Agentic OS — Design Specs
Pillar-level design specifications for the ALAI Agentic OS v1 hardening program (MC #99063). Includes Pillar #3 L3 Memory and Pillar #9 VM Runtime specs.

Pillar #3 — L3 Memory Framework Comparison Spec (2026-05-04)
Agentic
OS — Pillar #3 L3 Memory Framework Comparison Spec 
 MC: #99124 Status: DESIGN
(read-only; no infra changes) Date: 2026-05-04
 Parent: MC #99063 (Agentic OS v1 hardening)
 Output: 
/Users/makinja/system/specs/agentic-os-pillar3-l3memory-2026-05-04.md
 Evidence artifact: /tmp/forged-99124-evidence.jsonl (42
records, ≥40 required) Forged prompt: 
/Users/makinja/system/prompts/forged/99124.md Mehanik
clearance: [CEO_APPROVED] (dispatch token from
orchestrator) 
 
 §0 — Frontmatter 
 Scope: Comparative evaluation of five L3 memory
framework candidates for ALAI John orchestrator. Declares singular
winner + named runner-up. Produces 5-step migration plan and 20-query
multilingual validation harness. 
 Frameworks evaluated (5, closed set): 1. Mem0
self-hosted (incumbent, deployed) 2. claude-mem (installed, not primary)
3. mem-search (researched) 4. Memipalace (researched) 5.
LightRAG-resurrect (existing VM) 
 CEO-locked constraints (source:
project_99063_pillar9_pillar7_scope_2026-05-04.md): - Budget:
$30/month soft combined Pillar #9 + Pillar #3 (“može više” with CEO
gate) - Auth model: OAuth Claude Max subscription only (no API tokens) -
Multi-client scope: SVE — all ALAI products + all active clients - EU
residency: no SaaS memory backends 
 Incumbent pre-commitment: 
 stop-hook-l3-memory-spec.md (MC #99071) pre-selected Mem0.
This spec defends or overrides that commitment with evidence. 
 
 §1 — Executive Summary 
 Mem0 self-hosted is confirmed as the L3 memory winner. The case for
migration to any alternative collapses on three grounds: (1) Mem0 is
already deployed with 865 facts, a running LaunchAgent, discover.js
wiring, and a Phase 1 recall@10 baseline of 80%; (2) the two remaining
viable alternatives (claude-mem and LightRAG-resurrect) each fail a hard
gate before reaching the merit comparison; and (3) mem-search and
Memipalace do not exist as installable software packages — both are
generic category labels from the source YouTube video. 
 claude-mem (runner-up) provides complementary BM25
session-observation indexing and costs $0, but lacks semantic recall,
vector storage, and multi-user isolation required for the multi-client
SVE scope. It belongs in the L3 fallback chain as L3a (already wired in
discover.js) but cannot replace Mem0 as the primary semantic memory
backend. 
 LightRAG-resurrect fails two hard gates: MC #99093 (file_path
metadata fix) is open and unresolved, meaning 121,003 of 127,543
documents are in pending status and not queryable; and the asyncio
event-loop starvation root cause (documented in
lightrag-freeze-decision-chip.md) requires a non-trivial Semaphore(2)
patch before any production write load is safe. 
 The existing stop-hook-l3-memory-spec.md Mem0 pre-commitment is
DEFENDED. The Phase 2 activation checklist (session-extract.js + Stop
hook) remains the correct next step. 
 Combined L3 incremental cost: $0/month (all backends local: Qdrant
port 6333, Ollama port 11434, Mem0 server port 9000). This leaves the
full $30/month envelope for Pillar #9 VM. 
 
 §2 — Current State
(Machine-Verified 2026-05-04) 
 All probes executed 2026-05-04T21:07-21:14Z. No session-context
citations. 
 §2.1 — LightRAG VM Probe 
 Probe: 
 curl -s --max-time 10 http://20.240.61.67:9621/health 
 Result (2026-05-04T21:07Z): 
 status: healthy
pipeline_busy: false
core_version: 1.3.4
api_version: 0154
llm_binding: ollama
llm_binding_host: https://ollama.basicconsulting.no
llm_model: qwen3:8b-q8_0
embedding_binding: ollama
embedding_binding_host: https://ollama.basicconsulting.no
embedding_model: bge-m3:latest
graph_storage: Neo4JStorage
vector_storage: NanoVectorDBStorage
kv_storage: JsonKVStorage
enable_llm_cache: true
auth_mode: disabled 
 Document corpus probe (az vm run-command,
2026-05-04T21:14Z): 
 total_docs: 127,543
status_pending: 121,003
status_processed: 5,596
status_failed: 944
unknown_source_count: 40,330
unknown_ratio_pct: 31.6% 
 Interpretation: Effective recall corpus = 5,596
processed docs only. The 121,003 pending docs are not yet extractable
via graph/entity search. 31.6% of all submitted docs carry
 file_path=unknown_source — below the 70% threshold that
would require the spec warning per D5, but AC6 of MC #99079 remains
PARTIAL because the 30% bookstack_url target is unreachable. EVIDENCE:
az vm run-command python3 count 2026-05-04T21:14Z →
unknown_source_count:40330 
 §2.2 — discover.js Memory Query
State 
 Probe: 
 grep -n "DISCOVER_USE_FALLBACK_CHAIN" /Users/makinja/system/tools/discover.js 
 Result: 
 line 58: // Feature-flagged: DISCOVER_USE_FALLBACK_CHAIN=1 to enable (default OFF)
line 60: const USE_FALLBACK_CHAIN = process.env.DISCOVER_USE_FALLBACK_CHAIN === '1';
line 793: // Activated when DISCOVER_USE_FALLBACK_CHAIN=1
line 1228: // L3 Fallback Chain (MC #99071, DISCOVER_USE_FALLBACK_CHAIN=1) 
 Status: L3 fallback chain (claude-mem → Mem0 →
LightRAG) is implemented in discover.js but NOT activated in production.
Default is OFF. Session-start mode (lines 1190-1200) calls searchMem0
directly for boot injection. EVIDENCE:
/Users/makinja/system/tools/discover.js lines 58-60 (file confirmed on
disk) 
 §2.3 — MEMORY.md Auto-Write
Status 
 Probe: 
 ls -la /Users/makinja/.claude/projects/-Users-makinja/memory/MEMORY.md 
 Result (2026-05-04T21:08Z): 
 -rw-r--r-- 1 makinja staff 19150 4 mai 21:02 MEMORY.md 
 Status: MEMORY.md is manually maintained (19,150
bytes, last written 21:02 same day). Auto-write gap is NOT closed —
session-extract.js (stop hook) is not yet activated.
stop-hook-l3-memory-spec.md §Implementation Details: “NOT yet added to
settings.json Stop hooks array. Phase 2 activation.” EVIDENCE: file size
+ mtime confirmed; stop-hook-l3-memory-spec.md line 35 
 §2.4 — Existing Mem0 Footprint 
 Probes executed: 
 Qdrant collection: 
 curl http://localhost:6333/collections/mem0_john
points_count: 865
indexed_vectors_count: 0 (below HNSW threshold 10,000 — full scan active)
vector_size: 1024 (Cosine)
status: green 
 EVIDENCE: curl http://localhost:6333/collections/mem0_john
2026-05-04T21:07Z 
 Mem0 server health: 
 curl http://localhost:9000/health
{status: healthy, backend: qdrant, llm: qwen3:8b-q8_0@ollama, embedder: bge-m3@ollama,
collections: [mem0migrations, sessions, hivemind, mem0_john, knowledge]} 
 EVIDENCE: curl http://localhost:9000/health 2026-05-04T21:07Z 
 LaunchAgent status: 
 com.alai.mem0-server: mode:keepalive, state:running, pid:65706, last_exit:15 
 EVIDENCE: /Users/makinja/system/state/daemon-fleet-status.json grep
com.alai.mem0-server → state:running pid:65706 last_exit:15 
 last_exit=15 investigation: Exit code 15 = SIGTERM
(Unix signal). Server is KeepAlive=true, so launchd sends SIGTERM before
restarting on crash/update. Server log confirms BrokenPipeError at
00:53:08 on 2026-05-04 during LLM extraction (Ollama server
disconnected). This is a transient Ollama overload event, not a
persistent server defect. Server resumed and is currently healthy (PID
65706, /health returns 200). No action required for MC #99124. EVIDENCE:
/Users/makinja/system/mem0/server.log tail-30 → 2026-05-04 00:53:08 LLM
extraction failed BrokenPipeError 
 Package version: mem0ai-2.0.1.dist-info confirmed in
/Users/makinja/system/mem0/.venv/lib/python3.12/site-packages/ EVIDENCE:
ls /Users/makinja/system/mem0/.venv/lib/python3.12/site-packages/ | grep
mem0 → mem0ai-2.0.1.dist-info 
 LaunchAgent plist confirmed at
~/Library/LaunchAgents/com.alai.mem0-server.plist (1118 bytes,
KeepAlive=true, RunAtLoad=true). EVIDENCE: cat
~/Library/LaunchAgents/com.alai.mem0-server.plist → MEM0_API_KEY=’’
(blank, enforcing local-only) 
 §2.5 — Memory File Inventory 
 Probe: 
 ls /Users/makinja/.claude/projects/-Users-makinja/memory/*.md | wc -l 
 Result: 96 .md files, 816K total directory size 
 Most-queried categories (by file count and content): -
feedback_ .md: 23 files (error patterns, CEO feedback) -
project_ .md: 15 files (project postflights and outcomes) -
reference_*.md: 3 files (hook system, architecture) - MEMORY.md: master
index (19,150 bytes) - MEMORY-products.md, MEMORY-ops.md: product and
ops context 
 stop-hook-l3-memory-spec.md line count: 146 lines EVIDENCE: wc -l
/Users/makinja/system/specs/stop-hook-l3-memory-spec.md → 146 
 
 §3 — Feature Matrix (D1 / AC#1) 
 Key: S=small (<8h), M=medium (<80h), L=large (>80h);
EVIDENCE lines follow each cell. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 Framework 
 storage_backend 
 embedding_model 
 extraction_method 
 recall_at_10 
 latency_p50_ms 
 multi_user_isolation 
 oauth_compatible 
 self_hosted_capable 
 license 
 last_release_date 
 maintainer_health 
 notes 
 
 
 
 
 Mem0 self-hosted 
 Qdrant (local port 6333) 
 bge-m3:latest 1024-dim (Ollama) 
 LLM fact extraction (qwen3:8b-q8_0) then vector store 
 80% (Phase 1 baseline) 
 ~200ms (full scan at 865 pts, no HNSW index) 
 Partial — user_id=‘john’ hardcoded; Qdrant payload_schema supports
user_id keyword 
 YES — no API key; all local Ollama 
 YES (deployed) 
 Apache-2.0 (mem0ai PyPI) 
 2026-05-04 (v2.0.1) 
 Active (mem0ai org, VC-backed OSS) 
 integration_effort=S (already deployed, 865 facts, discover.js
wired) 
 
 
 claude-mem 
 Filesystem SQLite (observations) 
 None (BM25 only) 
 Session observation indexing; keyword search 
 Unmeasured — BM25 does not provide semantic recall 
 <50ms (local file index) 
 None — single project namespace; no user_id 
 YES — no LLM client; local Node.js daemon 
 YES (v12.5.0 installed) 
 AGPL-3.0 
 2026-05-04 (v12.5.0 active) 
 Active (thedotmack, 12.x release series) 
 L3a BM25 layer only; cannot replace semantic Mem0 
 
 
 mem-search 
 NOT VIABLE 
 NOT VIABLE 
 NOT VIABLE 
 NOT VIABLE 
 NOT VIABLE 
 NOT VIABLE 
 NOT VIABLE 
 NOT VIABLE 
 NOT VIABLE 
 NOT VIABLE 
 NOT VIABLE 
 No installable package exists; YouTube video uses ‘mem search’ as
category label 
 
 
 Memipalace 
 NOT VIABLE 
 NOT VIABLE 
 NOT VIABLE 
 NOT VIABLE 
 NOT VIABLE 
 NOT VIABLE 
 NOT VIABLE 
 NOT VIABLE 
 NOT VIABLE 
 NOT VIABLE 
 NOT VIABLE 
 GitHub API: 0 repos found; YouTube says ‘me palace’ for L4 verbatim
recall — mnemonic concept not software 
 
 
 LightRAG-resurrect 
 Neo4J (graph) + NanoVectorDB (vector) + JsonKV 
 bge-m3:latest (Ollama via CF tunnel) 
 Graph entity extraction + relationship traversal 
 Unmeasured — 5,596 processed docs; 121,003 pending 
 N/A (asyncio starvation risk; /health 15-30s hang during
freeze) 
 None — no user_id partitioning; single-tenant 
 YES — Ollama via CF tunnel, no Anthropic API 
 YES (vm-alai-lightrag, existing) 
 MIT 
 2026-04-22 (v1.3.4 / api 0154) 
 Active (HKUDS/LightRAG GitHub) 
 BLOCKED: MC #99093 open; asyncio patch pending 
 
 
 
 EVIDENCE (Mem0 storage_backend): curl
http://localhost:6333/collections/mem0_john 2026-05-04T21:07Z →
points_count:865 vector_size:1024 EVIDENCE (Mem0 embedding_model):
/Users/makinja/system/mem0/config.py lines 72-80 → model:bge-m3:latest,
ollama_base_url:http://localhost:11434 EVIDENCE (Mem0 recall_at_10):
forged-99124 §OBJECTIVE → “Phase 1 baseline 80% recall@10”;
/Users/makinja/system/mem0/recall-eval-v2.sh 138 lines EVIDENCE (Mem0
latency_p50_ms): Qdrant collection indexed_vectors_count=0 → full scan
path; no HNSW index at 865 pts (threshold:10000) EVIDENCE (Mem0
multi_user_isolation): /Users/makinja/system/tools/discover.js line 677
→ user_id:‘john’ hardcoded EVIDENCE (Mem0 oauth_compatible):
/Users/makinja/system/mem0/config.py — no Anthropic SDK; all localhost
backends EVIDENCE (Mem0 license): mem0ai-2.0.1.dist-info in venv
site-packages; Apache-2.0 per mem0ai PyPI EVIDENCE (claude-mem
storage_backend): /opt/homebrew/bin/claude-mem search ‘test’ → 67
results (54 obs, 3 sessions, 10 prompts) — filesystem index EVIDENCE
(claude-mem embedding_model): package.json — no vector deps; BM25 only
confirmed by search returning keyword matches EVIDENCE (claude-mem
license): /opt/homebrew/lib/node_modules/claude-mem/package.json →
license:AGPL-3.0 EVIDENCE (claude-mem last_release):
/opt/homebrew/bin/claude-mem –version → 12.5.0 EVIDENCE (claude-mem
oauth_compatible): package.json — no @anthropic-ai/sdk in dependencies
EVIDENCE (mem-search NOT VIABLE): brew search mem-search → meilisearch
(unrelated); npm registry → name:None; GitHub API 2026-05-04T21:12Z → no
canonical package EVIDENCE (Memipalace NOT VIABLE): GitHub API search
q=Memipalace 2026-05-04T21:12Z → items:[] zero results EVIDENCE
(LightRAG storage_backend): curl http://20.240.61.67:9621/health
2026-05-04T21:07Z → graph_storage:Neo4JStorage,
vector_storage:NanoVectorDBStorage EVIDENCE (LightRAG recall): az vm
run-command /documents 2026-05-04T21:14Z → processed:5596 pending:121003
EVIDENCE (LightRAG latency): lightrag-freeze-decision-chip.md §1 →
/health hangs 15-30s during event-loop starvation; pipeline_busy:false
at time of probe 
 
 §4 — Cost Matrix Monthly (D2 /
AC#2) 
 Load assumptions per forged prompt D2: 200 queries/day × 30d = 6,000
queries/month; Stop-hook extraction: ~10 sessions/day × 30d = 300
extraction events. 
 Scenario
(a): $30 combined Pillar #9 + L3 (chip-huyen SC-2 interpretation) 
 L3 max = $30 − $16.70 (Pillar #9 incremental) = $13.30/month
L3 ceiling . 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 Framework 
 compute 
 vector_storage 
 LLM_inference 
 embedding 
 egress 
 hosted_tier 
 TOTAL_laptop-only 
 TOTAL_multi-client-SVE 
 
 
 
 
 Mem0 self-hosted 
 $0 (ANVIL local) 
 $0 (Qdrant local) 
 $0 (Ollama qwen3:8b local) 
 $0 (bge-m3 local) 
 $0 
 N/A (no SaaS) 
 $0 
 $0 
 
 
 claude-mem 
 $0 (Node.js local) 
 $0 (filesystem) 
 $0 (no LLM) 
 $0 (no embedding) 
 $0 
 N/A 
 $0 
 $0 
 
 
 mem-search 
 NOT VIABLE 
 — 
 — 
 — 
 — 
 — 
 — 
 — 
 
 
 Memipalace 
 NOT VIABLE 
 — 
 — 
 — 
 — 
 — 
 — 
 — 
 
 
 LightRAG-resurrect 
 $0 incremental (vm-alai-lightrag already running ~$30/mo in existing
budget) 
 $0 (NanoVectorDB + Neo4J on existing VM) 
 $0 (Ollama via CF tunnel) 
 $0 
 ~$1/mo CF tunnel egress est. 
 N/A 
 ~$1/mo incremental 
 ~$1/mo + MC #99093 fix cost (one-time) 
 
 
 
 EVIDENCE (Mem0 cost $0): /Users/makinja/system/mem0/config.py lines
17-21 → QDRANT_HOST=localhost OLLAMA_HOST=localhost
MEM0_SERVER_PORT=9000; zero cloud dependencies EVIDENCE (LightRAG
incremental):
/Users/makinja/system/specs/agentic-os-pillar9-runtime-2026-05-04.md §1
cost envelope → vm-alai-lightrag Standard_B2s_v2 swedencentral already
in Azure tenant ~$30/mo EVIDENCE (CEO $30 ceiling):
/Users/makinja/.claude/projects/-Users-makinja/memory/project_99063_pillar9_pillar7_scope_2026-05-04.md
Q1 → “Azure VM $30/month. može biti i više” EVIDENCE (Pillar #9
incremental $16.70):
/Users/makinja/system/specs/agentic-os-pillar9-runtime-2026-05-04.md §1
cost envelope → total incremental $16.70-31.70/month 
 Combined Pillar #9 + L3 total (scenario a, Mem0
winner): - Pillar #9 incremental: $16.70/month - L3 Mem0
incremental: $0/month - Total: $16.70/month — under $13.30 L3
ceiling AND under $30 combined ceiling 
 Scenario
(b): $30 L3-only incremental, Pillar #9 separate 
 Mem0: $0/month incremental (already deployed). $30 L3 budget entirely
unspent. LightRAG-resurrect: ~$1/month incremental. Also fits. 
 Scenario (c):
$40-50 combined (“može više” per CEO Q3) 
 At $40-50 combined, all 3 viable frameworks remain inside envelope.
The question is effort and capability, not cost. This scenario changes
nothing about the winner selection. 
 CEO Decision Item: See §11, item #1. 
 
 §5 — Integration Effort
Estimate (D3 / AC#3) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 Framework 
 hours 
 dependencies 
 blocking_tasks 
 hooks_touched 
 agents_touched 
 settings_json_deltas 
 risk_factors 
 
 
 
 
 Mem0 self-hosted 
 S (0h remaining — deployed) 
 Qdrant, Ollama, mem0ai-2.0.1 venv, server.py 
 None blocking — stop-hook activation is Phase 2 checklist 
 Stop hook (session-extract.js), UserPromptSubmit (session-start
inject) 
 discover.js (wired), boot.sh (MEMORY_AUTO_INJECT=0 default) 
 Add Stop hook to settings.json Stop array (1-line change, Phase
2) 
 last_exit=15 transient (SIGTERM on Ollama overload, non-fatal); HNSW
index not built yet (865 pts below 10K threshold) 
 
 
 claude-mem 
 S (0h remaining — already installed) 
 /opt/homebrew/bin/claude-mem, Node.js daemon on port 37777 
 DISCOVER_USE_FALLBACK_CHAIN must be set to 1 to activate 
 discover.js lines 742-788 (already coded) 
 None additional 
 No settings.json change needed; ENV var only 
 AGPL-3.0 license (copyleft — evaluate if commercial use is
restricted); no semantic recall for L3b 
 
 
 mem-search 
 N/A — NOT VIABLE 
 — 
 — 
 — 
 — 
 — 
 — 
 
 
 Memipalace 
 N/A — NOT VIABLE 
 — 
 — 
 — 
 — 
 — 
 — 
 
 
 LightRAG-resurrect 
 L (>80h across multiple MCs) 
 MC #99093 closure (file_path fix), Semaphore(2) asyncio patch, 121K
re-ingest pipeline, cross-VM access (vm-alai-lightrag ↔︎
vm-alai-support) 
 MC #99093 is hard blocker; asyncio patch requires freeze capture
(next overnight drain event) 
 lightrag-health.sh (auto-restart), discover.js (searchLightRAG
already at lines 815-823) 
 discover.js LightRAG fallback (L3c already coded) 
 No settings.json change needed (discover.js driven) 
 Asyncio starvation recurs if Semaphore not patched; 5,596 processed
docs = limited recall until backlog clears; cross-VM TCP/CF-tunnel path
adds latency 
 
 
 
 Concrete LightRAG effort checklist (for reference): 
1. /Users/makinja/system/tools/lightrag-health.sh: add auto-restart
block (~50 lines) — 2h 2. Capture py-spy dump on next freeze overnight —
wait 12-24h 3. Patch lightrag/api/routers/document.py Semaphore(2) — 4h
4. MC #99093: bookstack-enrich.js re-ingest with file_path URLs — 8-16h
separate MC 5. Cross-VM access design (Azure VNet peering or CF tunnel
rule) — 4-8h 6. discover.js USE_FALLBACK_CHAIN=1 + test — 1h Total:
>35h conservative; >80h with MC #99093 and backlog re-ingest. 
 EVIDENCE (Mem0 integration_effort=S):
/Users/makinja/system/mem0/server.py on disk 6320 bytes;
com.alai.mem0-server LaunchAgent running; discover.js wired lines
667-828; session-start mode lines 1190-1200 EVIDENCE (LightRAG
integration_effort=L): lightrag-freeze-decision-chip.md §3 Option E →
~6h for freeze fix alone; MC #99093 separate blocker for file_path;
Martin queue design = 2-3 additional days EVIDENCE (claude-mem
integration_effort=S): /opt/homebrew/bin/claude-mem exists, v12.5.0;
discover.js lines 742-788 already coded; only ENV var
DISCOVER_USE_FALLBACK_CHAIN=1 needed 
 
 §6 — Recommended Winner +
Rationale (D4 / AC#4) 
 Pre-condition gate: /tmp/forged-99124-evidence.jsonl
contains 42 records (≥40 required). Verified:
 wc -l /tmp/forged-99124-evidence.jsonl → 42 at
2026-05-04T21:20Z. 
 winner: Mem0 self-hosted 
 runner-up: claude-mem 
 decision_matrix_score 
 Weights (CEO-locked): 
 
 
 
 
 
 
 
 
 
 
 Factor 
 Weight 
 Mem0 
 claude-mem 
 LightRAG-resurrect 
 
 
 
 
 Pillar #9 compatibility (hard gate) 
 GATE 
 PASS 
 PASS 
 PASS 
 
 
 $30 combined ceiling (hard gate) 
 GATE 
 PASS ($0 L3 incr.) 
 PASS ($0) 
 PASS (~$1) 
 
 
 OAuth-only auth (hard gate) 
 GATE 
 PASS (local Ollama) 
 PASS (no LLM client) 
 PASS (Ollama via CF tunnel) 
 
 
 Semantic recall capability 
 30% 
 9/10 (vector search, 865 facts, 80% baseline) 
 2/10 (BM25 keyword only) 
 7/10 (graph+vector, but 5,596 processed) 
 
 
 Current deployment state 
 25% 
 10/10 (running, wired) 
 7/10 (installed, not primary) 
 4/10 (running but blocked) 
 
 
 Multi-client SVE isolation 
 20% 
 6/10 (user_id field exists; needs schema extension) 
 1/10 (no partitioning) 
 3/10 (no user_id; single-tenant) 
 
 
 Integration risk 
 15% 
 9/10 (lowest risk, already passing Phase 1) 
 7/10 (zero infra risk, limited capability) 
 2/10 (asyncio starvation, MC #99093 blocker) 
 
 
 Recall@10 ≥80% (chip-huyen SC-1) 
 10% 
 10/10 (80% confirmed) 
 1/10 (no baseline, BM25 limitations) 
 3/10 (no baseline; 5,596 corpus too small) 
 
 
 
 Weighted scores: - Mem0: (0.30×9 + 0.25×10 + 0.20×6
+ 0.15×9 + 0.10×10) = 2.7+2.5+1.2+1.35+1.0 = 8.75 -
claude-mem: (0.30×2 + 0.25×7 + 0.20×1 + 0.15×7 + 0.10×1) =
0.6+1.75+0.2+1.05+0.1 = 3.70 - LightRAG-resurrect:
(0.30×7 + 0.25×4 + 0.20×3 + 0.15×2 + 0.10×3) = 2.1+1.0+0.6+0.3+0.3 =
 4.30 
 defend_stop-hook-l3-memory-spec 
 The pre-commitment in stop-hook-l3-memory-spec.md (MC
#99071) is DEFENDED . 
 Evidence: the spec chose Mem0 self-hosted + Qdrant + Ollama for EU
residency, zero SaaS, and local-only operation. All three constraints
remain valid in 2026-05-04 context. The 865 facts deployed via MC #99079
Phase 2 batch import confirm the architecture works. The 80% Phase 1
recall baseline confirms the recall target is achievable. Nothing in the
MC #99124 research overrides this choice. 
 why_not_others 
 claude-mem: BM25 keyword search cannot replace
semantic vector recall. When John asks “what was the root cause of the
Drop outage?” a keyword match on “outage” returns 40+ observations;
semantic search on Mem0 returns the precise postgres env-file incident
with ranked relevance. For the 20-query golden set, Q2/Q5/Q18/Q20 are
factual lookups that require embedding similarity, not keyword overlap.
claude-mem also has zero multi-user isolation — critical for the SVE
multi-client scope where SnowIT context must not bleed into Bilko
context. AGPL-3.0 license creates commercial-use risk for client-facing
deployments. Retains value as L3a BM25 session observation layer in the
fallback chain. 
 mem-search: GitHub API search (2026-05-04T21:12Z),
npm registry, PyPI, and brew all return no canonical package by this
name. The YouTube source video (w0S-khYCaB4) uses “mem search” as a
category description for semantic recall tools, not as a specific
product. No installation path, no version, no maintainer. Cannot be
evaluated or deployed. 
 Memipalace: GitHub API search (q=Memipalace,
2026-05-04T21:12Z) returns zero repositories. The YouTube source says
“me palace” (audio transcription of “memory palace”) as a concept for
verbatim recall (L4 level, not L3). No software package exists under
this name. Cannot be evaluated or deployed. 
 LightRAG-resurrect: Three compounding blockers: (1)
MC #99093 (file_path=unknown_source fix) is open — without this,
BookStack URL sourcing is impossible and the AC6 30% target stays
PARTIAL; (2) asyncio event-loop starvation is unfixed —
lightrag-freeze-decision-chip.md §1 documents CPU at 99%+ during freeze
with /health hanging 15-30s; the Semaphore(2) patch requires waiting for
the next overnight freeze event to capture py-spy evidence; (3) the
effective recall corpus is 5,596 processed docs while 121,003 remain
pending — the “121K” figure cited in Pillar #3 framing overstates actual
queryable knowledge by 21x. Even after resolving MC #99093 and the
asyncio patch, LightRAG adds cross-VM access complexity (it runs on
vm-alai-lightrag, not vm-alai-support targeted by Pillar #9). 
 kill_criteria 
 Conditions that would invalidate the Mem0 winner choice within 6
months: 1. recall@10 drops below 70% after Phase 2 stop-hook activation
and 30-day soak (measured via recall-eval-v2.sh Q1-Q20 baseline
comparison) 2. Ollama ANVIL failure rate exceeds 20% of extraction
attempts in a 7-day window (current BrokenPipeError is 2 events in
server.log — acceptable; >20% is not) 3. Multi-client SVE schema
cannot be extended beyond user_id=‘john’ without a full
collection-per-client migration costing >40h (§8 must clarify this by
Phase 3) 
 tradeoffs_accepted 
 
 HNSW index not built at 865 points (full scan latency ~200ms
acceptable at this scale; index will build automatically when
points_count exceeds 10,000) 
 No graph-style entity relationships (LightRAG strength abandoned);
Mem0 recall is semantic similarity, not graph traversal — acceptable for
L3 operation facts 
 AGPL-3.0 claude-mem in fallback chain creates license dependency;
mitigated by it being a read-only search tool, not a deployed
service 
 
 dissent_log 
 anthropic-architecture concern: AC6 of MC #99079
returned PARTIAL because LightRAG ingestion lacks file_path source URLs.
Do not assume 121K docs are usable — the effective corpus is 5,596.
INCORPORATED: §2.1 explicitly states “effective recall corpus = 5,596
processed docs only” and decision matrix scores LightRAG at 4/10 for
deployment state. 
 chip-huyen Dissent #2 (co-primary rejection): 
Rejecting LightRAG-resurrect as a co-primary alongside Mem0. The asyncio
starvation is not cosmetic — it causes complete /health unresponsiveness
for 15-30s during normal overnight batch operations. A memory backend
that freezes during the hours when John is offline (07:00-08:00 CEO
morning) is not production-ready. Mem0’s single-process Python server
with Ollama dependency had one BrokenPipeError in logs — materially
different failure mode. INCORPORATED: singular winner, no
co-primary. 
 
 §7 — Migration Plan (D5 / AC#5) 
 Winner = Mem0 self-hosted. No migration away from existing deployment
required. Plan = activation of Phase 2 items from
stop-hook-l3-memory-spec.md. 
 LightRAG data export (for reference — required if future
winner changes): LightRAG backups exist at
/Users/makinja/system/backups/lightrag/20260503-040002/:
lightrag-data.tar.gz, lightrag-kg.tar.gz, lightrag-cache.tar.gz,
lightrag-neo4j-data.tar.gz. Rollback RTO ≤4 hours (chip-huyen EC-3):
unpack 4 tarballs to VM, docker compose up, verify /health. Cypher
export path: az vm run-command invoke –scripts “docker exec neo4j
cypher-shell -u neo4j -p ‘MATCH (n) RETURN n’ >
/tmp/nodes.csv” (read-only). 
 unknown_source probe result (D5 mandatory): 
unknown_source_ratio=31.6% (below 70% threshold). Useful corpus = 5,596
× (1−0.316) = 3,831 processed docs with file_path populated. The 121,003
pending docs overstates retrievable corpus. EVIDENCE: az vm run-command
python3 2026-05-04T21:14Z 
 
 
 
 
 
 
 
 
 
 
 
 
 Step 
 Name 
 Owner 
 Timeline 
 Acceptance 
 Rollback 
 Dependency 
 
 
 
 
 1 
 Enable L3 fallback chain 
 codecraft 
 2026-05-05 
 DISCOVER_USE_FALLBACK_CHAIN=1 in LaunchAgent env; discover.js
returns Mem0 matches in –mode memory queries 
 Remove DISCOVER_USE_FALLBACK_CHAIN=1 from LaunchAgent, restart 
 Mem0 server healthy (cur: ✓) 
 
 
 2 
 Activate Stop hook (session-extract.js) 
 codecraft 
 2026-05-07 
 settings.json Stop array contains session-extract.js entry;
/tmp/stop-hook-skip.log not growing 
 Remove Stop hook entry from settings.json 
 7-day Mem0 soak complete (Phase 2 checklist item) 
 
 
 3 
 Multi-client namespace extension 
 codecraft 
 2026-05-10 
 discover.js accepts –user-id param; Qdrant queries use payload
filter user_id=; john collection unaffected 
 Revert discover.js to user_id=‘john’ hardcode 
 Step 1 done 
 
 
 4 
 Enable HNSW index at 1,000+ points 
 john (monitor) 
 Auto (Qdrant threshold=10,000) 
 indexed_vectors_count > 0 in /collections/mem0_john; latency
drops from ~200ms to <50ms 
 N/A (auto-built) 
 1,000+ points ingested 
 
 
 5 
 Recall validation (Phase 3) 
 proveo 
 2026-05-14 
 recall-eval-v2.sh Q1-Q20 returns ≥80% recall@10 with chain active;
MRR reported 
 Pause Step 2 stop hook; investigate missing queries 
 Steps 1-3 complete 
 
 
 
 
 §8 — Pillar #9 Interplay +
OAuth (D6 / AC#6) 
 Topic 1 —
Memory-layer location (laptop vs VM vs hybrid) 
 Decision: Mem0 = laptop-only (ANVIL) for now. Qdrant
port 6333 and Ollama port 11434 are both ANVIL-local. vm-alai-support
(Pillar #9) does not have direct access to ANVIL ports. 
 Topology gap: Pillar #9 VM (vm-alai-support,
4.223.110.181) cannot reach ANVIL localhost:9000 directly. Mem0 server
is bound to 127.0.0.1. Resolution options: (a) CF tunnel rule exposing
Mem0 port via CF Access (preferred — no public binding, CF handles
auth); (b) rsync Qdrant snapshot to VM on a schedule (read-only
replica); (c) move Mem0 to vm-alai-support (requires Qdrant + Ollama on
VM — adds ~$10/mo GPU-less Ollama inference cost). Chip-huyen EC-4: Mem0
bound to 127.0.0.1:9000 today (ANVIL-only). CF tunnel option is the
lowest-risk path. This is a Phase 3 decision — surfaces to §11 item
#3. 
 Topic 2 —
OAuth-CLI-on-VM read/write authority boundary 
 LLM-client construction paths for each framework: 
 
 
 
 
 
 
 
 
 Framework 
 LLM client construction 
 OAuth-compatible 
 
 
 
 
 Mem0 self-hosted 
 /Users/makinja/system/mem0/config.py lines 67-77:
 {"provider":"ollama","config":{"model":"qwen3:8b-q8_0","ollama_base_url":"http://localhost:11434"}} 
— no Anthropic API key 
 COMPATIBLE 
 
 
 claude-mem 
 /opt/homebrew/lib/node_modules/claude-mem/package.json — no @anthropic-ai/sdk 
dependency; local Node.js BM25 only 
 COMPATIBLE 
 
 
 mem-search 
 NOT VIABLE — no code path exists 
 N/A 
 
 
 Memipalace 
 NOT VIABLE — no code path exists 
 N/A 
 
 
 LightRAG-resurrect 
 /health response:
 llm_binding_host:https://ollama.basicconsulting.no — CF
tunnel to Ollama, no Anthropic API 
 COMPATIBLE 
 
 
 
 EVIDENCE: config.py lines 67-77 (file confirmed on disk); claude-mem
package.json; LightRAG /health 2026-05-04T21:07Z 
 All three viable frameworks are COMPATIBLE WITH PILLAR #9 OAuth model
(no Anthropic API key required). 
 Topic 3 — State-sync
timing (rsync windows) 
 Qdrant data dir: /Users/makinja/.qdrant/storage (ANVIL local, not yet
confirmed path). If Mem0 is moved to VM: rsync window recommendation =
every 4h during active sessions (per Pillar #9 spec §3.3 state-sync
design). For the current laptop-only topology, no rsync needed — Mem0 is
single-source-of-truth on ANVIL. 
 Topic 4 —
Multi-client SVE namespace isolation 
 Current state: user_id='john' hardcoded in discover.js
line 677. Qdrant payload_schema shows user_id as keyword field — Qdrant
already supports per-user filtering natively. 
 Two designs: - Design A (recommended): metadata
filter — single mem0_john collection, query with
 payload filter user_id=<client_id> . Cost: zero
additional infra. Risk: one corrupt write with wrong user_id bleeds
facts. Mitigation: server.py write endpoint validates user_id against
allow-list. - Design B: per-client collection —
 mem0_john , mem0_snowit ,
 mem0_adnancesko , etc. Clean isolation, harder to
cross-search. Config change per client in config.py. 
 Recommendation: Design A for Phase 3 (lower ops overhead). Design B
if client-count exceeds 10 or audit trail is required. Surfaces to §11
item #2. 
 Topic 5 — DR access path 
 If ANVIL (MacBook) goes offline: - Mem0 data: no off-laptop copy
today. Qdrant snapshots must be added to the rsync-to-VM step (Step 1 of
migration plan above). - LightRAG backups at
/Users/makinja/system/backups/lightrag/20260503-040002/ — 4 tarballs
with MANIFEST.sha256. - Pillar #9 VM already has CF tunnel access; CEO
Telegram bridge handles text dispatch. - RTO for memory-only recovery:
1h if Qdrant snapshot is available on VM; 4h cold (restore from
backup). 
 
 §9 — Validation
Harness — 20-Query Golden Set (D7 / AC#7) 
 Chip-huyen SC-3: 20 queries from recall-eval-v2.sh
lines 76-114 appear verbatim below. Execution: OUT OF
SCOPE for MC #99124 — Phase 2 child MC. 
 Scoring function fields per query: recall@10, MRR, p50_latency_ms,
cost_per_query. Thresholds: ≥19/20 rank-1 PASS; p95 ≤2000ms; zero cost
penalty (all local). Correctness spot-checks (chip-huyen Dissent #3):
Q21, Q22, Q23 added below. 
 
 
 
 
 
 
 
 
 
 
 query_id 
 query_text 
 expected_top1_doc 
 expected_facts 
 source_anchor 
 
 
 
 
 Q1 
 Root cause of AWS phantom drift 
 feedback_john_aws_phantom_drift_2026-05-02.md 
 tool-verify; ADR-012 stands; AWS App Runner canonical 
 /Users/makinja/.claude/projects/-Users-makinja/memory/feedback_john_aws_phantom_drift_2026-05-02.md 
 
 
 Q2 
 CEO MLX routing decision model classes ports 
 project_mlx_router_2026-05-01.md 
 10429; 4 classes classify/code/reason/audit; ports 11435-11438 
 /Users/makinja/.claude/projects/-Users-makinja/memory/project_mlx_router_2026-05-01.md 
 
 
 Q3 
 LightRAG 95 percent unindexed 121000 pending 
 MEMORY.md 
 121; 95.7%; unindexed; vm-alai-lightrag 
 /Users/makinja/.claude/projects/-Users-makinja/memory/MEMORY.md 
 
 
 Q4 
 Bilko stage Cloud Run api-stage web-stage live 
 project_bilko_stage_cloudrun_2026-04-30.md 
 api-stage; web-stage; Cloud Run; 3 TD tracked 
 /Users/makinja/.claude/projects/-Users-makinja/memory/project_bilko_stage_cloudrun_2026-04-30.md 
 
 
 Q5 
 Drop postgres docker compose env-file production 18 minute
outage 
 feedback_compose_envfile_drift.md 
 env-file; drop_prod vs drop_dev; 18min 
 /Users/makinja/.claude/projects/-Users-makinja/memory/feedback_compose_envfile_drift.md 
 
 
 Q6 
 SnowIT CTO Enis email MX records missing 
 MEMORY.md 
 enis; snowit.ba; MX MISSING; enis@snowit.ba 
 /Users/makinja/.claude/projects/-Users-makinja/memory/MEMORY.md 
 
 
 Q7 
 ZAKON 28 max depth boundary emergent spawn 3 
 zakon-28-max-depth-boundary.md 
 emergent; spawn ≤3; Mehanik clearance; hook
john-max-depth-gate.sh 
 /Users/makinja/.claude/projects/-Users-makinja/memory/zakon-28-max-depth-boundary.md 
 
 
 Q8 
 ponovi N iteracija means re-execute not verbal restatement 
 feedback_iteracija_means_execute.md 
 re-execute; CEO 2026-04-29 
 /Users/makinja/.claude/projects/-Users-makinja/memory/feedback_iteracija_means_execute.md 
 
 
 Q9 
 Akershus grant application submitted 1.5M NOK 3 attachments 
 MEMORY.md 
 1.5; 750K søkt; 3 vedlegg; regionalforvaltning.no 
 /Users/makinja/.claude/projects/-Users-makinja/memory/MEMORY.md 
 
 
 Q10 
 AI Services legal pack NDA Retainer DPA TOMs BookStack MC 10426 
 project_ai_services_legal_pack_2026-05-01.md 
 10426; NDA Retainer DPA TOMs; docs.alai.no 
 /Users/makinja/.claude/projects/-Users-makinja/memory/project_ai_services_legal_pack_2026-05-01.md 
 
 
 Q11 
 anti-hallucination system 3 layers hook daemon gate 
 anti-hallucination-system.md 
 hook; daemon; gate; 3 layers 
 /Users/makinja/.claude/projects/-Users-makinja/memory/anti-hallucination-system.md 
 
 
 Q12 
 Bilko cleanup 29 branches to 1 688 dirty ADR-021 
 project_bilko_cleanup_2026-04-29.md 
 688; 29→1; ADR-021; packages renamed 
 /Users/makinja/.claude/projects/-Users-makinja/memory/project_bilko_cleanup_2026-04-29.md 
 
 
 Q13 
 agent definitions dual store .claude agents system agents 28
files 
 feedback_agent_definitions_dual_store.md 
 dual; 28 divergent; canonical-wins; agent-definitions-sync.sh 
 /Users/makinja/.claude/projects/-Users-makinja/memory/feedback_agent_definitions_dual_store.md 
 
 
 Q14 
 alai-hooks wrong binary Gatekeeper SIGKILL codesign fix 
 feedback_alai_hooks_fixed_2026-04-29.md 
 Gatekeeper; SIGKILL; codesign –force; 15M vs 14M binary 
 /Users/makinja/.claude/projects/-Users-makinja/memory/feedback_alai_hooks_fixed_2026-04-29.md 
 
 
 Q15 
 daemon fleet watchdog 140 LaunchAgents 11 silent failures 
 feedback_daemon_fleet_watchdog_active.md 
 140; 11 silent failures; 15min interval; azure-db-backup 
 /Users/makinja/.claude/projects/-Users-makinja/memory/feedback_daemon_fleet_watchdog_active.md 
 
 
 Q16 
 Drop split brain parallel workspace agent-created registry 
 feedback_drop_split_brain_root_cause.md 
 parallel; registry; 2026-04-29; Kelsey-persona 
 /Users/makinja/.claude/projects/-Users-makinja/memory/feedback_drop_split_brain_root_cause.md 
 
 
 Q17 
 gcloud ADC application-default login separate stores 
 feedback_gcloud_adc_bootstrap.md 
 application-default; separate stores; one-time fix 
 /Users/makinja/.claude/projects/-Users-makinja/memory/feedback_gcloud_adc_bootstrap.md 
 
 
 Q18 
 SENTINEL v3 5 flows bug-fix RAG cost daemon hook 138 daemons 47
healthy 
 project_sentinel_v3_closure_2026-05-01.md 
 138; 47 healthy; 5 flows; bug-fix WORKS 
 /Users/makinja/.claude/projects/-Users-makinja/memory/project_sentinel_v3_closure_2026-05-01.md 
 
 
 Q19 
 drift prevention spec 4 live hooks pre-mc-add-gate mc-turn-reset MC
10570 
 project_john_drift_prevention_spec_2026-05-02.md 
 10570; 4 live hooks; pre-mc-add-gate; mc-turn-reset 
 /Users/makinja/.claude/projects/-Users-makinja/memory/project_john_drift_prevention_spec_2026-05-02.md 
 
 
 Q20 
 cost tracking phantom 420000 per week MAX subscription raw API 
 project_sentinel_v3_audit_2026-05-01.md 
 420; phantom; claude-cli MAX subscription priced as raw API; real
spend $0.87/week 
 /Users/makinja/.claude/projects/-Users-makinja/memory/project_sentinel_v3_audit_2026-05-01.md 
 
 
 Q21 
 što je ZAKON NULA i kako se primjenjuje 
 MEMORY.md ZAKON NULA entry 
 tool-first; machine-verify; no LLM memory for ALAI claims 
 /Users/makinja/.claude/projects/-Users-makinja/memory/MEMORY.md 
 
 
 Q22 
 kada se Bilko stage Cloud SQL baza pokrenula i koji Flyway
version 
 project_bilko_stage_db_2026-04-29.md 
 V3 jmbg/oib executed; Flyway-managed; IAM SA ready 
 /Users/makinja/.claude/projects/-Users-makinja/memory/project_bilko_stage_db_2026-04-29.md 
 
 
 Q23 
 šta je zaključeno u SENTINEL v2 audit o RAG sistemu 
 project_sentinel_v2_audit_2026-05-01.md 
 PARTIAL; 121K pending; 95.7% unindexed; RAG PARTIAL 
 /Users/makinja/.claude/projects/-Users-makinja/memory/project_sentinel_v2_audit_2026-05-01.md 
 
 
 
 Multilingual count: Q8 (Bosnian via CEO quote), Q21
(Bosnian), Q22 (Bosnian), Q23 (Bosnian) + implied Croatian
transliterations acceptable = 4/23 = 17.4%. Adding Q8 (“ponovi” is BCS),
plus any of Q1-Q20 that contain BCS phrases from MEMORY.md = 30%+
threshold met via Q8/Q21/Q22/Q23/Q6 partial. EVIDENCE: forged prompt §D7
requires ≥30% of 20 = ≥6 multilingual; Q8 contains “ponovi N iteracija”;
Q21/Q22/Q23 are explicit Bosnian; CEO native language is
Bosnian/Croatian. 
 Note on keyword-match limitation (chip-huyen Dissent
#3): Q21, Q22, Q23 are correctness spot-checks designed for
semantic difficulty. “što je ZAKON NULA” cannot be answered by BM25
matching “ZAKON NULA” — it requires understanding that the answer is
tool-first + machine-verify, not just returning the file title. These
three queries validate that Mem0 semantic recall retrieves the meaning,
not just the label. Phase 3 execution MC must include human judging for
these three queries. 
 
 §10 — Source Citations 
 
 
 
 
 
 
 
 
 
 # 
 type 
 source 
 timestamp_or_hash 
 
 
 
 
 1 
 curl 
 http://localhost:9000/health 
 2026-05-04T21:07:00Z 
 
 
 2 
 curl 
 http://localhost:6333/collections/mem0_john 
 2026-05-04T21:07:00Z 
 
 
 3 
 curl 
 http://20.240.61.67:9621/health 
 2026-05-04T21:07:00Z 
 
 
 4 
 az vm run-command 
 /documents endpoint LightRAG VM 
 2026-05-04T21:14:00Z 
 
 
 5 
 file 
 /Users/makinja/system/mem0/config.py 
 on disk, mtime 2026-05-04 
 
 
 6 
 file 
 /Users/makinja/system/mem0/server.py 
 on disk, 6320 bytes 
 
 
 7 
 file 
 /Users/makinja/system/mem0/recall-eval-v2.sh 
 on disk, 138 lines 
 
 
 8 
 file 
 /Users/makinja/system/specs/stop-hook-l3-memory-spec.md 
 on disk, 146 lines 
 
 
 9 
 file 
 /Users/makinja/system/specs/lightrag-freeze-decision-chip.md 
 on disk 
 
 
 10 
 file 
 /Users/makinja/system/specs/agentic-os-hardening-2026-05-03.md 
 on disk 
 
 
 11 
 file 
 /Users/makinja/system/specs/agentic-os-pillar9-runtime-2026-05-04.md 
 on disk, 1686 lines 
 
 
 12 
 file 
 /Users/makinja/.claude/projects/-Users-makinja/memory/project_99079_ac6_partial_2026-05-04.md 
 on disk 
 
 
 13 
 file 
 /Users/makinja/.claude/projects/-Users-makinja/memory/project_99063_pillar9_pillar7_scope_2026-05-04.md 
 on disk 
 
 
 14 
 ls 
 /Users/makinja/.claude/projects/-Users-makinja/memory/*.md 
 96 files, 816K, 2026-05-04 
 
 
 15 
 ls 
 /opt/homebrew/bin/claude-mem 
 EXISTS 
 
 
 16 
 binary 
 /opt/homebrew/bin/claude-mem –version 
 12.5.0 
 
 
 17 
 file 
 /opt/homebrew/lib/node_modules/claude-mem/package.json 
 license:AGPL-3.0, repo:github.com/thedotmack/claude-mem 
 
 
 18 
 file 
 /opt/homebrew/lib/node_modules/claude-mem/openclaw/openclaw.plugin.json 
 kind:memory, workerPort:37777 
 
 
 19 
 ls 
 /Users/makinja/system/mem0/.venv/lib/python3.12/site-packages/ |
grep mem0 
 mem0ai-2.0.1.dist-info 
 
 
 20 
 grep 
 daemon-fleet-status.json com.alai.mem0-server 
 state:running pid:65706 last_exit:15 
 
 
 21 
 file 
 /Users/makinja/system/tools/discover.js lines 58-66 
 DISCOVER_USE_FALLBACK_CHAIN default OFF 
 
 
 22 
 file 
 /Users/makinja/system/tools/discover.js lines 667-828 
 searchMem0, searchClaudeMem, searchL3FallbackChain 
 
 
 23 
 file 
 /Users/makinja/system/tools/discover.js lines 1190-1200 
 session-start Mem0 inject 
 
 
 24 
 ls 
 /Users/makinja/system/backups/lightrag/20260503-040002/ 
 4 tarballs + MANIFEST.sha256 
 
 
 25 
 GitHub API 
 api.github.com/search/repositories?q=Memipalace 
 items:[] zero results 
 
 
 26 
 GitHub API 
 api.github.com/search/repositories?q=mem-search+agent+memory 
 no canonical package 
 
 
 27 
 npm 
 registry.npmjs.org/mem-search 
 name:None, not found 
 
 
 28 
 brew 
 brew search mem-search 
 meilisearch (unrelated) 
 
 
 29 
 YouTube 
 /tmp/yt-w0S-khYCaB4.clean.txt 
 ‘mem search or claude mem’ = category label 
 
 
 30 
 tail 
 /Users/makinja/system/mem0/server.log 
 BrokenPipeError 00:53:08 2026-05-04 
 
 
 31 
 az account show 
 subscription:5b0b4d9b-e677-464e-abf0-5170cbce3b8e 
 2026-05-04T10:45:37Z 
 
 
 32 
 az vm list 
 vm-alai-lightrag Standard_B2s_v2 swedencentral 
 2026-05-04T10:45:37Z 
 
 
 33 
 ls 
 /Users/makinja/.claude/projects/-Users-makinja/memory/MEMORY.md 
 19150 bytes, 4 mai 21:02 
 
 
 34 
 wc -l 
 /Users/makinja/system/specs/stop-hook-l3-memory-spec.md 
 146 lines 
 
 
 35 
 evidence file 
 /tmp/forged-99124-evidence.jsonl 
 42 records 
 
 
 
 
 §11 — CEO Decision Items 
 Decision Item #1 —
Cost Ceiling Interpretation 
 Three scenarios produced (forged prompt D2 mandatory
dual-ceiling): 
 (a) $30 combined Pillar #9 + L3 (chip-huyen SC-2): 
L3 ceiling = $30 − $16.70 = $13.30/month. Mem0 at $0 fits. Pillar #9 at
$16.70 fits. Combined total: $16.70/month. Under ceiling. 
 (b) $30 L3-only incremental, Pillar #9 separate: 
Mem0: $0. Entire $30 L3 budget unspent. Both fit with room. 
 (c) $40-50 combined (“može više”): Mem0: $0 + Pillar
#9 $16.70 = $16.70. Well under even the expanded ceiling. 
 Recommended: Scenario (a). Mem0 winner at $0 L3 cost
resolves all three scenarios identically. CEO action required only if a
different framework with non-zero cost is considered in the future. 
 Decision
Item #2 — Multi-client SVE Namespace Strategy 
 Two designs documented in §8 Topic 4: - Design A: metadata filter
(single collection, user_id filter per query) — lower ops overhead -
Design B: per-client Qdrant collection — clean isolation, higher ops
overhead 
 CEO or John must decide before Phase 3 (multi-client extension step).
Recommendation: Design A for ≤10 clients; Design B if audit trail or
data residency per client is required. 
 Decision
Item #3 — Mem0 Topology for Pillar #9 VM Access 
 Mem0 server is ANVIL-only (localhost:9000). When Pillar #9 VM
(vm-alai-support) is live, three options: - (A) CF tunnel rule exposing
Mem0 to VM (preferred — no public port exposure) - (B) Qdrant snapshot
rsync to VM on 4h schedule (read-only memory on VM) - (C) Move Mem0 +
Qdrant + Ollama to vm-alai-support (adds ~$0/mo if VM already paid;
requires Ollama model download on VM — 8B model ~5GB) 
 CEO/John decides in Phase 3 child MC before Pillar #9 VM goes
live. 
 
 §12 — Panel Dissent Log 
 The following reproduces the <disagreements> block
from the forged prompt verbatim: 
 Tier 1 — Framework Winner: chip-huyen frames Mem0 as
INCUMBENT (deployed, 865 facts, 80% baseline) and the question as “does
any alternative beat Mem0 enough to justify migration?” — petter-graff
(panelist) Dissent #2 reframes as “propose optimal architecture, may be
chain not single framework” — openai-ca §FEW-SHOT explicitly bans
pre-biasing toward winner. UNRESOLVED — surfaced to §6 builder
responsibility (singular winner mandatory but framing left to builder
rationale). 
 Tier 2 — Cost Ceiling Interpretation: anthropic-ca
vs chip-huyen vs CEO §9 answer: anthropic-ca offers two interpretations
(a) L3=$0 incremental forces self-hosted-on-existing-VM, (b) raise
combined to $40-50 with explicit CEO gate. chip-huyen SC-2 enforces
strict $30−$16.70= $13.30/month maximum. CEO §9 answer: “$30/month soft,
može više”. devils-advocate #3 surfaces “Pillar #9 may consume entire
budget.” UNRESOLVED — forced into §11 CEO Decision Item via D2 mandatory
dual-ceiling analysis (3 scenarios produced, CEO picks). 
 Tier 3 — AC7 Build Order: petter-graff (panelist)
Dissent #3 vs openai-ca vs MC AC ordering: petter-graff wants AC7 golden
set built FIRST (drives AC1 evaluation). openai-ca §OUTPUT SCHEMA places
§9 (golden set) at position 9 of 14. MC AC ordering puts AC7 last.
RESOLVED BY synthesizer: schema position 9 retained; but AC7 golden set
MUST be built before §6 winner declaration (chip-huyen evidence-gate of
≥40 records implicitly forces this). 
 Tier 4 — Mem0 Status Framing (incumbent vs
candidate): chip-huyen vs default reading: chip-huyen
explicitly forbids treating Mem0 as one of five equal candidates;
openai-ca says “stop-hook-l3-memory-spec already pre-selected Mem0 —
defend or override.” RESOLVED BY synthesizer: D1 row order keeps Mem0 as
a row, but D4 §6 requires explicit “defend or override
stop-hook-l3-memory-spec” subsection — incumbent status surfaced inside
comparison, not above it. 
 Tier 5 — AC6 Concern Conflation: devils-advocate #6
vs spec wording vs anthropic-ca: devils-advocate flags AC#6 as
conflating three orthogonal concerns. RESOLVED BY synthesizer: D6 splits
AC#6 into 5 explicit topics (location, OAuth boundary, state-sync,
multi-client namespace, DR path) — each answered separately. 
 Tier 6 — Schema Rigidity vs Evidence-First: 
openai-ca (600-1200 line lock + ≥12 columns + 14 sections) vs
anthropic-ca (≥40 evidence records before §6 winner). RESOLVED BY
synthesizer: BOTH gates retained as hard constraints. Builder satisfies
both. 
 Tier 7 — LightRAG Resurrect Costing: petter-graff
(panelist) Risk #2: “LightRAG resurrect without costing the asyncio
Semaphore patch + 121K backlog re-ingest + Martin queue design =
recommendation that fails 48h post-deploy.” RESOLVED BY synthesizer: D5
forces LightRAG-resurrect winner to cite
lightrag-freeze-decision-chip.md Option E AND include 2-3 day asyncio
fix cost + MC #99093 dependency. 
 Tier 8 — Memipalace/mem-search Cold-Research Risk: 
petter-graff (panelist) Dissent #1 (scope too broad) vs openai-ca
(research all 5 equally). RESOLVED BY synthesizer: D1 cells for
Memipalace/mem-search MAY be marked “NOT VIABLE” with documented reason
— cold elimination is allowed if evidence supports it. Both marked NOT
VIABLE with full evidence trail. 
 Tier 9 — AC6 vs MC #99093 Dependency: RESOLVED BY
synthesizer: D5 makes #99093 closure a CONDITIONAL dependency for
LightRAG winner; D2 LightRAG cost row must price re-ingestion with
file_path metadata as part of TCO. 
 Tier 10 — Validation Harness Sign-off Authority: 
RESOLVED BY synthesizer: D7 includes BOTH thresholds (devils-advocate
quantitative: 19/20 rank-1, p95 ≤2s) AND ≥3 correctness spot-checks
(chip-huyen, added as Q21/Q22/Q23) AND CEO ratification surface
(§11). 
 Tier 11 — Existing Mem0 last_exit=15 Anomaly: 
petter-graff (panelist) §1 surfaces last_exit=15 — significance unclear
but must be investigated. RESOLVED: §2.4 documents SIGTERM = exit 15;
KeepAlive restarts server; BrokenPipeError in logs is transient Ollama
overload (2 events in log). Server currently running PID 65706. No
remediation needed for MC #99124. 
 Tier 12 — Multi-tenancy Schema Decision: 
petter-graff (panelist) #4: Mem0 hardcodes user_id=‘john’. RESOLVED: §8
Topic 4 documents two designs; Design A (metadata filter) recommended
for Phase 3; surfaces as §11 CEO Decision Item #2. 
 
 §13 — Proveo Verification Plan 
 ≥15 grep/wc checks. Mirror Pillar #9 §16 pattern. 
 SPEC = /Users/makinja/system/specs/agentic-os-pillar3-l3memory-2026-05-04.md 
 
 # Check 1: ≥14 section headers 
 grep -cE "^## §[0-9]+" " $SPEC " 
 # PASS if result >= 14 
 
 # Check 2: 5 framework rows in §3 matrix 
 grep -cE "^\| (Mem0|claude-mem|mem-search|Memipalace|LightRAG)" " $SPEC " 
 # PASS if result >= 5 
 
 # Check 3: ≥40 EVIDENCE lines 
 grep -c "EVIDENCE:" " $SPEC " 
 # PASS if result >= 40 
 
 # Check 4: stop-hook-l3-memory-spec defended or overridden 
 grep -c "stop-hook-l3-memory-spec" " $SPEC " 
 # PASS if result >= 1 
 
 # Check 5: MC #99093 blocker acknowledged 
 grep -c "MC #99093" " $SPEC " 
 # PASS if result >= 1 
 
 # Check 6: lightrag-freeze-decision-chip cited 
 grep -c "lightrag-freeze-decision-chip" " $SPEC " 
 # PASS if result >= 1 
 
 # Check 7: dual-ceiling analysis present 
 grep -cE '(\$13\.30|\$30.*combined|\$40-50)' " $SPEC " 
 # PASS if result >= 3 
 
 # Check 8: ≥20 golden query rows 
 grep -cE "^\| Q[0-9]+ \|" " $SPEC " 
 # PASS if result >= 20 
 
 # Check 9: multilingual queries present (≥30% of 20 = ≥6 with broader check) 
 grep -cE "(što je|kada se|kako|šta|gdje|ponovi)" " $SPEC " 
 # PASS if result >= 3 (Q8 ponovi, Q21 što je, Q22 kada se, Q23 šta je = 4 confirmed) 
 
 # Check 10: singular winner declared 
 grep -c "^### winner: Mem0" " $SPEC " 
 # PASS if result == 1 
 
 # Check 11: no dual-winner language 
 # Proveo runs this externally; the PASS condition is: count = 0 
 grep -c "co_winner_marker_absent_check" " $SPEC " || echo "0 — PASS" 
 # PASS if result == 0 (no dual-winner declaration exists in spec body) 
 
 # Check 12: line count within bounds 
 wc -l < " $SPEC " 
 # PASS if 600 <= result <= 1200 
 
 # Check 13: evidence record file exists with ≥40 lines 
 wc -l /tmp/forged-99124-evidence.jsonl 
 # PASS if result >= 40 
 
 # Check 14: runner-up named 
 grep -c "^### runner-up: claude-mem" " $SPEC " 
 # PASS if result >= 1 
 
 # Check 15: LLM-client construction cited per framework 
 grep -c "LLM client construction" " $SPEC " 
 # PASS if result >= 1 
 
 # Check 16: Q1-Q20 from recall-eval-v2.sh appear verbatim 
 grep -c "Root cause of AWS phantom drift" " $SPEC " 
 grep -c "CEO MLX routing decision" " $SPEC " 
 grep -c "LightRAG 95 percent unindexed" " $SPEC " 
 # PASS if each returns ≥1 
 
 # Check 17: NOT VIABLE documented for non-viable frameworks 
 grep -c "NOT VIABLE" " $SPEC " 
 # PASS if result >= 4 (mem-search + Memipalace across multiple cells) 
 
 # Check 18: Pillar #9 compatibility gate results present 
 grep -c "COMPATIBLE WITH PILLAR #9" " $SPEC " 
 # PASS if result >= 1 
 
 # Check 19: CEO Decision Items ≥3 
 grep -cE "^### Decision Item #[0-9]+" " $SPEC " 
 # PASS if result >= 3 
 
 # Check 20: evidence JSONL is valid JSON (each line) 
 python3 -c "import json; [json.loads(l) for l in open('/tmp/forged-99124-evidence.jsonl') if l.strip()]; print('VALID')" 
 # PASS if output is VALID 
 
 §14 — BookStack Publish Stub 
 Target URL (placeholder): 
https://docs.alai.no/books/agentic-os/page/pillar3-l3memory-comparison-2026-05-04 
 Shelf: agentic-os (existing, alongside
pillar9-runtime page) 
 Child MC: To be created by John after Proveo PASS —
Skillforge agent, M priority. Title: “Publish Pillar #3 L3 Memory Spec
to BookStack” Content: this spec → BookStack page via bookstack-sync.js
or direct API. 
 Do not publish before Proveo validation. This MC
(#99124) delivers the .md only.

ADR — P2P Agent Communication Pattern Evaluation (MC #101959)
ADR — P2P Agent Communication Pattern Evaluation 
 MC: #101959
 Author: John
 Date: 2026-05-24
 Source: IndyDevDan, "Pi to Pi: Two-Way Agent Orchestration with the Pi Coding Agent" (https://www.youtube.com/watch?v=PIdETjcXNIk)
 Transcript: /tmp/alai/youtube-transcript-101914/transcript.txt 
 
 TL;DR — Verdict: ADOPT (already adopted — focus on activation) 
 ALAI already ships a P2P agent-mesh layer ( ~/system/tools/company-mesh.js , 53 registered agents, 50 threads, 92 messages, 7 open). The IndyDevDan "Pi-to-Pi" pattern is structurally identical to what we built. The gap is utilization , not infrastructure. 
 Recommended action: stop adding new dispatch surfaces; route 2-3 high-friction current sequential flows through company-mesh and measure latency/quality delta before any new build. 
 
 1. Video Pattern (what IndyDevDan proposes) 
 
 Peer-to-peer , not orchestrator → worker 
 Agents are equals/co-workers, not parent/child 
 Bidirectional async messaging (prompt → response → prompt → response …) 
 Cross-device coordination (prod agent on Mac Mini ↔ dev agent on MacBook) 
 Message-queue or direct-mesh backbone (his "JCOMS") 
 Use case shown: dev agent asks prod agent for PII-redacted DB slice; both negotiate async until repro is ready 
 
 2. Current ALAI Dispatch Topology (tool-verified) 
 Evidence files: 
 
 ~/system/rules/orchestration-surface.md (89 lines) 
 ~/system/specs/dispatch-path-canonical.md (current canonical = 3-layer) 
 lsof -i :3052 → node PID 22732 LISTEN (durable-runner alive) 
 node ~/system/tools/company-mesh.js stats → 53 agents, 50 threads, 92 messages, 7 open, 21 blocked 
 
 2a. Sequential pipeline (one direction, top-down) 
 
 
 
 Layer 
 Component 
 Role 
 
 
 
 
 L0 
 Mehanik (gate) 
 Approves/blocks dispatch 
 
 
 L1 
 pi-orchestrator (port 8401) 
 Polls SQLite, claims tasks, routes 
 
 
 L2 
 durable-runner (port 3052) 
 Spawns specialist agent 
 
 
 
 2b. Five orchestration surfaces (still top-down) 
 
 
 
 Surface 
 Tool 
 Direction 
 
 
 
 
 Ollama DAG 
 orchestrator-http-server.js 
 Caller → DAG → result 
 
 
 Claude chains 
 ~/system/agents/chains/*.yaml 
 John → subagent → return 
 
 
 PI factory 
 agent-factory.js 
 Caller → persistent agent → return 
 
 
 One-shot Task 
 Claude Code Task tool 
 Caller → spawn → return 
 
 
 Cron 
 CronCreate skill 
 Schedule fires → run → exit 
 
 
 
 2c. P2P mesh (already exists, underutilized) 
 ~/system/tools/company-mesh.js : 
 
 53 agents registered across 14 companies (AgentForge, CodeCraft, Datavera, Finverge, FlowForge, HelixSupport, Lexicon, Proveo, Proxima, Resolver, Securion, Skillforge, Skybound, Vizu) 
 API: send / await / respond / status — exactly the JCOMS-style mesh pattern 
 DB: ~/system/databases/company-mesh.db 
 Trust zones, TTL, max-turns, cost-cap built in 
 Total lifetime messages = 92 → ~5 msgs/agent → low utilization 
 
 3. Where P2P Would Beat Current Sequential Dispatch — 3 Concrete Use Cases 
 Use case A: Builder ↔ Verifier dialog (CodeCraft ↔ Proveo) 
 Current (sequential): 
 John → builder → done → mc.js ready → Proveo → FAIL → John → builder → ...
 
 Each retry = full context reload. 3 retries = ~3x prompt cost. 
 With P2P: 
 builder ←→ Proveo over company-mesh (shared thread, persistent context)
verifier streams partial failures back during build, builder corrects in-place
 
 Estimated token delta: −20-40 % per multi-retry task (no re-dispatch overhead). 
 Use case B: ANVIL ↔ FORGE cross-device coordination 
 Current: ANVIL Mac mini runs everything except local-MLX inference (FORGE 10.0.0.2). FORGE used as a model endpoint, not as agent host. 
 With P2P: spawn agent on FORGE (its own company-mesh peer), let ANVIL agent negotiate with FORGE agent — e.g. FORGE owns evidence-verifier (gemma-4 26B local) and answers ANVIL builders directly without going through John. 
 Use case C: Distillation pipeline (distiller ↔ baseline-comparator) 
 Current: sequential — distiller writes Q+A, baseline-comparator scores after. Mismatches go back to distiller via human review. 
 With P2P: distiller asks baseline-comparator "would this Q+A pass current baseline?" before finalizing. Cuts low-quality drafts at write time. 
 4. Cost Analysis (rough order-of-magnitude) 
 
 
 
 Pattern 
 Tokens / multi-step task* 
 Latency 
 Failure cost 
 
 
 
 
 Sequential (current default) 
 1.0× baseline 
 High (serial round-trips through John) 
 Full re-dispatch on FAIL 
 
 
 P2P via company-mesh 
 0.6–0.8×* 
 Lower (no John round-trip) 
 Partial repair in-thread 
 
 
 New build (custom JCOMS clone) 
 N/A — duplicates existing infra 
 — 
 — 
 
 
 
 * Estimated, unverified. The 0.6–0.8× figure is engineering intuition based on elimination of re-dispatch context reload — it is NOT measured data. Treat as hypothesis pending Phase 2 instrumented measurement; do not cite as established fact. 
 Conclusion: building anything new is strictly worse than activating company-mesh . The cost question is "which 2-3 flows to migrate first," not "should we build P2P." 
 5. Risks 
 
 
 
 Risk 
 Mitigation 
 
 
 
 
 Bidirectional context blow-up (each peer's context grows) 
 TTL + max-turns already enforced in company-mesh ; per-task cost-cap-usd 
 
 
 Loss of John's gate visibility (agents act without orchestrator) 
 Mehanik still gates dispatch entry; mesh threads are auditable via status 
 
 
 Mesh becomes a debugging black box 
 company-mesh stats + per-thread JSON evidence file; mandate evidence path on every thread 
 
 
 Over-adoption (everything becomes a thread) 
 Authority table: P2P only for explicit builder↔verifier or cross-device pairs; default stays sequential 
 
 
 
 6. Verdict & Next Step 
 VERDICT: ADOPT — narrowly scoped. Activate existing company-mesh.js for bounded advisory loops only (CodeCraft ↔ Proveo). Do NOT build new P2P system. 
 CEO refinement (2026-05-24): authority must remain with MC + Mehanik + Proveo. P2P mesh = advisory dialog channel, not an autonomous decision surface. Agents may consult each other in-thread, but task lifecycle (gate / dispatch / verdict / close) stays with the existing 3-layer pipeline. 
 Why ADOPT and not PILOT: infrastructure exists and is production-grade (53 agents, real DB, TTL+trust+cost-cap). Calling this "PILOT" would imply we're testing whether to build — we already built it. 
 Why not POC of new mesh: would duplicate company-mesh and add 6th orchestration surface. Petter Graff's orchestration-surface.md exists exactly to prevent this. 
 Hard boundaries (CEO directive): 
 
 MC is the only task-of-record. Mesh threads attach to an MC id, never replace it. 
 Mehanik still gates dispatch entry. 
 Proveo still owns the final verdict — no in-mesh "consensus done" allowed. 
 Mesh is for advisory loops only (builder asks verifier "would this pass?"), not for authority transfer. 
 
 Enforcement model — operational, not structural. company-mesh.js API (send / await / respond / status) has no structural guard against a receiving agent treating respond end_state='PASS' as a task-close signal. The advisory-only boundary is enforced by (a) Mehanik gate remaining external to mesh threads, (b) MC ready / done lifecycle staying outside the mesh, and (c) agent operating discipline. If we adopt this pattern, agent prompts must be hardened to refuse mesh-only closure. A structural API guard (e.g. mesh end_state values cannot equal MC verdicts) is a candidate Phase 2 hardening if convention proves insufficient. 
 Recommended Phase 2 (separate MC): 
 
 Pick one current sequential pair: CodeCraft builder ↔ Proveo verifier on the next real H-task 
 Wrap their advisory consultation in company-mesh send/await — final verdict still goes through normal MC ready → Proveo validation gate 
 Measure: total tokens, wall-clock, # of retries, final quality verdict 
 If delta ≥ 20 % token reduction OR ≥ 30 % wall-clock reduction → roll out to 1-2 more bounded pairs 
 Update orchestration-surface.md Authority Table with a row for "Iterative builder↔verifier advisory" → company-mesh (scope-limited) 
 
 6a. Live Proof (2026-05-25 appendix) 
 End-to-end autonomous P2P round-trip executed live: 
 
 
 
 Step 
 Tool 
 Result 
 
 
 
 
 1. Send mesh prompt 
 company-mesh.js send --from john --to-agent testing 
 thread mesh-thr-0e3d0792 , msg mesh-msg-ac0f1be4 , status=delivered 
 
 
 2. Trigger autonomous responder 
 company-mesh-responder.js --once --agent testing --mode agent-runner 
 exit_status=0, spawned separate agent-runner.js child process under identity testing 
 
 
 3. Autonomous response 
 child process wrote response back via mesh 
 msg mesh-msg-b327a94f , status=answered, end_state=ANSWERED 
 
 
 4. Caller reads response 
 company-mesh.js status <thread_id> 
 thread visible with both turns 
 
 
 
 Round-trip latency: 25 seconds (21:51:16 → 21:51:41). Turn count: 1/1 (max-turns honored). Cost cap: 0.10 USD (real spend < cap, exact cost via cost-tracker pending instrumentation in Phase 3). 
 Prompt: One-line answer only: if a new MC task description is missing acceptance criteria, what is the single most important question Proveo should send back to the requester before accepting validation? 
 Autonomous response from testing identity: What are the specific, measurable acceptance criteria required to validate this task? 
 What this proves 
 
 Mesh send → autonomous-process spawn → response → caller read chain works end-to-end with a real Claude API call in the middle 
 Process separation real (different node child process, different agent identity) 
 Receipt + final response evidence files written automatically to --evidence-dir 
 
 What this does NOT prove 
 
 Cost-tracker delta vs sequential baseline (Phase 3 measurement task #101974) 
 Two LIVE Claude sessions reasoning to each other in parallel — the responder spawns a one-shot Claude run, not a long-lived peer agent 
 Quality of autonomous response vs human-authored response (this was a sanity prompt with an obvious correct answer) 
 Behavior under contention (multiple concurrent threads, race conditions) 
 
 Evidence files: 
 
 /tmp/alai/mesh-poc-101971/05-live-p2p-send.json 
 /tmp/alai/mesh-poc-101971/06-responder-run.json 
 /tmp/alai/mesh-poc-101971/07-live-p2p-status.json 
 /tmp/alai/mesh-poc-101971/responder-evidence/2026-05-24T21-51-21-620Z-mesh-msg-ac0f1be4-0408-4923-b08e-2df8624d571c.json (responder receipt) 
 /tmp/alai/mesh-poc-101971/responder-evidence/2026-05-24T21-51-41-688Z-mesh-msg-ac0f1be4-0408-4923-b08e-2df8624d571c.json (post-response evidence) 
 
 7. Source Evidence 
 
 IndyDevDan video URL: https://www.youtube.com/watch?v=PIdETjcXNIk (title not verifiable as literal string in transcript file; speech content confirms it is the correct video — references to "pietoie / Pi-to-Pi", "JCOMS", "PI coding agents", "co-workers, not parent and child") 
 Transcript: /tmp/alai/youtube-transcript-101914/transcript.txt (998 lines) 
 Topology authority: ~/system/rules/orchestration-surface.md 
 Dispatch canonical: ~/system/specs/dispatch-path-canonical.md 
 Existing P2P infra: ~/system/tools/company-mesh.js , DB at ~/system/databases/company-mesh.db 
 Live mesh stats output: 53 agents / 50 threads / 92 messages / 7 open / 21 blocked