Results-Driven n8n: The First Hub for Workflow Mastery

n8n is an open-source workflow automation platform. Created in 2019 by Jan Oberhauser in Berlin, it enables the modelling of business processes and the integration and interaction of applications. It's designed to connect various services and APIs to automate repetitive tasks, saving time, money, and reducing human error.

How does n8n work?

In essence, n8n provides a powerful yet accessible platform for building sophisticated automations. While its visual interface is intuitive, a basic understanding of JavaScript can unlock even more advanced customizations and logic within your workflows.

What are the alternatives to n8n?

Popular alternatives include Zapier and IFTTT. Zapier, for instance, offers a vast number of app integrations and is often praised for its user interface, though it operates on a different pricing model.

How much does n8n cost? (The Best Part!)

While the raw JSON file of an n8n workflow cannot contain comments, n8n provides a user-friendly way to annotate your workflows directly within its visual interface: Sticky Notes.

Sticky Notes are like digital post-it notes that you can place anywhere on your workflow canvas. They are invaluable for:

• Explaining complex logic within a section of your workflow.
• Leaving reminders for yourself or collaborators.
• Documenting the purpose of specific nodes or groups of nodes.
• Providing context or instructions for using the workflow.

These notes are part of the n8n workflow's UI data but are not included in the core `nodes` or `connections` JSON that defines the execution logic, thus keeping your executable JSON clean.

Typically, you add a Sticky Note via a right-click menu or a toolbar option in the n8n UI.

An n8n workflow JSON file has a few key top-level properties. Click on each key to learn more about its role. This structure is the blueprint of your entire automation.

Key Components of a Workflow File:

• name: Human-readable workflow title.
• nodes: (As above) Array of node definitions.
• connections: (As above) Object linking nodes.
• active: Boolean (true/false) for workflow status (usually `false` for new imports).
• settings: Workflow-specific configurations (e.g., error workflow, timezone).
• id: A unique identifier for the workflow itself.
• meta (Optional): Contains metadata like the workflow creator ID.
• tags (Optional): An array of tag strings associated with the workflow.

Each node in the `nodes` array has several common fields that define its identity, appearance, and behavior. Hover over or tap the field names for a quick explanation.

Let's explore the specific nodes mentioned in the example workflow. Each tab provides details on the node's purpose, key parameters, and a representative JSON snippet based on the source document.

"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"webhookId": "b7b3d6fd-f8f8-4832-b2c5-82d6b58acfde",
"parameters": {
  "public": true,
  "options": {}
}

"type": "n8n-nodes-base.set",
"parameters": {
  "assignments": {
    "assignments": [
      {
        "name": "query",
        "value": "={{ $json.body.query }}",
        "type": "string"
      }
      // ... other assignments
    ]
  },
  "options": {}
}

"type": "@n8n/n8n-nodes-langchain.agent",
"parameters": {
  "promptType": "define",
  "text": "=Okay, I understand. You want the previous guide...", // Truncated for brevity
  "options": {}
}

"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"credentials": {
  "openAiApi": { "id": "KVs7jziNjpp9jJus", "name": "LM Studio Local" }
},
"parameters": {
  "model": { "value": "lmstudio-community/gemma-3-12B-it-qat-GGUF" },
  "options": {}
}

"type": "n8n-nodes-base.set",
"parameters": {
  "assignments": {
    "assignments": [
      { "name": "success", "value": "=true", "type": "boolean" }
    ]
  },
  "options": {}
}

"type": "n8n-nodes-base.respondToWebhook",
"parameters": {
  "respondWith": "allIncomingItems",
  "options": {
    "responseHeaders": {
      "entries": [ { "name": "X-n8n-Signature" } ]
    }
  }
}

For more sophisticated AI tasks, n8n offers various "Agents" (often via Langchain integration). Agents use an LLM to choose a sequence of actions using "Tools." These are distinct from the single "AI Agent" node shown in the JSON example section, which is a specific instance.

This section outlines a sophisticated Retrieval Augmented Generation (RAG) system built with n8n. It allows users to chat with an AI agent that can answer questions based on documents ingested from Google Drive or local files, supporting various formats and leveraging vector databases for intelligent retrieval.

Workflow Architecture (High-Level)

Core Technologies Stack

Simplified RAG Data Flow

This RAG system is a powerful example of n8n's capabilities. The original report contains more granular details on specific node configurations and potential optimizations.

The `connections` object maps out how data flows between nodes. It's a critical part of the workflow definition.

The structure for one connection from a source node's output port to a target node's input port is:

// "SourceNodeNameOrID": {
  "OutputPortName": [ // e.g., "main" or "ai_languageModel"
    [ // Inner array - CRUCIAL! This allows one output to connect to multiple inputs.
      {
        "node": "TargetNodeNameOrID",
        "type": "InputPortNameOnTarget", // e.g., "main" or "ai_languageModel"
        "index": 0 // Index of the input port on the target, usually 0.
      }
      // , { ... another target connection object for the same output port ... }
    ]
  ]
// }

The key is the source node's name or IDUsing the node ID is generally more robust than using the name, especially if names are not unique or contain special characters.. Its value is an object where keys are the output port namesMost nodes have a default output port named 'main'. Specialized nodes, like AI or conditional nodes, might have other named output ports (e.g., 'ai_languageModel', 'true', 'false'). from that source node. Each output port maps to an array. This array MUST contain an inner arrayThis inner array is essential and a common point of error if missed. It's designed this way to allow a single output port to potentially connect to multiple different input ports on various target nodes.. This inner array holds one or more connection objects. Each object specifies the `node` (the name or ID of the target node), `type` (the name of the input port on that target node), and `index` (the specific slot of that input port, which is almost always `0` for standard inputs).

n8n expressions allow you to dynamically use data from previous nodes in your parameters.

To access data from the directly preceding node's output itemThis is the most common way to get data from the node immediately connected.:

{{ $json.fieldName }}

To access data from any specific node's output itemUse the node's unique name (if unique and simple) or its ID for reliability. in the workflow (assuming it has executed):

{{ $('NodeNameOrID').item.json.fieldName }}

Key parts:

• {{ ... }}: Denotes an n8n expression.
• $json: Refers to the main JSON data of the current item being processed by the current node.
• $('NodeNameOrID'): References the output data of a specific node (identified by its name or, more reliably, its ID).
• .item: Accesses the current item being processed (n8n often processes items one by one; this ensures you're getting data from the correct iteration if there are multiple items).
• .json (after .item): Accesses the main JSON data object of that specific node's item.
• .fieldName: The actual key of the data you want from within that JSON object.

Tip: Always run the preceding node and inspect its output in the n8n UI (JSON or Table view) to find the exact path and `fieldName` you need. This is the most reliable way to build correct expressions and avoid guesswork.

The source document describes an example workflow for an AI-powered chat system (distinct from the RAG system above). Here's a simplified visual representation of its data flow. Click on a node name to jump to its detailed explanation in the "Node Deep Dive" section.

Summary of Logic:

1. Workflow starts when a chat message is received via a webhook.
2. Input fields (query, user_id, etc.) are extracted and prepared from the incoming message.
3. The AI Agent (example from JSON guide) processes this input. It utilizes the configured LM Studio Chat Model and its own system prompt/instructions to generate a response.
4. A `success: true` field is added to the output data, likely for status tracking.
5. The final result, including the AI's response, is sent back as an HTTP response to the system that originally sent the chat message.

Remember these crucial points when working with n8n JSON, especially for AI-driven workflows:

• Model Identifiers: Double-check that `model.value` in LLM nodes matches the exact model identifier in your LLM service.
• Expressions are Key: Use n8n expressions accurately. Verify data paths by inspecting previous node outputs.
• Node-Specific Parameters: For complex nodes, consult n8n documentation for all parameters. The UI panel is your first exploration point.
• Strictly Valid JSON: Remember, n8n workflow files must be pure JSON. No comments in the actual `.json` file!

We're constantly working to enhance your n8n experience on resultsfirst.ca. Here's a sneak peek at what we're brewing:

Stay tuned for these and more updates designed to help you achieve your automation goals with n8n!