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About

Long-term memory for AI coding agents — remembers project rules across sessions at $0 AI cost (MCP server, SQLite/PostgreSQL). 90.26% on Snap LoCoMo.

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README.md

Original

Krusch Context MCP

Krusch Context MCP — DolphinBench 91.17% Queued (UUID c047b063) & 90.67% Clean-Room Grounded v0.3, LoCoMo (90.26%), ICLR 2026 BEAM (93.00% 10M IE Recall)

Local MCP server: five tools to store, retrieve, and retire project decisions. SQLite by default — powering the $0.00 Zero-LLM Raw-RAG + 4-Way RRF memory substrate behind 91.17% (547/600 @ $24.54, Gemini 4 Argon Low — uploaded dolphinbench.ai submission UUID c047b063, status: "queued" awaiting leaderboard acceptance; exceeds current 70.67% published leaderboard entry in independent 600/600 evaluation), 90.67% (544/600 @ $30.54, Argon Low + Grounded v0.3 — self-run clean-room evaluation with highest Riley slice 181/200 = 90.50% and published SHA-256 audit logs; 98.33% Global Oracle@28), and 57.33% (344/600 @ $0.00, Wave-6 krush420/gemma4-31b-sre-lora + Grounded — highest open-weights score in self-run 28-run evaluation; 54.17% [325/600] stock submission UUID 288671ad uploaded & queued on dolphinbench.ai) across 28 Official DolphinBench runs (16,800 traces), 90.26% (1,390/1,540, 92.66% Oracle@2, 95.77% recall, 182 ms) on Snap LoCoMo (ACL 2024), and 93.00% 10M-token IE recall on ICLR 2026 BEAM.

Node.js 22+ License: MIT MCP: 5 Verbs Storage: SQLite-First DolphinBench Queued Submission DolphinBench Open-Weights LoCoMo ACL 2024 ICLR 2026 BEAM Paper #27 PDF Article: What Makes It Special Docs: What Makes It Special HOL Guard Catalog M8ven Score

📖 Read the Systems & Benchmark Deep Dives: Read Paper #27 — Return on Memory (91.17% Queued dolphinbench.ai Submission UUID c047b063 & 90.67% Clean-Room Grounded v0.3) (PDF), Paper #26 — What DolphinBench Taught Me About Local AI (Highest Open-Weights Score 57.33% Wave-6 / 56.67% Stock+Grounded / 54.17% Queued Stock UUID 288671ad Gemma 4 31B @ $0.00), and Beyond Flat Vector RAG: What Makes krusch-context-mcp Special (docs/WHAT_MAKES_IT_SPECIAL.md).

Krusch Context MCP gives AI coding agents (Cursor, Claude Code, Windsurf, Antigravity) persistent working memory across sessions. It captures architectural decisions, invariants, lessons, and bug diagnoses, warns on near-duplicate memories, maintains temporal lineage when facts are superseded or invalidated, and audits diffs against active constraints.

🎬 90-Second Narrated Walkthrough (1080p)

Watch the Krusch Context MCP v2.0 Narrated Walkthrough (01:31 · 1080p HD)

▶️ Stream 1080p HD Walkthrough (01:31, with audio) · Interactive Player on krusch.dev · Local MP4 (15.6 MiB) · Closed Captions (.srt)

🚫 What This Is Not

  • Not a codebase AST or Git indexer: Code symbol search, call graphs, and Git DAG traversal belong in krusch-git or your IDE's native search.
  • Not a 61-tool cafeteria: Collapsed to strictly 5 canonical verbs (~350 prompt tokens) for boring reliability.
  • Not a cloud SaaS platform: Runs 100% locally on your machine with zero external cloud dependencies.
  • Not a monolithic domain suite: Legal compliance (krusch-law), document ingestion (krusch-nexus), and contract graphs (krusch-biz) live in separate companion repositories.

⚡ 30-Second Quickstart

Requires Node.js >= 22.0.0. Run once inside your project repository:

npx krusch-context-mcp init

This command will:

  1. Validate Node >= 22.0.0 and initialize .agent/context.db via node:sqlite.
  2. Clear stale MCP tool schemas from local IDE caches.
  3. Create your project .env with sensible defaults.
  4. Seed initial project context and run a diagnostic health check.
  5. Print copy-paste JSON configurations for Cursor, Claude Code, and Claude Desktop.

🛠️ The 5-Verb Agent Loop

Krusch Context MCP exposes strictly 5 canonical verbs designed to fit into standard coding workflows:

Verb Short Alias Purpose
krusch_context_retrieve retrieve Pulls active memories, steering rules, and project state briefings within a strict limit_tokens budget. Returns citations.
krusch_context_remember remember Writes episodic facts and steering nuggets. Detects near-duplicates (similarity >= 0.85) and proposes revise(action: 'supersede') without blocking contrasting rules.
krusch_context_revise revise Updates stale knowledge (supersede with lineage tracking) or revokes obsolete rules (invalidate with mandatory reason).
krusch_context_nudge nudge Pre-commit auditor that checks proposed diffs against active project invariants (max 1–3 findings). Trigger defaults to pre_commit or manual; rejects every_turn.
krusch_context_health health Reports storage mode, memory counts by closed taxonomy, and 30-day TTL decay review.

🤖 Recommended Agent Protocol

Coding agents should interact with memory at specific lifecycle boundaries:

┌─────────────────────────────────────────────────────────────┐
│ 1. SESSION START                                            │
│    Agent calls retrieve(query: '*', include_state: true)    │
│    -> Obtains active invariants, recent decisions, blockers │
└──────────────────────────────┬──────────────────────────────┘
                               │
┌──────────────────────────────▼──────────────────────────────┐
│ 2. DURING WORK                                              │
│    Agent calls remember(content: '...', category: '...')    │
│    -> If near-duplicate returned, agent calls revise(...)   │
└──────────────────────────────┬──────────────────────────────┘
                               │
┌──────────────────────────────▼──────────────────────────────┐
│ 3. PRE-COMMIT / PRE-EDIT                                    │
│    Agent calls nudge(trigger: 'pre_commit')                 │
│    -> Audits code against active invariants (max 3 findings)│
└─────────────────────────────────────────────────────────────┘

🛡️ Safe Memory Writes: Closed Taxonomy & Deduplication

To prevent memory hallucination and database pollution:

  1. Closed Taxonomy: Every write must use one of 5 categories:
    • decision: Architectural choices, design directions, trade-offs.
    • invariant: Non-negotiable rules (e.g., "Use node:sqlite, zero native compiles").
    • bug: Solved defects, regressions, and root-cause analyses.
    • lesson: Tactical insights and framework discoveries.
    • blocker: Active dependencies or external obstacles.
  2. Near-Duplicate Detection: If a proposed memory matches an existing active record ($\ge 0.85$ cosine similarity), the memory is saved with a warning proposing revise(action: 'supersede', target_id: ...). This protects contrasting rules (e.g., "Allow CORS" vs "Do not allow CORS") while flagging twin facts.
  3. Compulsory Invalidation Reasons: Calling revise(action: 'invalidate') strictly requires a non-empty reason.
  4. Focused Audits: nudge caps findings at 3 with concrete evidence, rejecting every_turn triggers to eliminate auditor nagware.
  5. Span-Grounded Provenance & Drift Self-Healing: Passing provenance: { file, locator, quoted_sentence } to remember computes a normalized SHA-256 anchor_hash and line range (start_line..end_line). On subsequent retrieve(include_state: true) calls, the engine verifies the span on disk—automatically healing line numbers when code shifts above it, or flagging [DRIFTED] when the underlying code changes.

🧠 Embedding Reality & Offline Mode

  • Zero-GPU Default: If neither Ollama nor an API key is present, the engine runs 100% offline using deterministic keyword extraction, recency scoring, and heuristic tag indexing.
  • Local Ollama (Optional): Set OLLAMA_URL="http://127.0.0.1:11434" with EMBED_MODEL="snowflake-arctic-embed2" (8,192-token context, 1024 dims, ~664 MB VRAM, asymmetric query: retrieval via /api/embed; also supports qwen3-embedding MRL 1024d, nomic-embed-text, and legacy bge-large).
  • Remote Cloud (Optional): Set OPENROUTER_API_KEY="sk-..." with EMBED_MODEL="baai/bge-large-en-v1.5" (or any 1024-dim OpenRouter embedding model) for zero-local-VRAM embeddings.

🐘 When Do You Need PostgreSQL?

  • Use SQLite (Default): Perfect for single developers, local IDE sessions, and projects with thousands of decisions—featuring compact Float32Array binary vector blobs (4 KB/row) and an external-content Porter-stemmed FTS5 lexical index. Zero Docker, zero setup.
  • Use PostgreSQL (STORAGE_MODE=postgres): Required only when scaling to multi-agent swarms across different machines needing centralized persistence and server-side pgvector HNSW indexing.

🔄 Migration from v1.6 / v1.7

  • Tool Aliases: Legacy tool names (add_memory, search_memory, compile_state, supersede_memory, invalidate_memory, proactive_nudge) are automatically aliased to the 5 verbs with a deprecation notice.
  • Database Path: v1.7 used .agent/memory.db. v1.8+ uses .agent/context.db. Run npx krusch-context-mcp init to migrate.
  • See docs/MIGRATION.md for the complete mapping table.

🧩 Sibling Architecture

┌─────────────────────────────────────────────────────────────┐
│                 AI Coding Agent (Cursor / Claude)           │
└──────────────────────────────┬──────────────────────────────┘
                               │
               ┌───────────────┴───────────────┐
               ▼                               ▼
┌─────────────────────────────┐ ┌─────────────────────────────┐
│  krusch-context-mcp (v2.0)  │ │   krusch-git (External)     │
│   Project Memory & Steering │ │   Codebase RAG & AST Search │
│       (5 Canonical Verbs)   │ │      (7 Specialized Tools)  │
└──────────────┬──────────────┘ └─────────────────────────────┘
               │
               ▼
┌─────────────────────────────┐
│     .agent/context.db       │
│   (node:sqlite, Node ≥22)   │
└─────────────────────────────┘

Other specialized tools (krusch-nexus, krusch-law, krusch-biz) run as independent MCP servers in their own repositories.

💻 IDE Configuration

Cursor (.cursor/mcp.json)

{
  "mcpServers": {
    "krusch-context": {
      "command": "npx",
      "args": ["-y", "krusch-context-mcp"]
    }
  }
}

Claude Code

claude mcp add krusch-context -- npx -y krusch-context-mcp

Claude Desktop (claude_desktop_config.json)

{
  "mcpServers": {
    "krusch-context": {
      "command": "npx",
      "args": ["-y", "krusch-context-mcp"]
    }
  }
}

📊 External Benchmarks & 3-Category Verification Taxonomy

Ecosystem Verification Taxonomy: Evaluated across 335,000+ traces and 14 external benchmark suites, with published SHA-256 audit logs and reproducible evaluation artifacts — separating (1) CI-Verified Upstream PRs such as RouteWorks/RouterArena PR #169 (0.7263 across 8,400/8,400 live receipts — github-actions[bot] CI-verified upstream PR), (2) Uploaded & Queued Leaderboard Submissions on dolphinbench.ai (status: "queued" awaiting leaderboard acceptance, led by UUID c047b063 at 91.17% [547/600 @ $24.54] and open-weights stock UUID 288671ad at 54.17% [325/600 @ $0.00]), and (3) Independently Reproducible Clean-Room Evaluations with SHA-256-pinned audit logs in krush420/sovereign-g4-benchmarks.

Official DolphinBench (91.17% Queued UUID c047b063 @ $24.54, 90.67% Clean-Room Grounded v0.3 @ $30.54 & 57.33% Open-Weights @ $0.00) — Return on Memory

Evaluating all 600/600 multi-app workspace tasks across 13,539 conversational sessions (1,500,265 o200k_base tokens) under the official openai/gpt-5.6-sol judge (28 official 600-task evaluations = 16,800 official openai/gpt-5.6-sol traces across 6 model families: 8 uploaded & queued on dolphinbench.ai + 20 clean-room controls = 28/28 submission.validate passed), my byte-identical 0o444 read-only frozen Zero-LLM Raw-RAG (256/32-token windows) + Porter-FTS5 BM25 + snowflake-arctic-embed2 (1024d) 4-Way RRF node:sqlite store ($0.00 ingestion) achieves 91.17% (547/600 @ $24.54, Gemini 4 Argon Low — uploaded dolphinbench.ai submission UUID c047b063, status: "queued" awaiting leaderboard acceptance; exceeds current 70.67% published leaderboard entry in independent 600/600 evaluation), 90.67% (544/600 @ $30.54, Argon Low + Grounded v0.3 — self-run clean-room evaluation with highest Riley slice 181/200 = 90.50% and published SHA-256 audit logs; Argon Low Oracle@2 = 94.67% [568/600], Argon Oracle@4 = 96.83% [581/600], Global Oracle@28 = 98.33% [590/600]), 81.50% (489/600 @ $1.35, 1.57x faster, +10.83 pp > published 70.67% Mem0 entry @ 71.1x lower cost) model-matched on OpenAI GPT 5.6 Luna (High + Grounded) (81.33% Std.), and 57.33% (344/600 @ $0.00, Wave-6 krush420/gemma4-31b-sre-lora + Grounded — highest open-weights score in self-run 28-run evaluation; 56.67% Stock + Grounded; 54.17% [325/600] stock submission UUID 288671ad uploaded & queued on dolphinbench.ai; $0.00 Open-Weights Oracle@17 = 83.17% [499/600]):

Agent Reader + Memory Substrate Overall (600) Persona Breakdown (Morgan / Alex / Riley) Checks / Key Delta Total Cost
Sovereign Raw-RAG + Gemini 4 Argon (Low) 91.17% (547/600, uploaded dolphinbench.ai submission UUID c047b063, status: "queued") 95.5% (191) / 89.5% (179) / 88.5% (177) +20.50 pp (+123 tasks) > current 70.67% published Mem0 entry in independent 600/600 eval · Mech 92.22% (1,221/1,324) · Sem 93.62% (558/596) · 35.38s $24.54 ($0.00 ingest, 57.1% cache hit, ~3.9x cheaper)
Sovereign Raw-RAG + Gemini 4 Argon (Low) + Grounded v0.3 90.67% (544/600, clean-room eval, highest Riley slice 90.50%) 94.5% (189) / 87.0% (174) / 90.5% (181, highest Riley slice) Argon Low Oracle@2 = 94.67% (568/600) · Argon Oracle@4 = 96.83% (581/600) · 89.17% (535) v0.2 control · +21 unique wins vs. Std. $30.54 ($23.92 v0.2, SHA-256 665fb7ad...)
Sovereign Raw-RAG + Gemini 4 Argon (Medium) 89.17% (535/600, queued UUID 29389157) 92.0% (184) / 87.0% (174) / 88.5% (177) Mech 89.77% (1,193/1,329) · Sem 92.22% (545/591) · 55.27s $24.57 (56.5% cache hit)
Sovereign Raw-RAG + GPT 5.6 Luna (High, + Grounded / Std. / + 2PC) 81.50% (489) / 81.33% (488) / 80.50% (483) 87.0% (174) / 78.5% (157) / 79.0% (158) (+Grounded) · 176/156/156 (Std.) +10.83 pp (+65 tasks) > published 70.67% Mem0 entry @ 71.1x lower cost · 30.03s (1.57x faster) · Oracle@2 = 89.33% (536/600), Luna Oracle@5 = 91.83% (551/600) $1.35 / $1.38 (62.9% cache hit)
Sovereign Raw-RAG + Gemini 4 Argon (Turn-0 Briefing-Only, Med / Low) 79.17% (475) / 78.00% (468) 86.0%/82.5% · 77.5%/77.5% · 74.0%/74.0% 0 follow-up search_persona_memory calls (26.93s / 21.49s; 2×2 $\Delta\Delta = +3.17\text{ pp}, p = 0.0320$) $16.91 / $15.76 (+7.33 pp > Mem0 @ 6.1x lower cost)
Sovereign Raw-RAG + GPT 5.6 Luna (Low, Std. / + 2PC) 76.67% (460/600) / 76.50% (459/600) 85.0% (170) / 72.5% (145) / 72.5% (145) (Std. Harness) · 176/144/139 (+2PC) +6.00 pp (+36 tasks) > published 70.67% Mem0 entry @ 87.4x lower cost · Luna Oracle@5 = 91.83% (551/600) · 40.85s $1.10 / $1.12
Current Published Leaderboard Entry: Hermes + GPT 5.6 Luna + Mem0 70.67% (424/600) 78.0% (156) / 66.0% (132) / 68.0% (136) 37.69s median · Current highest published dolphinbench.ai entry (Supermemory 59.17% @ $345.64) $96.21
krush420/gemma4-31b-sre-lora & Google Gemma 4 31B (30.7B Dense Q4_K_M) 57.33% (344, highest open-weights in 28-run self-run evaluation) / 56.67% (340, Stock+Grounded) / 54.17% (325, uploaded & queued UUID 288671ad) 74.5% (149) / 51.5% (103) / 46.0% (92) (Wave-6+Grounded) · 146/97/97 (Stock+Grounded) · 144/95/86 (Stock) 54.83% (329) Wave-5 · 53.67% (322) Wave-4 · 52.00% (312) OFF · Gemma 4 Oracle@6 = 71.83% (431/600) · Beats ALL 8 non-GPT-5.6-Luna cloud stacks up to $1,830.57 $0.00
krush420/qwen3-coder-30b-sre-lora & Qwen3-Coder-30B-A3B (3.3B active) 43.00% (258, highest Qwen/MoE +Grounded) / 42.67% (256, Wave-4) / 42.00% (252, +2PC & Stock) 62.5% (125) / 31.5% (63) / 35.0% (70) (Wave-4+Grounded) · 111/70/75 (Wave-4) INV-STACK-10 2PC: -95.2% dup MCP calls (42 -> 2) & 30B Oracle@2 = 50.33% · 38.50% (231) Wave-6 control · 30B Oracle@5 = 61.17% (367/600) $0.00
gpt-oss:120b Wave-4 QLoRA+DPO / Stock (5.1B active) 39.50% (237) / 39.17% (235) 54.5% (109) / 31.5% (63) / 32.5% (65) (Wave-4) +7.33 pp Raw-RAG + 4-Way RRF lift over raw turns (9.62s Stock / 16.83s Wave-4) $0.00
Qwen3-Coder-Next-80B (38.50%) · Qwen2.5-Coder-32B (35.33%) · Hermes-4-14B (34.50%) 231 / 212 / 207 of 600 104/68/59 · 97/59/56 · 99/57/51 Scaling & domain controls: Gemma 4 31B > Qwen2.5-Coder-32B by +22.00 pp Wave-6 Grounded / +18.83 pp Stock ($p = 1.35 \times 10^{-16}$); 14B ties 32B ($p = 0.729$) $0.00
Hermes-3-Llama-3.1-8B Wave-4 LoRA+DPO (Q4_0) 18.33% (110/600) 26.0% (52) / 14.5% (29) / 14.5% (29) 15.7x lift over 1.17% stock (Ollama .System fix + BF16 LoRA+DPO) $0.00
  • Four Return on Memory Scaling Laws & 4-Vertex Pareto Frontier: (1) 98.33% (590/600: Morgan 196, Alex 199, Riley 195) Global Oracle@28 retrieval sufficiency (<= 1.67% zero-LLM Raw-RAG indexing miss) across the frozen 0o444 SQLite store (94.67% [568/600] Argon Low Oracle@2, 96.83% [581/600] Argon Oracle@4, and 83.17% [499/600] $0.00 Open-Weights Oracle@17 (+12.50 pp above published 70.67% Mem0 entry)) and controlled memory-vs.-reader attribution via model-matched GPT 5.6 Luna (High) at 81.50% (489/600 Grounded) / 81.33% (488/600 Std.) @ $1.35 (52.0% [+10.67 pp, $p = 5.96 \times 10^{-9}$] of my +20.50 pp lead over the 70.67% published Mem0 baseline comes purely from $0.00 Raw-RAG memory, 48.0% [+9.83 pp] from Argon Low), establishing a 4-vertex 100% Sovereign Raw-RAG Pareto frontier ($0.00 [57.33% gemma4-31b-sre-lora / 54.17% Stock] → $1.10 [76.67% Luna Low] → $1.35 [81.50% Luna High + Grounded] → $24.54 [91.17% Argon Low]); (2) 4.19x superlinear LBF=4+ multiplier (89.33% [67/75] Argon Low vs. 21.33% [16/75] 30B & Gemma 4 31B Stock, 16.75x vs. 8B); (3) Scale-dependent Goldilocks reasoning & +22.00 pp / +18.83 pp ($p = 1.35 \times 10^{-16}$) post-training domain specialization (2×2 Thinking × Search $\Delta\Delta = +3.17\text{ pp}, p = 0.0320$ on Argon; +2.17 pp thinking lift, +2.50 pp Grounded lift, & +3.17 pp Wave-6 QLoRA+DPO lift on Gemma 4 31B); and (4) Flatter corpus-entropy decay (-4.00 pp Argon Low + Grounded v0.3 [94.5% → 90.5%, highest Riley slice 181/200] / -7.00 pp Argon Low / -3.50 pp Argon Medium Morgan → Riley vs. -10.00 pp on Mem0 [78.0% → 68.0%, -12.00 pp to Alex 66.0%] and -32.50 pp on MiniMax M3 + Hindsight).
  • Verified Upstream Queue & Open Artifacts: 8 sovereign submission .zip archives (Argon Low UUID c047b063-822c-49fb-8b36-27c3763d6ca7 [91.17% / 547/600], SHA-256 80a199a60aef65f0d963d0402a2e3b0af99b82ed63e2f422001234b6ed770f0b; Argon Medium UUID 29389157-b01b-42b5-8193-17d9a496f580 [89.17%], SHA-256 9a7fa41fb3d1cf5f71f0c3cf74a0c97f8f39d50e26a7dd484c323f7719ecb7a0; uploaded & queued open-weights stock Gemma 4 31B UUID 288671ad-afe0-40a3-93af-b834c9bbd306 [54.17% / 325/600], SHA-256 2a23c2a023473a6642f5a61f1d99ff569f3d50ac53e60576f8195eef659c254b; plus 30b_wave4, 30b_stock, 120b_wave4, 120b_stock, and 8b_wave4) hold status: "queued" on dolphinbench.ai awaiting maintainer merge (dolphinbench.ai/leaderboard/results.json currently lists the 13 commercial cloud baselines), and all 28/28 official archives (8 queued flagships + 20 clean-room controls) passed submission.validate (error_code: null). Read Paper #27 (HTML) · Paper #27 (PDF) · Paper #26 (Local AI Ablation) · 49 SHA-256 Hugging Face Audit Logs across 64 files (48 raw JSON + 1 live PR #169 receipt bundle, 95/95 ecosystem gates) (krush420/sovereign-g4-benchmarks).

Official Snap LoCoMo (ACL 2024, 1,540/1,540 QA Probes)

  • Zero-gaming, clean-room evaluation across all 10 multi-month conversations (5,882 turns, 1,515 256/32-token Raw-RAG windows) and 1,540/1,540 standard Category 1–4 QA probes (snap-research/locomo SHA-256 79fa87e9...ea698ff4) using the unmodified SovereignMemoryStore (0o444 read-only frozen SQLite stores, $0.00 / 0-LLM ingestion via local snowflake-arctic-embed2 1024d + Porter FTS5 4-Way RRF) and verbatim mem0ai/mem0@aae5989e ANSWER_PROMPT_ZEP + ACCURACY_PROMPT (gpt-4o-mini temperature=0.0 judge):
    • argon_matched (9,983 mean cl100k tokens, 3.01× compression): 90.26% (1,390/1,540) judged accuracy (94.17% Single-Hop, 92.83% Temporal, 84.04% Multi-Hop, 65.62% Open-Domain), 95.77% ground-truth dia_id evidence recall (98.87% Single-Hop, 97.98% Temporal), 63.97% official Porter-stemmed token F1, 181.8 ms median local retrieval, and +19.86 pp over LivingBrain / SuperLocalMemory V3.3's zero-LLM mode (70.4%) (trace_argon_matched.jsonl SHA-256 68363e5f7c6cf733b42681c6e73911b129e71a43a41ac385cf56daf96a80d70d).
    • argon_tight (4,465 mean cl100k tokens, 6.73× compression — <0.5× peer token budget): 89.68% (1,381/1,540) judged accuracy, 91.05% dia_id recall (96.37% Single-Hop, 95.25% Temporal), 63.11% official F1, 186.4 ms median retrieval, and 92.66% (1,427/1,540) Oracle@2 across both frozen runs (trace_argon_tight.jsonl SHA-256 e48f3cc00f690eda2d8345283fc822f05527eb95844aa923001f74a2b82f87f1; 5/5 zero-leakage & BlindMappingGuard checks passed; full report).

Official ICLR 2026 BEAM (100K–10M Tokens, 2,000/2,000 Probes)

  • Closed-system full-suite evaluation across all 100 conversations (~178.34M tokens) and 2,000/2,000 questions (100K, 500K, 1M, 10M splits) on node:sqlite + snowflake-arctic-embed2 (8K ctx) in a fixed ~8K prompt budget: reaches 92.58% (100K) & 93.00% (10M IE) recall (78.67% 4-split weighted), exhibits zero E2E QA degradation from 100K → 1M (70.60% → 71.82% → 72.86% on hermes3), and outperforms cloud past.dev (openai/gpt-5.6-luna) on Contradiction Resolution across all 4/4 splits and on 10M Multi-Session Reasoning across both cross-family referees (29.54% / 38.33% vs. 28.17%) with 10/10 zero-leakage verification (full report).

📚 Documentation & Specifications

  • Canonical Tool Reference: Generated directly from live schemas (npm run docs:generate).
  • Architecture & Failure Modes: 2-page specification covering schemas, indexes, and v2.0.0 multi-scale retrieval invariants.
  • Setup & Operations Guide: Zero-Docker setup and optional configurations.
  • Migration Guide: Step-by-step v1.5 → v1.6 → v1.7 → v1.8 → v1.9.0 → v2.0.0 mapping.
  • Episodic Memory Guide: Deep dive into the closed taxonomy and lifecycle.
  • Changelog: Version release notes.
  • Official Snap LoCoMo Empirical Report: Clean-room 1,540/1,540-probe ACL 2024 evaluation (90.26% matched / 89.68% tight / 92.66% Oracle@2, 95.77% dia_id recall, $0.00 zero-LLM ingest) and JSON receipts (benchmarks/locomo/).
  • Official ICLR 2026 BEAM Empirical Report: Full 100-conversation (~178.34M-token), 2,000/2,000-probe methodology, per-ability tables, and 10/10 zero-leakage verification gates.
  • Official DolphinBench Whitepapers & Clean-Room Audit Logs: Paper #27 (HTML), Paper #27 (PDF), Paper #26, and all 28 Official DolphinBench runs (16,800 official gpt-5.6-sol traces across 6 model families, 8 uploaded & queued on dolphinbench.ai + 20 clean-room controls = 28/28 submission.validate passed — led by 91.17% [547/600 @ $24.54] uploaded dolphinbench.ai submission UUID c047b063-822c-49fb-8b36-27c3763d6ca7 [status: "queued" awaiting leaderboard acceptance; exceeds current 70.67% published leaderboard entry in independent 600/600 evaluation], 90.67% [544/600 @ $30.54] self-run clean-room Argon Low + Grounded v0.3 [highest Riley slice 181/200 = 90.50%, published SHA-256 audit logs], 89.17% Argon Medium queued UUID 29389157-b01b-42b5-8193-17d9a496f580, & 57.33% [344/600 @ $0.00] Wave-6 krush420/gemma4-31b-sre-lora + Grounded [highest open-weights score in self-run 28-run evaluation; 56.67% Stock+Grounded; 54.17% (325/600) stock submission UUID 288671ad-afe0-40a3-93af-b834c9bbd306 uploaded & queued on dolphinbench.ai]; Global Oracle@28 = 98.33%, $0.00 Oracle@17 = 83.17%; 49 SHA-256 Hugging Face audit logs across 64 files) in krush420/sovereign-g4-benchmarks.
  • Evaluation Baseline Notes: Preliminary benchmark fixtures and token-budget metrics.

🧪 Testing

# Run full unit & contract test suite (100% SQLite, zero Docker)
npm test

# Check storage and memory health
npm run health

License

MIT © kruschdev