Key concepts

GoRules uses a visual decision graph model. Understanding these building blocks helps you work effectively with the platform.

Decision graph

A decision graph is a visual canvas where you connect nodes to model decision logic. Data flows from an input node, through processing nodes, to an output node. Every decision graph has:

Input node — Receives the data you want to evaluate
Processing nodes — Transform and evaluate the data (decision tables, expressions, functions, switches)
Output node — Returns the final result (optional unless you need output validation)

You build graphs by dragging nodes onto the canvas and connecting them. Data flows through the connections, with each node receiving input from the previous node and passing output to the next.

Decision table

A decision table is a spreadsheet-like component for conditional logic. Rows define rules: conditions on the left, outcomes on the right. The engine evaluates rows top-to-bottom. By default, it returns the first matching row (first-hit policy). You can also collect all matching rows when needed. When to use: Conditional logic with multiple rules, lookup tables, classification, eligibility checks.

Unary tests vs standard expressions

When an input column has a field name defined, cells use unary test syntax — shorthand expressions evaluated against that field:

Operator	Example	Matches
Comparison	`> 100`, `<= 50`, `!= 0`	Values matching the comparison
Range (inclusive)	`[1..10]`	Values from 1 to 10
Range (exclusive)	`(0..100)`	Values between 0 and 100
List	`'US', 'GB', 'CA'`	Any value in the list
Combined	`> 5 and < 10`	Values matching both conditions
Any	(empty)	Matches any value

When an input column has no field name (empty), cells use standard expressions instead. This lets you write full expressions like customer.age > 18 and customer.country == 'US'.

Hit policies

Hit policies control how the engine handles multiple matching rows:

Policy	Behavior
First	Returns the first matching row (default)
Collect	Returns all matching rows as an array

Expression node

An expression node transforms data using the ZEN expression language. Use it for calculations, mappings, and data manipulation. Use $ to reference the current expression node’s output. In the example above, $.subtotal refers to the subtotal field calculated earlier in the same node.

The operators and functions below work throughout GoRules — in expression nodes, decision table cells, and switch conditions.

Operators

Type	Operators
Arithmetic	`+`, `-`, `*`, `/`, `%`, `^` (power)
Comparison	`==`, `!=`, `>`, `<`, `>=`, `<=`
Logical	`and`, `or`, `not`
Ternary	`condition ? then : else`
Null coalescing	`value ?? fallback`
Range check	`x in [1..10]`, `x not in (0..100)`

See Operators for the complete reference.

Built-in functions

Category	Functions
Math	`abs`, `floor`, `ceil`, `round`, `min`, `max`, `sum`, `avg`, `median`
String	`len`, `upper`, `lower`, `trim`, `contains`, `startsWith`, `endsWith`, `matches`, `split`
Array	`map`, `filter`, `some`, `all`, `one`, `none`, `count`, `flatMap`, `keys`, `values`
Date	`d()`, `duration`
Type	`string`, `number`, `bool`, `type`, `isNumeric`

See Built-in functions for the complete reference. When to use: Calculations, data transformation, mapping values, combining fields.

Function node

A function node runs custom JavaScript for complex logic that expressions can’t handle. Use it when you need external API calls, complex algorithms, or operations that require full programming language capabilities.

/** @type {Handler} **/
export const handler = async (input) => {
  const { customerId, orderTotal } = input;

  // Complex loyalty calculation
  const loyaltyPoints = Math.floor(orderTotal * 1.5);

  // Date-based promotions
  const today = dayjs();
  const isWeekend = today.day() === 0 || today.day() === 6;
  const bonusMultiplier = isWeekend ? 2 : 1;

  return {
    loyaltyPoints: loyaltyPoints * bonusMultiplier,
    earnedOn: today.format('YYYY-MM-DD')
  };
};

Function nodes support:

ES6+ JavaScript — Modern syntax including async/await
Built-in libraries — dayjs for dates, big.js for precision math, zod for validation
Async operations — Await promises for API calls or async logic

When to use: Complex algorithms, async operations, logic that requires full JavaScript capabilities.

Switch node

A switch node routes data through different paths based on conditions. It evaluates conditions in order and sends data down the first matching branch.

Each branch leads to its own chain of nodes, allowing you to build parallel processing paths that merge back into a single output. When to use: Conditional branching, routing logic, different processing paths based on input values.

How data flows

When you evaluate a decision, data flows through the graph:

Input — You provide a JSON object with your data
Processing — Each node receives data, processes it, and passes results forward
Output — The final node returns the decision result

Output node is optional and most often it’s not used. Without one, the engine returns the results from all endpoint nodes combined.

Pass-through behavior

By default, nodes use pass-through mode — they carry forward all incoming data plus their own outputs. This means downstream nodes can access both the original input and any values added by previous nodes.

Input: { customer: { tier: "gold" }, order: { total: 150 } }
     ↓
Decision Table adds: { discount: 0.15 }
     ↓
Output: { customer: { tier: "gold" }, order: { total: 150 }, discount: 0.15 }

You can disable pass-through on any node when you want to return only that node’s output fields. The → icon on a node indicates pass-through is enabled.

// Input
{
  "customer": { "tier": "gold", "yearsActive": 3 },
  "order": { "subtotal": 150, "items": 5 }
}

// Output (after flowing through the graph)
{
  "discount": 15,
  "freeShipping": true,
  "loyaltyBonus": 225
}

Nodes can access data from:

Direct input — Data passed directly into the node
Previous nodes — Output from upstream nodes in the graph

Referencing previous nodes

Use $nodes to access the output of any upstream node by its name:

$nodes.DiscountTable.discount      // Output from a node named "DiscountTable"
$nodes.RiskScore.level             // Output from a node named "RiskScore"
$nodes["My Node"].value            // Use brackets for names with spaces

This lets you build multi-stage decisions where later nodes use results from earlier ones. Example: Multi-stage loan decision

[Input] → [Credit Score] → [Income Check] → [Final Decision]

The Credit Score node outputs { rating: "good", score: 720 }. The Income Check node outputs { sufficient: true }. In the Final Decision expression node, you can combine both:

approved = $nodes.CreditScore.rating in ['good', 'excellent'] and $nodes.IncomeCheck.sufficient

In function nodes, access $nodes through the input parameter:

const creditRating = input.$nodes.CreditScore.rating;
const incomeOk = input.$nodes.IncomeCheck.sufficient;

Special symbols

The $ symbol has different meanings depending on context:

Symbol	Context	Meaning
`$`	Expression node	Reference to current node’s output (e.g., `$.subtotal`)
`$`	Unary test (decision table)	The field value being tested (e.g., `len($) > 5`)
`$nodes`	Any node	Access output from previous nodes
`$root`	Expression node	The entire context object
`#`	Array iteration	Current element in `map`, `filter`, etc.

JDM file format

Decision graphs are stored as JSON Decision Model (JDM) files. This portable format lets you:

Version control rules in Git
Move rules between environments
Share rules across applications
Edit rules programmatically

You don’t need to understand the JDM format to use GoRules — the visual editor handles it automatically. But if you want to work with rules programmatically, the format is documented in the JDM Format section.

Getting Started

Authoring

ZEN Language

Tutorials

Decision graph

Decision table

Unary tests vs standard expressions

Hit policies

Expression node

Operators

Built-in functions

Function node

Switch node

How data flows

Pass-through behavior

Referencing previous nodes

Special symbols

JDM file format

​Decision graph

​Decision table

​Unary tests vs standard expressions

​Hit policies

​Expression node

​Operators

​Built-in functions

​Function node

​Switch node

​How data flows

​Pass-through behavior

​Referencing previous nodes

​Special symbols

​JDM file format

Decision graph

Decision table

Unary tests vs standard expressions

Hit policies

Expression node

Operators

Built-in functions

Function node

Switch node

How data flows

Pass-through behavior

Referencing previous nodes

Special symbols

JDM file format