Integrations

OpenAI

What it does

@loop-engine/adapter-openai uses OpenAI chat completions with response_format: { type: "json_object" } to produce structured actor decisions. It returns an AIAgentActor and AIActorDecision for runtime submission where policy guards are enforced.

Quick setup

1. Install

   ⧉

   1npm install @loop-engine/adapter-openai openai

2. Create the actor

   ⧉

   1import { createOpenAIActorAdapter } from "@loop-engine/adapter-openai";
   2 
   3const adapter = createOpenAIActorAdapter(process.env.OPENAI_API_KEY!, {
   4  modelId: "gpt-4o",
   5  confidenceThreshold: 0.75
   6});

3. Submit to a loop transition

   ⧉

   1const { actor, decision } = await adapter.createSubmission({
   2  loopId: "procurement",
   3  loopName: "SCM Procurement",
   4  currentState: "pending_analysis",
   5  availableSignals: [
   6    {
   7      signalId: "submit_recommendation",
   8      name: "Submit Recommendation",
   9      allowedActors: ["ai-agent"]
   10    }
   11  ],
   12  instruction: "Analyze demand data and recommend a purchase order.",
   13  evidence: { demandForecast: 0.89, currentStock: 42, reorderPoint: 50 }
   14});
   15 
   16await loopSystem.transition({
   17  aggregateId: "procurement-1",
   18  signal: decision.signalId,
   19  actor,
   20  evidence: decision
   21});

How it works

1. Builds a structured prompt from loop context
2. Calls OpenAI with JSON object response format
3. Parses response and validates signalId + confidence
4. Computes promptHash for auditability
5. Returns actor + decision for runtime enforcement

Configuration reference

| Option | Type | Default | Description | |---|---|---|---| | modelId | string | gpt-4o | OpenAI model ID (gpt-4o-mini, o1, o3-mini also supported) | | maxTokens | number | 1024 | Max tokens in response | | systemPrompt | string | — | Optional system prompt prepended to loop context | | confidenceThreshold | | | Minimum confidence required (0–1) |