Ralph Wiggum - OpenKit

Implementation of the Ralph Wiggum technique for iterative, self-referential AI development loops in OpenCode.

What is Ralph?

Ralph is a development methodology based on continuous AI agent loops. As Geoffrey Huntley describes it: "Ralph is a Bash loop" - a simple while true that repeatedly feeds an AI agent a prompt file, allowing it to iteratively improve its work until completion.

The technique is named after Ralph Wiggum from The Simpsons, embodying the philosophy of persistent iteration despite setbacks.

Core Concept

This plugin implements Ralph using an OpenCode plugin that listens for the session.idle event:

# You run ONCE:
/ralph-loop "Your task description" --completion-promise "DONE"

# Then OpenCode automatically:
# 1. Works on the task
# 2. Session goes idle (completes)
# 3. Plugin intercepts session.idle event
# 4. Plugin sends the SAME prompt back via client.session.prompt()
# 5. Repeat until completion

The loop happens by re-injecting the prompt after each session.idle event. The ralph-wiggum.ts plugin in plugins/ creates the self-referential feedback loop by calling client.session.prompt() with the same task prompt.

This creates a self-referential feedback loop where:

• The prompt never changes between iterations
• The agent's previous work persists in files
• Each iteration sees modified files and git history
• The agent autonomously improves by reading its own past work in files

Quick Start

Installation

Add the plugin to your project's .opencode/opencode.json file and restart opencode:

{
    "plugin": ["openkit-ralph-wiggum"]
}

Usage

/ralph-loop "Build a REST API for todos. Requirements: CRUD operations, input validation, tests. Output <promise>COMPLETE</promise> when done." --completion-promise "COMPLETE" --max-iterations 50

OpenCode will:

• Implement the API iteratively
• Run tests and see failures
• Fix bugs based on test output
• Iterate until all requirements met
• Output the completion promise when done

Commands

/ralph-loop

Start a Ralph loop in your current session.

Usage:

/ralph-loop "<prompt>" --max-iterations <n> --completion-promise "<text>"

Options:

• --max-iterations <n> - Stop after N iterations (default: unlimited)
• --completion-promise <text> - Phrase that signals completion

/cancel-ralph

Cancel the active Ralph loop.

Usage:

/cancel-ralph

Prompt Writing Best Practices

1. Clear Completion Criteria

❌ Bad: "Build a todo API and make it good."

✅ Good:

Build a REST API for todos.

When complete:
- All CRUD endpoints working
- Input validation in place
- Tests passing (coverage > 80%)
- README with API docs
- Output: <promise>COMPLETE</promise>

2. Incremental Goals

❌ Bad: "Create a complete e-commerce platform."

✅ Good:

Phase 1: User authentication (JWT, tests)
Phase 2: Product catalog (list/search, tests)
Phase 3: Shopping cart (add/remove, tests)

Output <promise>COMPLETE</promise> when all phases done.

3. Self-Correction

❌ Bad: "Write code for feature X."

✅ Good:

Implement feature X following TDD:
1. Write failing tests
2. Implement feature
3. Run tests
4. If any fail, debug and fix
5. Refactor if needed
6. Repeat until all green
7. Output: <promise>COMPLETE</promise>

4. Escape Hatches

Always use --max-iterations as a safety net to prevent infinite loops on impossible tasks:

# Recommended: Always set a reasonable iteration limit
/ralph-loop "Try to implement feature X" --max-iterations 20

# In your prompt, include what to do if stuck:
# "After 15 iterations, if not complete:
#  - Document what's blocking progress
#  - List what was attempted
#  - Suggest alternative approaches"

Note: The --completion-promise uses exact string matching, so you cannot use it for multiple completion conditions (like "SUCCESS" vs "BLOCKED"). Always rely on --max-iterations as your primary safety mechanism.

Philosophy

Ralph embodies several key principles:

1. Iteration > Perfection

Don't aim for perfect on first try. Let the loop refine the work.

2. Failures Are Data

"Deterministically bad" means failures are predictable and informative. Use them to tune prompts.

3. Operator Skill Matters

Success depends on writing good prompts, not just having a good model.

4. Persistence Wins

Keep trying until success. The loop handles retry logic automatically.

When to Use Ralph

Good for:

• Well-defined tasks with clear success criteria
• Tasks requiring iteration and refinement (e.g., getting tests to pass)
• Greenfield projects where you can walk away
• Tasks with automatic verification (tests, linters)

Not good for:

• Tasks requiring human judgment or design decisions
• One-shot operations
• Tasks with unclear success criteria
• Production debugging (use targeted debugging instead)