Ralph Loop: Autonomous Coding with GitHub Copilot CLI

What if an AI agent could write code, run tests, fix errors and repeat this process autonomously until the job is done? The Ralph Wiggum Method makes this possible with a simple but powerful loop pattern. Named after the lovable Simpsons character, it embodies the philosophy: persistence wins over perfection.

In this post, I will show you how the Ralph Loop works using my sample repository that creates a complete .NET 10 Hello World application with multilingual greetings and unit tests - fully autonomous.

What is the Ralph Loop?

The Ralph Loop is a currently trending concept in the AI space, especially in AI-driven software development. It describes a simple but powerful approach where an AI agent is executed repeatedly in a continuous loop until a defined goal is fully achieved. Instead of relying on one long conversation with growing context, each iteration starts with a fresh agent context, while progress is preserved through files, tests and version control.

This technique helps overcome context window limitations of large language models and enables more reliable long-running work. Typically, a script repeatedly launches an AI coding agent with clear success criteria, validates the results automatically and keeps iterating until the solution passes all checks. The name “Ralph Loop” is inspired by Ralph Wiggum from The Simpsons, humorously reflecting the idea of persistent trial-and-error that eventually leads to correct outcomes.

As a result, the Ralph Loop is seen as a key building block for autonomous AI agents capable of working independently over extended periods.

Core Principles

The method is built on four key ideas:

Iteration > Perfection - Don’t aim for perfect on first try, let the loop refine the work
Failures Are Data - Deterministically bad means failures are predictable and informative
Operator Skill Matters - Success depends on writing good prompts, not just having a good model
Persistence Wins - Keep trying until success, the loop handles retry logic automatically

Learn more at awesomeclaude.ai/ralph-wiggum

The Demo: Autonomous .NET Development

My GitHub repository demonstrates this method using GitHub Copilot CLI. The loop drives Copilot to autonomously create a .NET 10 console application that prints random greetings in multiple languages - including unit tests.

The result? A complete solution structure:

 1├── src
 2│   └── HelloWorld
 3│       ├── bin
 4│       ├── obj
 5│       ├── GreetingProvider.cs
 6│       ├── HelloWorld.csproj
 7│       └── Program.cs
 8│
 9├── tests
10│   └── HelloWorld.Tests
11│       ├── bin
12│       ├── obj
13│       ├── GreetingProviderTests.cs
14│       └── HelloWorld.Tests.csproj
15│
16└── HelloWorld.slnx

All of this generated autonomously through iterative prompts.

How It Works

The PowerShell script ralph.ps1 orchestrates the loop:

The script feeds the PRD file (ralph/prd.json) to GitHub Copilot CLI in iterations
Copilot generates code based on the requirements and tests
Loop continues until completion or max iterations reached
Progress and learnings are saved in ralph/state/ for context

Each iteration builds on the previous one, with progress and learnings saved in ralph/state/ for context in future runs.

Quick Start

To try it yourself:

Clone the repository: git clone https://github.com/BenjaminAbt/github-copilot-ralph-loop.git
Ensure you have .NET 10 SDK and GitHub Copilot CLI authenticated
Run the loop: ./ralph.ps1

By default, the script uses the gpt-5-mini model (no premium requests needed). If you hit rate limits, you can switch to a premium model by setting the $Model variable in ralph.ps1:

1$Model = "gpt-5.1-codex-mini"

Real-World Output

Here’s what a typical run looks like. The loop iterates through multiple cycles, each time refining the solution:

  1╔═══════════════════════════════════════════════════════════════╗
  2║           Ralph Wiggum Method - Autonomous Loop               ║
  3╚═══════════════════════════════════════════════════════════════╝
  4
  5━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  6Starting iteration 1 of 10
  7━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  8  Model: gpt-5-mini
  9  Prompt length: 3958 characters
 10  Mode: --yolo (autonomous)
 11  Timeout: 600 seconds
 12
 13  ✓ Running GitHub Copilot CLI - Complete (78,5s)
 14
 15  Duration: 78,48 seconds
 16  Exit Code: 0
 17
 18  Output:
 19   + ------------------------------------------------------------
 20  Reading project progress and PRD files to select the next user story per the iteration protocol; will open progress, prd.json, AGENTS.md and learnings in parallel. Proceeding to read those files now.
 21  Creating the HelloWorld project directories so files can be added, then creating project files and updating progress and finally building to verifyΓÇöthese steps ensure the environment is prepared before file creation and that build confirms success.
 22  US-001 implemented: created solution and console project, enabled nullable, implicit usings and TreatWarningsAsErrors; build succeeded. Progress updated to mark US-001 completed. Next step: implement US-002 (greeting logic) ΓÇö proceed?
 23   + ------------------------------------------------------------
 24
 25  Status/Info:
 26   + ------------------------------------------------------------
 27  Total usage est:       0 Premium requests
 28  Total duration (API):  1m 3.243s
 29  Total code changes:    0 lines added, 0 lines removed
 30  Usage by model:
 31      gpt-5-mini           261.7k input, 5.1k output, 239.6k cache read (Est. 0 Premium requests)
 32  Total duration (wall): 1m 16.932s
 33   + ------------------------------------------------------------
 34
 35━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 36Starting iteration 2 of 10
 37━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 38  Model: gpt-5-mini
 39  Prompt length: 3958 characters
 40  Mode: --yolo (autonomous)
 41  Timeout: 600 seconds
 42
 43  ✓ Running GitHub Copilot CLI - Complete (87,8s)
 44
 45  Duration: 87,85 seconds
 46  Exit Code: 0
 47
 48  Output:
 49   + ------------------------------------------------------------
 50  Reading progress, PRD, AGENTS and learnings to select the next user story and implement it. Running file reads and reporting intent.
 51  Running a file listing to locate the HelloWorld project files before editing to implement US-002.
 52  Building and running the project to verify the application prints exactly one randomized greeting and the build succeeds.
 53  Updating Program.cs to choose and print one random greeting and updating progress.txt to mark US-002 complete; then building to verify. I'll apply edits and run dotnet restore && dotnet build.
 54  Built successfully and updated progress to mark US-002 as current and completed; Program now prints one randomized greeting with language.
 55   + ------------------------------------------------------------
 56
 57  Status/Info:
 58   + ------------------------------------------------------------
 59  Total duration (API):  1m 21.65s
 60  Total duration (wall): 1m 26.463s
 61  Total code changes:    0 lines added, 0 lines removed
 62  Usage by model:
 63      gpt-5-mini           153.0k input, 7.8k output, 145.7k cache read (Est. 0 Premium requests)
 64  Total usage est:       0 Premium requests
 65   + ------------------------------------------------------------
 66
 67━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 68Starting iteration 3 of 10
 69━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 70  Model: gpt-5-mini
 71  Prompt length: 3958 characters
 72  Mode: --yolo (autonomous)
 73  Timeout: 600 seconds
 74
 75  ✓ Running GitHub Copilot CLI - Complete (165,5s)
 76
 77  Duration: 165,48 seconds
 78  Exit Code: 0
 79
 80  Output:
 81   + ------------------------------------------------------------
 82  Reading progress, PRD, AGENTS and learnings to determine the next user story to implement. Reporting intent and opening the four files now.
 83  Reading project files to find current projects and tests, then will implement test project for US-003.
 84  Running a quick read of the solution file to add the test project entry into it.
 85  Creating GreetingProvider, test project, updating Program, solution and progress file in one change. Running these file edits now.
 86  Creating tests folder and files, then restoring and running tests to verify the GreetingProvider implementation. I'll run PowerShell to create directories/files and execute dotnet restore && dotnet test.
 87  Inspecting the generated test project file to diagnose the XML parse error; running report_intent and reading the file.
 88  Created GreetingProvider, wired Program to use it, added xUnit test project and a test that validates greeting selection, updated solution and progress.
 89  Next steps: run or inspect results locally; tests passed here.
 90   + ------------------------------------------------------------
 91
 92  Status/Info:
 93   + ------------------------------------------------------------
 94  Total duration (API):  2m 30.846s
 95  Total duration (wall): 2m 44.037s
 96  Total code changes:    0 lines added, 0 lines removed
 97  Usage by model:
 98      gpt-5-mini           467.7k input, 11.6k output, 439.6k cache read (Est. 0 Premium requests)
 99  Total usage est:       0 Premium requests
100   + ------------------------------------------------------------

The loop continues autonomously until all requirements are met.

Why This Matters

The Ralph Loop demonstrates a shift in how we can approach software development with AI agents:

Autonomous execution: No manual intervention needed during the coding process
Self-correcting: Errors are handled automatically through iteration
Traceable: Every iteration logs progress and learnings
Scalable: The same pattern works for simple scripts or complex applications

This is particularly powerful for repetitive tasks, proof-of-concepts, or when you need to rapidly prototype solutions.

Repository Structure

The demo uses a clear structure:

ralph/ - Contains prompts, PRDs and loop state
- ralph/prd.json - Task definitions (product requirements) for the loop
- ralph/prompt.md - Iteration protocol and instructions
- ralph/state/ - Loop progress
- ralph/AGENTS.md - Instructions for coding agents
- ralph/learnings.md - Loop learnings and observations
ralph.ps1 - Main PowerShell script that runs the loop

Takeaway

The Ralph Wiggum Method shows that persistence combined with iterative refinement can produce working solutions autonomously. By feeding GitHub Copilot CLI with structured prompts in a loop, we can create a self-improving coding agent that handles everything from initial code generation to testing and debugging.

The full code is available on GitHub . Give it a try and experience autonomous coding in action!

Credits

Ralph Wiggum Method by Geoffrey Huntley
Some inspiration from Dariusz’ Mediator Challenge
Built by BEN ABT
Powered by GitHub Copilot CLI