n8n OpenAI Integration: GPT-4o Prompt Chain Workflow Tutorial

n8n connects to OpenAI via the native OpenAI Chat Model node—a LangChain sub‑node that authenticates with your API key and sends prompts to GPT‑4o, GPT‑4o‑mini, or any model available to your account. A prompt chain uses multiple OpenAI calls in sequence, where each call’s output feeds the next, enabling multi‑step reasoning that a single prompt cannot achieve. This tutorial walks through credential setup, node parameters, dynamic prompt construction, and a complete multi‑node chain for generating, evaluating, and publishing content.

How do you set up OpenAI credentials and choose the right model in n8n?

Go to Settings → Credentials → New Credential, select OpenAI API, and paste your key from platform.openai.com/api-keys. n8n encrypts the key with AES‑256. Then add an AI Agent or Basic LLM Chain root node to the canvas, drop an OpenAI Chat Model sub‑node inside it, and select gpt-4o from the Model dropdown. [1]

The model list is dynamically loaded from OpenAI, so you will only see models available to your account. For high‑volume content generation, start with GPT‑4o‑mini at $0.15 per 1 M input tokens; switch to full GPT‑4o only for complex multi‑step reasoning tasks where simple models produce unacceptable output [2]. For a broader view of how OpenAI fits into n8n’s AI ecosystem, see the AI Agents & LLM Orchestration guide.

How do temperature, max tokens, and system prompts control GPT‑4o output in n8n?

The OpenAI Chat Model node exposes four key parameters. Temperature (0–2, default 1) controls randomness: 0 produces deterministic output for fact‑based tasks, 0.7–1.0 suits creative writing, and 2 generates maximum variability. Max Tokens (1–32,768) caps the response length. The System Prompt sets assistant behavior and persists across all messages in the conversation. [1]

The Response Format option toggles between Text and JSON. JSON mode forces the model to return valid, parseable JSON—ideal for Structured Output Parsers downstream. Additional controls include Frequency Penalty (−2.0 to 2.0) to reduce repetition, and Presence Penalty (−2.0 to 2.0) to encourage new topics. For a complete reference of all node parameters, consult the Code node transformation guide for custom post‑processing of AI outputs.

How do you build dynamic prompts using a Set node before the OpenAI call?

A Set node placed between the trigger and the AI Agent node constructs the prompt dynamically. You map incoming fields—topic, keywords, or customer name—into a combined prompt string. For example: topic = {{ "Write a blog about " + $json.topic + " targeting " + $json.audience }}. The downstream OpenAI node then receives a complete, context‑rich prompt without any hardcoding. [2]

For more advanced prompt assembly, use a Code node to inject conditional logic—for example, appending different instructions based on content type, or merging data from multiple upstream nodes into a single structured prompt. The Code node also supports JavaScript template literals for complex multi‑line prompts with embedded variables. For detailed Code node examples, see the Code node transformation guide.

How do you build a working OpenAI‑powered content generator in 25 minutes?

Create a five‑node workflow: Manual Trigger → Set node (build the prompt) → AI Agent with OpenAI Chat Model sub‑node (GPT‑4o, temperature 0.7, max tokens 2048) → Parse Output node (extract the text) → Google Sheets or Notion node (store the result). Test with a specific topic; the entire chain runs in 2–5 seconds and costs approximately $0.03 per generation with GPT‑4o. [2]

How do you chain multiple OpenAI calls so each output feeds the next prompt?

A prompt chain connects multiple AI Agent (or Basic LLM Chain) nodes in sequence. The first node generates a blog outline; a Set node extracts that outline and feeds it into a second AI Agent node that writes the full article; a third evaluates the draft against SEO criteria. Each node’s system prompt specialises the model for that specific sub‑task. [3]

For branching chains, insert an IF node after the first AI call to check output quality—for example, verifying that a generated outline contains at least five sections. If the check fails, route back to the same AI Agent node with a revised prompt; if it passes, continue to the writing stage. This self‑correcting loop dramatically improves output consistency. For more on conditional logic, refer to the IF & Switch branching logic guide.

How do you add retry logic and rate‑limit handling for production OpenAI workflows?

Wrap the OpenAI call in an error workflow: if the OpenAI node returns a 429 (rate limit) or 5xx error, the error workflow waits using exponential backoff—1 s, 2 s, 4 s, up to 60 s—then retries. For batch processing of 100+ items, place a SplitInBatches node before the AI Agent to process 5–10 items at a time, preventing quota exhaustion mid‑workflow. [2]

Track costs by logging token usage from the OpenAI node’s output metadata to Google Sheets or Data Tables. The response includes completion_tokens, prompt_tokens, and total_tokens—multiply by your model’s per‑token price to calculate exact cost per execution. Set a daily budget alert via a Schedule Trigger that sums the logs and notifies Slack if the total exceeds a threshold. For comprehensive error handling architecture, see n8n Error Workflow: Catch, Retry & Alert.