Structure, not improvisation
AI works best when decisions are bounded, outcomes are measurable, and every action leaves evidence. Loop Engine provides that structure.
Open Infrastructure · Apache-2.0
The control layer for AI-operated enterprises
Loop Engine gives AI finite states, deterministic guards, and structured feedback. Not improvisation - control.
SIGNAL
LOOP ENGINE
ACTOR
TRANSITION
EVIDENCE
LEARNING
Every actor accounted for
Human, automation, AI agent - the actor model treats all three identically. No action is anonymous. Every transition has attributed evidence.
Loops learn
Each closed loop emits structured training signals. Forecasts improve. Lead times sharpen. The system gets better automatically.
Quick start
Running in 60 seconds
- 1. Install the SDK
- 2. Define your loop
- 3. Start and transition
- 4. Subscribe to events
import { LoopBuilder } from '@loop-engine/sdk'
const approval = LoopBuilder
.create('expense.approval', 'finance')
.state('SUBMITTED')
.state('APPROVED', { isTerminal: true })
.state('REJECTED', { isTerminal: true })
.initialState('SUBMITTED')
.transition({ id: 'approve', from: 'SUBMITTED', to: 'APPROVED', actors: ['human'] })
.transition({ id: 'reject', from: 'SUBMITTED', to: 'REJECTED', actors: ['human'] })
.outcome({ id: 'expense_approved', valueUnit: 'expense_approved', description: 'Expense approved', measurable: true })
.build()Everything you need to build on
@loop-engine/sdk
High-level entry point for loop systems.
@loop-engine/core
Canonical types and model contracts.
@loop-engine/runtime
Loop engine execution lifecycle.
@loop-engine/dsl
YAML and builder loop definition APIs.
@loop-engine/events
Event contracts and event bus types.
@loop-engine/guards
Built-in and custom guard registry.
@loop-engine/actors
Actor model and attribution helpers.
@loop-engine/observability
Metrics, timelines, replay.