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Overview
- When clicking "Execute Workflow"
- Structured Output Parser
- Auto-fixing Output Parser
- OpenAI Chat Model
- OpenAI Chat Model1
- Prompt
- LLM Chain
An n8n workflow that asks an OpenAI model a question and holds the answer to a JSON shape you define. A reply that does not fit goes to a second model call, which repairs it once.
JSON answers in a fixed format with an auto-fix retry is an n8n workflow in the Developer category, listed by Mindket. It is free. This is version 1, last updated on . Download it on this page or copy its JSON, then import it into an n8n canvas. Nothing runs until you activate it in n8n.
At a glance
- Runs in
- n8n Cloud: import the file into a new workflow there
- Your own n8n server: the same file and steps
- Other workflows: copy LLM Chain and its two parsers into a workflow that has its own trigger and prompt
- Another chat model: swap the two OpenAI model nodes for a different chat model node
Platform
How it works
Trigger
Manual run
It starts when you press Execute workflow, and there is no form:
- No input from you: the question is set inside the workflow
Actions
- A step sets the question: the five largest US states by area, each with its three largest cities and their populations
- The chain sends it to an OpenAI model with the instructions of the schema
- The Structured Output Parser checks the reply against the schema
- A reply that does not fit goes to a second OpenAI call with the error, which writes one new attempt
- The parsed answer leaves the chain, as one item or as a list in a data field
Result
The model’s answer as data in the shape of your schema, repaired once if it first came back wrong
Integrations
What you need before you start.
OpenAI
Language model
Answers the question and, in a second node, repairs a reply that does not fit the schema.
NeedsOpenAI API key
Workflow check
Checked by Mindket on 13 Nov 2026
- Steps
- 3 steps, starting by hand
- AI
- 1 step calls an AI model: LLM Chain
- Sign-ins
- Signs in to: OpenAI
- API keys
- No API key is left in the file
- Step types
- Mindket’s checker reads all 3 of its step types
About
What it is
This n8n workflow shows how to make a language model answer in a fixed JSON shape. You press a button, and a prompt asks OpenAI for the five largest US states by area, each with its three largest cities and their populations. A Structured Output Parser holds the answer to a schema. An Auto-fixing Output Parser wraps it and sends a bad reply to a second model call, which repairs it once. Eleven nodes do the work, four of them sticky notes.
Who it suits
Builders of n8n workflows who need a model’s answer as data that later nodes can read, such as a row to store or a field to branch on. Skip it if you want free text. It runs by hand from the editor, so it is a pattern to copy, not a service.
| Industry | Who uses it | How they use it | Example |
|---|---|---|---|
| Software | Workflow builders | Make a model return fields that a later node can use | A builder asks for a ticket’s category and urgency as two fields. |
| Operations | Operations analysts | Turn free text into rows | An analyst pulls the supplier, amount and date out of an email. |
| Research | Data teams | Collect facts from a passage in one fixed layout | A team lists the people and companies an article names. |
| Marketing | Content teams | Get product copy back in labelled parts | A marketer asks for a headline, a subtitle and three bullets. |
You need a little n8n: import a file, add an OpenAI credential and run it.
Where to use it
Any n8n that can reach OpenAI will run it. The reply appears in the output of the LLM Chain node.
- n8n Cloud: import the file into a new workflow there
- Your own n8n server: the same file and steps
- Other workflows: copy
LLM Chainand its two parsers into a workflow that has its own trigger and prompt - Another chat model: swap the two OpenAI model nodes for a different chat model node
Setup
Download saves a zip with workflow.json and Mindket’s setup notes in your downloads folder. Setup is one credential, used in two nodes, and a model choice in each.
| Need | Detail |
|---|---|
| n8n | On n8n Cloud or on a server of your own |
| OpenAI | An API key |
- In n8n, create a workflow, open its menu, choose Import from File and pick
workflow.json. - Add an OpenAI credential with your key and select it in
OpenAI Chat ModelandOpenAI Chat Model1. - Pick a model in each of those two nodes. The default,
gpt-5-mini, is due to shut down on 11 December 2026. - Press Execute workflow, then open
LLM Chainto read the answer.
How to use it
Change the question in Prompt and the schema in Structured Output Parser, then run it again. The schema tells the model which fields to return, and the parser rejects anything else. A rejected reply goes to OpenAI Chat Model1 with the error, and that model writes one new attempt.
| To change | Where |
|---|---|
| The question | The input value in Prompt |
| The shape of the answer | The jsonSchema in Structured Output Parser |
| The repair instructions | The prompt option of Auto-fixing Output Parser, which must keep {error} |
| The models | OpenAI Chat Model for the answer and OpenAI Chat Model1 for the repair |
Example
A builder keeps the question as it ships and reads what comes back.
The model answers in JSON. If the parser accepts the reply, LLM Chain outputs it: a single object comes out as the item, and a list comes out as an item whose data field holds the list. If not, the repair call tries once more. Check the figures against a source, since the parser checks shape and not truth.
For newcomers and experts
If you are new. n8n draws a workflow as boxes joined by lines, and each box, a node, does one job. A model’s reply is plain text, so a later node cannot count on its layout. A parser reads the reply against a schema and hands on data in the shape you asked for. The two models hang under the chain instead of sitting in the line.
For experts. LLM Chain is the Basic LLM Chain with its output parser switched on. Structured Output Parser takes the JSON schema. In n8n’s current code it accepts one object or a list of that object. Auto-fixing Output Parser wraps it. On a failure it sends the instructions, the bad reply and the error to its own model.
| Node | What it does | What to know |
|---|---|---|
Prompt | Sets the input field | The chain reads its prompt from input |
LLM Chain | Sends the prompt and applies the parser | An object comes out as the item, a list as a data field |
Structured Output Parser | Holds the schema | name and population are bare words, so any value passes |
Auto-fixing Output Parser | Repairs a bad reply once | A second failure stops the run with an error |
OpenAI Chat Model1 | Writes the repair | It can be a different model from the first |
Good to know
The workflow runs by hand and leans on a default model that is going away.
- Models: the two model nodes set no model, so each takes the node’s default,
gpt-5-miniin n8n’s repository on 12 November 2026. OpenAI’s deprecations page, read that day, listsgpt-5-minifor shutdown on 11 December 2026, withgpt-5.6-terraas the replacement. Both nodes also set a temperature of 0, so clear it if OpenAI refuses it for the model you pick - Schema: it describes one state with a list of cities, while the question asks for five states. In n8n’s current code a list of five passes, so the run works, but write the shape you mean
- Loose fields:
nameandpopulationare bare words, not{"type": "string"}, so any value passes. Write each as an object with atypeto enforce it - One repair: if the second reply still fails the schema, the run stops with an error
- Billing: each run makes at least one model call, and a repair makes a second
- Trigger: manual, so nothing runs until you press the button
- Your data: the question and the reply go to OpenAI
- Credentials: the JSON holds none
README and files
JSON answers in a fixed format with an auto-fix retry
An n8n workflow that asks an OpenAI model a question and holds the answer to a JSON shape you define. A Structured Output
Parser keeps the shape. An Auto-fixing Output Parser sends a reply that does not fit to a second model call, which repairs it.
What is inside
workflow.json: the workflow as n8n exports it, 11 nodes, four of them sticky notes. It holds no credential. The notes
label the four parts of the canvas: the first model, the auto-fixing parser, the model that repairs and the schema.README.md: these notes.
What we changed from the original copy
- Both model nodes were linked to a credential in their author's n8n. The links are gone, so select your own.
- The export's instance id and version id are gone.
- Nothing else. The file has no Code node, and its expressions needed no repair.
Before you start
- An n8n instance, on n8n Cloud or your own server.
- An OpenAI API key.
Set it up
- In n8n, create a workflow, open its menu and choose Import from File. Pick
workflow.json. - Add an OpenAI credential with your key. Select it in
OpenAI Chat ModelandOpenAI Chat Model1. - Pick a model in each of those two nodes. The default,
gpt-5-mini, is due to shut down on 11 December 2026. - Press Execute workflow and open
LLM Chainto read the answer. - Change the question in
Prompt, and the schema inStructured Output Parserto match it.
How a run goes
When clicking "Execute Workflow"starts the run by hand.Promptsets one field,input, to a question: the 5 largest US states by area, each with its 3 largest cities and
their population.LLM Chainreadsinputas its prompt and sends it toOpenAI Chat Model, which has a temperature of 0.- The chain is told to answer in the shape held by
Structured Output Parser.Auto-fixing Output Parserwraps that parser. - A reply that does not parse, or does not fit the schema, goes to
OpenAI Chat Model1. It receives the instructions, the bad
reply and the error in n8n's default repair prompt, and writes one new attempt. - A reply that fits leaves
LLM Chain. In n8n's current code a single object comes out as the item itself, and a list of
objects comes out as an item whosedatafield holds the list.
Change the look
- The question: the
inputvalue inPrompt. - The shape: the
jsonSchemainStructured Output Parser. - The repair instructions: add a custom prompt in the options of
Auto-fixing Output Parser. It must keep the{error}placeholder. - The models:
OpenAI Chat Modelfor the answer andOpenAI Chat Model1for the repair. They can differ.
Good to know
- Models: the two model nodes set no model, so they take the node's own default,
gpt-5-miniin n8n's repository on
12 November 2026. OpenAI's deprecations page, read that day, gives 11 December 2026 as the shutdown ofgpt-5-mini, withgpt-5.6-terraas the replacement. Pick a model in each node. Both nodes also set a temperature of 0, so clear it if
OpenAI refuses it for the model you choose. - The schema describes one state with a list of cities, while the question asks for five states. In n8n's current code the
parser accepts one object or a list of them, so a list of five passes. nameandpopulationare written as bare words, as in"name": "string", not as{"type": "string"}. n8n's converter
lets such a property take any value, so a population written as text also passes. Write each as an object with atypeto
enforce it.- The repair is a single attempt. If the second reply still fails the schema, the run stops with an error.
- Billing: each run makes at least one model call, and a reply that needs repair makes a second.
- Your data: the question and the model's reply go to OpenAI.
- The trigger is manual, so nothing runs until you press the button.
NOTICE.md 602 B README.md 4 KB workflow.json 5 KB
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