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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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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10 repositories
A node-based builder for diffusion pipelines: wire up image, video and audio generation visually. Runs locally and exposes API endpoints.
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 low-level library for agents modelled as a state graph. It gives you long-running flows, saved progress and pauses for human approval.
A lightweight alternative to GraphRAG: it builds a knowledge graph from documents and answers questions using both the graph and plain vector search. Ships with a server and a web UI.
A modular Microsoft system that builds an entity graph and community summaries from texts, so it can answer questions about a whole corpus rather than one passage.
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 long-term memory layer for agents: it remembers facts, links them into a graph and retrieves them on demand. The basic flow runs locally, even without an LLM key.
Tooling for computer-use agents: drivers for several OSes, virtual machines and benchmarks for training and evaluation.
ETL for documents: it splits PDFs, emails, Word, HTML and images into typed elements (titles, paragraphs, tables) ready for chunking and embeddings.
A security scanner for agentic workflows: it analyzes LangGraph, CrewAI, n8n and other code, builds a report and can test agents for vulnerabilities.
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