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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26 repositories
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.
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.
A visual builder for AI agents and workflows in Python: drag components, test the chain in chat and publish it as an API.
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 library that lets an agent drive a browser: open sites, click, fill in forms. Works with a local browser or a cloud one.
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.
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 collection of examples and guides for the OpenAI API: from basic requests to function calling and RAG. Most code is Python.
Feeds fresh, version-specific library docs into the prompt so the model stops inventing outdated APIs. Works as an MCP server or through a CLI with a skill.
Microsoft's framework for agents that talk to each other. It has the high-level AgentChat API and AutoGen Studio, a no-code graphical interface.
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.
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.
A long-running builder of "agents" that watch the web and events and act on your behalf: send emails, collect data, trigger chains. Runs on your own server.
A local replacement for cloud APIs: an OpenAI-compatible server that runs text, voice and image models on any hardware, no GPU required.
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 framework and runtime for agent platforms: an SDK to build, AgentOS to serve agents as a service with an API, and a web UI to manage them. Your data stays with you.
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.
An SDK that drives the browser with a mix of code and natural language: extract data and click through any site without brittle selectors.
Hugging Face's library for post-training models: SFT, DPO, GRPO and reward-model training. The base for shaping model behavior after pretraining.
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.
A unified interface to different generative AI providers: you only change the model string, such as `openai:gpt-4o` to `anthropic:...`, and the rest of the code stays the same.
Gives a coding agent the structure of a Figma design: layers, spacing, colors. The code ends up closer to the design than it would from a screenshot.
Firecrawl's official MCP server: scraping, search and site crawling for an agent. Offers a hosted keyless free tier or a local run through npx.
A gateway and observability platform: change the API address in your code and all model requests get logged, costed and compared. Can be self-hosted.
Notion's official MCP server: an agent searches, reads and edits pages and databases. Access follows the integration's permissions, so you can grant read-only.
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