Ralph is an autonomous AI agent loop that repeatedly executes coding tasks until all requirements are complete.
Ralph addresses the problem of breaking down large features into manageable pieces and executing them autonomously. It works by reading a structured PRD in JSON format, selecting the highest-priority incomplete user story, spawning a fresh AI coding agent instance to implement that story, running quality checks, committing successful work, and updating progress before repeating. Memory persists across iterations through git history, a progress file that captures learnings, and the PRD JSON that tracks which stories have passed their quality checks. Each iteration runs with clean context, preventing context bloat while maintaining continuity through these persistent artifacts.
Ralph suits developers who want to delegate feature implementation to AI coding tools while maintaining control over requirements and quality gates. It works best for projects using either Amp or Claude Code, and requires a git repository, jq, and one of those two AI tools installed and authenticated. The tool is particularly valuable for handling stories that exceed a single AI context window, since the autonomous loop can hand off work across multiple iterations. The README does not compare Ralph to alternative approaches.
The project receives issue reports primarily from outside users rather than the core team, indicating a meaningful base of real-world adopters. Maintainers typically respond to new issues and pull requests within a few days.
