Inputs

Workflow inputs allow you to parameterize workflows with typed values. Define inputs at the workflow level and provide them when triggering the workflow manually or through schedules.

Input types

Workflows support three input types, each with type-safe default values:

Type	Description	Default value type
string	Text values	String
bool	Boolean true/false values	Boolean
int	Integer numeric values	Integer

Defining inputs

Define inputs as a map where keys are input names and values are input objects.

Example with all types:

inputs:
  model_type:
    type: string
    required: true
    description: Type of ML model to train

  debug_mode:
    type: bool
    default: false
    description: Enable debug logging

  epochs:
    type: int
    default: 100
    description: Number of training epochs

Input properties

Common properties

These properties are available for all input types.

type (required)

The data type of the input.

Type: "string" | "bool" | "int"

Example:

type: string

required (optional)

Whether this input must have a value.

Type: bool

Default: false

Behavior:

• If you provide a default, the input always has a value (satisfied by the default)
• If you don't provide a default, you must provide the input when triggering the workflow

Example:

required: true

description (optional)

Human-readable description of the input.

Type: string

Example:

description: Model architecture (xgboost, random-forest, etc.)

String-specific properties

String inputs support an additional secret property.

secret (optional)

Whether this input contains sensitive data that should be masked in logs and the UI.

Type: bool

Default: false

Availability: Only available for type: string

Example:

inputs:
  api_key:
    type: string
    secret: true

Type-specific defaults

Default values must match the input type.

String defaults:

environment:
  type: string
  default: "production"

Boolean defaults:

dry_run:
  type: bool
  default: false

Integer defaults:

max_retries:
  type: int
  default: 3

Using inputs

Reference inputs in expressions using ${{ .inputs.<name> }}. For more details, see Expressions.

In environment variables

inputs:
  model_type:
    type: string
    required: true

env:
  MODEL_TYPE: "${{ .inputs.model_type }}"

In shell commands

steps:
  - name: Train model
    run: python train.py --model ${{ .inputs.model_type }}

In Drive paths

upload:
  path: models/
  destination: "drive://bucket/${{ .inputs.model_type }}/models/"

Providing input values

Schedule triggers

Schedules provide input values through the inputs field:

inputs:
  dataset:
    type: string
    required: true

trigger:
  schedule:
    - cron: "0 0 * * *"
      inputs:
        dataset: full-training-set
    - cron: "0 12 * * *"
      inputs:
        dataset: validation-set

Manual triggers

When you manually start a workflow, provide input values through the trigger mechanism (UI, API, or CLI).

Type coercion in expressions

When you use inputs in expressions, they automatically convert to strings. This allows inputs of different types to work anywhere string values are expected.

Input type	Expression result
string	Used as-is
bool	"true" or "false"
int	Decimal string (for example, "100")

Example:

inputs:
  debug_mode:
    type: bool
    default: false

env:
  DEBUG: ${{ .inputs.debug_mode }}  # Result: "false"

steps:
  - run: |
      if [ "$DEBUG" = "true" ]; then
        echo "Debug mode enabled"
      fi

For more details on type coercion, see Expressions.

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