Architecture: Can Vitto achieve Jarvis-like persistent intelligence across...

Documentation for Sessions

🏗️ Architecture: Can Vitto achieve Jarvis-like persistent intelligence across...

Date: 2026-02-27
Time: 20:16:14 UTC
Session: gspace1-150.10
Agent: backend
Confidence: 88%
Status: ✅ Documented

📋 Decision Context

Question:

Can Vitto achieve Jarvis-like persistent intelligence across sessions, and what are the critical architectural gaps?

Task: Analyze Vitto Jarvis-like memory architecture feasibility

🎯 Decision Made

Chosen:

Yes, with 5 specific gaps to close: (1) Importance decay on memories (exponential, based on daysSinceLastAccess), (2) Request-aware cognitive load budgeting (classify request → budget tokens → retrieve strategically), (3) Auto-generated User Brief per user (200-token daily summary replacing raw memory dumps), (4) Knowledge graph for multi-hop reasoning (entities + typed relationships via MongoDB $graphLookup), (5) VM-as-memory integration (Chrome cookies/logins as a physical memory layer outside the context window). Current system is B+ — tiered recall, vector search, pattern promotion work well. The persistent VM on Hetzner is the missing physical layer that no competitor offers.

Options Considered:

  1. 5-gap fix roadmap (chosen)
  2. Full knowledge graph rewrite
  3. MemGPT-style self-editing memory
  4. Just increase context window and stuff everything in
  5. Keep current system as-is

💭 Reasoning

ROOT CAUSE ANALYSIS: The fundamental constraint is that LLMs are stateless — every API call starts from zero. The 'always on' feeling is an illusion built from memory injection. Current system injects ~2,000-3,000 tokens of context per call (user identity, memories, rules, patterns, decisions). Claude's 200K context window has room, but a user with 6 months of history has ~50,000 lines of context that cannot all fit. WHAT EXISTS (B+ grade): 3-tier memory (hot/warm/cold), vector embeddings (1536-dim OpenAI), execution traces (90-day TTL), strategy memories, Intelligence Pyramid (rules→patterns→decisions), memory consolidation (7-day merging). WHAT'S MISSING: (a) No importance decay — memories stay at initial importance forever, hot cache dominated by stale high-importance items after months; (b) No request-aware budgeting — same context injected whether user asks about TypeScript or their weekend; (c) Vector search is 1-hop only — misses tangential relevance (checkout→payment→Stripe→webhooks); (d) No pre-computed User Brief — raw memories injected instead of a 200-token summary; (e) VM Chrome state not queryable as memory. COMPETITORS: MemGPT/Letta self-edits memory (corruption risk), ChatGPT has flat fact list, no one combines persistent VM + knowledge graph + tiered recall. CONFIDENCE: 0.88 — high on the diagnosis, medium on the specific solutions (need validation with real user data).

📁 Files Involved

🔗 Related

Auto-generated by MCP Smart Consolidation