Ralph Orchestrator

A hat-based multi-agent orchestration framework that keeps AI agents in a loop until the task is done.

"Me fail English? That's unpossible!" - Ralph Wiggum

Alpha Notice: Ralph Orchestrator is under active development. It works today, but expect rough edges and breaking changes between releases.

v1.0.0 was ralphed into existence with little oversight and guidance. v2.0.0 is a simpler, more-structured implementation. Looking for the old version? See v1.2.3.

Table of Contents

• What is Ralph?
• Features
• Installation
• Quick Start
• Configuration
• Presets
• Key Concepts
• CLI Reference
• Architecture
• Building & Testing
• Contributing
• License
• Acknowledgments

What is Ralph?

Ralph implements the Ralph Wiggum technique — autonomous task completion through continuous AI agent iteration. Unlike simple loops, Ralph v2 introduces hat-based orchestration: specialized agent roles that coordinate through events.

"The orchestrator is a thin coordination layer, not a platform. Agents are smart; let them do the work."

The Ralph Tenets

1. Fresh Context Is Reliability — Each iteration clears context. Re-read specs, plan, code every cycle.
2. Backpressure Over Prescription — Don't prescribe how; create gates that reject bad work.
3. The Plan Is Disposable — Regeneration costs one planning loop. Cheap.
4. Disk Is State, Git Is Memory — Files are the handoff mechanism.
5. Steer With Signals, Not Scripts — Add signs, not scripts.
6. Let Ralph Ralph — Sit on the loop, not in it.

See AGENTS.md for the full philosophy.

Features

• Multi-Backend Support — Works with Claude Code, Kiro, Gemini CLI, Codex, and Amp
• Hat System — Specialized agent personas with distinct behaviors
• Event-Driven Coordination — Hats communicate through typed events with glob pattern matching
• Backpressure Enforcement — Gates that reject incomplete work (tests, lint, typecheck)
• Presets Library — 20+ pre-configured workflows for common development patterns
• Interactive TUI — Real-time terminal UI for monitoring agent activity (experimental)
• Session Recording — Record and replay sessions for debugging and testing (experimental)

Installation

Prerequisites

• Rust 1.75+
• At least one AI CLI:
  • Claude Code (recommended)
  • Kiro
  • Gemini CLI
  • Codex
  • Amp

Via npm (Recommended)

# Install globally
npm install -g @ralph-orchestrator/ralph

# Or run directly with npx
npx @ralph-orchestrator/ralph --version

Via Cargo

cargo install ralph-cli

From Source

git clone https://github.com/mikeyobrien/ralph-orchestrator.git
cd ralph-orchestrator
cargo build --release

# Add to PATH
export PATH="$PATH:$(pwd)/target/release"

# Or create symlink
sudo ln -s $(pwd)/target/release/ralph /usr/local/bin/ralph

Verify Installation

ralph --version
ralph --help

Migrating from v1 (Python)

If you have the old Python-based Ralph v1 installed, uninstall it first to avoid conflicts:

# If installed via pip
pip uninstall ralph-orchestrator

# If installed via pipx
pipx uninstall ralph-orchestrator

# If installed via uv
uv tool uninstall ralph-orchestrator

# Verify removal
which ralph  # Should return nothing or point to new Rust version

The v1 Python version is no longer maintained. See v1.2.3 for historical reference.

Quick Start

1. Initialize a Project

# Minimal config for Claude (recommended)
ralph init --backend claude

# Use a preset workflow
ralph init --preset tdd-red-green

# Combine preset with different backend
ralph init --preset spec-driven --backend kiro

# See all available presets
ralph init --list-presets

This creates ralph.yml in your current directory.

2. Define Your Task

Option A: Create a PROMPT.md file:

cat > PROMPT.md << 'EOF'
Build a REST API with the following endpoints:
- POST /users - Create a new user
- GET /users/:id - Get user by ID
- PUT /users/:id - Update user
- DELETE /users/:id - Delete user

Use Express.js with TypeScript. Include input validation
and proper error handling.
EOF

Option B: Pass inline prompt when running:

ralph run -p "Add input validation to the user API endpoints"

3. Run Ralph

# Autonomous mode (headless, default)
ralph run

# With inline prompt
ralph run -p "Implement the login endpoint with JWT authentication"

# Interactive TUI mode (experimental)
ralph run -i

# Resume interrupted session
ralph resume

# Dry run (show what would execute)
ralph run --dry-run

Configuration

Ralph uses a YAML configuration file (ralph.yml by default).

Minimal Configuration

# ralph.yml
cli:
  backend: "claude"

event_loop:
  completion_promise: "LOOP_COMPLETE"
  max_iterations: 100

Full Configuration Reference

# Event loop settings
event_loop:
  completion_promise: "LOOP_COMPLETE"  # Output that signals completion
  max_iterations: 100                   # Maximum orchestration loops
  max_runtime_seconds: 14400            # 4 hours max runtime
  idle_timeout_secs: 1800               # 30 min idle timeout
  starting_event: "task.start"          # First event published

# CLI backend settings
cli:
  backend: "claude"                     # claude, kiro, gemini, codex, amp, custom
  prompt_mode: "arg"                    # arg (CLI argument) or stdin
  experimental_tui: false               # Enable TUI mode support

# Core behaviors (always injected into prompts)
core:
  scratchpad: ".agent/scratchpad.md"    # Shared memory across iterations
  specs_dir: "./specs/"                 # Directory for specifications
  guardrails:                           # Rules injected into every prompt
    - "Fresh context each iteration - scratchpad is memory"
    - "Don't assume 'not implemented' - search first"
    - "Backpressure is law - tests/typecheck/lint must pass"

# Custom hats (omit to use default planner/builder)
hats:
  my_hat:
    name: "My Hat Name"                 # Display name
    triggers: ["some.event"]            # Events that activate this hat
    publishes: ["other.event"]          # Events this hat can emit
    instructions: |                     # Prompt instructions
      What this hat should do...

Presets

Presets are pre-configured workflows for common development patterns.

Development Workflows

Debugging & Investigation

Review & Quality

Documentation

Specialized

Using Presets

# List all available presets
ralph init --list-presets

# Initialize with a preset
ralph init --preset tdd-red-green

# Use preset with different backend
ralph init --preset spec-driven --backend gemini

# Override existing config
ralph init --preset debug --force

Key Concepts

Hats

Hats are specialized agent personas. Each hat has:

• Triggers: Events that activate this hat
• Publishes: Events this hat can emit
• Instructions: Prompt injected when hat is active

View event history:

ralph events

Scratchpad

All hats share .agent/scratchpad.md — persistent memory across iterations. This enables hats to build on previous work rather than starting fresh.

The scratchpad is the primary mechanism for:

• Task tracking (with [ ], [x], [~] markers)
• Context preservation between iterations
• Handoff between hats

Backpressure

Ralph enforces quality gates through backpressure. When a builder publishes build.done, it must include evidence:

tests: pass, lint: pass, typecheck: pass

CLI Reference

Commands

Global Options

ralph run Options

ralph init Options

Architecture

Ralph is organized as a Cargo workspace with six crates:

Building & Testing

Build

cargo build           # Debug build
cargo build --release # Release build

Test

# Run all tests (includes smoke tests with JSONL replay)
cargo test

# Run smoke tests specifically
cargo test -p ralph-core smoke_runner

# Run Kiro-specific smoke tests
cargo test -p ralph-core kiro

Smoke Tests

Smoke tests use recorded JSONL fixtures instead of live API calls — fast, free, and deterministic.

Fixture locations:

• crates/ralph-core/tests/fixtures/basic_session.jsonl — Claude CLI session
• crates/ralph-core/tests/fixtures/kiro/ — Kiro CLI sessions

Recording new fixtures:

# Record a session
ralph run -c ralph.yml --record-session session.jsonl -p "your prompt"

# Or capture raw CLI output
claude -p "your prompt" 2>&1 | tee output.txt

Linting

cargo clippy --all-targets --all-features
cargo fmt --check

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Write tests for new functionality
4. Ensure cargo test passes
5. Run cargo clippy and cargo fmt
6. Commit your changes (git commit -m 'Add amazing feature')
7. Push to the branch (git push origin feature/amazing-feature)
8. Open a Pull Request

See AGENTS.md for development philosophy and conventions.

License

MIT License — See LICENSE for details.

Acknowledgments

• Geoffrey Huntley — Creator of the Ralph Wiggum technique
• Harper Reed — Spec-driven development methodology
• Strands Agent SOPs — Natural language workflows that enable AI agents to perform complex, multi-step tasks with consistency and reliability.
• ratatui — Terminal UI framework
• portable-pty — Cross-platform PTY support

"I'm learnding!" - Ralph Wiggum