Logging
Coming soon
Lleverage automatically logs all workflow executions and their individual components, providing detailed insights into how your workflows run. This helps you monitor, debug, and optimize your AI workflows by tracking every step in real time.
Key Features of Logging
Automatic Logging: Every time a workflow is executed, Lleverage captures detailed logs, including the input, output, and status of each node in the workflow. This ensures full visibility into how your workflow operates.
Component-Level Logs: Logs are generated for each part of the workflow, from the execution of nodes to the transitions between them. This granular logging allows you to understand how data moves through the workflow and how each node behaves.
Error Tracking: If a node or workflow fails, logs will capture the error details, enabling you to quickly identify the issue and troubleshoot effectively.
Performance Insights: In addition to functionality, logs also capture execution times, allowing you to optimize your workflows by identifying any bottlenecks or delays in specific nodes or data processes.
How to Use Logging
View Workflow Logs: You can access the logs of each workflow execution through the Logs tab in your project. Each log entry contains detailed information about the nodes that were executed, their inputs and outputs, and the overall status of the workflow.
Node-Level Insights: For each node within a workflow, you can drill down into the logs to view the exact inputs, outputs, and any errors or warnings generated during execution. This helps you understand how individual components are performing.
Troubleshooting and Debugging: Logs allow you to identify where and why a workflow may have failed. Whether it's an incorrect input, a failed API call, or a timeout, the logs provide a clear path to resolving issues.
Optimization: By reviewing logs, you can identify slow-running nodes or inefficient data flows and optimize them for faster, more cost-effective execution.
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