Plugins

Plugins are the building blocks you assemble on the canvas to create a workflow. Each plugin is a node type with defined inputs, outputs, and settings.

Trigger plugins start a workflow. Every workflow must have exactly one trigger.

Plugin	Description
Manual Trigger	Run from the canvas, CLI, or as a callable subworkflow
Webhook	Receive events from external systems (Stripe, GitHub, etc.)
API Endpoint	Expose a programmable HTTP endpoint backed by a workflow

Action plugins do work — call APIs, run code, transform data, send messages.

Plugin	Description
HTTP Request	Make HTTP requests to any external API
Transform	Reshape data declaratively without writing code
Code	Run custom Python logic against an input value
Subworkflow	Call another workflow and use its return value
Send Email	Send transactional email via Resend

Logic plugins route execution. Control plugins terminate or return from a path.

Plugin	Description
If	Branch on a boolean condition — true or false
Switch	Route across multiple named cases
Stop	Explicitly terminate the current execution path
Return	Return a value from a subworkflow or synchronous API workflow

Every plugin is defined by a manifest that specifies:

• What input handles it accepts and what types they expect
• What output handles it produces and what shape the data has
• What settings fields appear in the node's configuration panel
• What Python packages it requires at runtime

When you connect an output handle to an input handle, Tensorify validates that the types are compatible and generates the Python data-passing code automatically.

1. Open the node palette in the canvas editor
2. Browse or search for a plugin by name
3. Drag it onto the canvas
4. Click the node to open its settings panel and configure it
5. Connect edges from upstream nodes to its input handles