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
35 repositories
The easiest way to run an open model on your own machine: one command downloads and starts it, plus a local REST API and libraries for Python and JavaScript.
The classic browser UI for Stable Diffusion: text-to-image, inpainting, upscaling and thousands of community extensions.
A self-hosted ChatGPT-style interface that connects to Ollama and any OpenAI-compatible API. Installs with a single Docker command and runs on your own server.
A node-based builder for diffusion pipelines: wire up image, video and audio generation visually. Runs locally and exposes API endpoints.
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.
A library that lets an agent drive a browser: open sites, click, fill in forms. Works with a local browser or a cloud one.
An open AI-developer platform: the agent writes code, runs commands and browses the web. Runs locally or on a shared server for a team.
An open-source Python crawler: it visits pages with a real browser and returns clean Markdown for LLMs. Runs locally, in Docker or as a cloud 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.
A coding agent, a fork of Codex tuned for cheap and open models. It can emulate other agents' harnesses to get the most out of a weaker model.
A ready-made «chat with your documents» app: plug in any model and vector database, upload files and talk to them. Has a desktop version, Docker and agents.
An open, extensible agent that goes beyond code: research, writing, automation. Works with 15+ providers and 70+ extensions through MCP.
Whisper rewritten in C/C++: runs fast on a plain CPU and on Macs with no Python or heavy dependencies. Easy to embed in apps.
A local replacement for cloud APIs: an OpenAI-compatible server that runs text, voice and image models on any hardware, no GPU required.
One of the first terminal AI pair programmers: it edits your repo's files and makes git commits itself. Works with almost any model.
Joins several devices into one AI cluster so you can run models too big for a single machine's memory. Devices discover each other automatically; there is a dashboard and an API.
A vector database for embedding search at large scale. It scales to cloud workloads, and for prototypes there is a lightweight local version, Milvus Lite.
A desktop ChatGPT alternative that works fully offline: download a model and chat, with nothing leaving your machine.
GitHub's official MCP server: an agent reads repositories, issues and pull requests and works with them. Comes as a hosted version from GitHub and a local one in Docker.
A long-term memory layer for agents: it remembers facts, links them into a graph and retrieves them on demand. The basic flow runs locally, even without an LLM key.
The easiest vector database to start with: it runs inside your app process, stores documents and finds similar ones. Great for prototypes, with a client-server mode and a cloud.
Apple's array framework for its own chips: a NumPy- and PyTorch-like interface that uses the Mac's unified memory. The base for running and training models on a Mac.
A polished terminal coding agent from the Charm team, written in Go: a neat interface, many models, MCP support. A single binary with no heavy dependencies.
A terminal coding agent from the Qwen team, tuned for their models but open and configurable. A good way to try open models in practice.
The open Qwen3 model family from Alibaba, from small to large, with reasoning modes. The README collects run examples for Transformers, llama.cpp and Ollama.
A compact multimodal model that understands images and video and fits on a phone or laptop. Ships an online demo and a web demo you can self-host.
Packs a model and its engine into one executable file: download, make it executable, run. Works across operating systems with no install.
A library for working with datasets: one line loads thousands of ready-made sets from the Hub or your local files and processes them fast. It can stream data without a full download.
Open video-generation models: turn text or an image into a clip. The 1.3B version fits in roughly 8 GB of VRAM.
A structured-generation library: the model returns an answer strictly matching a type or Pydantic schema. Works with different providers and local models.
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