Performance Benchmark
August 14, 2026 ยท View on GitHub
The benchmark is deterministic, local, and does not read the user's memory database or call a model. Each of three runs creates 1,001 synthetic claims and executes 300 mixed Chinese/English queries after warm-up. The report uses the median metric across the three runs and also records the maximum observed p95.
Run it with:
npm run benchmark
The script measures both an in-memory SQLite database and a temporary database on the WSL filesystem. The pass threshold is 25 ms p95 for the complete recall path, including trigger search, capsule merge, CBDC decisions, and audit writes.
Representative Result
Environment: WSL, Node.js 22.22.1, 2026-08-14.
| Storage/path | mean | p50 | p95 | p99 |
|---|---|---|---|---|
| v1 baseline trigger search, memory | 2.445 ms | 2.151 ms | 3.657 ms | 4.076 ms |
| v1.1 trigger search, memory | 4.097 ms | 3.907 ms | 5.198 ms | 5.669 ms |
| v1.1 full recall + audit, memory | 4.126 ms | 3.885 ms | 5.478 ms | 5.972 ms |
| v1.1 trigger search, WSL disk | 4.208 ms | 4.015 ms | 5.343 ms | 5.859 ms |
| v1.1 full recall + audit, WSL disk | 8.801 ms | 8.512 ms | 11.151 ms | 13.701 ms |
The maximum observed WSL-disk full-recall p95 across the three runs was 11.663 ms. Both quality probes passed: a lightweight Chinese synonym query found the expected memory, and an unrelated query still received the trusted global preference through the bounded memory capsule.
Interpretation
Dual-channel matching adds about 2 ms p95 to the in-memory trigger search in this workload. The realistic WSL-disk full path remains around 11 ms p95, far smaller than normal model latency. Results vary by hardware and filesystem, so the checked-in script is the source of truth rather than this single run.