Co-Pilot / 辅助式
更新于 a month ago

ralph-orchestrator

Mmikeyobrien
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mikeyobrien/ralph-orchestrator
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Agent 评分

💡 摘要

Ralph Orchestrator是一个通过迭代任务完成来管理AI代理的框架。

🎯 适合人群

寻找编排工具的AI开发者实施自动化工作流程的软件工程师监督AI驱动项目的项目经理集成AI解决方案的DevOps专业人士探索自主任务管理的研究人员

🤖 AI 吐槽:一个AI编排的帽子戏法,但别指望它能把所有的帽子都戴好。

安全分析中风险

自述文件暗示了潜在风险,例如依赖供应链问题和对外部AI服务的网络访问。确保审查依赖项并使用安全的网络实践。

Ralph Orchestrator

License Rust Build Coverage Mentioned in Awesome Claude Code Docs

A hat-based orchestration framework that keeps AI agents in a loop until the task is done.

"Me fail English? That's unpossible!" - Ralph Wiggum

Documentation | Getting Started | Presets

Installation

Via npm (Recommended)

npm install -g @ralph-orchestrator/ralph-cli

Via Homebrew (macOS)

brew install ralph-orchestrator

Via Cargo

cargo install ralph-cli

Quick Start

# 1. Initialize Ralph with your preferred backend ralph init --backend claude # 2. Plan your feature (interactive PDD session) ralph plan "Add user authentication with JWT" # Creates: specs/user-authentication/requirements.md, design.md, implementation-plan.md # 3. Implement the feature ralph run -p "Implement the feature in specs/user-authentication/"

Ralph iterates until it outputs LOOP_COMPLETE or hits the iteration limit.

For simpler tasks, skip planning and run directly:

ralph run -p "Add input validation to the /users endpoint"

What is Ralph?

Ralph implements the Ralph Wiggum technique — autonomous task completion through continuous iteration. It supports:

  • Multi-Backend Support — Claude Code, Kiro, Gemini CLI, Codex, Amp, Copilot CLI, OpenCode
  • Hat System — Specialized personas coordinating through events
  • Backpressure — Gates that reject incomplete work (tests, lint, typecheck)
  • Memories & Tasks — Persistent learning and runtime work tracking
  • 31 Presets — TDD, spec-driven, debugging, and more

Documentation

Full documentation is available at mikeyobrien.github.io/ralph-orchestrator:

Contributing

Contributions are welcome! See CONTRIBUTING.md for guidelines and CODE_OF_CONDUCT.md for community standards.

License

MIT License — See LICENSE for details.

Acknowledgments


"I'm learnding!" - Ralph Wiggum

五维分析
清晰度8/10
创新性8/10
实用性9/10
完整性8/10
可维护性7/10
优缺点分析

优点

  • 支持多种AI后端
  • 促进迭代任务完成
  • 包括多种不同工作流程的预设

缺点

  • 新用户可能需要学习曲线
  • 依赖外部AI服务
  • 随着项目规模增大,复杂性可能增加

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版权归原作者所有 mikeyobrien.