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.
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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
29 repositories
Formerly Awesome ChatGPT Prompts: a huge community library of prompts plus an interactive book on prompting. You can deploy your own private copy.
A free 21-lesson Microsoft course that goes from generative AI basics and prompting to working applications. Each lesson ships with code.
Code for Sebastian Raschka's book: you build a GPT-like model in PyTorch step by step, then pretrain and finetune it. The best way to see how an LLM works inside.
A 12-week course on classic machine learning: 26 lessons and 52 quizzes. The foundation that makes modern models easier to understand.
A three-part LLM roadmap: fundamentals, the LLM Scientist (building models) and the LLM Engineer (building apps). Topics come with Colab notebooks.
A detailed guide to prompt engineering: techniques from few-shot to chains of thought, papers, lectures and notebooks. Recently extended with context engineering and agents.
A Microsoft course of 18 lessons on building AI agents: tools, memory, planning, multiple agents. Code examples live in the code_samples folder.
A collection of examples and guides for the OpenAI API: from basic requests to function calling and RAG. Most code is Python.
Recipes from Anthropic as notebooks: tools, RAG, multimodality, subagents and other techniques for working with Claude. The code is ready to copy into your projects.
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.
Anthropic's interactive prompting course: 9 chapters with exercises where you can try things right away and see how Claude's answer changes.
More than 90 projects on LLMs, RAG and agents, sorted by difficulty from beginner to advanced. Each project has its own write-up and code.
More than 40 runnable notebooks with RAG techniques, from basic retrieval to advanced setups. Each one has the idea, the code and references.
Code for every example in Jay Alammar and Maarten Grootendorst's book Hands-On Large Language Models. Twelve chapter notebooks, from tokens and transformers to RAG, multimodality and fine-tuning.
A layer on top of PyTorch that reduces training a network to a few lines. It comes with the well-known free fast.ai course, which makes it a handy place to start.
A curated list on large language models: milestone papers, open models, training and inference frameworks, courses and books. A good reference for a first look at the field.
A model that parses a UI screenshot into buttons and fields so an agent can work from the screen alone, without access to page code.
Andrej Karpathy's video course: you code neural networks from scratch, from backpropagation up to a GPT-style language model. Lectures come with notebooks.
A language for controlling generation: you mix text and constraints right in Python, and the model must produce exactly the required format, including schema-bound JSON.
Code notebooks for François Chollet's Deep Learning with Python (third edition, on Keras 3). Runs on JAX, TensorFlow or PyTorch. Legacy notebooks for the two earlier editions are included.
The Qwen team's family of multimodal models: understand images, documents and video, locate objects in a frame and work with UIs.
Meta's Segment Anything 2: picks out any object in an image or video from a click and tracks it across frames. The repo has code, checkpoints and notebooks.
A tiny autograd engine with a small neural net on top and a PyTorch-like API. Very little code, yet it shows exactly how backpropagation works.
Companion materials for Chip Huyen's book AI Engineering: table of contents, chapter summaries, study notes, prompt examples and case studies. The book covers adapting foundation models to real tasks.
A free MCP course from Microsoft: from basics and security to first servers, with examples in C#, Java, JavaScript, Rust, Python and TypeScript.
Code for the first edition of Sebastian Raschka's Python Machine Learning (2015): classic algorithms in NumPy and scikit-learn, from the perceptron to clustering. A newer edition lives in a separate repo.
Companion materials for Chip Huyen's Designing Machine Learning Systems: table of contents, chapter summaries, an MLOps tools overview and a resource list. There is no code, the book is about design decisions.
A suite of eight models of different sizes trained on identical data, with 154 saved checkpoints each. It exists to study how a model learns and when knowledge appears in it.
Materials for research on indirect prompt injection: how malicious instructions in emails, web pages and code break LLM-integrated apps. Includes attack demos.
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