📊 n8n Workflow Health Check

Free Complexity Analyzer for n8n Workflows

Get maintainability scores, identify bottlenecks, and optimize your automations.

Health Score

Optimization Tips

Best Practices

Try Free Complexity Analyzer

What Is Workflow Complexity?

Workflow complexity measures how difficult your automation is to understand, maintain, and debug. Factors include node count, branching depth, number of external integrations, error handling coverage, and code node usage. High complexity isn't always bad, but it increases the risk of failures and makes troubleshooting harder.

Why Workflow Complexity Matters

Complex workflows are harder to debug, slower to execute, and more likely to fail. They consume more memory (leading to 'heap out of memory' errors), take longer to load in the editor, and are difficult to hand off to team members. Understanding complexity helps you decide when to refactor into smaller sub-workflows.

Managing Workflow Complexity

Keep workflows under 20-25 nodes when possible
Use sub-workflows for reusable logic
Avoid deep nesting of IF/Switch nodes (max 3-4 levels)
Add comments via sticky notes for complex sections
Process large datasets in batches to avoid memory issues
Use Execute Workflow node to break apart monolithic workflows

Frequently Asked Questions

What does the complexity score measure?

The score considers node count, branching depth (IF/Switch nesting), number of external integrations, Code node usage, error handling presence, and overall maintainability. A score of 1-3 is simple, 4-6 is moderate, and 7-10 indicates high complexity that may need refactoring.

Is high complexity always bad?

Not necessarily. Complex business processes require complex workflows. However, high complexity increases debugging difficulty, memory usage, and maintenance burden. The goal is appropriate complexity—not more than your use case requires.

When should I split a workflow into sub-workflows?

Consider splitting when you have more than 20-25 nodes, reusable logic that appears in multiple workflows, or distinct functional phases (like 'fetch data' vs 'process data' vs 'send output'). Sub-workflows improve maintainability and enable parallel execution.

What causes 'heap out of memory' errors?

Memory errors typically occur with very large workflows (50+ nodes), processing large datasets without batching, or multiple Code nodes with heavy computation. The complexity analyzer flags workflows at risk and suggests optimization strategies.

Is this analyzer free?

Yes, completely free with no signup required. We built this to help n8n users build maintainable automations. For workflow optimization consulting or refactoring help, we offer professional services.

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