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.
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7 repositories
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.
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 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.
A Postgres extension that adds a vector type and nearest-neighbor search. It lets you keep embeddings next to regular data without running a separate database.
A vector database that stores both objects and vectors and combines semantic search with ordinary filters. It can call embedding models itself.
An embedded search database, like SQLite for embeddings: it stores data in files and searches by keyword, vector and SQL. Handy for multimodal data.
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