Symbolic Learning in Python and Julia
A next-generation automatic algorithm design platform, making automated algorithm design more accessible and easier to use
Distributed GPU-Accelerated Framework for Evolutionary Computation. Comprehensive Library of Evolutionary Algorithms & Benchmark Problems.
Open-source autoresearch powered by autonomous coding agents. Run Claude Code, OpenCode, and Codex with grading, shared knowledge, and multi-agent evolution. Accepted at COLM 2026.
Gradient descent, but the parameters are agents β a parallel, asynchronous framework for self-evolving agents (skills, prompts, harnesses). Diffs are the gradients; the aggregator is the optimizer.
Open-source implementation of AlphaEvolve
An auto-evolution framework that optimizes anything β your 7Γ24 team of algorithm engineers.
Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).
Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.
turns your codebase into an autoresearch loop β discovers what to measure, instruments the benchmark, then runs tree search with parallel subagents.
Advanced evolutionary computation library built directly on top of PyTorch, created at NNAISENSE.
Automated modeling and machine learning framework FEDOT
Evolutionary multi-agent runtime that breeds, evaluates, and improves autonomous agents across reproducible epochs to converge on optimization of a goal.
Hillclimbing on verifiable rewards. Use Claude Code and Codex to autonomously search for python programs that optimize a score you define. ποΈ