ralph-orchestrator
💡 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
🤖 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。”
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
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
- Geoffrey Huntley — Creator of the Ralph Wiggum technique
- Strands Agents SOP — Agent SOP framework
- ratatui — Terminal UI framework
"I'm learnding!" - Ralph Wiggum
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
相关技能
免责声明:本内容来源于 GitHub 开源项目,仅供展示和评分分析使用。
版权归原作者所有 mikeyobrien.
