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Beginner AI dataset generator using OpenAI + LangChain in n8n

Robert BreenRobert Breen
1614 views
2/3/2026
ZendeskWebhookTicket ManagementHTTP RequestAPI Integration
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This n8n workflow dynamically generates a realistic sample dataset based on a single topic you provide. It uses OpenAI (via LangChain) and n8n’s built-in nodes to:

  1. Generate structured JSON data for 5 columns with 3–5 values each
  2. Flatten that data into a single text blob
  3. Infer meaningful column names via a second AI call
  4. Pivot, split, merge, and rename columns automatically
  5. Output a clean, labeled dataset ready for export or further processing

⚙️ Prerequisites

  1. OpenAI API Key

    • Visit: https://platform.openai.com/account/api-keys
    • Create a new key
    • In n8n: Credentials → New → OpenAI API, paste key, name it “OpenAi account”

  2. LangChain nodes enabled in your n8n instance

🥇 Step 1: Set Up OpenAI Credential

  1. Go to OpenAI API Keys
  2. Create and copy your key
  3. In n8n: Credentials → New → OpenAI API → paste key as “OpenAi account”

🥈 Step 2: Manual Trigger

  • Add Manual Trigger to start the workflow

🥉 Step 3: Set Topic

  • Add a Set node named Set Topic to Search
  • Field: Topic = n8n use cases (or any topic you choose)

✨ Step 4: Generate Structured Data

  • LangChain Agent node Generate Random Data
  • Connect to OpenAI Chat Model1 and Tool: Inject Creativity1
  • System prompt: instruct AI to output 5 columns of realistic values in JSON

🔧 Step 5: Parse AI Output

  • Structured Output Parser to validate JSON

🔄 Step 6: Flatten Data

  • Code node Outpt all Data to One Field
  • Joins all values into a comma-separated string for column naming

🧠 Step 7: Generate Column Names

  • LangChain Agent Generate Column Names
  • Connect to OpenAI Chat Model2
  • Prompt: infer 5 column names from the string

🔢 Step 8: Pivot Names Row

  • Code node Pivot Column Names transforms array into { column1: name1, … }

🪓 Step 9: Split Columns

  • 5 SplitOut nodes to break each array back into rows per column

🔗 Step 10: Merge Rows

  • Merge node Merge Columns together using combineByPosition

🏷️ Step 11: Rename Columns

  • Set node Rename Columns assigns the AI-generated names to each column

🔗 Step 12: Final Output

  • Merge Append Column Names combines data and header row

🏁 Done! You now have a fully AI-driven, labeled dataset generated from a single topic—no external services needed. Easily extend by adding a Google Sheets or HTTP node to export.

📬 Need Help or Want to Customize This?

📧 robert@ynteractive.com
🔗 LinkedIn

n8n Beginner AI Dataset Generator using OpenAI and Langchain

This n8n workflow demonstrates a foundational setup for generating structured AI datasets using OpenAI's chat models and Langchain's capabilities within n8n. It provides a starting point for creating diverse data points based on a user-defined prompt.

What it does

This workflow takes a user-defined prompt, processes it through an AI agent powered by OpenAI, and then structures the output into a usable dataset format.

  1. Manual Trigger: Initiates the workflow when manually executed.
  2. Edit Fields (Set): Defines the initial prompt that will be fed to the AI model. This is where you specify what kind of data you want to generate.
  3. AI Agent (Langchain): Acts as the orchestrator, taking the prompt and deciding which tools to use to fulfill the request. In this workflow, it uses an internal "Think" tool.
  4. Think Tool (Langchain): A placeholder tool within the Langchain agent that can be expanded to perform specific actions or reasoning steps.
  5. OpenAI Chat Model (Langchain): The core AI model that generates responses based on the prompt and the agent's reasoning.
  6. Structured Output Parser (Langchain): Takes the raw AI output and attempts to parse it into a structured format (e.g., JSON), making it easier to consume and use as a dataset.
  7. Split Out: If the structured output contains an array of items, this node splits them into individual items, allowing for further processing of each data point.
  8. Merge: Combines data from different branches of the workflow, though in this basic setup, it primarily serves to consolidate the final output.
  9. Code: A custom JavaScript code block that can be used for advanced data manipulation or logging if needed. (Currently empty, ready for customization).

Prerequisites/Requirements

  • n8n Instance: A running n8n instance (cloud or self-hosted).
  • OpenAI API Key: An API key for OpenAI to use their chat models. This will need to be configured as a credential in n8n for the "OpenAI Chat Model" node.
  • Langchain Nodes: Ensure the @n8n/n8n-nodes-langchain package is installed in your n8n instance.

Setup/Usage

  1. Import the workflow: Download the JSON provided and import it into your n8n instance.
  2. Configure Credentials:
    • Locate the "OpenAI Chat Model" node.
    • Click on the "Credentials" field and select or create a new "OpenAI API" credential.
    • Enter your OpenAI API Key when prompted.
  3. Define your prompt:
    • Locate the "Edit Fields (Set)" node.
    • Modify the prompt field to define the type of dataset you want to generate (e.g., "Generate 5 examples of product descriptions for a new line of eco-friendly water bottles, including product name, features, and a short marketing slogan, in JSON format.").
  4. Execute the workflow: Click the "Execute Workflow" button in the "Manual Trigger" node or activate the workflow to run it.
  5. Review Output: Inspect the output of the "Structured Output Parser" and "Split Out" nodes to see your generated dataset. The "Merge" and "Code" nodes will contain the final processed data.

This workflow provides a flexible foundation for generating various AI-powered datasets. You can extend it by adding more tools to the AI agent, integrating with databases or storage services, or refining the output parsing logic.

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