEY='your-gemini-api-key'`
  3. Run your first evolution using the example config: `python openevolve-run.py examples/function_minimization/initial_program.py examples/function_minimization/evaluator.py --config examples/function_minimization/config.yaml --iterations 50`
  4. Configure OpenEvolve to use a different OpenAI-compatible provider by modifying the `config.yaml` file
  5. Try evolving a simple sorting algorithm to verify that OpenEvolve works: `python openevolve-run.py examples/sorting/initial_program.py examples/sorting/evaluator.py --config examples/sorting/config.yaml --iterations 50`

About

Open-source implementation of AlphaEvolve

Category & Tags

Category: coding

Tags: alpha-evolve, alphacode, alphaevolve, coding-agent, deepmind, deepmind-lab, discovery, distributed-evolutionary-algorithms, evolutionary-algorithms, evolutionary-computation, genetic-algorithm, genetic-algorithms, iterative-methods, iterative-refinement, llm-engineering, llm-ensemble, llm-inference, openevolve, optimize

Market Context

Competitive positioning with GEPA and LEVI