Debugging Workflows
Learn how to identify and fix issues in your workflows.
Execution History
Section titled “Execution History”Every workflow run is logged in the execution history.
Viewing History
Section titled “Viewing History”- Open your workflow
- Click the History tab
- See all past executions with status
Execution Details
Section titled “Execution Details”Click any execution to see:
- Start time and duration
- Trigger data that started the run
- Each node’s input and output
- Errors with stack traces
- Final status and output
Execution States
Section titled “Execution States”| State | Icon | Description |
|---|---|---|
| Running | 🔵 | Currently executing |
| Completed | ✅ | Finished successfully |
| Failed | ❌ | Encountered an error |
| Cancelled | ⚪ | Manually stopped |
| Waiting | ⏸️ | Paused (delay or waiting) |
Common Issues
Section titled “Common Issues””Node failed: Agentic Node”
Section titled “”Node failed: Agentic Node””Cause: AI agent processing error
Debug steps:
- Check the task description for issues
- Verify input context exists and has values
- Check if inputs are too long
- Review the agent configuration
Solutions:
- Simplify the task description
- Check the agent’s instructions and tools
- Break into multiple smaller nodes
”Trigger not firing”
Section titled “”Trigger not firing””Cause: Configuration issue
Debug steps:
- Verify the workflow is Published
- Check trigger configuration
- For scheduled workflows: check timezone settings
- Check the execution history for any runs
”Context data is missing”
Section titled “”Context data is missing””Cause: Previous node didn’t output expected data
Debug steps:
- Check if previous nodes completed successfully
- Verify the previous node’s output structure
- Check execution history for node outputs
- Look for null/empty values in context
”Workflow is slow”
Section titled “”Workflow is slow””Cause: Performance issues
Debug steps:
- Check execution history for timing per node
- Identify the slowest nodes
- Look for unnecessary sequential operations
- Check for large data transfers
Solutions:
- Parallelize independent operations
- Reduce data transferred between nodes
- Cache repeated lookups
- Optimize AI prompts to be more concise
Logging
Section titled “Logging”Execution Logs
Section titled “Execution Logs”View detailed execution information:
- Open execution history
- Click an execution
- View node execution details, inputs, and outputs
Error Handling
Section titled “Error Handling”When nodes fail:
- Errors are logged in the execution history
- You can view detailed error messages and stack traces
- Failed nodes are marked in the workflow execution view
- Workflows may continue or stop depending on configuration
Testing Best Practices
Section titled “Testing Best Practices”- Test with realistic data: Use production-like test data
- Test edge cases: Empty arrays, null values, very long strings
- Test error paths: Intentionally trigger errors
- Test timing: Run at different times if schedule-dependent
- Test with fresh state: Don’t rely on leftover test data
Monitoring
Section titled “Monitoring”Track workflow execution:
- Execution history: View all runs with status and duration
- Success rate: Monitor successful vs failed runs
- Performance: Track execution time per node
- Error frequency: Identify patterns in failures
Getting Help
Section titled “Getting Help”If you can’t resolve an issue:
- Check the FAQ
- Search the Troubleshooting guide
- Contact support with:
- Workflow ID
- Execution ID
- Steps to reproduce
- Expected vs actual behavior