A skills set and methodology for coding agents: clarify the task, plan, write tests, then code. The agent works with more discipline.
The AI movement's repository database
The best AI repositories
224 living projects — from agent frameworks to prompt-injection defense. Each comes with our own note: why it matters, who it is for and how to start in three steps.
Stars, forks and activity refresh daily via the GitHub API · updated October 6, 2026
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A service that turns any website into clean Markdown or structured data for models. It can search, scrape, crawl a whole site and even click through a page.
A small Microsoft utility that converts PDF, Word, PowerPoint, Excel and other files into Markdown that is easy to hand to a language model.
An open coding agent for the terminal with a desktop app. It is not tied to one provider: plug in whichever models you have.
A toolkit for spec-driven development: principles and a spec first, then a plan and tasks, and only then code. Works with several coding agents.
OpenAI's lightweight coding agent that runs in the terminal and works on your project's code. Written in Rust.
Categories
Agent frameworksFrameworks and orchestration: what agents and multi-agent systems are built from.23 repositoriesCoding agents & CLIAgents that write code in the terminal and editor, and the methods for working with them.21 repositoriesMCPThe Model Context Protocol spec, official SDKs and the best servers.20 repositoriesOpen models & inferenceOpen weights, local runs, fast inference and fine-tuning.38 repositoriesRAG & vector DBsSearch over your own data: document parsing, crawlers, vector databases, knowledge graphs.19 repositoriesEvals & observabilityHow to measure a model or agent and see what happens inside it.17 repositoriesPrompting & contextPrompts, structured output, skills and context engineering.16 repositoriesVoice, video, multimodalSpeech recognition and synthesis, voice agents, images and video.19 repositoriesAutomationNo-code workflows, browser and computer-use agents.15 repositoriesAwesome lists & learningCourses, books and awesome lists worth starting with.21 repositoriesSecurityPrompt injection, guardrails, red teaming and agent scanners.15 repositoriesMissing your favorite?Suggest a repository — we will review it and add it if it earns a spot.Suggest a repo →
Catalog
19 repositories
The classic browser UI for Stable Diffusion: text-to-image, inpainting, upscaling and thousands of community extensions.
A C/C++ engine that runs language models on ordinary hardware: laptops, phones, GPU-less servers. Much of local AI, Ollama included, is built on it.
The foundation under most modern neural networks: GPU-accelerated tensors and automatic differentiation, wrapped in ordinary Python. Transformers, Llama and nearly every open model are written on it.
An engine for fast model serving on GPUs: frugal with memory and handles many requests at once. The default pick when a model has to run as a service.
A tool for running and fine-tuning models while saving memory: there is a desktop app, a web UI and a library. Fine-tuning works on a modest GPU.
The whole path from zero to your own ChatGPT-like chat in one repo: tokenizer, pretraining, fine-tuning and UI. Designed to train for roughly a hundred dollars.
A local replacement for cloud APIs: an OpenAI-compatible server that runs text, voice and image models on any hardware, no GPU required.
Meta's classic library for fast similarity search and clustering of vectors. It is not a database but an index engine that many other systems are built on.
A fast engine for serving language and multimodal models. A vLLM rival, especially strong on large models like DeepSeek.
GPT-2 training in plain C and CUDA, no PyTorch. Shows what happens under the hood while a model learns.
The official inference code for FLUX.1 models: text-to-image generation and editing, including Kontext mode where you edit an image with words.
Whisper on the CTranslate2 engine: same output, noticeably faster and lighter on memory, including 8-bit mode.
A fast, memory-efficient implementation of attention that large-model training and inference on GPUs lean on. The result is exact, with no approximation.
An alternative to the transformer: a state space model whose runtime grows linearly with text length. A good fit for long sequences.
Open video-generation models: turn text or an image into a clip. The 1.3B version fits in roughly 8 GB of VRAM.
A small tensor library in plain C/C++ with no dependencies. It underpins llama.cpp and whisper.cpp and runs quantized models on CPU, GPU and in the browser.
The standard toolkit for running models through hundreds of academic benchmarks. Many open model leaderboards are built on it.
Squeezes models down to 8 and 4 bits so they fit on an ordinary GPU. QLoRA and running large models in half the memory both rely on it.
EleutherAI's library for training large language models from scratch across many GPUs: ZeRO and 3D parallelism, launching via Slurm and MPI. Its own README says to use it only for models with billions of parameters.
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