#Loop-engineering
Showing 16 of 16 repositories tagged #loop-engineering, ranked by stars
🤯 LobeHub is your Chief Agent Operator, organizing your agents into 7×24 operations by hiring, scheduling, and reporting on your entire AI team.
Personal memory for agents - fast memory retrieval, self-evolving skills, and lower cost.
Practical patterns, starters & CLI tools for loop engineering with AI coding agents. Design systems that prompt and orchestrate agents (inspired by Addy Osmani and Boris Cherny). Includes loop-audit, loop-init, loop-cost.
Your autonomous engineering team in a CLI. The agent loop produces senior-level code that you can actually trust in prod because of non-negotiable feedback from independent reviewers. Supports Claude Code, OpenAI Codex, OpenCode, and Gemini CLI with trivial setup.
Private control plane for AI agents
OpenLoomi is a Claude Cowork-style open-source alternative for builders who want local-first work memory around their AI agents
Self-evolving second brain with 17 AI skills, 6 worker agents, and people CRM — inspired by Garry Tan's gstack and gbrain. Works with Claude Code, Cursor, Kiro, Gemini CLI, Codex.
Spec-driven, agentic workflow framework for AI coding agents. Turn a request into a verifiable goal loop — plan, act, verify — with durable specs and evidence in your repo. Works with Claude Code, Codex, Gemini, OpenCode, and plain CLI.
Self-hosted Personal AI + agent runtime in .NET (NativeAOT-friendly)
Inference-native Tokenmaxxing Agent Harness for Loop Engineering
Hands-on tutorials for building AI agents from scratch. Learn LLM APIs, prompt engineering, tool calling, and the agent loop through practical examples.
Open-source, self-hostable alternative to Claude Tag — a Slack-style workspace where your team and its AI agents (Claude Code, Codex, GitHub Copilot, and more) work as teammates in channels, threads, DMs, and shared tasks. Your data stays on your machines.
Build the system that prompts your agents. A teaching repo for loop engineering: chapters, an annotated reading list, copy-paste prompts, a runnable example, and a portable agent skill
给你的 Agent 配个「纪律委员」—— 约束工作习惯（先读后写/验证再干/谨慎修改），从错误中沉淀教训；核心层纯 bash 零依赖，编排引擎可选，OpenClaw first。
🪢 Multi-Agent framework family for AI-powered product development — PM · Design · Engineering | 面向 AI 原生产品开发的多智能体（Multi-Agent）框架家族 — 覆盖产品 · 设计 · 研发
《智能体工程：从一句话到一个闭环》（Prompt · Context · Harness · Loop），本书将大语言模型驱动的智能体系统拆解为四种递进的工程范式，四种范式从 Token 到 System，从一次调用到完整的自主闭环，构成了智能体系统的完整工程视图。