Analytics & Monitoring

Lleverage automatically logs all workflow executions and their individual components, providing detailed insights into how your workflows run. This comprehensive logging and monitoring system helps you track, debug, and optimize your AI workflows in real-time.

Key Features

• Automatic logging of all workflow executions

• Component-level logs for detailed analysis

• Error tracking with specific failure points

• Performance insights with execution times

• Usage metrics and resource consumption tracking

• System health and uptime monitoring

How to Access Workflow Logs

1. Navigate to your project in Lleverage

2. Select the "Logs" tab in your project dashboard

3. Browse through the list of workflow executions

4. Click on a specific execution to view detailed information

5. Review the overall status, execution time, and workflow result

💡 Logs are automatically generated when you publish and run your workflows, requiring no additional setup.

How to Review Component-Level Logs

1. Open a specific workflow execution from the Logs tab

2. View the list of nodes that were executed during the workflow

3. Click on any node to expand its detailed logs

4. Examine the inputs received by the node

5. Review the outputs generated by the node

6. Check for any warnings or errors that occurred

💡 Component-level logs provide granular visibility into how data flows through your workflow and how each node processes that data.

How to Troubleshoot Using Logs

1. Identify the failed workflow execution in the Logs tab

2. Open the detailed view to see which specific node failed

3. Examine the error message and stack trace if available

4. Review the inputs that were provided to the failed node

5. Check for any upstream issues that might have caused incorrect inputs

6. Make necessary adjustments to your workflow based on the findings

⚠️ When troubleshooting complex workflows, start by checking error messages, then verify the data flow between nodes to identify potential issues.

How to Monitor Performance Metrics

1. Access the Monitoring section of your project

2. Review model performance metrics (accuracy, response time, efficiency)

3. Track usage metrics (frequency, types of tasks, request volume)

4. Analyze user feedback and error rates

5. Monitor system health and uptime statistics

6. Examine resource consumption during workflow execution

💡 Performance metrics help identify bottlenecks or inefficient components that may need optimization.

How to Use Analytics for Workflow Optimization

1. Review execution times for each node in your workflow

2. Identify nodes that take longer to execute than expected

3. Analyze patterns in workflow usage (peak times, common inputs)

4. Compare performance across different versions of your workflow

5. Use these insights to optimize slow-running nodes or inefficient data flows

💡 Regular analysis of workflow analytics can help you create more efficient, cost-effective automations.

Future Monitoring Enhancements

1. Enhanced visualizations of workflow execution paths

2. More detailed analytics for enterprise users

3. Expanded logging capabilities

4. Improved error tracking and resolution suggestions

5. Advanced performance optimization recommendations

⚠️ Lleverage is continuously improving its logging and monitoring capabilities. The available features may expand beyond what's documented here.