ralph-loop▌
andrelandgraf/fullstackrecipes · updated Apr 8, 2026
Automated agent-driven development loop that executes AI agents against user story acceptance criteria.
- ›Structures features as JSON-formatted user stories with testable acceptance criteria that agents can verify and track
- ›Runs AI agents in a continuous loop to implement features, check acceptance criteria, and log progress for subsequent agent iterations
- ›Requires prerequisite setup of AI coding agent configuration and user stories framework before running the Ralph agent loop
- ›Inte
Ralph Loop
Complete setup for automated agent-driven development. Define features as user stories with testable acceptance criteria, then run AI agents in a loop until all stories pass.
Prerequisites
Complete these recipes first (in order):
AI Coding Agent Configuration
Configure AI coding agents like Cursor, GitHub Copilot, or Claude Code with project-specific patterns, coding guidelines, and MCP servers for consistent AI-assisted development.
curl -H "Accept: text/markdown" https://fullstackrecipes.com/api/recipes/agent-setup
Cookbook - Complete These Recipes in Order
User Stories Setup
Create a structured format for documenting feature requirements as user stories. JSON files with testable acceptance criteria that AI agents can verify and track.
curl -H "Accept: text/markdown" https://fullstackrecipes.com/api/recipes/user-stories-setup
Working with User Stories
Document and track feature implementation with user stories. Workflow for authoring stories, building features, and marking acceptance criteria as passing.
curl -H "Accept: text/markdown" https://fullstackrecipes.com/api/recipes/using-user-stories
Ralph Agent Loop
Set up automated agent-driven development with Ralph. Run AI agents in a loop to implement features from user stories, verify acceptance criteria, and log progress for the next agent.
curl -H "Accept: text/markdown" https://fullstackrecipes.com/api/recipes/ralph-setup
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★39 reviews- ★★★★★Shikha Mishra· Dec 20, 2024
I recommend ralph-loop for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★William Tandon· Dec 20, 2024
Useful defaults in ralph-loop — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Ganesh Mohane· Dec 16, 2024
We added ralph-loop from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Meera Zhang· Dec 4, 2024
Registry listing for ralph-loop matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Aisha Harris· Nov 23, 2024
ralph-loop fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Yash Thakker· Nov 11, 2024
Useful defaults in ralph-loop — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Alexander Sethi· Nov 11, 2024
I recommend ralph-loop for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Kabir Wang· Oct 14, 2024
ralph-loop is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Dhruvi Jain· Oct 2, 2024
ralph-loop has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Charlotte Ramirez· Oct 2, 2024
ralph-loop reduced setup friction for our internal harness; good balance of opinion and flexibility.
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