🎓 Optimize Speed-Critical Workflows Using Parallel Processing (Fan-Out/Fan-In)
Ready for 3.0Ready for 3.0. This template uses no nodes that n8n 3.0 removes. No removed nodes, versions or helpers found. Static check, last run 2026-10-19.
What this workflow does
This AI workflow has 18 nodes and runs manually, when you click “Execute workflow” (plus 1 other trigger). Along the way it connects Google Gemini Chat Model.
How it runs
Trigger: Manually, when you click “Execute workflow” (plus 1 other trigger).
- The Project Brief (Edit Fields (Set)): sets 1 field: tasks
- Check Work Order Status (If): checks a condition and branches into true / false
- Split Out: splits a list into separate items
- The Dispatcher (Switch): routes items into 3 branches
- The Project Dashboard (Code) (Code): runs custom JavaScript code
- Assign Tasks to Teams (Execute Sub-workflow): calls another n8n workflow
- Description Team (AI Agent): runs an AI agent, with model: The AI Specialist
- Ad Copy Team (AI Agent): runs an AI agent, with model: The AI Specialist
- Email Team (AI Agent): runs an AI agent, with model: The AI Specialist
- Is Project Complete? (If): checks a condition and branches into true / false
…plus 5 more steps. See the full node list below.
Services it uses
Before you upgrade to n8n 3.0
Nothing in this workflow is removed in n8n 3.0, so it should import and run after the upgrade.
Nodes used (12)
- Manual Trigger
n8n-nodes-base.manualTrigger - Switch
n8n-nodes-base.switch - Split Out
n8n-nodes-base.splitOut - Edit Fields (Set)
n8n-nodes-base.set - Wait
n8n-nodes-base.wait - Execute Sub-workflow
n8n-nodes-base.executeWorkflow - Execute Workflow Trigger
n8n-nodes-base.executeWorkflowTrigger - Google Gemini Chat Model
@n8n/n8n-nodes-langchain.lmChatGoogleGemini - AI Agent
@n8n/n8n-nodes-langchain.agent - Code
n8n-nodes-base.code - If
n8n-nodes-base.if - HTTP Request
n8n-nodes-base.httpRequest
From the author
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…