What is Loop Engineering?

Loop Engineering turns a prompt into one intelligent agent that repeats a cycle: plan, execute, validate, improve, retry — until an explicit success condition or stop condition is met. It trades a single answer for a converged answer.

Should I learn Prompt Engineering first?

Yes. Every loop is built from prompts and every graph node is a loop or a prompt. The Academy is sequenced deliberately: Prompt, then Loop, then Graph.

When should I use Graph Engineering?

When a task has genuinely distinct roles, needs different models or tools per step, requires a human approval before an irreversible action, or when you need explicit, inspectable failure paths instead of one agent doing everything.

Your prompts, loops, graphs, generated content, learning progress and Gemini API key are designed to stay local on your device. Only purchase entitlement metadata uses Firebase. No graph uploads, no prompt uploads, no hidden cloud sync.

Can I export to LangGraph?

Yes — LangGraph Python and TypeScript, plus Google ADK, OpenAI Agents SDK, CrewAI, AutoGen, n8n, Make, Mermaid and a framework-neutral spec. Exports generate scaffolds and implementation guidance, not deployed applications or executed workflows.

Can I use my own Gemini key?

Yes. Bring Your Own Key (BYOK) is supported. Your Gemini API key is stored locally on your device and used directly for your own generations.

Now available on iOS and Android

Engineer AI Systems with Graphs and Loops

Q: What is Graph Engineering?

Graph Engineering designs an AI workflow as an explicit graph: multiple specialised nodes do the work, edges route information, shared state connects everything, validators enforce quality, human approvals keep control, and stop conditions end the run safely. It is an emerging engineering practice rather than a universally standardised term.

Turn complex ideas into visual AI graphs, structured execution loops, and reusable workflows. Design systems beyond one-off prompting with Loop Engineering AI.

Download on theApp Store Get it onGoogle Play

Windows and macOS apps are coming soon.

Windows — Coming Soon
macOS — Coming Soon

iOS Android Local-first Gemini BYOK

Workflow overview illustrating an agent loop of plan, validate, guardrails, retries, stop conditions and final report

Visual graph editor showing an AI agents workflow of connected planner, router, specialist, validator and human approval nodes

Graph Analyzer screen showing an architecture score of 81 with warnings and a quality breakdown for structure, routing, state design and validation

Prompt

One instruction, one response

Loop

Plan → Execute → Validate → Retry

Graph

Multi-node intelligent organisation

Platform availability

Available now on mobile

Loop Engineering AI is live on the Apple App Store and Google Play. Desktop apps are on the way.

Available now

iPhone and iPad

Native iOS app with the full graph, loop and prompt workspace.

Download on the App Store

Available now

Android phones and tablets

Native Android app with the same local-first project library.

Get it on Google Play

Coming Soon

Windows

A desktop canvas for larger graphs is in development.

Coming Soon

macOS

A native Mac workspace is in development.

Inside the app

See Graph and Loop Engineering in Action

Explore the complete mobile workspace for creating visual AI graphs, structured execution loops, reusable prompts and AI engineering projects.

All screens

Swipe on mobile, or use the arrows. Select any screen for a larger preview.

Create hub
Workspace
Visual graph
Graph Engineering
Routes and branching
Graph Engineering
Graph analyzer
Quality analysis
Improve graph
Quality analysis
Loop node detail
Loop Engineering
Agent loop cycle
Loop Engineering
Agent graph systems
Graph Engineering
Mermaid export
Export
Graph Engineering track
Learning Academy
Practice exercises
Learning Academy
Improve and learn
Workspace

Why Loop Engineering AI

Most AI users write prompts.
Professionals engineer systems.

Our platform teaches you how AI actually evolves — from a single instruction, to a self-correcting agent, to a coordinated multi-node workflow you can inspect, validate and export.

Prompt Engineering

One instruction. One response.

Perfect for writing, coding and brainstorming
Role, context, constraints and output format
Fastest path from idea to usable output

Loop Engineering

One intelligent agent that refuses to settle.

Plan → Execute → Validate → Improve → Retry
Runs until an explicit success condition is met
Stop conditions cap iterations, time and cost

Graph Engineering

A complete AI workflow — an intelligent organisation.

Multiple specialised nodes and shared state
Routing, validation and human approvals
Framework exports for production teams

The evolution

The Evolution of AI Engineering

Each stage does not replace the last — it contains it. Graphs are made of loops, and loops are made of prompts.

Stage 1
Prompt Engineering
Single instruction
You ask once and judge the answer yourself. Precision lives entirely in the wording.
Stage 2
Loop Engineering
Single intelligent worker
The agent critiques and rewrites its own output until it passes your criteria — or safely stops.
Stage 3
Graph Engineering
Entire intelligent organisation
Specialists collaborate over shared state with explicit routing, quality gates and human control.

Bounded retry node detail showing purpose, inputs, outputs, instruction, validations, failure behaviour and human approval settings — Loop node validation, retries and approval settings

The next evolution

What is Graph Engineering?

Graph Engineering is the next evolution of AI system design. Instead of one AI repeating work, multiple specialised nodes collaborate together.

Nodes perform work

Each node is a specialist with one job, one contract and one output shape.

Edges route information

Conditional routing sends results forward, backwards, or to a fallback path.

Shared state connects everything

A typed state object every node reads and writes — the real contract of the graph.

Validators ensure quality

Quality gates score output and can return work to an earlier node automatically.

Human approvals keep control

Deliberate checkpoints before anything irreversible is published or executed.

Stop conditions finish safely

Budgets, iteration caps and success criteria end the run without runaway cost.

Graph Engineering is an emerging engineering discipline for orchestrating AI-agent workflows as explicit graphs — nodes, routing, shared state, validation, approvals, retries and observability. It builds on established workflow orchestration concepts while extending Loop Engineering to coordinated multi-node systems. It is an emerging practice rather than a universally standardised term.

Reference: Graph Engineering for AI agents — CodesDevs

Live example

Research → Publish graph

Edges animate as state flows

Graph view showing a workflow planner and risk router branching into standard path, retry with validator feedback and parallel review paths — Conditional routing between nodes

Everything included

One app. The whole AI engineering workflow.

Build, analyze, edit, learn and export — without leaving your device.

Prompt Builder

Compose structured prompts from role, context, constraints and output contracts.

Loop Builder

Design plan-execute-validate-retry cycles with explicit success and stop conditions.

Graph Builder

Assemble multi-node agent workflows with routing, shared state and approvals.

Graph Visualization

See the whole system at a glance — nodes, edges, gates and failure paths.

Graph Analyzer

Architecture scoring, warnings, critical issues and suggested improvements.

Visual Graph Editor

Drag nodes, rewire edges, edit state and auto-layout the canvas.

Framework Export

Scaffolds for LangGraph, ADK, OpenAI Agents SDK, CrewAI, AutoGen, n8n and Make.

Mermaid Export

Documentation-ready diagrams you can paste straight into a README or wiki.

Gemini AI

Bring your own Gemini key for generation, analysis and rewriting.

Learning Academy

Structured tracks across Prompt, Loop and Graph Engineering.

Local Library

Every prompt, loop and graph saved and searchable on your device.

Privacy First

No prompt uploads, no graph uploads, no hidden cloud sync.

Offline Storage

Your library stays available even without a network connection.

Version History

Track how a graph evolved and compare revisions side by side.

Framework Scaffolding

Generated project structure and implementation guidance to start fast.

Graph Analyzer

Know your architecture is sound before you build it

The analyzer reviews your graph like a senior engineer would — structure, routing, safety and cost.

Architecture Score

A single quality number for structure, routing and safety.

Warnings

Missing stop conditions, unused nodes, ambiguous edges.

Critical Issues

Unreachable paths, infinite loops, unapproved irreversible actions.

Suggested Improvements

Concrete rewrites ranked by impact on reliability.

Automatic Fix Planning

A step-by-step remediation plan you can apply node by node.

Version Comparison

Diff two revisions and see exactly what changed and why.

Visual Highlights

Problem nodes and edges highlighted directly on the graph.

Architecture Score

Critical — Publisher node runs without a preceding approval gate.
Warning — Validator has no maximum retry budget.
Improve — Split Research Agent into search and synthesis nodes.

Illustrative analyzer output. Scores depend on your own graph.

Improve Graph panel listing options to simplify the graph, add stronger validation, add failure recovery, reduce node count and add human approval — Suggested improvements

Visual Graph Editor

Edit the system, not the syntax

A canvas built for thinking: reposition nodes, rewire routes, adjust shared state and validate — all with full undo history.

content-pipeline.graph

Drag nodes

Edit routes

Change state

Undo

Redo

Auto layout

Validation

Planner

Research

Writer

Validator

Approval

Publisher

Illustrative editor mockup — the live canvas ships inside the mobile app.

Interoperability

Export Anywhere

Design once, ship into the stack your team already uses.

LangGraph Python

LangGraph TypeScript

Google ADK

OpenAI Agents SDK

CrewAI

AutoGen

n8n

Make

Framework Neutral

Exports generate scaffolds and implementation guidance — not deployed applications or executed workflows. You keep full control of credentials, tools, hosting and deployment.

Learning Academy