ИИ-агенты
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A TypeScript toolkit for building agents: unified LLM API, loop, TUI, and CLI.
★ 113,3 тыс. звёздMIT
Смотреть руководство →Loop Engineering — проект с открытым исходным кодом из категории «ИИ-агенты» от cobusgreyling (cobusgreyling/loop-engineering на GitHub). У него 11 554 звезды на GitHub и 1 572 форка, он распространяется по лицензии MIT и последний раз обновлялся на GitHub 15 октября 2026.
Loop Engineering — practical patterns, starters and a CLI for designing the loops that prompt and orchestrate AI coding agents, with a Loop Ready score
Browse the pattern library — daily triage, PR babysitter, thin loop and more — and choose by cadence and cost.
Run the loop CLI init with your pattern and tool to generate the starter files in your repo.
Run doctor and the audit to get your Loop Ready score, then iterate on the loop instead of the prompts.
Each pattern documents its cadence, Week 1 deliverable and cost level, so loops are designed deliberately.
One front door — @cobusgreyling/loop — with init, doctor, status, audit and cost; the older single-purpose packages stay supported.
The audit scores how ready a loop is, turning agent operations into something measurable.
The cost command reports spend per pattern and autonomy level before a loop runs away with the budget.
Initialise the daily-triage pattern for Claude Code in the current project.
npx @cobusgreyling/loop init . --pattern daily-triage --tool claudeDoctor verifies the loop is wired correctly.
npx @cobusgreyling/loop doctor .Report spend for the pattern at a given autonomy level.
npx @cobusgreyling/loop cost --pattern daily-triage --level L1Cobus Greyling; the showcase site is cobusgreyling.github.io/loop-engineering.
Yes, MIT-licensed; the CLI is published on npm. Agent usage itself runs on your own model subscriptions or keys.
No key for the patterns or CLI; the agents your loops drive use your existing tooling and accounts.
Prompt libraries optimise single prompts. Loop Engineering optimises the recurring system around the agent — cadence, handoffs, audit and cost — and scores it.
Run npx @cobusgreyling/loop init . --pattern daily-triage --tool claude in a project, then npx @cobusgreyling/loop doctor . to verify it.
A TypeScript toolkit for building agents: unified LLM API, loop, TUI, and CLI.
Mobile app automation and verification built for AI coding agents — CLI, MCP server, and Node API
Babysitter enforces the process: deterministic, quality-gated orchestration for fleets of coding agents.
Compression gateway that cuts AI coding agents' token bills up to 85%.
Test your agents against prompt-injection attacks.
A CLI that writes and maintains agent-ready docs for your codebase.
Другие проекты из категории «ИИ-агенты» с наибольшим числом звёзд в каталоге LibreAI — реалистичный список альтернатив, если Loop Engineering вам не подходит.
A personal AI that really does things — on any OS, with your data staying yours.
An agentic skills framework & software development methodology that works.
Agent harness optimization system with skills, memory and security for Claude Code, Codex and more
The agent that grows with you — a Python framework for adaptive AI agents.
DeepSeek's plugin-based agent harness where everything is a plugin.
Соседние проекты из категории «ИИ-агенты» в каталоге, ближайшие по числу звёзд к Loop Engineering — продолжайте изучать полку по обе стороны от него.
The elegant testing framework for PHP developers and AI agents.
Local-first agentic workspace where Claude Code, Codex and a built-in agent share memory, apps and tools.
Observability and evaluation for AI apps — see what your agents do.
Open-source framework for conversational voice AI agents
Self-evolving memory OS for LLMs and agents: persistent graph memory, hybrid retrieval, skill reuse.
Agentic AI Framework for Java Developers
Бесплатно и открыто — забирайте исходный код, ставьте звезду репозиторию и начинайте создавать.