Loop Engineering

🤯 LobeHub is your Chief Agent Operator, organizing your agents into 7×24 operations by hiring, scheduling, and reporting on your entire AI team.

Your Personal AI Assistant; easy to install, deploy on your own machine or on the cloud; supports multiple chat apps with easily extensible capabilities.

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.

OpenLoomi is an open-source AI coworker. It connects your work tools, understands what you’re working on, and tells you what needs your attention, why it matters, and what to do next. It’s your open-source Attention Agent.

Private control plane for AI agents

Inference-native Tokenmaxxing Agent Harness for Loop Engineering

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.

An all-in-one local AI Agent workspace with a fully self-developed stack

A loop engineering framework — state a Goal; Rounds run until a skeptical, read-only Verify agent settles it.

OpenCode++: a Coding Agent Reliability Harness for OpenCode, adding context, edit boundaries, command evidence, verification gates, impact analysis, and repair loops.OpenCode++：面向 OpenCode 的 AI 编程可靠性增强框架，为其增加上下文管理、编辑边界、命令证据、验证门禁、影响分析与修复闭环能力。

Build stateful agent workflows with typed outputs, reusable tools, session forks, and ordinary TypeScript.

🚀 Multi-agent orchestration for Qwen Code CLI. 24 specialized AI agents and 82+ skills working as a professional dev department. Built for Alibaba's open-source AI coding assistant.

AI agent harness tool allowing it to wipe its own session context and re-run the first message

The AI engineer roadmap for agent builders: a staged path with runnable labs to build a coding agent from scratch - by AI Builder Club

For an organization to trust AI with its code, three things must hold — trust, traceability, and stability at scale. cladding wraps your AI coding agent: your intent goes in before it writes, and the result is verified against your spec after, so those three are earned, not assumed. First L4 implementation of the Ironclad standard.

내 맥에 상주하는 AI 팀. 텔레그램·슬랙에서 일을 맡기면 누가 담당인지 정하고, 진행이 대시보드에 남습니다.

Own the Outer Loop. Evidence → Verdict → Answerability. Governance layer for agentic engineering.

🤖 An automated setup kit for implementing loop engineering based on issue-driven development in your repository; since 2026.5

Give your coding agent a delivery process you can trust. Spec → Plan → Build → Acceptance → Ship.

An open, natural-language DSL for self-correcting AI coding loops — say what an AI coding agent should build and how to verify it in plain English, and it loops until the check passes. Runs in Claude Code, Cursor, and Copilot.