Loop over items — beginner example

This workflow introduces beginners to one of the most fundamental concepts in n8n: looping over items. Using a simple use case—generating LinkedIn captions for content ideas—it demonstrates how to split a dataset into individual items, process them with AI, and collect the output for review or export.

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✅ Key Features

• 🧪 Create Dummy Data: Simulate a small dataset of content ideas.
• 🔁 Loop Over Items: Process each row independently using the SplitInBatches node.
• 🧠 AI Caption Creation: Automatically generate LinkedIn captions using OpenAI.
• 🧰 Tool Integration: Enhance AI output with creativity-injection tools.
• 🧾 Final Output Set: Collect the original idea and generated caption.

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🧰 What You’ll Need

• ✅ An OpenAI API key
• ✅ The LangChain nodes enabled in your n8n instance
• ✅ Basic knowledge of how to trigger and run workflows in n8n

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🔧 Step-by-Step Setup

1️⃣ Run Workflow

• Node: Manual Trigger (Run Workflow)
• Purpose: Manually start the workflow for testing or learning.

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2️⃣ Create Random Data

• Node: Create Random Data (Code)
• What it does: Simulates incoming data with multiple content ideas.
• Code:

return [
  {
    json: {
      row_number: 2,
      id: 1,
      Date: '2025-07-30',
      idea: 'n8n rises to the top',
      caption: '',
      complete: ''
    }
  },
  {
    json: {
      row_number: 3,
      id: 2,
      Date: '2025-07-31',
      idea: 'n8n nodes',
      caption: '',
      complete: ''
    }
  },
  {
    json: {
      row_number: 4,
      id: 3,
      Date: '2025-08-01',
      idea: 'n8n use cases for marketing',
      caption: '',
      complete: ''
    }
  }
];

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3️⃣ Loop Over Items

• Node: Loop Over Items (SplitInBatches)
• Purpose: Sends one record at a time to the next node.
• Why It Matters: Loops in n8n are created using this node when you want to iterate over multiple items.

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4️⃣ Create Captions with AI

• Node: Create Captions (LangChain Agent)
• Prompt:

idea: {{ $json.idea }}

• System Message:

You are a helpful assistant creating captions for a LinkedIn post. Please create a LinkedIn caption for the idea.

• Model: GPT-4o Mini or GPT-3.5
• Credentials Required:
  • OpenAI Credential
    • Go to: OpenAI API Keys
    • Create a key and add it in n8n under credentials as “OpenAi account”

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5️⃣ Inject Creativity (Optional)

• Node: Tool: Inject Creativity (LangChain Tool)
• Purpose: Demonstrates optional LangChain tools that can enhance or manipulate input/output.
• Why It’s Cool: A great way to show chaining tools to AI agents.

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6️⃣ Output Table

• Node: Output Table (Set)
• Purpose: Combines original ideas and generated captions into final structure.
• Fields:
  • idea: ={{ $('Create Random Data').item.json.idea }}
  • output: ={{ $json.output }}

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💡 Educational Value

This workflow demonstrates:

• Creating dynamic inputs with the Code node
• Using SplitInBatches to simulate looping
• Sending dynamic prompts to an AI model
• Using Set to structure the output data

Beginners will understand how item-level processing works in n8n and how powerful looping combined with AI can be.

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📬 Need Help or Want to Customize This?

Robert Breen
Automation Consultant | AI Workflow Designer | n8n Expert
📧 [email protected]
🌐 ynteractive.com
🔗 LinkedIn

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🏷️ Tags

n8n loops OpenAI LangChain workflow training beginner LinkedIn automation caption generator