Workflow Execution

Understanding how workflows execute helps you build reliable automations and debug issues.

Running Workflows

Manual Execution

1. Open the workflow in the editor
2. Click Execute Workflow in the toolbar
3. Watch the execution progress in real-time

Triggered Execution

Workflows run automatically when their trigger fires:

• Schedule triggers run at the configured time
• Webhook triggers run when the endpoint is called
• Error triggers run when a monitored workflow fails

Execution Flow

1. Trigger fires - The workflow starts
2. Nodes execute - Each node runs in sequence
3. Data flows - Output from one node becomes input for the next
4. Branching - Logic nodes route data to different paths
5. Completion - Workflow finishes (success or error)

Real-Time Monitoring

During execution, the editor shows:

• Green highlight - Currently executing node
• Checkmark - Successfully completed node
• Red X - Failed node
• Data count - Number of items processed

Execution History

View past executions in the Executions page:

Viewing Execution Details

Click an execution to see:

• Data at each node
• Error messages (if any)
• Timing breakdown
• Full execution path

Execution States

Error Handling

When Nodes Fail

By default, a node failure stops the workflow. You can configure:

• Continue on fail - Skip the failed node and continue
• Retry - Attempt the node again
• Error workflow - Trigger a separate error-handling workflow

Common Errors

Debugging Workflows

Check Node Output

1. Click on a node after execution
2. View the output data in the panel
3. Verify the data matches expectations

Use Data Preview

Add a Data Preview node to inspect data at any point without affecting the flow.

Test Incrementally

1. Build your workflow step by step
2. Execute after adding each node
3. Verify output before continuing

Check Expressions

If expressions aren't working:

1. Verify the source node executed successfully
2. Check the exact field path
3. Use the expression editor's autocomplete

Performance Tips

Optimize Data Flow

• Filter data early to reduce processing
• Use Split/Merge for parallel processing
• Avoid unnecessary data transformations

Handle Large Datasets

• Process in batches when possible
• Use pagination for API calls
• Consider memory limits

Reduce API Calls

• Cache responses when appropriate
• Batch requests when APIs support it
• Use webhooks instead of polling

Execution Limits