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Core Concepts

Understanding Nodes, Edges, and Data Flow

Core Concepts

To build workflows effectively in Nodes2Cloud, you need to understand the three fundamental building blocks: Nodes, Edges, and Data References.

Nodes

A Node is a single step in your workflow. Nodes perform specific actions, such as receiving a webhook, sending an email, or transforming data.

There are three main categories of nodes:

  1. Trigger Nodes: These start the workflow. Every workflow must have exactly one active trigger (e.g., Webhook, Cron).
  2. Action Nodes: These perform an operation (e.g., Send Slack Message, Query Database).
  3. Control Flow Nodes: These route the execution path based on logic (e.g., If/Else, Loop).

Edges

An Edge is the visual connection between two nodes on the canvas.

  • Execution Flow (Solid Lines): These determine the order in which nodes execute. When Node A finishes, the engine follows the solid edge to execute Node B.
  • Data Flow (Implicit): You do not need to wire "data" edges explicitly. In Nodes2Cloud, any node can read the output of any node that executed before it using a Data Reference.

Data References (Variables)

When a node executes, it produces output data. You can reference this data in subsequent nodes using our expression syntax, denoted by {{ }}.

$item

This is the most common reference. It allows you to access the output of a specific node by its ID.

{{ $item("webhook_trigger").body.email }}

$loop

When you are inside a Loop node, you can access the current item being processed using the $loop variable.

{{ $loop.currentItem.email }}

$error

Used primarily in error-handling branches. If a node fails, the execution can be routed to an error branch where you can inspect the failure.

{{ $error.message }}

Built-in Helpers

You also have access to standard context helpers:

  • {{ $now }} - The current timestamp.
  • {{ $env("MY_SECRET") }} - Access an environment variable securely.

Executions

An Execution is a single run of your workflow. When a trigger fires, the Cloudflare Worker wakes up, instantiates a runtime context, and processes the graph node by node. Because it runs on the edge, executions are stateless and highly parallel.

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