Implement Repeated Sub-Workflows With Loop Nodes

Loop Node Overview

The FastGPT Loop Node is a dedicated workflow building block that enables repeated execution of a nested sub-workflow. It supports two standardized use case patterns to streamline repetitive automation tasks. An official visual reference for the node is available at /imgs/fastgpt-loop-run-node.png in the FastGPT documentation library. This node eliminates manual duplication of workflow steps for repeated tasks, reducing configuration overhead for repetitive automation workflows.

Core Loop Functionality Modes

There are two native loop modes available within the Loop Node, aligned to common automation needs:

1. Array Loop: Processes each individual entry within a provided data array sequentially, running the attached sub-workflow once per array item. This mode is purpose-built for batch processing structured list data.
2. Conditional Loop: Executes the nested sub-workflow repeatedly, terminating only when a predefined success condition is satisfied. This mode supports iterative task refinement until output meets required quality or operational standards.

Step-by-Step Configuration Guide

This configuration workflow uses only native FastGPT Loop Node parameters as defined in official documentation:

1. Navigate to the workflow canvas in your FastGPT project, and add the Loop Node from the official component library.
2. Select your desired loop mode from the node's configuration menu: either Array Loop or Conditional Loop.
3. For Array Loop setups: Attach your pre-defined array data input to the node, then configure the nested sub-workflow to run for each individual array entry.
4. For Conditional Loop setups: Build the nested sub-workflow to include a condition check step (e.g., validating an AI draft quality score). Connect the condition check output to restart the sub-workflow until the predefined termination condition is satisfied.
5. Validate the configured workflow to confirm all connections are correctly aligned, then save the final workflow setup.

Typical Deployment Scenarios

The Loop Node is optimized for three common developer and enterprise automation scenarios: first, summarizing individual paragraph chunks from a long-form article to produce a consolidated, cohesive summary; second, iteratively refining an AI-generated draft until a specified quality threshold (e.g., score ≥80) is achieved; third, sequentially calling external APIs in batch to process multiple data entries without manual intervention.

Source: FastGPT official source

Applicability and version scope

Use this page for the documented Workflow nodes scenario. Confirm the FastGPT, dependency, API, and deployment versions in the official source before applying a change.

Safety guardrails

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Rollback guidance

Restore the prior technical-content authority snapshot. Restore saved configuration and data snapshots, then repeat the smallest verification scenario.