First comprehensive guide to Ralph Wiggum Loops

Your AI forgets everything.
What if that's the point?

The Ralph Loop transforms context degradation from a bug into a feature. Fresh instances. Filesystem memory. Autonomous iteration.

Get the Book Read Chapter 1 Free

ralph-loop.sh

$ ralph --mode build --prd tasks.json

# Spawning fresh Claude instance...

[task 1/12] Implementing user authentication

✓ Committed: feat(auth): add JWT validation

# Context cleared. Starting fresh...

[task 2/12] Adding password reset flow

✓ Committed: feat(auth): password reset endpoint

# Memory persists in git, not context window

The Research

Context degradation isn't a feeling.
It's measured.

11/12

models fall below 50% accuracy at 32K tokens

NoLiMa Research, 2024

99→70%

accuracy degradation in extended sessions

GPT-4o Study

~50%

cost reduction with fresh-context iteration

JetBrains Research

66%

of developers spend time correcting AI output

Stack Overflow Survey

40%

of agentic AI projects will be scrapped by 2027

Gartner Prediction

79%

hallucination rate in reasoning-heavy scenarios

Academic Research

⚠

The Problem

AI coding assistants degrade over time. Context windows fill with accumulated errors. Compaction introduces drift. What started precise ends generic.

Generic responses late in sessions
"Forgotten" decisions made earlier
Compaction (summarisation) that drifts
Accumulated errors that compound
Costs spiralling as context fills with garbage

✓

The Solution

Fresh-context iteration. Instead of one long degrading conversation, spawn a new instance for each task. Let the filesystem remember.

Give the AI one specific task
Let it work autonomously until done
Record results to filesystem (git commits)
Terminate that instance
Start fresh for the next task

What's Inside

Seven chapters. From theory to troubleshooting.

History and Discovery

Origin story, key contributors, how the technique emerged and spread

Free Preview

Theory and Foundations

The science of context degradation, research citations, economic case

Research

Modes, Workflows, and PRD Writing

Build, plan, and reverse modes. How to structure work for autonomous execution

Core

Practical Application with Claude Code

Step-by-step implementation, snarktank/ralph setup, configuration

Hands-on

Warnings and Anti-patterns

Security considerations, the "fire and forget" myth, when Ralph is wrong

Critical

Beyond Claude Code

Goose, Ralphy, Ralph Orchestrator, Vercel AI SDK comparisons

Tools

Troubleshooting and Failure Modes

Decision trees, debugging, cost optimisation, recovery procedures

Reference

Who This Is For

From first context window to fiftieth iteration.

Beginner

New to AI Coding

Clear explanation of context windows, why they matter, step-by-step first Ralph Loop setup.

Intermediate

Using AI Daily

PRD writing guidance, task sizing, workflow selection, avoiding common mistakes.

Advanced

Building Systems

Cross-tool comparisons, troubleshooting decision trees, nuanced "when not to use" guidance.

Harry Munro

Chartered Engineer & Author

Founder of School of Simulation. Author of Vibe Modelling (2025). His background in probabilistic modelling for London Underground taught him that accounting for real-world variability matters more than theoretical perfection - a lesson that applies directly to autonomous AI systems.

Chartered Engineer School of Simulation AI in Production Since 2024

FAQ

Common questions, honest answers.

Do I need to know Claude Code already?

Basic CLI and git knowledge is enough. Chapter 4 walks through setup step-by-step.

Is this just for Claude?

Chapter 6 covers Goose, Ralphy, Ralph Orchestrator, and Vercel AI SDK. The principles are tool-agnostic.

Will this make my AI coding "fire and forget"?

No - and the book is honest about that. Chapter 5 specifically debunks this myth.

I've been using Ralph Loops for months. Is this useful?

Yes - the troubleshooting chapter and cross-tool comparison serve experienced users.

Stop fighting context degradation.

The first comprehensive guide to fresh-context iteration. Theory. Practice. Troubleshooting.