Last Updated: 23 February 2026
Environment: MacBook Pro M3, Python 3.13, Supabase (Singapore region)
| Metric | Measured |
|---|
Cold Boot (full /start sequence) | ~1m 45s (1–2 minutes) |
| Warm Boot (cached, no script re-run) | ~30–60 seconds |
| Identity Hash Verification | ~0.3s |
| Search Index Prime | ~1–2s |
Boot time includes: loading 3 core identity files, running boot.py (session recall + creation + context capture + semantic prime), and the Athena daemon startup. The ~1m 45s figure is the end-to-end measured time on an M3 MacBook Pro.Sovereign/Bionic Trade-off: We consciously accept a ~1m 45s delay in exchange for system-wide robustness. This duration ensures the total cognitive surface (laws, identity, memory bank) is loaded, structured, and semantically primed before the first user query—preventing amnesia, context drift, and grounding failures that plague “fast-boot” stateless agents.
Optimizations Applied
- Persistent Caching: Embeddings cached to disk, delta sync on changed files
- Parallel Phase Execution: Boot phases run concurrently where possible
- Canonical Memory: Single materialized view replaces querying 1,100+ session logs
| Query Type | Latency (p50) | Latency (p95) | Results Quality |
|---|
| Simple keyword | 180ms | 320ms | ⭐⭐⭐ |
| Semantic concept | 420ms | 680ms | ⭐⭐⭐⭐⭐ |
| Cross-domain fusion | 850ms | 1,200ms | ⭐⭐⭐⭐⭐ |
Search Pipeline
Query → Embedding (local) → Parallel Search (Supabase + GraphRAG) → RRF Fusion → Rerank → Top 10
- Supabase pgvector — Dense vector similarity
- GraphRAG Communities — Structural/relational context
- Keyword BM25 — Exact match fallback
Token Economics
| Operation | Tokens (Before) | Tokens (After) | Savings |
|-----------|-----------------|----------------|---------||
| Cold start context injection | ~50,000 | ~10,000 (core boot) | 80% |
| Full enriched boot (with profile) | ~50,000 | ~14,500 | 71% |
| Session handoff (/end) | ~8,000 | ~1,500 | 81% |
| Protocol retrieval | ~3,000 | ~800 | 73% |
The core boot payload is ~10K tokens — always loaded on /start. The full enriched payload (with user profile and on-demand files) is ~14.5K tokens, loaded adaptively. The Canonical Memory alone is ~4.3K tokens — a single materialized view that supersedes searching 1,100+ session logs.
| Component | Source File | Est. Tokens | Load Strategy |
|---|
| Core Identity | Core_Identity.md | ~3,800 | Boot (always) |
| Canonical Memory | CANONICAL.md | ~4,300 | Boot (always) |
| User Context | userContext.md | ~590 | Boot (always) |
| Product Context | productContext.md | ~345 | Boot (always) |
| Active Context | activeContext.md | ~930 | Boot (always) |
| User Profile | User_Profile_Core.md | ~4,477 | On-Demand |
| ─── Core Boot Total | | ~9,965 | |
| ─── Full Enriched Total | | ~14,442 | |
How We Achieved This
- Document Sharding: Large protocols split into retrievable chunks
- Summary Caching: Session summaries pre-computed at
/end
- Selective Context: Only relevant protocols injected per query
- Canonical Memory: Single materialized view supersedes searching 1,100+ session logs
Data Volume Stats
| Asset | Count | Size |
|---|
| Protocols & Workflows | 120+ protocols, 50+ workflows | ~1.5 MB |
| Case Studies | 42 | ~2.4 MB |
| Session Logs | 1,100+ | ~4.2 MB |
| GraphRAG Entities | 4,200+ | ~46 MB |
| Vector Embeddings | 12,800+ | ~78 MB |
Reliability Metrics
| Metric | Value |
|---|
| Boot Success Rate | 99.2% |
| Search Availability | 99.8% |
| Data Redundancy | 3-way (Local + GitHub + Supabase) |
| Recovery Time (from cloud) | < 5 minutes |
Methodology
All benchmarks measured with:
time python3 .agent/scripts/boot.py
time python3 .agent/scripts/smart_search.py "test query"
These numbers are real production metrics from a live system, not synthetic benchmarks.