Multi-Node Workflow Example

This example demonstrates a complex workflow with multiple execution branches, parallel processing, conditional routing, and data merging. Perfect for advanced automation scenarios.

Workflow Overview

The workflow implements a data enrichment pipeline:

Webhook Trigger - Receives batch data requests
Split In Batches - Processes items individually
IF Node - Routes based on data type
Parallel Processing - Multiple API calls simultaneously
Merge Node - Combines enriched data
Set Node - Final formatting
Respond to Webhook - Returns processed results

Use Cases

Data enrichment pipelines
Multi-source data aggregation
Parallel API processing
Complex business logic workflows
ETL (Extract, Transform, Load) operations

Complete Workflow JSON

{
  "name": "Multi-Node Data Pipeline",
  "nodes": [
    {
      "id": "webhook-1",
      "name": "Webhook Trigger",
      "type": "n8n-nodes-base.webhook",
      "typeVersion": 2,
      "position": [250, 400],
      "parameters": {
        "httpMethod": "POST",
        "path": "enrich-data",
        "responseMode": "lastNode",
        "options": {}
      },
      "webhookId": "data-enrichment"
    },
    {
      "id": "split-1",
      "name": "Split In Batches",
      "type": "n8n-nodes-base.splitInBatches",
      "typeVersion": 3,
      "position": [450, 400],
      "parameters": {
        "batchSize": 5,
        "options": {}
      }
    },
    {
      "id": "if-1",
      "name": "Route By Type",
      "type": "n8n-nodes-base.if",
      "typeVersion": 2,
      "position": [650, 400],
      "parameters": {
        "conditions": {
          "options": {
            "caseSensitive": true,
            "leftValue": "",
            "typeValidation": "strict"
          },
          "conditions": [
            {
              "id": "condition-1",
              "leftValue": "={{$json.type}}",
              "rightValue": "user",
              "operator": {
                "type": "string",
                "operation": "equals"
              }
            }
          ],
          "combinator": "and"
        },
        "options": {}
      }
    },
    {
      "id": "http-user-1",
      "name": "Fetch User Data",
      "type": "n8n-nodes-base.httpRequest",
      "typeVersion": 4.2,
      "position": [850, 300],
      "parameters": {
        "method": "GET",
        "url": "https://api.example.com/users/{{$json.id}}",
        "authentication": "genericCredentialType",
        "genericAuthType": "httpHeaderAuth",
        "sendHeaders": true,
        "headerParameters": {
          "parameters": [
            {
              "name": "X-Request-ID",
              "value": "={{$workflow.id}}-{{$runIndex}}"
            }
          ]
        },
        "options": {
          "timeout": 10000,
          "retry": {
            "maxRetries": 3,
            "retryInterval": 1000
          }
        }
      },
      "credentials": {
        "httpHeaderAuth": {
          "id": "1",
          "name": "API Auth"
        }
      }
    },
    {
      "id": "http-metadata-1",
      "name": "Fetch Metadata",
      "type": "n8n-nodes-base.httpRequest",
      "typeVersion": 4.2,
      "position": [850, 400],
      "parameters": {
        "method": "GET",
        "url": "https://api.example.com/metadata/{{$json.id}}",
        "authentication": "genericCredentialType",
        "genericAuthType": "httpHeaderAuth",
        "options": {
          "timeout": 10000
        }
      },
      "credentials": {
        "httpHeaderAuth": {
          "id": "1",
          "name": "API Auth"
        }
      }
    },
    {
      "id": "http-company-1",
      "name": "Fetch Company Data",
      "type": "n8n-nodes-base.httpRequest",
      "typeVersion": 4.2,
      "position": [850, 500],
      "parameters": {
        "method": "GET",
        "url": "https://api.example.com/companies/{{$json.id}}",
        "authentication": "genericCredentialType",
        "genericAuthType": "httpHeaderAuth",
        "options": {
          "timeout": 10000
        }
      },
      "credentials": {
        "httpHeaderAuth": {
          "id": "1",
          "name": "API Auth"
        }
      }
    },
    {
      "id": "merge-1",
      "name": "Merge Data Sources",
      "type": "n8n-nodes-base.merge",
      "typeVersion": 3,
      "position": [1050, 400],
      "parameters": {
        "mode": "combine",
        "combinationMode": "mergeByPosition",
        "options": {}
      }
    },
    {
      "id": "set-1",
      "name": "Format Enriched Data",
      "type": "n8n-nodes-base.set",
      "typeVersion": 3.4,
      "position": [1250, 400],
      "parameters": {
        "mode": "manual",
        "assignments": {
          "assignments": [
            {
              "id": "assign-1",
              "name": "id",
              "value": "={{$json.id}}",
              "type": "string"
            },
            {
              "id": "assign-2",
              "name": "type",
              "value": "={{$json.type}}",
              "type": "string"
            },
            {
              "id": "assign-3",
              "name": "enriched",
              "value": "={{{
  user: $('Fetch User Data').item?.json,
  metadata: $('Fetch Metadata').item?.json,
  company: $('Fetch Company Data').item?.json
}}}",
              "type": "object"
            },
            {
              "id": "assign-4",
              "name": "processed_at",
              "value": "={{$now}}",
              "type": "string"
            },
            {
              "id": "assign-5",
              "name": "status",
              "value": "completed",
              "type": "string"
            }
          ]
        },
        "options": {}
      }
    },
    {
      "id": "loop-1",
      "name": "Loop Over Batches",
      "type": "n8n-nodes-base.splitInBatches",
      "typeVersion": 3,
      "position": [1450, 400],
      "parameters": {
        "batchSize": 5,
        "options": {}
      }
    },
    {
      "id": "aggregate-1",
      "name": "Aggregate Results",
      "type": "n8n-nodes-base.aggregate",
      "typeVersion": 1,
      "position": [1650, 400],
      "parameters": {
        "aggregate": "aggregateAllItemData",
        "options": {}
      }
    },
    {
      "id": "respond-1",
      "name": "Respond to Webhook",
      "type": "n8n-nodes-base.respondToWebhook",
      "typeVersion": 1,
      "position": [1850, 400],
      "parameters": {
        "respondWith": "json",
        "responseBody": "={{{
  total_processed: $json.length,
  results: $json,
  workflow_id: $workflow.id,
  execution_time: $executionTime
}}}",
        "options": {}
      }
    }
  ],
  "connections": {
    "Webhook Trigger": {
      "main": [
        [
          {
            "node": "Split In Batches",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Split In Batches": {
      "main": [
        [
          {
            "node": "Route By Type",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Route By Type": {
      "main": [
        [
          {
            "node": "Fetch User Data",
            "type": "main",
            "index": 0
          },
          {
            "node": "Fetch Metadata",
            "type": "main",
            "index": 0
          }
        ],
        [
          {
            "node": "Fetch Company Data",
            "type": "main",
            "index": 0
          },
          {
            "node": "Fetch Metadata",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Fetch User Data": {
      "main": [
        [
          {
            "node": "Merge Data Sources",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Fetch Metadata": {
      "main": [
        [
          {
            "node": "Merge Data Sources",
            "type": "main",
            "index": 1
          }
        ]
      ]
    },
    "Fetch Company Data": {
      "main": [
        [
          {
            "node": "Merge Data Sources",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Merge Data Sources": {
      "main": [
        [
          {
            "node": "Format Enriched Data",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Format Enriched Data": {
      "main": [
        [
          {
            "node": "Loop Over Batches",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Loop Over Batches": {
      "main": [
        null,
        [
          {
            "node": "Split In Batches",
            "type": "main",
            "index": 0
          }
        ],
        [
          {
            "node": "Aggregate Results",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Aggregate Results": {
      "main": [
        [
          {
            "node": "Respond to Webhook",
            "type": "main",
            "index": 0
          }
        ]
      ]
    }
  },
  "settings": {
    "executionOrder": "v1"
  },
  "active": false
}

Architecture Overview

Webhook → Split Batches → Route by Type
                              |
                              |-- User Type → [Fetch User + Fetch Metadata] → Merge
                              |                                                  |
                              |-- Company Type → [Fetch Company + Fetch Metadata] → Merge
                                                                                      |
                                                                        Format → Loop → Aggregate → Respond

Step-by-Step Breakdown

Batch Processing Setup

The Split In Batches node processes items in groups of 5 to prevent overwhelming external APIs.Benefits:

Memory efficient for large datasets
Respects rate limits
Progress tracking
Can resume on errors

Conditional Routing

The IF node routes data to different processing branches based on type.Routes:

TRUE branch (type=“user”) → User + Metadata API calls
FALSE branch (other types) → Company + Metadata API calls

Parallel API Calls

Multiple HTTP nodes execute simultaneously within each branch:

User branch: 2 parallel calls
Company branch: 2 parallel calls

Performance: 2x faster than sequential processing

Data Merging

The Merge node combines results from parallel API calls.Mode: Combine by position (preserves order)

Input 0: Primary data (User or Company)
Input 1: Metadata

Data Formatting

The Set node structures the enriched data into a consistent format using n8n expressions to reference multiple node outputs.

Batch Loop & Aggregation

The Loop node returns to process next batch, then Aggregate collects all results into final response.Output: Single array with all processed items

Advanced Patterns Explained

Parallel Execution

When an IF node has multiple connections on one output, they execute in parallel:

"Route By Type": {
  "main": [
    [
      {"node": "Fetch User Data"},
      {"node": "Fetch Metadata"}  // Executes simultaneously
    ]
  ]
}

Cross-Node References

The Set node accesses data from multiple previous nodes:

$('Fetch User Data').item?.json    // Access specific node output
$('Fetch Metadata').item?.json     // Optional chaining prevents errors

Important: Use $('Node Name') syntax to reference specific nodes. The ?. optional chaining prevents errors when a branch didn’t execute.

Batch Loop Pattern

The Loop Over Batches node has three outputs:

Output 0 (null) - Not used
Output 1 - Loop back to continue processing
Output 2 - Final output when all batches complete

Expected Response

{
  "total_processed": 4,
  "results": [
    {
      "id": "user-123",
      "type": "user",
      "enriched": {
        "user": {"name": "John Doe", "email": "[email protected]"},
        "metadata": {"created_at": "2024-01-15", "updated_at": "2024-03-10"},
        "company": null
      },
      "processed_at": "2024-03-15T10:30:00.000Z",
      "status": "completed"
    },
    {
      "id": "company-456",
      "type": "company",
      "enriched": {
        "user": null,
        "metadata": {"created_at": "2023-05-20", "updated_at": "2024-03-10"},
        "company": {"name": "Acme Corp", "industry": "Technology"}
      },
      "processed_at": "2024-03-15T10:30:01.000Z",
      "status": "completed"
    }
  ],
  "workflow_id": "wf-12345",
  "execution_time": "2.3s"
}

Performance Optimization

Adjust Batch Size

For faster processing with good APIs:

{"batchSize": 10}  // Process 10 items at once

For rate-limited APIs:

{"batchSize": 3}  // Smaller batches, more requests

Add Retries

All HTTP nodes include retry configuration:

{
  "options": {
    "retry": {
      "maxRetries": 3,
      "retryInterval": 1000
    }
  }
}

Add Rate Limiting

Insert a Wait node to control request rate:

{
  "name": "Rate Limit",
  "type": "n8n-nodes-base.wait",
  "parameters": {
    "amount": 100,
    "unit": "ms"
  }
}

Common Modifications

Add More Route Types

Extend the IF node conditions:

{
  "conditions": [
    {"leftValue": "={{$json.type}}", "operator": "equals", "rightValue": "user"},
    {"leftValue": "={{$json.type}}", "operator": "equals", "rightValue": "admin"}
  ],
  "combinator": "or"
}

Add Database Storage

Insert after Format Enriched Data:

{
  "name": "Save to Database",
  "type": "n8n-nodes-base.postgres",
  "parameters": {
    "operation": "insert",
    "table": "enriched_data"
  }
}

Add Error Handling

See Error Handling Example for comprehensive patterns.

Debugging Tips

Enable Save Execution Data

In workflow settings:

{
  "saveDataSuccessExecution": "all",
  "saveDataErrorExecution": "all"
}

Add Debugging Nodes

Insert Set nodes to inspect data:

{
  "name": "Debug Point",
  "parameters": {
    "assignments": {
      "assignments": [
        {"name": "debug_data", "value": "={{$json}}"},
        {"name": "node_name", "value": "={{$node.name}}"},
        {"name": "run_index", "value": "={{$runIndex}}"}
      ]
    }
  }
}

Monitor Performance

Use execution time metadata:

Check $executionTime in final node
Review execution logs
Track batch processing times

Next Steps

Implement Error Handling for production resilience
Build AI Agent Workflows with intelligent routing
Review Basic Workflows for foundational patterns

Resources

Examples

Multi-Node Workflow

Multi-Node Workflow Example

Workflow Overview

Use Cases

Complete Workflow JSON

Architecture Overview

Step-by-Step Breakdown

Advanced Patterns Explained

Parallel Execution

Cross-Node References

Batch Loop Pattern

Expected Response

Performance Optimization

Adjust Batch Size

Add Retries

Add Rate Limiting

Common Modifications

Add More Route Types

Add Database Storage

Add Error Handling

Debugging Tips

Next Steps

Build docs developers (and LLMs) love

Resources

Examples

​Multi-Node Workflow Example

​Workflow Overview

​Use Cases

​Complete Workflow JSON

​Architecture Overview

​Step-by-Step Breakdown

​Advanced Patterns Explained

​Parallel Execution

​Cross-Node References

​Batch Loop Pattern

​Expected Response

​Performance Optimization

​Adjust Batch Size

​Add Retries

​Add Rate Limiting

​Common Modifications

​Add More Route Types

​Add Database Storage

​Add Error Handling

​Debugging Tips

​Next Steps

Build docs developers (and LLMs) love

Multi-Node Workflow Example

Workflow Overview

Use Cases

Complete Workflow JSON

Architecture Overview

Step-by-Step Breakdown

Advanced Patterns Explained

Parallel Execution

Cross-Node References

Batch Loop Pattern

Expected Response

Performance Optimization

Adjust Batch Size

Add Retries

Add Rate Limiting

Common Modifications

Add More Route Types

Add Database Storage

Add Error Handling

Debugging Tips

Next Steps