# Loop Engineering — Patterns and CLI Tools for AI Agent Orchestration

> A practical toolkit of patterns, starter templates, and CLI utilities for designing deterministic loop-based systems that prompt and orchestrate AI coding agents.

## Install

Save in your project root:

# Loop Engineering — Patterns and CLI Tools for AI Agent Orchestration

## Quick Use
```bash
npx loop-init my-project
cd my-project
npx loop-audit ./src
npx loop-cost --estimate
```

## Introduction
Loop Engineering provides practical patterns and CLI tools for building deterministic, repeatable AI agent workflows. Instead of one-shot prompting, it helps developers design structured loops where agents iterate on tasks with clear convergence criteria and cost controls.

## What Loop Engineering Does
- Provides CLI tools (loop-init, loop-audit, loop-cost) for agent workflow management
- Includes starter templates for common loop patterns like review-fix-verify cycles
- Estimates token cost before running expensive multi-turn agent interactions
- Audits existing agent configurations for anti-patterns and inefficiencies
- Documents proven orchestration patterns from production deployments

## Architecture Overview
The toolkit consists of three CLI utilities backed by a shared configuration format. loop-init scaffolds a project with loop definitions in YAML. loop-audit analyzes your prompt chains and flags unbounded loops or missing exit conditions. loop-cost simulates token usage across the loop topology without calling any model.

## Self-Hosting & Configuration
- Install tools globally via npm or use npx for zero-install execution
- Configure loop definitions in a loops.yaml file at project root
- Set cost budgets and iteration caps per loop in configuration
- Integrates with any AI provider via environment variable configuration
- Works alongside Claude Code, Codex, Cursor, or custom agent setups

## Key Features
- Cost estimation before execution prevents budget overruns
- Audit tool catches infinite loops and missing convergence criteria
- Starter templates for 12 common patterns (review, migrate, test, etc.)
- Provider-agnostic design works with any LLM backend
- Deterministic execution order makes debugging reproducible

## Comparison with Similar Tools
- **LangGraph** — code-heavy DAG definition vs YAML-based loop patterns
- **CrewAI** — role-based multi-agent vs single-agent loop orchestration
- **Temporal** — general workflow engine vs AI-loop-specific tooling
- **Dagger** — CI/CD pipelines vs prompt-loop pipelines
- **Claude Code workflows** — built-in orchestration vs external loop management

## FAQ
**Q: Does this replace my AI coding agent?**
A: No. It orchestrates how you use your agent by defining structured iteration patterns around it.

**Q: What is a loop in this context?**
A: A loop is a defined cycle of prompt-execute-evaluate steps with explicit exit conditions, iteration caps, and cost limits.

**Q: Can I use this with local models?**
A: Yes, the tools are provider-agnostic. Configure any OpenAI-compatible endpoint.

**Q: How does loop-cost estimate without calling a model?**
A: It counts tokens in your prompt templates and multiplies by expected iterations based on the loop topology.

## Sources
- https://github.com/cobusgreyling/loop-engineering
- https://github.com/cobusgreyling/loop-engineering#readme

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Source: https://tokrepo.com/en/workflows/asset-eb07290b
Author: AI Open Source