Based on a tutorial by n8n Official
Struggling with workflow errors in n8n? You're not alone. Managing errors effectively is crucial for maintaining reliable automations that run smoothly without constant supervision.
I've summarized this comprehensive tutorial to help you understand how n8n stores execution histories and implement proper error handling strategies that will make your workflows more robust.
Quick Navigation
How n8n Stores Workflow Executions
When you're working with n8n, understanding how executions are stored is the first step to effective error handling.
Key Points:
- By default, only activated workflow executions (production) are saved in the execution log
- Manual executions are not saved by default, but you can enable logging for them on a per-workflow basis
- Access execution history by clicking on "All Executions" in the n8n interface
- You can filter executions by workflow, status, date ranges, or execution data
My Take:
I recommend enabling logging for manual executions during development and testing phases. This creates a clear record of your testing process that will be invaluable when troubleshooting issues later.
Understanding Execution Histories
Execution histories provide a snapshot of how your workflow performed, making them essential for debugging and optimization.
Key Points:
- Each execution history shows the final state of all nodes from that execution
- Histories are static snapshots and cannot be changed
- Double-click any node to view its input data, output data, and settings
- For nodes with errors, double-clicking will also reveal detailed error information
My Take:
Get in the habit of regularly reviewing execution histories, even for successful runs. This practice helps you understand typical data patterns and makes it easier to spot anomalies when they occur.
Types of Error Handling
When workflows fail, you need a strategy to identify and address these failures quickly to maintain reliable automations.
Key Points:
- The Error Workflow is a special workflow that executes whenever another workflow encounters an error
- Error workflows need to be configured for each workflow you create
- The Stop and Error node lets you deliberately raise errors for specific conditions
- Error triggers capture details like workflow name, execution ID, error message, and links to the failed execution
My Take:
Error workflows are not just for notification—they're your first line of defense against workflow failures. I recommend setting up a dedicated communication channel (like Slack or Teams) specifically for workflow errors to ensure they're addressed promptly.
Building an Error Workflow
Creating a basic error workflow requires just a few steps but provides immense value for maintaining your automations.
Key Points:
- Start with an Error Trigger node that captures error information
- Connect the trigger to a notification channel (like Slack, Email, or Teams)
- Include key information in your notifications: workflow name, execution URL, and error message
Error message example:
n8n error:
Workflow: [Workflow Name]
Execution URL: [URL to failed execution]
Error message: [Specific error message]
My Take:
For larger teams, consider extending your error workflow to tag specific team members based on which workflow failed. This ensures the right person is notified immediately rather than requiring someone to triage each error.
Practical Error Handling Example
The tutorial demonstrates practical error handling with a real workflow example that processes webhook data.
Key Points:
- Implement data validation checks before processing (e.g., verifying email format)
- Use the Stop and Error node to halt execution with custom error messages
- Configure error behavior in node settings: stop workflow, continue execution, or continue with error output
- Handle potential edge cases like missing data or invalid event types
// Example of simple email validation
// In an Function node:
return {
json: {
emailValid: $input.item.json.email && $input.item.json.email.includes('@')
}
}
My Take:
I recommend creating a dedicated "validation" section at the beginning of your workflows. This approach catches data issues early before they propagate through multiple nodes, making debugging much simpler.
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