dag-performance-profiler

DAG performance profiling requires collecting a trace with dag-execution-tracer and feeding it to the profiler to generate a report. It analyzes latency, token usage, cost, and resource consumption across individual nodes to surface actionable bottlenecks.

DAG profiling metrics cover end-to-end latency, token usage, cost computation, and resource consumption. Per-node metrics collection allows granular visibility into AI agent pipeline workflows to pinpoint specific execution bottlenecks.

Cost bottleneck detection in DAG workflows is achieved by computing per-node costs during profiling and generating optimization recommendations. The profiler provides cost-aware analysis and parallelization guidance to reduce overall DAG execution expenses.

Dag-execution-tracer is required to collect the representative DAG trace before profiling. You run the tracer on your DAG workflow, then feed the collected trace data into the profiler to generate the performance report.

Parallelization guidance for DAG workloads is generated by analyzing per-node latency and resource usage metrics. The profiler detects sequential bottlenecks and provides actionable optimization recommendations to improve DAG throughput.

DAG profiling operates without external dependencies or components. The profiler independently processes trace data to compute costs, detect bottlenecks, generate optimizations, and present a performance report for your AI agent pipelines.