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What are flow loops?

Flow loops allow you to process multiple items through the same series of steps automatically. Think of a loop as a container that takes a list of items—like rows in a spreadsheet or sentences in a document—and runs each item through the steps you've placed inside.

Real-world analogy: It's like having an assembly line worker who processes each item from a batch using the same procedure.

Key capabilities

Automatic splitting: Convert files, text, or data into individual items
Parallel or sequential processing: Process all items at once, or one-by-one with feedback
Flexible input: Works with spreadsheets, text files, comma-separated values, and more
Configurable limits: Control how many iterations run (1-20)

When to use loops

Use loops when you need to:

Process multiple items

Analyze each row in a spreadsheet
Translate multiple sentences
Generate summaries for multiple documents
Process a list of customer names or IDs

Iterate and improve

Refine a result based on previous attempts
Build upon previous outputs (e.g., write a story chapter-by-chapter)
Implement retry logic with learning

Batch operations

Send personalized emails to a list
Generate reports for multiple data points
Transform data in bulk

How loops work

The basic structure

A loop consists of three main components:

1. Input and splitting

The loop takes input data and splits it into individual items based on your chosen strategy (comma, rows, sentences, or paragraphs).

2. Processing steps

You add steps inside the loop container. Each item passes through these steps in sequence.

3. Output

The loop collects results and outputs them either all together (parallel processing) or one at a time with feedback (sequential processing).

Two types of processing

Parallel processing (standard output)

How it works: All items are processed simultaneously
Speed: Fast—items don't wait for each other
Best for: Independent tasks where items don't need to know about each other

Sequential processing (feedback output)

How it works: Items are processed one at a time, in order
Speed: Slower—each item waits for the previous one to complete
Best for: Tasks where later items build on earlier results

Splitting strategies

Comma

Use for: Comma-separated lists, CSV data. Each value becomes one item.

Rows

Use for: Spreadsheets, CSV files, line-separated text. Each row becomes one item.

Sentences

Use for: Natural language processing, text analysis, translation. Each sentence becomes one item.

Paragraphs

Use for: Document processing, long-form content analysis. Each paragraph becomes one item.

Understanding loop outputs

Loop output (standard)

Waits for ALL items to finish, combines all results. Use when processing items independently.

Iteration output (feedback)

Feeds each result back into the loop for the next iteration. Use when building upon previous results.

Best practices

Use Rows for structured data, Sentences for natural language, Comma for simple lists, Paragraphs for documents
Start with a low max iterations (5-10) for testing
Use standard output for independent items; feedback output only when items need previous results
Test with 2-3 items first before scaling up
Monitor costs: each iteration counts as a separate AI call