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Performance Benchmarks

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Test Environment

  • Hardware: 8 vCPU (Intel 13th Gen), 7.6 GB RAM
  • Stack: Docker Compose — PostgreSQL 16 + pgvector, FastAPI
  • Dataset: 1000 memories, 100 queries, 20 cross-agent queries (seeded, deterministic)
  • Concurrency: 10 concurrent clients
  • Embedding: OpenAI text-embedding-3-small (1536 dimensions)

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Results

Total: 1060 operations, 0% error rate.

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Key Findings

  • Writes scale well under concurrency — 85 ops/s at p50=100ms with 10 concurrent clients.
  • Query tail latency is OpenAI-bound — p95/p99 spikes on queries are dominated by the external embedding API call, not Aegis or PostgreSQL.
  • Votes and dedup are cheap — pure database operations with no embedding overhead, consistently under 75ms at p50.

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Reproduce

The harness generates a seeded dataset (--seed 42), captures machine profile, and writes results to results.json. Configure via environment variables:

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Benchmark Scripts

  • generate_dataset.py — Seeded JSONL dataset generator
  • query_workload.py — Async workload runner with latency percentiles
  • machine_profile.py — Captures hardware profile for reproducibility
  • run_benchmark.sh — End-to-end orchestrator