ralphify 0.3.0

Put your AI coding agent in a while True loop and let it ship.

Ralphify is a minimal harness for running autonomous AI coding loops, inspired by the Ralph Wiggum technique. The idea is simple: pipe a prompt to an AI coding agent, let it do one thing, commit, and repeat. Forever. Until you hit Ctrl+C.

while :; do cat RALPH.md | claude -p ; done

Ralphify wraps this pattern into a proper tool with commands, iteration tracking, and clean shutdown.

Install

uv tool install ralphify    # recommended

Or if you don't have uv:

pipx install ralphify       # isolated install via pipx
pip install ralphify         # plain pip (use a virtualenv or --user)

Any of these gives you the ralph command.

Quickstart

A ralph is a directory with a RALPH.md file. Scaffold one:

ralph init my-ralph

Then edit my-ralph/RALPH.md:

---
agent: claude -p --dangerously-skip-permissions
commands:
  - name: tests
    run: uv run pytest
---

You are an autonomous coding agent working in a loop.

## Test results

{{ commands.tests }}

If any tests are failing, fix them before continuing.

## Task

Implement the next feature from the TODO list.

Run it:

ralph run my-ralph           # Starts the loop (Ctrl+C to stop)
ralph run my-ralph -n 5      # Run 5 iterations then stop

What ralph run does

Each iteration:

1. Runs commands — executes all commands, captures output
2. Assembles prompt — reads RALPH.md body, replaces {{ commands.<name> }} placeholders with output
3. Pipes to agent — executes the agent command with the assembled prompt on stdin
4. Repeats — goes back to step 1

What it looks like

$ ralph run my-ralph -n 3

── Iteration 1 ──
  Commands: 1 ran
✓ Iteration 1 completed (52.3s)

── Iteration 2 ──
  Commands: 1 ran
✗ Iteration 2 failed with exit code 1 (23.1s)

── Iteration 3 ──
  Commands: 1 ran
✓ Iteration 3 completed (41.7s)

Done: 3 iteration(s) — 2 succeeded, 1 failed

The technique

The Ralph Wiggum technique works because:

• One thing per loop. The agent picks the most important task, implements it, tests it, and commits. Then the next iteration starts fresh.
• Fresh context every time. No context window bloat. Each loop starts clean and reads the current state of the codebase.
• Progress lives in git. Code, commits, and a plan file are the only state that persists between iterations. If something goes wrong, git reset --hard and run more loops.
• The prompt is a tuning knob. When the agent does something dumb, you add a sign. Like telling Ralph not to jump off the slide — you add "SLIDE DOWN, DON'T JUMP" to the prompt.

Read the full writeup: Ralph Wiggum as a "software engineer"

Core concepts

A ralph is a directory containing a RALPH.md file. That's it. Everything the ralph needs lives in that directory.

my-ralph/
├── RALPH.md              # the prompt (required)
├── check-coverage.sh     # script (optional)
├── style-guide.md        # reference doc (optional)
└── test-data.json        # any supporting file (optional)

RALPH.md is the only file the framework reads. It has YAML frontmatter for configuration and a body that becomes the prompt:

Commands run before each iteration. Their output replaces {{ commands.<name> }} placeholders in the prompt. Use them for test results, git history, lint output — anything that changes between iterations.

No project-level configuration. No ralph.toml. No .ralphify/ directory. A ralph is fully self-contained.

AI-guided setup

ralph new my-task

Launches an interactive agent conversation to scaffold a new ralph with the right commands and prompt for your project.

Documentation

Full documentation at ralphify.co/docs — getting started tutorial, prompt writing guide, cookbook, and troubleshooting.

Requirements

• Python 3.11+
• Claude Code CLI (or any agent CLI that accepts piped input)

License

MIT