RALPH Loop
/rælf luːp/
What is a RALPH Loop?
A RALPH loop (also called "Ralph Wiggum" after the Simpsons character) is an autonomous development technique for AI coding agents like Claude Code. The approach involves running the same prompt repeatedly in a loop, allowing the AI agent to iteratively work through a list of tasks from a PRD (Product Requirements Document) until completion.
The core mechanic is simple: when the AI agent tries to exit after completing a task, a stop hook blocks the exit and feeds the same prompt back in. Because the files it just changed persist between iterations, each loop can build on the previous one.
Key Characteristics
- Iterative execution: Agent works on one task, documents progress, then moves to the next
- Persistent context: Code changes carry over between iterations
- Exit detection: Hooks prevent premature termination
- Progress tracking: Typically uses a progress.txt file to document completed work
- Test integration: Best implementations run tests after each feature before proceeding
Why RALPH Loops Matter
RALPH loops represent a shift from interactive pair programming with AI to fully autonomous development. When combined with well-crafted PRDs, they enable developers to start a loop and return hours later to working code.
However, experts caution that RALPH loops amplify both good and bad planning:
"If you have a terrible plan, if you have a terrible PRD, this doesn't matter. You're just donating money to Anthropic." — Ross Mike
The technique is most effective when:
- The PRD is exhaustively detailed (using tools like
ask_user_question) - Each feature has clear acceptance criteria
- Tests validate each feature before proceeding
- Context limits are respected (staying under 50% token usage)
Historical Context
The RALPH technique was developed by Geoffrey Huntley as a simple bash loop pattern for AI agents. The name references Ralph Wiggum from The Simpsons, embodying the philosophy of persistent iteration despite setbacks. Claude Code includes an official RALPH plugin, though some practitioners prefer custom implementations with additional safeguards like test-first verification.
Notable results include developers running multi-day loops that built complete applications, and YC hackathon teams shipping multiple repositories overnight for minimal API costs.
Related Reading
- AI Agents - The autonomous systems that power RALPH loops
- Rise of AI Agents - The broader trend toward autonomous development
Mentioned In
Ross Mike at 00:15:00
"A Ralph loop is basically you have a list of things that need to get done. You give it to the AI model. The model works on the first task, finishes it, documents it in another file, and then it goes again until it's completed the whole list."