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
16 repositories
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.
Reference MCP servers from the protocol team: memory, git, filesystem and more. A solid pattern for how a server is built and a ready set to start with.
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 Microsoft course of 18 lessons on building AI agents: tools, memory, planning, multiple agents. Code examples live in the code_samples folder.
A memory layer for agents: it remembers facts about the user across sessions and pulls up the right ones at the right moment, keeping context small.
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 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.
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 TypeScript framework for agents and AI apps: agents, workflows, memory and quality evaluation in one place. A good fit for Node-based web teams.
A framework for assembling LLM apps from modular pipelines with explicit control over retrieval, routing and memory. Strong at RAG and search systems.
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.
Formerly MemGPT: a platform for agents with long-term memory that learn from experience. Today it lives on as the Letta Code terminal client with a server mode.
A parameter-efficient fine-tuning library: LoRA and related methods train only a small fraction of the weights. Large models fine-tune on consumer hardware.
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.
A modern rethink of the recurrent LSTM with exponential gating and matrix memory. The authors trained an xLSTM 7B language model on it, with weights on Hugging Face.
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