trace-qa

To debug an agent execution trace, you can analyze specific trace IDs to inspect individual steps, LLM calls, and tool usage. This allows you to pinpoint the exact step and reason for failure by examining detailed inputs, outputs, and errors.

The best way to analyze LLM calls for performance review is to inspect the agent execution trace. You can analyze token consumption per LLM call to identify areas for optimization and get an overview of trace duration and tool calls.

Yes, you need Python scripts to fetch and segment trace data from a local API endpoint. The skill requires these scripts to properly retrieve and process the local trace data for detailed analysis and debugging.

Yes, you can drill down into specific steps or LLM calls within an agent trace to inspect tool usage, inputs, and outputs. This detailed inspection helps understand what happened during an agent run and locate errors.

An agent execution trace summary includes the overall trace status, duration, token usage, and tool calls. This overview provides a quick snapshot of the agent run's efficiency and execution path.

You can review agent run efficiency through trace analysis by examining the duration and token consumption per LLM call. This helps identify areas for optimization and understand the overall performance of the agent execution.