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
13 repositories
OpenAI's lightweight coding agent that runs in the terminal and works on your project's code. Written in Rust.
A fast multiplayer code editor in Rust from the creators of Atom and Tree-sitter, with built-in AI agent support. A lightweight alternative to VS Code.
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 single gateway to 100+ LLM providers in OpenAI format: a Python SDK and a proxy server with cost tracking, load balancing and logging. Swap models without changing code.
An open, extensible agent that goes beyond code: research, writing, automation. Works with 15+ providers and 70+ extensions through MCP.
A desktop ChatGPT alternative that works fully offline: download a model and chat, with nothing leaving your machine.
A fast Rust vector database with payload filtering, hybrid search, quantization and a distributed mode. Available as a container and as a cloud service.
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.
Tooling for computer-use agents: drivers for several OSes, virtual machines and benchmarks for training and evaluation.
A developer platform that turns scripts into webhooks, workflows and UIs. A fast job engine and an open alternative to Retool and Temporal.
A free MCP course from Microsoft: from basics and security to first servers, with examples in C#, Java, JavaScript, Rust, Python and TypeScript.
An embedded search database, like SQLite for embeddings: it stores data in files and searches by keyword, vector and SQL. Handy for multimodal data.
A dedicated language for describing LLM calls as typed functions: prompt, response schema and tests live together and compile into a client for your language.
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