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: Apr 21, 2026

GitHub Statistics

Community Sentiment

Community Buzz: The OpenEvolve community is buzzing with enthusiasm, as one user exclaimed on Reddit, 'OpenEvolve has had more real world impact than many other related open source projects!' and another praised its scalability on GitHub. However, it's worth noting that some users are still experimenting with the tool and have not yet reached a definitive conclusion.

Pros & Cons

What People Love

Reddit users praise OpenEvolve's flexibility and customization options, which allow them to tailor the tool to their specific research workflows., GitHub contributors appreciate OpenEvolve's scalability and ability to handle large datasets.

Common Complaints

Some users on Reddit mention that OpenEvolve can be difficult to use, especially for those without extensive experience in machine learning and population evolution., Others on GitHub express concerns about the tool's stability and reliability, particularly in high-stress environments.

Biggest Positive: Scalable population evolution tool

Biggest Negative: Difficult to use without extensive experience

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

The AI tool space is highly competitive, with various solutions catering to specific needs.