A skills set and methodology for coding agents: clarify the task, plan, write tests, then code. The agent works with more discipline.
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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 →
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32 repositories
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.
Anthropic's official collection of Agent Skills: folders with instructions and scripts that Claude loads for a task. A good reference for writing your own.
A free 21-lesson Microsoft course that goes from generative AI basics and prompting to working applications. Each lesson ships with code.
Google's open terminal agent: it connects Gemini to your codebase and can edit files, run commands and search the web.
The largest community-curated list of MCP servers, sorted by category from databases to browsers. The place to look for an existing server before writing your own.
A ready-made RAG engine with a UI: it parses documents with templates, chunks them, searches with citations and supports agentic retrieval. Deploys with Docker.
Few-shot voice cloning: about a minute of recorded speech is enough to fine-tune a text-to-speech model. Ships a web UI for data prep and training.
A framework where agents get roles, goals and tasks and work like a small team. The mental model is simple: crew, roles, process.
The whole path from zero to your own ChatGPT-like chat in one repo: tokenizer, pretraining, fine-tuning and UI. Designed to train for roughly a hundred dollars.
An open, extensible agent that goes beyond code: research, writing, automation. Works with 15+ providers and 70+ extensions through MCP.
A local replacement for cloud APIs: an OpenAI-compatible server that runs text, voice and image models on any hardware, no GPU required.
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.
A speech-synthesis model tuned for natural conversational dialogue: handles pauses, laughter and intonation. Good for voice assistants and dialogue voice-over.
Google's library for extracting structured data from text with LLMs: each field is tied to a spot in the source, and results can be viewed in an interactive visualization.
Anthropic's interactive prompting course: 9 chapters with exercises where you can try things right away and see how Claude's answer changes.
A Playwright-based MCP server that lets an agent drive a browser through page structure rather than screenshots. Good for testing sites and automating web actions.
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.
A free Hugging Face course on agents in four units: fundamentals, the smolagents, LlamaIndex and LangGraph frameworks, agentic RAG and a final benchmark assignment.
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 polished terminal coding agent from the Charm team, written in Go: a neat interface, many models, MCP support. A single binary with no heavy dependencies.
A terminal coding agent from the Qwen team, tuned for their models but open and configurable. A good way to try open models in practice.
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.
Twelve principles for building LLM apps good enough to put in customers' hands: own your prompts, own your context, small agents, explicit control flow.
A collection of 280+ free n8n templates: email, Telegram, Slack, Google Drive, Notion, OpenAI, RAG chatbots and document processing. Each template is a ready-made JSON.
Google's code-first agent framework: build agents in Python, run evaluations and deploy. Tuned for Gemini but works with other models too.
An alternative to the transformer: a state space model whose runtime grows linearly with text length. A good fit for long sequences.
A vector database that stores both objects and vectors and combines semantic search with ordinary filters. It can call embedding models itself.
DeepMind's official AlphaFold 2 code: predicts a protein's 3D structure from its sequence. A showcase of AI changing science far beyond chatbots.
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.
An OpenTelemetry-based toolkit that automatically traces calls to models, vector databases and frameworks. Data can go to any compatible monitoring system.
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