OpenEvolve is an open-source evolutionary coding agent that uses large language models (LLMs) to iteratively improve programs. It implements a MAP-Elites quality-diversity algorithm with island-based evolution, supporting multiple LLM backends (OpenAI, Claude Code, etc.) and a cascade evaluation pattern. The system accepts a user-provided initial program and an evaluator script, then repeatedly mutates the program via LLM-generated SEARCH/REPLACE diffs or full rewrites, evaluates the fitness, and maintains a diverse population across feature dimensions. The core architecture is a library with a CLI entry point (`openevolve-run`), a high-level API (`run_evolution`, `evolve_function`, `evolve_code`), and a controller that orchestrates the evolution loop, prompt sampling, LLM inference, parallel evaluation, and database storage. The repository includes 20+ diverse examples ranging from algorithm optimization (AlgoTune, TSP, circle packing) to mathematical discovery (Heilbronn, kissing number) and hardware-specific kernel tuning (MLX Metal, MLIR). The codebase is well-structured with extensive unit and integration tests, typed configuration, and a modular design that separates concerns (evaluator, LLM, database, prompt templates). It is actively maintained with frequent commits and responsive issue triage.
Autoresearch-opencode is an OpenCode plugin and skill that enables autonomous code optimization experiments. It provides a slash command (`/autoresearch`) and a TypeScript plugin that injects context into the AI agent's system prompt to guide it through an iterative experiment loop. The system defines a protocol for the agent: it reads/writes a JSONL state file (`autoresearch.jsonl`), generates experiment scripts (`autoresearch.sh`), and maintains a worklog (`experiments/worklog.md`). The core logic is documented in markdown files (SKILL.md, commands/autoresearch.md) rather than executable code, making it a hybrid of documentation and configuration. The repository includes a well-crafted bash backup utility (`scripts/backup-state.sh`) and example optimization targets (bogo_sort.py). The system is designed to be used within the OpenCode environment and does not function as a standalone application.
This repository is a curated list (awesome list) of AutoResearch use cases, implementations, benchmarks, and forks. It does not contain any source code, data, or executable components. The sole content is a README.md file that organizes links to external projects, each with descriptions and links to optimization traces. The repository serves as a directory for the AutoResearch community, not as a software application or data research pipeline. It is a static documentation resource with no runtime behavior, architecture, or data flows.