openclaw-test-performance

To benchmark OpenClaw plugin runtime performance, you measure wall time, CPU usage, and RSS memory consumption across test suites. This diagnostic process identifies high-latency imports and CPU-intensive operations to pinpoint exact dependencies causing regressions.

CI test time spikes when a new OpenClaw plugin introduces high-latency imports or memory leaks. Generating an import breakdown and memory profile distinguishes between real memory leaks and retained module graphs to isolate the problematic dependency.

Identifying import hotspots in a Vitest integration workflow requires measuring import durations and tracking heap growth patterns. This diagnostic analysis targets plugin-inspector data to distinguish retained module graphs from active memory leaks.

Yes, performance benchmarking supports package-boundary validation by measuring wall time and RSS memory consumption during plugin composition. Collecting precise metrics ensures efficient resource management across development workflows involving validated package boundaries.

The best way to distinguish real memory leaks from retained module graphs in OpenClaw is diagnostic analysis of heap growth patterns. Tracking RSS memory consumption alongside import durations reveals whether memory retention stems from leaks or module graph retention.

No specific external dependencies are required to measure RSS and import durations for OpenClaw plugins. The benchmarking process operates independently to collect wall time, CPU usage, and memory utilization metrics within your existing development workflow.