snarktank/ralphPublic

Ralph is an autonomous AI agent loop that runs repeatedly until all PRD items are complete.

AI summary: An autonomous AI agent loop that drives coding tools repeatedly to complete tasks defined in a PRD.

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TypeScriptMITCreated Jan 7, 2026Last push 8mo ago+39 stars this week+199 this month

Quick answers

What is ralph?
An autonomous AI agent loop that drives coding tools repeatedly to complete tasks defined in a PRD.
What does ralph do?
Ralph implements an autonomous bash loop that continuously runs AI coding agents, like Amp or Claude Code, against a structured set of Product Requirements Document (PRD) items until completion. Each iteration of the loop is spun up as a fresh instance with a clean context, effectively mitigating the context window degradation that typically ruins long-running AI tasks. It maintains state and memory purely through git history, a persistent progress log, and the structured PRD JSON file. This architecture ensures reliable, long-running agent execution without accumulating unnecessary conversational context, allowing agents to tackle much larger features autonomously.
Who is ralph for?
Developers using AI coding agents who need to automate long, complex tasks defined by a PRD, maximizing agent autonomy while avoiding context bloat.
How do I get started with ralph?
./scripts/ralph/ralph.sh [max_iterations]
How popular is ralph on GitHub?
snarktank/ralph has 21,893 stars and 2,097 forks on GitHub, and gained 39 stars in the last 7 days.
What license does ralph use?
snarktank/ralph is released under the MIT license.

Star history

since Jul 29, 2026
010K20KJul 2026Aug 2026Sep 2026Oct 2026
21.9K stars as of Oct 3, 2026. Measured daily since Jul 29, 2026; GitHub no longer exposes earlier star timestamps.

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15 commits in the last yearLessMore

Signals and awards

derived from tracked data
  • Widely adopted

    21,893 stars

  • Permissive license

    MIT

  • Continuous integration

    Automated checks passing

  • Repeat trending

    7 trending appearances

What ralph does

Ralph implements an autonomous bash loop that continuously runs AI coding agents, like Amp or Claude Code, against a structured set of Product Requirements Document (PRD) items until completion. Each iteration of the loop is spun up as a fresh instance with a clean context, effectively mitigating the context window degradation that typically ruins long-running AI tasks. It maintains state and memory purely through git history, a persistent progress log, and the structured PRD JSON file. This architecture ensures reliable, long-running agent execution without accumulating unnecessary conversational context, allowing agents to tackle much larger features autonomously.

Developers using AI coding agents who need to automate long, complex tasks defined by a PRD, maximizing agent autonomy while avoiding context bloat.

  • Continuous execution loop: Automatically loops agent invocations until all defined tasks in the PRD are marked as finished.
  • Stateless iterations: Launches completely fresh agent instances for each loop to maintain clean, highly focused context windows.
  • Git-backed memory: Relies entirely on git history and specific progress text files for state persistence between isolated runs.
  • Multi-tool compatibility: Designed to work seamlessly with various AI coding tools, including both Amp and Claude Code.
  • PRD-driven logic: Uses a specifically formatted JSON Product Requirements Document to guide and track the agent's actions.

Where teams use it

Automated Feature Development

Feed a structured PRD to Ralph and let it continuously drive an agent until the entire feature is fully implemented and tested.

Long-Running Tasks

Execute complex refactors or additions that would normally exceed a single AI agent's context window by breaking them into fresh iterations.

Unattended Coding

Start the bash loop and allow the agent to work autonomously in the background without requiring constant human prompting or intervention.

Context Preservation

Ensure long development sessions don't degrade in code quality by forcing fresh context starts for every major user story.

Getting started: ./scripts/ralph/ralph.sh [max_iterations]

README

main branch

Ralph

Ralph

Ralph is an autonomous AI agent loop that runs AI coding tools (Amp or Claude Code) repeatedly until all PRD items are complete. Each iteration is a fresh instance with clean context. Memory persists via git history, progress.txt, and prd.json.

Based on Geoffrey Huntley's Ralph pattern.

Read my in-depth article on how I use Ralph

Prerequisites

  • One of the following AI coding tools installed and authenticated:
  • jq installed (brew install jq on macOS)
  • A git repository for your project

Setup

Option 1: Copy to your project

Copy the ralph files into your project:

# From your project root
mkdir -p scripts/ralph
cp /path/to/ralph/ralph.sh scripts/ralph/

# Copy the prompt template for your AI tool of choice:
cp /path/to/ralph/prompt.md scripts/ralph/prompt.md    # For Amp
# OR
cp /path/to/ralph/CLAUDE.md scripts/ralph/CLAUDE.md    # For Claude Code

chmod +x scripts/ralph/ralph.sh

Option 2: Install skills globally (Amp)

Copy the skills to your Amp or Claude config for use across all projects:

For AMP

cp -r skills/prd ~/.config/amp/skills/
cp -r skills/ralph ~/.config/amp/skills/

For Claude Code (manual)

cp -r skills/prd ~/.claude/skills/
cp -r skills/ralph ~/.claude/skills/

Option 3: Use as Claude Code Marketplace

Add the Ralph marketplace to Claude Code:

/plugin marketplace add snarktank/ralph

Then install the skills:

/plugin install ralph-skills@ralph-marketplace

Available skills after installation:

  • /prd - Generate Product Requirements Documents
  • /ralph - Convert PRDs to prd.json format

Skills are automatically invoked when you ask Claude to:

  • "create a prd", "write prd for", "plan this feature"
  • "convert this prd", "turn into ralph format", "create prd.json"

Configure Amp auto-handoff (recommended)

Add to ~/.config/amp/settings.json:

{
  "amp.experimental.autoHandoff": { "context": 90 }
}

This enables automatic handoff when context fills up, allowing Ralph to handle large stories that exceed a single context window.

Workflow

1. Create a PRD

Use the PRD skill to generate a detailed requirements document:

Load the prd skill and create a PRD for [your feature description]

Answer the clarifying questions. The skill saves output to tasks/prd-[feature-name].md.

2. Convert PRD to Ralph format

Use the Ralph skill to convert the markdown PRD to JSON:

Load the ralph skill and convert tasks/prd-[feature-name].md to prd.json

This creates prd.json with user stories structured for autonomous execution.

3. Run Ralph

# Using Amp (default)
./scripts/ralph/ralph.sh [max_iterations]

# Using Claude Code
./scripts/ralph/ralph.sh --tool claude [max_iterations]

Default is 10 iterations. Use --tool amp or --tool claude to select your AI coding tool.

Ralph will:

  1. Create a feature branch (from PRD branchName)
  2. Pick the highest priority story where passes: false
  3. Implement that single story
  4. Run quality checks (typecheck, tests)
  5. Commit if checks pass
  6. Update prd.json to mark story as passes: true
  7. Append learnings to progress.txt
  8. Repeat until all stories pass or max iterations reached

Key Files

File Purpose
ralph.sh The bash loop that spawns fresh AI instances (supports --tool amp or --tool claude)
prompt.md Prompt template for Amp
CLAUDE.md Prompt template for Claude Code
prd.json User stories with passes status (the task list)
prd.json.example Example PRD format for reference
progress.txt Append-only learnings for future iterations
skills/prd/ Skill for generating PRDs (works with Amp and Claude Code)
skills/ralph/ Skill for converting PRDs to JSON (works with Amp and Claude Code)
.claude-plugin/ Plugin manifest for Claude Code marketplace discovery
flowchart/ Interactive visualization of how Ralph works

Flowchart

Ralph Flowchart

View Interactive Flowchart - Click through to see each step with animations.

The flowchart/ directory contains the source code. To run locally:

cd flowchart
npm install
npm run dev

Critical Concepts

Each Iteration = Fresh Context

Each iteration spawns a new AI instance (Amp or Claude Code) with clean context. The only memory between iterations is:

  • Git history (commits from previous iterations)
  • progress.txt (learnings and context)
  • prd.json (which stories are done)

Small Tasks

Each PRD item should be small enough to complete in one context window. If a task is too big, the LLM runs out of context before finishing and produces poor code.

Right-sized stories:

  • Add a database column and migration
  • Add a UI component to an existing page
  • Update a server action with new logic
  • Add a filter dropdown to a list

Too big (split these):

  • "Build the entire dashboard"
  • "Add authentication"
  • "Refactor the API"

AGENTS.md Updates Are Critical

After each iteration, Ralph updates the relevant AGENTS.md files with learnings. This is key because AI coding tools automatically read these files, so future iterations (and future human developers) benefit from discovered patterns, gotchas, and conventions.

Examples of what to add to AGENTS.md:

  • Patterns discovered ("this codebase uses X for Y")
  • Gotchas ("do not forget to update Z when changing W")
  • Useful context ("the settings panel is in component X")

Feedback Loops

Ralph only works if there are feedback loops:

  • Typecheck catches type errors
  • Tests verify behavior
  • CI must stay green (broken code compounds across iterations)

Browser Verification for UI Stories

Frontend stories must include "Verify in browser using dev-browser skill" in acceptance criteria. Ralph will use the dev-browser skill to navigate to the page, interact with the UI, and confirm changes work.

Stop Condition

When all stories have passes: true, Ralph outputs <promise>COMPLETE</promise> and the loop exits.

Debugging

Check current state:

# See which stories are done
cat prd.json | jq '.userStories[] | {id, title, passes}'

# See learnings from previous iterations
cat progress.txt

# Check git history
git log --oneline -10

Customizing the Prompt

After copying prompt.md (for Amp) or CLAUDE.md (for Claude Code) to your project, customize it for your project:

  • Add project-specific quality check commands
  • Include codebase conventions
  • Add common gotchas for your stack

Archiving

Ralph automatically archives previous runs when you start a new feature (different branchName). Archives are saved to archive/YYYY-MM-DD-feature-name/.

References

View on GitHub

Recent activity

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When work happens

weekday and hour
SunMonTueWedThuFriSat036912151821Sun 0:00 — 0 commitsSun 1:00 — 0 commitsSun 2:00 — 0 commitsSun 3:00 — 0 commitsSun 4:00 — 0 commitsSun 5:00 — 0 commitsSun 6:00 — 0 commitsSun 7:00 — 0 commitsSun 8:00 — 0 commitsSun 9:00 — 0 commitsSun 10:00 — 0 commitsSun 11:00 — 1 commitsSun 12:00 — 0 commitsSun 13:00 — 0 commitsSun 14:00 — 0 commitsSun 15:00 — 0 commitsSun 16:00 — 0 commitsSun 17:00 — 0 commitsSun 18:00 — 0 commitsSun 19:00 — 0 commitsSun 20:00 — 0 commitsSun 21:00 — 0 commitsSun 22:00 — 0 commitsSun 23:00 — 0 commitsMon 0:00 — 0 commitsMon 1:00 — 0 commitsMon 2:00 — 1 commitsMon 3:00 — 0 commitsMon 4:00 — 0 commitsMon 5:00 — 0 commitsMon 6:00 — 0 commitsMon 7:00 — 0 commitsMon 8:00 — 0 commitsMon 9:00 — 0 commitsMon 10:00 — 0 commitsMon 11:00 — 0 commitsMon 12:00 — 0 commitsMon 13:00 — 0 commitsMon 14:00 — 0 commitsMon 15:00 — 0 commitsMon 16:00 — 0 commitsMon 17:00 — 0 commitsMon 18:00 — 0 commitsMon 19:00 — 0 commitsMon 20:00 — 0 commitsMon 21:00 — 0 commitsMon 22:00 — 0 commitsMon 23:00 — 0 commitsTue 0:00 — 0 commitsTue 1:00 — 0 commitsTue 2:00 — 0 commitsTue 3:00 — 0 commitsTue 4:00 — 0 commitsTue 5:00 — 0 commitsTue 6:00 — 0 commitsTue 7:00 — 0 commitsTue 8:00 — 0 commitsTue 9:00 — 0 commitsTue 10:00 — 0 commitsTue 11:00 — 0 commitsTue 12:00 — 0 commitsTue 13:00 — 0 commitsTue 14:00 — 0 commitsTue 15:00 — 0 commitsTue 16:00 — 0 commitsTue 17:00 — 0 commitsTue 18:00 — 0 commitsTue 19:00 — 0 commitsTue 20:00 — 0 commitsTue 21:00 — 0 commitsTue 22:00 — 0 commitsTue 23:00 — 0 commitsWed 0:00 — 0 commitsWed 1:00 — 0 commitsWed 2:00 — 0 commitsWed 3:00 — 0 commitsWed 4:00 — 0 commitsWed 5:00 — 0 commitsWed 6:00 — 0 commitsWed 7:00 — 0 commitsWed 8:00 — 0 commitsWed 9:00 — 4 commitsWed 10:00 — 0 commitsWed 11:00 — 0 commitsWed 12:00 — 0 commitsWed 13:00 — 0 commitsWed 14:00 — 0 commitsWed 15:00 — 3 commitsWed 16:00 — 0 commitsWed 17:00 — 0 commitsWed 18:00 — 0 commitsWed 19:00 — 0 commitsWed 20:00 — 0 commitsWed 21:00 — 0 commitsWed 22:00 — 0 commitsWed 23:00 — 0 commitsThu 0:00 — 0 commitsThu 1:00 — 0 commitsThu 2:00 — 0 commitsThu 3:00 — 0 commitsThu 4:00 — 0 commitsThu 5:00 — 0 commitsThu 6:00 — 0 commitsThu 7:00 — 0 commitsThu 8:00 — 0 commitsThu 9:00 — 0 commitsThu 10:00 — 0 commitsThu 11:00 — 0 commitsThu 12:00 — 0 commitsThu 13:00 — 1 commitsThu 14:00 — 1 commitsThu 15:00 — 0 commitsThu 16:00 — 0 commitsThu 17:00 — 0 commitsThu 18:00 — 0 commitsThu 19:00 — 0 commitsThu 20:00 — 0 commitsThu 21:00 — 1 commitsThu 22:00 — 0 commitsThu 23:00 — 0 commitsFri 0:00 — 0 commitsFri 1:00 — 0 commitsFri 2:00 — 0 commitsFri 3:00 — 0 commitsFri 4:00 — 0 commitsFri 5:00 — 0 commitsFri 6:00 — 0 commitsFri 7:00 — 0 commitsFri 8:00 — 1 commitsFri 9:00 — 0 commitsFri 10:00 — 0 commitsFri 11:00 — 0 commitsFri 12:00 — 0 commitsFri 13:00 — 0 commitsFri 14:00 — 0 commitsFri 15:00 — 0 commitsFri 16:00 — 0 commitsFri 17:00 — 0 commitsFri 18:00 — 0 commitsFri 19:00 — 0 commitsFri 20:00 — 0 commitsFri 21:00 — 0 commitsFri 22:00 — 0 commitsFri 23:00 — 0 commitsSat 0:00 — 0 commitsSat 1:00 — 0 commitsSat 2:00 — 0 commitsSat 3:00 — 0 commitsSat 4:00 — 0 commitsSat 5:00 — 0 commitsSat 6:00 — 0 commitsSat 7:00 — 0 commitsSat 8:00 — 0 commitsSat 9:00 — 2 commitsSat 10:00 — 0 commitsSat 11:00 — 0 commitsSat 12:00 — 0 commitsSat 13:00 — 0 commitsSat 14:00 — 0 commitsSat 15:00 — 0 commitsSat 16:00 — 0 commitsSat 17:00 — 0 commitsSat 18:00 — 0 commitsSat 19:00 — 0 commitsSat 20:00 — 0 commitsSat 21:00 — 0 commitsSat 22:00 — 0 commitsSat 23:00 — 0 commits
Commit volume by weekday and hour (UTC). Larger dots mean more commits.

Who is committing

last 52 weeks
Maintainer commits9 (45%)
Community commits11 (55%)

20 commits in total over the last year.

DateListRankStars gained
Jan 22, 2026daily#19+170
Jan 21, 2026daily#15+197
Jan 20, 2026daily#23+152
Jan 12, 2026daily#9+277
Jan 11, 2026daily#3+373
Jan 10, 2026daily#6+295
Jan 9, 2026daily#11+111
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