mikeyobrien/ralph-orchestrator

What it solves

Ralph Orchestrator prevents AI agents from failing on complex tasks by keeping them in a continuous iteration loop until a goal is achieved. It moves beyond single-shot AI responses by implementing a system where agents can plan, execute, and self-correct based on feedback from tests, linters, and human input.

How it works

Ralph uses a "hat system" where specialized personas (such as research, debug, and review) coordinate via events to handle different stages of a task. It employs "backpressure gates"—automated checks like type-checking and linting—that reject incomplete or broken work, forcing the agent to iterate. The framework supports multiple AI backends (including Claude Code and Gemini CLI) and can be integrated as an MCP server. It also features a human-in-the-loop system called RObot that allows users to guide agents or answer their questions via Telegram.

Who it’s for

Developers who want to automate complex software engineering tasks using AI agents and ensure the output is production-ready through automated validation and iterative refinement.

Highlights

• Multi-Backend Support: Works with a wide variety of AI coding assistants including Claude Code, Gemini CLI, and Copilot CLI.
• Hat System: Uses specialized roles (hats) to structure multi-step task execution.
• Backpressure Gates: Integrates tests and linting to automatically reject failing code.
• Human-in-the-Loop: Real-time steering and communication with agents via Telegram.
• Web Dashboard: A visual interface for monitoring and managing orchestration loops.