Iteration

Process arrays by applying workflows to each element

The Iteration node processes arrays by running the same workflow steps on each element sequentially or in parallel. Use it for batch processing tasks that would otherwise hit limits or be inefficient as single operations.

Iteration node processing workflow

How Iteration Works

The node takes an array input and creates a sub-workflow that runs once for each array element. During each iteration, the current item and its index are available as variables that internal nodes can reference.

Core Components:

• Input Variables - Array data from upstream nodes
• Internal Workflow - The processing steps to perform on each element
• Output Variables - Collected results from all iterations (also an array)

Configuration

Array Input

Connect an array variable from upstream nodes such as Parameter Extractor, Code nodes, Knowledge Retrieval, or HTTP Request responses.

Built-in Variables

Each iteration provides access to:

• items[object] - The current array element being processed
• index[number] - The current iteration index (starting from 0)

Processing Mode

Sequential Mode

Sequential Processing - Items processed one after another in order

Streaming Support - Results can be output progressively using Answer nodes

Resource Management - Lower memory usage, predictable execution order

Best For - When order matters or when using streaming output

Sequential vs parallel processing comparison

Enable parallel mode in iteration settings

Error Handling

Configure how to handle processing failures for individual array elements:

Terminate - Stop processing when any error occurs and return the error message

Continue on Error - Skip failed items and continue processing, outputting null for failed elements

Remove Failed Results - Skip failed items and return only successful results

Input-output correspondence examples:

• Input: [1, 2, 3]
• Output with Continue on Error: [result-1, null, result-3]
• Output with Remove Failed: [result-1, result-3]

Long Article Generation Example

Generate lengthy content by processing chapter outlines individually:

Long article generation workflow

Workflow Steps:

1. Start Node - User provides story title and outline
2. LLM Node - Generate detailed chapter breakdown
3. Parameter Extractor - Convert chapter list to structured array
4. Iteration Node - Process each chapter with internal LLM
5. Answer Node - Stream chapter content as it’s generated

Start node configuration for story input

Parameter extraction for chapter structure

Iteration configuration with LLM processing

Parameter extraction effectiveness depends on model capabilities and instruction quality. Use stronger models and provide examples in instructions to improve results.

Output Processing

Iteration nodes output arrays that often need conversion for final use:

Convert Array to Text

Using Code Node

def main(articleSections: list):
    return {
        "result": "\n".join(articleSections)
    }

Code node array conversion