assignment - Frayme

assignment nodes are how you initialise variables. Each assignment has a name and an expression evaluated once and written to the top-level run context.

Configuration

{
  "id": "asg-1",
  "type": "assignment",
  "data": {
    "label": "Initialize Variables",
    "description": "Set up initial variables for the pipeline",
    "assignments": [
      { "name": "risk_score",         "expression": "0" },
      { "name": "rejection_reasons",  "expression": "[]" },
      { "name": "processing_start",   "expression": "datetime.now()" }
    ]
  }
}

Expression evaluation

Expressions are small Python-like literals or function calls. The supported surface is intentionally narrow:

Expression	Result
`0`, `'pending'`, `[]`, `true`, `false`	JSON literals
`datetime.now()`	ISO-8601 datetime string at evaluation time
`uuid()`	New v4 UUID
`{{some_variable}}`	Interpolation from the run context (rarely needed in assignment — use the variable directly downstream)

For anything more complex, prefer the code node.

Why initialise

Accumulator variables (rejection_reasons: []) that downstream nodes append to via code transforms.
Tracking timestamps (processing_start) for SLA calculations.
Status placeholders (kyc_status: 'pending') that change as the workflow advances.

A simpler workflow can skip the assignment node entirely and let rules + decision tables write directly to the top-level context — more compact, fewer moving parts.

Rule

rule is the workhorse downstream of assignment — single-pass first-match conditions producing named output fields. See the rule node.