Context Object Reference

The context object is the central runtime state container for every workflow execution. It stores inputs, intermediate data, node-level status, final outputs, and metadata needed for debugging and session rehydration.

Top-Level Structure

Every workflow execution has a single context object with three top-level categories:

Category	Key Examples	Description
Run Metadata	`startDate`, `status`, `flowType`, `appId`, `versionId`, `cfProcessId`	Identifiers and timestamps for the workflow instance.
Execution State	`steps`, `branch`, `activeSteps`, `status`	Progress and outcome data for each node.
Environment & Config	`env`, `agentTimeout`, `asyncExecDetails`	Runtime and environment parameters.

The `steps` Object

The steps object is a map that holds the execution state of every node in the workflow, indexed by node name.

"steps": {
  "NodeNameA": { ... },
  "NodeNameB": { ... },
  "NodeNameC": { ... }
}

Each node entry contains:

Field	Description
`input`	Data received by the node at execution time.
`output`	Result produced by the node — model outputs, processed data, or API responses.
`statusCode` / `isSuccessful`	Whether execution succeeded, failed, or was skipped.
`logs`	Optional log text or debug messages.
`executionTime`, `totalTime`, `pausedAt`, `resumedAt`	Node-level timing data for performance analysis.
`reqObj` / `resObj`	For AI nodes, the raw request and response envelopes.
`metadata`	Node-specific information such as schema references, task IDs, or external job handles.

Example node structure:

"steps": {
  "ExampleNode": {
    "input": {
      "parameters": { "param1": "value1" },
      "contextRefs": { "previousNodeOutput": "..." }
    },
    "output": {
      "result": "some value",
      "summary": "optional explanation",
      "structuredData": { "key": "value" }
    },
    "statusCode": 200,
    "isSuccessful": true,
    "logs": "Node executed successfully",
    "executionTime": "1250ms",
    "metadata": {
      "model": "gpt-5",
      "nodeType": "LLM",
      "dependencies": ["SchemaRetriever", "Sanitizer"]
    }
  }
}

How `steps` evolves during execution

The platform populates steps as the workflow runs:

Initialization — The steps object starts empty.
Node execution — An entry is created for each node as it runs.
Completion — Outputs and timing data are appended.
Error handling — Logs and status are updated; branch reflects failures.

Common Node Types

Node Type	Typical Fields	Purpose
Input/Start	`user_query`, `metadata`	Captures initial requests.
API	`body`, `statusCode`, `totalTime`	Fetches external data.
AI	`reqObj`, `resObj`, `modelElapsedTime`	Records prompt and response data.

Cross-Node Referencing

Nodes reference each other’s data using the {{ }} syntax. Values resolve at runtime. Example: Pass the schema output from one node as input to the next.

"steps": {
  "SchemaRetriever": {
    "output": { "schema": [ ... ] }
  },
  "QueryGenerator": {
    "input": {
      "schema": "{{steps.SchemaRetriever.output.schema}}"
    }
  }
}

Supported reference patterns

{{context.appId}}
{{context.steps.QueryGenerator.output.sql_query}}
{{context.schema[0].column_name}}
{{context.steps.Start.variable_name}}

context refers to the full context object.
steps provides scoped access to individual node data.
References work in input fields, API node parameters, LLM prompts, and conditions.

Evaluation behavior

Lazy evaluation — Values resolve immediately before the node executes.
Missing keys — If a referenced key doesn’t exist, the expression resolves to null or an empty string in string contexts.
Circular references — Automatically detected and blocked.

Extending Context with Script Nodes

Use Script Nodes to add or modify context keys dynamically. Any key you add is accessible in later nodes using {{context.<key>}}.

// Add a simple key-value pair
context.newKey = "Hello World";

// Store structured data
context.customData = {
  runId: context.cfProcessId,
  stage: "validation",
  timestamp: new Date().toISOString()
};

// Access it in later nodes
console.log(context.customData.stage);

Guidelines

Use camelCase naming — for example, customerInfo, retryCounter, tempResults.
Don’t override system-reserved keys: steps, branch, status, dbquery.
Keep data lightweight — avoid storing large arrays or raw API responses.

Loop Node Context

When a Loop Node executes, it creates its own context object that tracks iteration progress and results. This object is accessible throughout the workflow.

Structure

{
  "loopId": "string",
  "startTime": "timestamp",
  "iterationCount": "number",
  "inputs": ["array of input values"],
  "outputArray": ["array of iteration results"],
  "totalCount": "number",
  "childNodesExecuted": "number"
}

Field	Type	Description
`loopId`	String	Unique identifier for the loop node, matching the node ID in your flow.
`startTime`	Timestamp	When the loop started executing.
`iterationCount`	Number	Number of iterations completed so far.
`inputs`	Array	The original list of items the loop processes.
`outputArray`	Array of Objects	Results from each iteration, in execution order.
`totalCount`	Number	Total number of items to process (same as `inputs` length).
`childNodesExecuted`	Number	Total count of child nodes that ran across all iterations.

Accessing loop data

Inside the loop — Use iteration-scoped variables:

Variable	Description
`{{context.currentItem}}`	The item being processed in the current iteration.
`{{context.currentIndex}}`	Zero-based index of the current item.
`{{context.currentOutput[x]}}`	Output from a specific previous iteration.

Outside the loop — Access results using the loop node’s ID:

Variable	Description
`{{context.steps.Loop0001.output}}`	The complete array of all iteration results.
`{{context.steps.Loop0001.output[0]}}`	The result from a specific iteration.

Example: Processing a list of email addresses.

{
  "loopId": "Add2DB_0",
  "startTime": "2025-11-12T09:23:05.306Z",
  "iterationCount": 2,
  "inputs": ["user1@example.com", "user2@example.com"],
  "outputArray": [
    { "output": { "success": "user1@example.com added to the Employee Database" } },
    { "output": { "success": "user2@example.com added to the Employee Database" } }
  ],
  "totalCount": 2,
  "childNodesExecuted": 10
}

Best Practices

Use consistent node naming: Names like QueryGenerator or DataCleaner make context references traceable and readable.
Mask sensitive data: Don’t write credentials or PII directly to the context object.
Prefer declarative references: Use {{context.<key>}} syntax wherever possible. Reserve Script Nodes for computed or dynamic values.
Keep context data lightweight: Avoid storing large arrays or raw API payloads.

Building Workflows

Platform Services

Administration

References

Context Object Reference

Top-Level Structure

The `steps` Object

How `steps` evolves during execution

Common Node Types

Cross-Node Referencing

Supported reference patterns

Evaluation behavior

Extending Context with Script Nodes

Guidelines

Loop Node Context

Structure

Accessing loop data

Best Practices

Building Workflows

Platform Services

Administration

References

Documentation Index

​Top-Level Structure

​The steps Object

​How steps evolves during execution

​Common Node Types

​Cross-Node Referencing

​Supported reference patterns

​Evaluation behavior

​Extending Context with Script Nodes

​Guidelines

​Loop Node Context

​Structure

​Accessing loop data

​Best Practices

Top-Level Structure

The `steps` Object

How `steps` evolves during execution

Common Node Types

Cross-Node Referencing

Supported reference patterns

Evaluation behavior

Extending Context with Script Nodes

Guidelines

Loop Node Context

Structure

Accessing loop data

Best Practices