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🎓 Optimize Speed-Critical Workflows Using Parallel Processing (Fan-Out/Fan-In)

Lucas PeyrinLucas Peyrin
2343 views
2/3/2026
Content PublishingTranslationAutomationRSSTelegram
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How it works

This template is a hands-on tutorial for one of the most advanced and powerful patterns in n8n: asynchronous parallel processing, also known as the Fan-Out/Fan-In model.

When should you use this? Use this pattern when speed is your top priority and you have multiple independent, long-running tasks. Instead of running them one after another (which is slow), this workflow runs them all at the same time and waits for them all to finish.

We use a Construction Project analogy to explain the architecture:

  • The Main Workflow (Top): This is the Project Manager. It defines the project, assigns all the tasks to specialist teams, and then pauses, waiting for a final report.
  • The Sub-Workflow (Bottom): This represents the Specialist Teams. It's a single, reusable workflow that can perform any task it's assigned.
  • Static Data (The Brains): A hidden Project Dashboard is used to track the status of every task in real-time.

The process follows three key phases:

  1. Fan-Out: The Project Manager starts multiple sub-workflows at once without waiting for them to finish.
  2. Asynchronous Execution: Each Specialist Team works on its task independently and in parallel. When a team finishes, it updates its status on the Project Dashboard.
  3. Fan-In: The Project Manager, which has been paused by a Wait node, is only resumed when the Project Dashboard confirms that all tasks are complete. It then receives the aggregated results from all the parallel tasks.

Set up steps

Setup time: < 1 minute

This workflow is a self-contained tutorial. The only setup required is to configure the AI model.

  1. Configure Credentials:
    • Go to the The AI Specialist node in the sub-workflow (bottom flow).
    • Select your desired AI credential (Gemini in that case).
  2. Execute the Workflow:
    • Click the "Execute Workflow" button on the Start Project node.
  3. Explore and Learn:
    • Follow the execution path to see how the main workflow fans out, and how the sub-workflow is called multiple times.
    • Click on each node and read the detailed sticky notes to understand its specific role in this advanced pattern.

Optimize Speed-Critical Workflows Using Parallel Processing (Fan-Out/Fan-In)

This n8n workflow demonstrates a powerful pattern for optimizing speed-critical tasks: fan-out/fan-in parallel processing. It allows you to distribute a single task into multiple sub-tasks, process them concurrently using sub-workflows, and then aggregate the results. This approach significantly reduces the overall execution time for complex operations that can be broken down into independent units.

What it does

This workflow showcases how to:

  1. Initiate a task: It starts with a manual trigger, simulating the initiation of a speed-critical process.
  2. Define parallel sub-tasks: A "Code" node generates a list of items, each representing a sub-task to be processed in parallel.
  3. Fan-out to sub-workflows: The "Split Out" node takes the list of sub-tasks and sends each item individually to an "Execute Sub-workflow" node. This node then calls a separate, dedicated sub-workflow for each item, enabling parallel execution.
  4. Process sub-tasks concurrently: The sub-workflow (triggered by "When Executed by Another Workflow") simulates a processing step for each item. In this example, it uses an "AI Agent" with a "Google Gemini Chat Model" to perform a task, followed by a "Wait" node to simulate a time-consuming operation.
  5. Aggregate results (Fan-in): After all sub-workflows complete their processing, their results are automatically collected back into the main workflow.
  6. Conditional processing: An "If" node checks a condition on the aggregated results, demonstrating how to introduce conditional logic after parallel processing.
  7. Further processing: A "Switch" node and an "Edit Fields (Set)" node are included to show how to continue processing the combined results based on different conditions.
  8. External API interaction: An "HTTP Request" node is present, suggesting the possibility of interacting with external services as part of the overall process.

Prerequisites/Requirements

To run this workflow, you will need:

  • n8n instance: A running n8n instance.
  • Google Gemini Chat Model Credential: An API key or credentials for the Google Gemini Chat Model, configured within n8n.
  • AI Agent Configuration: The "AI Agent" node will require specific configuration, including tools and prompts, depending on the actual AI task you intend to perform.
  • Sub-workflow: A separate n8n workflow that is designed to be executed by the "Execute Sub-workflow" node. This sub-workflow should contain the logic for processing a single item from the fanned-out list.

Setup/Usage

  1. Import the workflow: Download the JSON provided and import it into your n8n instance.
  2. Create the sub-workflow: You will need to create a separate n8n workflow that starts with an "Execute Workflow Trigger" node. This sub-workflow should contain the "AI Agent" and "Wait" nodes as shown in the description above, or any other logic you want to execute in parallel.
  3. Configure "Execute Sub-workflow": In the main workflow, open the "Execute Sub-workflow" node and select the sub-workflow you created in the previous step.
  4. Configure Google Gemini Chat Model: Ensure your Google Gemini Chat Model credentials are set up correctly in n8n and selected in the "Google Gemini Chat Model" node within your sub-workflow.
  5. Configure "AI Agent": Adjust the "AI Agent" node in your sub-workflow with the desired prompt, tools, and model to perform your specific AI task.
  6. Adjust "Code" node: Modify the "Code" node to generate the actual data/items you want to process in parallel.
  7. Execute the workflow: Click "Execute Workflow" on the "Manual Trigger" node to start the process.

This workflow provides a robust framework for implementing parallel processing, allowing you to scale your n8n automations for speed-critical and resource-intensive operations.

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