Ralph Loop
Not Just a Silly Loop
The Ralph Loop isn't just a programming pattern—it's a philosophy for building reliable AI systems. Named after the now-legendary question from Geoffrey Huntley's son during Christmas 2024: "Dad, why don't you just put it in a loop?"
What started as a simple observation became the foundation for how we think about AI-assisted work. The Ralph Loop represents a fundamental shift: from hoping your AI agent does the right thing to knowing it will, because you've defined what "done" actually means.
This visualization demonstrates the core concept: you can program outside the loop. Inject validation, transformation, or enrichment at any point. The loop isn't just iteration—it's structured iteration with deterministic checkpoints.
"The Ralph Loop isn't a hack or a workaround—it's now table stakes. In 2026, every serious CLI agent implements some version of this pattern. The question isn't whether to use it, but how to tune it for your definition of done."
Origin & Attribution
Geoffrey Huntley
Creator of the Ralph Loop Pattern
"Why don't you just put it in a loop?"
The Philosophy
The Ralph Loop isn't just a code pattern—it's a way of thinking about AI-assisted work.
Human ON the Loop
You don't set it and forget it. The Ralph Loop philosophy requires you to watch, tune, and guide until the agent is going in the right direction. It's supervision, not automation—at least until you've built enough trust and guardrails.
Definition of Done
Deterministic ways for the agent to declare 'done.' No ambiguity, no interpretation—the loop needs clear exit conditions. This is the difference between an agent that spins forever and one that knows when to stop.
Table Stakes in 2026
What started as a novel insight is now standard in most CLI agents. Oh My OpenCode's Sisyphus Agent, OpenAI Codex's ReAct loop, Claude Tasks with Skills—they all implement variants of this pattern. The question isn't whether to use it, but how.
Kingly's Approach
When the Ralph Loop pattern started gaining traction, we split our tooling into a modular system that separates concerns and bakes validation into every step.
Pre-flight Validation
Before any work begins: Is the plan correct? Is definition of done established? Are depth requirements clear? Does this fit the architecture? Pre-flight catches problems when they're cheap to fix.
Post-flight & Context Splicing
Unit of work agents, context window management, YAML-driven configs for handoff at 50% context length. The monumental shift: validation is baked into the tooling, not yet another agent watching the first agent.
The Future
The Ralph Loop was the beginning, not the end. Here's where we see AI-assisted work heading.
Chat/Voice Decisions
Quick iteration through conversation, not keyboard—Ralph loops in dialogue.
Planner Agent as Interface
The primary interaction becomes planning, with execution as the background process.
Vibe Engineering for Edge Cases
Traditional hands-on debugging reserved for deep PoCs and unusual problems.
AI-Assisted Design
Hands-on but with AI suggestions—tools like Pencil.dev making this manifest.
Tech Stack
Features
Animated Sequence Playback
Watch the deterministic loop unfold through an interactive animation with terminal feedback.
Terminal Emulation
See validation gates in action with real terminal-style output showing pass/fail states.
Validation Gate Visualization
Visual representation of deterministic + genetic checks before work enters the loop.
Responsive Layout
Circular loop on desktop, vertical flow on mobile with bottom sheet for details.
What This Is Used For
Human ON the Loop
Watch and tune until the agent is going in the right direction, not set-and-forget
Definition of Done
Implement deterministic ways for the agent to declare completion, removing ambiguity
Pre-flight Validation
Verify plans, architecture, and requirements before work begins
Post-flight Context Splicing
Hand off cleanly when context windows fill, maintaining coherence
Install
NPM Package Coming Soon
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