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.
This repository is a structured, comprehensive AI engineering curriculum spanning 20 phases and over 500 lessons, from math foundations and ML fundamentals to deep learning, computer vision, NLP, speech, transformers, generative AI, reinforcement learning, LLMs from scratch, agent engineering, multi-agent systems, production infrastructure, and ethics/safety. It is an educational resource, not a production application or data research project. The code consists of self-contained pedagogical implementations (primarily Python, with some TypeScript, Rust, and Julia) that demonstrate core algorithms from scratch—such as attention mechanisms, BPE tokenization, diffusion models, RLHF, and agent loops—using synthetic data and deterministic stubs. Capstone projects include small web servers (Hono/TypeScript) and test harnesses. The curriculum is actively maintained with CI tooling, certification prep materials, and translation support. While the code is simplified for learning, it exhibits clean patterns, consistent lesson structure, and testing practices that make individual components (e.g., tool registries, state machines, evaluation harnesses) reusable in other contexts.
llm-action is a comprehensive resource hub for large language model engineering that combines theoretical explanations with practical implementation code. It covers the full LLM lifecycle including data engineering, training (full fine-tuning and parameter-efficient methods like LoRA/QLoRA), model compression (quantization, pruning, distillation), inference serving, deployment, and evaluation. The repository contains both educational documentation and runnable code examples, with particular emphasis on Chinese-language resources and domestic AI hardware adaptation (especially Huawei Ascend NPUs). While it serves as a knowledge base, its inclusion of actual training scripts, inference servers, and utility tools makes it primarily application-oriented rather than purely research-focused.