Agentes de IA
superpowers
An agentic skills framework & software development methodology that works.
★ 294,9 mil estrelasMIT
Ver o guia →claude-code-from-scratch é um projeto de agentes de IA de código aberto de Windy3f3f3f3f (Windy3f3f3f3f/claude-code-from-scratch no GitHub). Tem 2.727 estrelas no GitHub e 547 forks, é publicado sob a licença MIT e foi atualizado pela última vez no GitHub em 9 de julho de 2026.
Rebuild Claude Code’s core in ~5,000 lines: an 11-chapter coding-agent masterclass.
Follow the guided tutorial sequence.
Implement the agent’s reasoning cycle.
Wire up file, shell, and search tools.
End with a working Claude Code-style agent.
Step-by-step guided learning.
Core architecture, nothing extra.
Two implementations to study.
No magic; every piece explained.
You finish with a real coding agent.
MIT licensed learning resource.
Download the source and move into the project folder.
git clone https://github.com/Windy3f3f3f3f/claude-code-from-scratch.git
cd claude-code-from-scratchEvery project documents its own setup; start with install requirements.
cat README.md | head -60Follow the project’s launch command — Docker, pip, npm, or a binary release.
# see README for the exact launch commandIt is an open project at Windy3f3f3f3f/claude-code-from-scratch with 547 forks.
Yes. MIT licensed.
Yes. The finished agent still calls a real model API.
Eleven chapters; pace yourself over days or weeks.
Parts are; this project exists because the full source is too large to study.
An agentic skills framework & software development methodology that works.
A lightweight, powerful framework for multi-agent workflows
Self-hosted ai agents: minimal ai coding agent (~1,000 lines of python) inspir.
A Claude Code-style coding agent that runs anywhere and works with almost any model provider
Community-built terminal coding agent: bring your own model, keep code local.
Local-first agentic workspace where Claude Code, Codex and a built-in agent share memory, apps and tools.
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Chinese Java course: build AI agents with Spring AI, RAG, MCP, and tool calling.
MCP server, CLI and skills for searching and downloading academic papers from arXiv, PubMed and 20+ sources
Persistent memory layer for AI agents, backed by Markdown and Milvus.
Recursive self-improving physical agent OS — verified, evidence-based execution for robots and simulators
Sync your AI coding setup: skills, agents, rules, MCP, hooks, everywhere.
Grátis e aberto: pegue o código-fonte, dê uma estrela no repositório e comece a criar.