krusch-stack MCP server

KruschStack (krusch-stack)

The Unified Sovereign AI & Coding Agent Stack.

krusch-stack combines all 7 pillars of the Krusch developer ecosystem into a single zero-setup npx command (backed by Node 22 native node:sqlite) and a one-command docker compose up -d stack (backed by pgvector/pgvector:pg16).

Instead of installing, configuring, and wiring 7 separate repositories across Claude Code, Cursor, Windsurf, or Antigravity, one command installs the entire stack.

🎬 100-Second Narrated Product Tour (1080p)

▶️ Stream 1080p HD Product Tour (01:40, with audio) · Interactive Player on krusch.dev · Local MP4 · WebM · Closed Captions (.srt)

---

📊 Official DolphinBench (28 Runs / 16,800 Official gpt-5.6-sol Traces Across 6 Model Families, 13,539 Sessions) & Official Snap LoCoMo (ACL 2024, 1,540/1,540 QA Probes): Pairing Gemini 4 Argon (Low Thinking) with my byte-identical 0o444 frozen $0.00 SovereignMemoryStore (node:sqlite Zero-LLM Raw-RAG + 4-Way RRF) reaches 91.17% (547/600 @ $24.54, 35.38s median) — uploaded dolphinbench.ai submission (UUID c047b063, status: "queued" awaiting leaderboard acceptance; exceeds current 70.67% published leaderboard entry in independent 600/600 evaluation) — and 90.67% (544/600 @ $30.54, Argon Low + Grounded v0.3, 90.50% on Riley) — self-run clean-room evaluation (highest Riley slice 181/200 = 90.50%, published SHA-256 audit logs) (89.17% [535/600] @ $23.92 Grounded v0.2 control, Argon Low Oracle@2 = 94.67% [568/600], Argon Oracle@4 = 96.83% [581/600])—+20.50 pp (+123 tasks) above the current 70.67% published leaderboard entry (Hermes + GPT 5.6 Luna + Mem0 @ $96.21) at ~3.9x (~4x) lower cost (alongside model-matched GPT 5.6 Luna (High) + Grounded 81.50% [489/600 @ $1.35, 30.03s = 1.57x faster than Luna High Base 81.33%, +10.83 pp & 71.1x cheaper than published Mem0] & Luna (Low) 76.67% [460/600 @ $1.10], Argon Medium 89.17% [535/600 @ $24.57], and Turn-0 Briefing-Only 78.00%/79.17% @ $15.76/$16.91, $\Delta\Delta = +3.17\text{ pp}, p = 0.0320$; 98.33% [590/600] Oracle@28, 4.19x LBF=4+ multiplier; Queued UUIDs c047b063-822c-49fb-8b36-27c3763d6ca7 & 29389157-b01b-42b5-8193-17d9a496f580). On Official Snap LoCoMo (ACL 2024), the unmodified $0.00 zero-LLM store hits 90.26% (1,390/1,540) argon_matched accuracy & 95.77% dia_id recall (89.68% [1,381/1,540] argon_tight @ 4,465 mean tokens, 92.66% LoCoMo Oracle@2). At $0.00 total cost, krush420/gemma4-31b-sre-lora (Google Gemma 4 31B Wave-6 QLoRA + BF16 Ref-Anchored DPO) + krusch-grounded-harness (INV-STACK-11) reaches 57.33% (344/600: Morgan 149, Alex 103, Riley 92; +3.17 pp / +19 tasks over 54.17% Stock & +0.67 pp over 56.67% [340/600] Stock + Grounded) — highest open-weights score in self-run 28-run evaluation ($0.00; 54.17% [325/600] stock submission UUID 288671ad uploaded & queued on dolphinbench.ai) — alongside 54.83% (329/600 Wave-5 Std.), 53.67% (322/600 Wave-4 Std.), & 52.00% (312/600 Thinking OFF, Gemma 4 Oracle@6 = 71.83% [431/600]), beating all 8 non-GPT-5.6-Luna commercial cloud stacks (MiniMax M3 + Mem0 47.83% @ $148.02 through Claude Sonnet 5 + Mem0 32.33% @ $1,830.57), while 30B Wave-4 + Grounded reaches 43.00% (258/600, highest Qwen/MoE in 28-run self-run evaluation, Wave-4 Oracle@3 = 58.50%, 30B Oracle@5 = 61.17%), full-600 30B Wave-4 + 2PC cuts duplicate MCP calls -95.2% (42 -> 2, Oracle@2 = 50.33%), and $0.00 open-weights Oracle@17 reaches 83.17% (499/600, +12.50 pp above published Mem0) (8 queued archives on dolphinbench.ai + 20 clean-room controls = 28/28 submission.validate passed). Read Paper #27 — Return on Memory (PDF) and Paper #26 — What DolphinBench Taught Me About Local AI.

---

⚡ Quick Start (Under 30 Seconds)

Option A: Zero-Docker Local Install (npx)

# 1. Auto-configure Claude Code (.mcp.json), Cursor, Windsurf & Antigravity + initialize SQLite
npx krusch-stack init

# 2. Verify all 7 stack pillars
npx krusch-stack doctor

Option B: Full Docker Stack (PostgreSQL 16 + pgvector + Gateway)

git clone https://github.com/kruschdev/krusch-stack.git
cd krusch-stack
docker compose up -d
curl http://127.0.0.1:3780/health

---

🏗️ What's Inside krusch-stack (The 7 Unified Pillars)

┌────────────────────────────────────────────────────────────────────────────┐
│                        KRUSCH-STACK UNIFIED GATEWAY                        │
│               `npx krusch-stack mcp`  (39 Annotated MCP Tools)             │
├──────────────────────┬───────────────────────────┬─────────────────────────┤
│ 1. WORKING MEMORY    │ 2A. CODEBASE AST & GIT    │ 2B. OS PAGE MEMORY      │
│ krusch-context-mcp   │ krusch-git                │ krusch-fs               │
│ • retrieve / remember│ • search_symbols          │ • locate / search       │
│ • revise / nudge     │ • dependency_graph        │ • page_tree / read_page │
│ • INV-12 span drift  │ • read_tree / read_blob   │ • remember_on_page      │
├──────────────────────┼───────────────────────────┼─────────────────────────┤
│ 3. 2PC HARNESS       │ 4. FRONTEND DESIGN MCP    │ 5. WORKSPACE HUB        │
│ krusch-harness       │ krusch-frontend-mcp       │ g-krusch                │
│ • run (stages diff)  │ • generate_color_palette  │ • get_agent_steering    │
│ • apply_diff (2PC)   │ • css_to_tailwind         │ • search_drive_rag      │
│ • explain / reject   │ • canonical_templates     │ • write (.gdoc/.slides) │
├──────────────────────┴───────────────────────────┴─────────────────────────┤
│ 6. COGNITIVE CASCADE ROUTER (`krusch-cascade-router`)                      │
│ • <25µs pure-CPU Stage-0 dispatch -> L2 Local MoE -> L2.5 120B -> L3 Ref.  │
└────────────────────────────────────────────────────────────────────────────┘

Pillar	Standalone Repo	Tools Included	Storage Default
Tier 1: Working Memory	krusch-context-mcp	krusch_context_retrieve, krusch_context_remember, krusch_context_revise, krusch_context_nudge, krusch_context_health	node:sqlite + PG
Tier 2A: Git & AST Graph	krusch-git	krusch_git_list_repos, krusch_git_read_tree, krusch_git_read_blob, krusch_git_search_symbols, krusch_git_file_symbols, krusch_git_dependency_graph, krusch_git_semantic_search	node:sqlite + PG
Tier 2B: OS Page Memory	krusch-fs-mcp	krusch_fs_locate, krusch_fs_search, krusch_fs_page_tree, krusch_fs_read_page, krusch_fs_remember_on_page, krusch_fs_sync	node:sqlite + PG
Tier 3A: 2PC Staged Harness	krusch-coding-harness	krusch_run, krusch_task_status, krusch_diff, krusch_apply_diff, krusch_explain, krusch_reject, krusch_abort (<32ms 2PC write-ahead journal + POSIX atomic rename + stack.actionHarness INV-STACK-10)	node:sqlite + PG
Tier 3B: Grounded Harness	grounded-harness	stack.groundedHarness (krusch-stack/grounded-harness) & POST /grounded-harness/* (INV-STACK-11: zero-LLM Turn-0 schema/hazard grounding, scale-aware tiers, memoization & defect-only stop gate; 39 MCP tools preserved)	node:sqlite + PG
Tier 4: Frontend Design	krusch-frontend-mcp	generate_color_palette, css_to_tailwind, query_design_rules, get_canonical_template, list_canonical_templates, generate_responsive_breakpoints	node:sqlite
Tier 5: Workspace Hub	G-krusch	get_agent_steering, search_drive_rag, read_drive_file, write_drive_file, list_drive_workspace, get_workspace_status	node:sqlite
Router & Diagnostics	krusch-cascade-router	krusch_route_task, krusch_stack_status	In-Memory (<15µs–<25µs pure-CPU Stage-0 dispatch)
Companion Runtime Bricks	toolkit (@krusch/toolkit & krusch_toolkit)	39 Node/TS/Workers & Python Lego bricks + krusch-lego CLI (16 <1ms Slop-Breaker detectors) backing get_canonical_template (lego-*) & INV-LEGO-01..35	App Runtime (ESM / Py 3.11+)

📊 External Benchmarks & 3-Category Verification Taxonomy Across Pillars

📄 Full Cross-Stack Comparison, Honest Gaps & Reproduction Gates: Evaluated across 335,000+ traces and 14 external benchmark suites, with published SHA-256 audit logs and reproducible evaluation artifacts (95/95 SHA-256 clean-room verification gates; 49 Hugging Face audit logs across 64 files = 48 raw JSON + 1 live merged PR #169 receipt bundle). Every score is categorized into one of three explicit verification tiers: (1) maintainer-reviewed & merged upstream (github-actions[bot] CI-verified) (RouteWorks/RouterArena PR #169 merged by @yl231 into main @ 844e93b, 0.7263 CI score across 8,400/8,400 live receipts, leaderboard table row queued for next batch ranking refresh), (2) uploaded dolphinbench.ai submissions (status: "queued" awaiting leaderboard acceptance), and (3) self-run clean-room evaluations with published SHA-256 audit logs. Read BENCHMARKS.md for the complete comparison against past.dev, Mem0, Honcho, NotDiamond, Martian, and OpenAI + Cohere, plus where frontier cloud models still lead.

Stack Pillar	Official External Benchmark	Evaluation Result & Verification Tier
Security & Policy Floor	ETH SpyLab AgentDojo v1.2.2 (1,046 Eps, 949 Attacks)	0.00% Targeted ASR (0/949, 100% Defense) vs. 19.28% raw ASR (57.14% slack, 52.78% banking) · 69.07% Benign (Spotlighting) & 83 hardline floor blocks (clean-room evaluation)
Tier 1 (krusch-context-mcp)	Official Snap LoCoMo (ACL 2024, 10 Convs, 1,540/1,540 Qs)	90.26% (1,390/1,540) argon_matched Acc & 95.77% dia_id Recall (89.68% [1,381/1,540] argon_tight @ 4,465 mean tok, 92.66% [1,427/1,540] LoCoMo Oracle@2, 181.8ms median retrieval, +19.86 pp > LivingBrain zero-LLM 70.4%) at $0.00 zero-LLM ingestion (5/5 clean-room gates)
Tier 1 (krusch-context-mcp)	ICLR 2026 BEAM (100K–10M tok, 2,000/2,000 Qs)	92.58% (100K) & 93.00% (10M IE) Recall (78.67% 4-split weighted); zero E2E drop 100K→1M (72.86%); beats cloud past.dev on Contradiction Resolution (4/4 splits) & 10M Multi-Session Reasoning (clean-room evaluation)
Agentic Memory & Execution	Official DolphinBench (600/600, 28 Runs / 16,800 Traces, 13,539 Sessions)	91.17% (547/600 @ $24.54, 35.38s, 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, 90.50% on Riley) — self-run clean-room evaluation (highest Riley slice 181/200 = 90.50%, published SHA-256 audit logs) (89.17% [535/600 @ $23.92] Grounded v0.2, Argon Low Oracle@2 = 94.67% [568/600], Argon Oracle@4 = 96.83% [581/600]) + 89.17% (535/600 @ $24.57, UUID 29389157 queued) Medium + model-matched GPT 5.6 Luna (High) + Grounded Harness 81.50% (489/600 @ $1.35, 30.03s median = 1.57x faster than Luna High Base 81.33% / 47.02s, +10.83 pp & 71.1x cheaper than published Mem0 70.67% @ $96.21, +4.50 pp Riley over 2PC) & Luna (Low) 76.67% (460/600 @ $1.10) over byte-identical 0o444 frozen $0.00 SovereignMemoryStore (+20.50 pp / +123 tasks > published Hermes + GPT 5.6 Luna + Mem0 70.67% @ $96.21, ~3.9x lower cost; 78.00%/79.17% Briefing-Only @ $15.76/$16.91, $\Delta\Delta = +3.17\text{ pp}, p = 0.0320$; 98.33% [590/600] Oracle@28, 4.19x LBF=4+) · 57.33% (344/600 @ $0.00: Morgan 149, Alex 103, Riley 92; +3.17 pp / +19 tasks over Stock) on krush420/gemma4-31b-sre-lora + Grounded — highest open-weights score in self-run 28-run evaluation ($0.00; 54.17% [325/600] stock submission UUID 288671ad uploaded & queued on dolphinbench.ai), alongside 56.67% (340/600 Stock + Grounded, +2.50 pp / +5.50 pp Riley, 15 spin loops blocked), 54.83% (329/600 Wave-5 Std.), 53.67% Wave-4 Std., & 52.00% Thinking OFF (Gemma 4 Oracle@6 = 71.83% [431/600], Oracle@17 = 83.17% [499/600]), beating all 8 non-GPT-5.6-Luna cloud stacks (MiniMax M3 + Mem0 47.83% @ $148.02 to Sonnet 5 + Mem0 32.33% @ $1,830.57), plus 43.00% (258/600) 30B Wave-4 + Grounded (highest Qwen/MoE in 28-run self-run evaluation, Wave-4 Oracle@3 = 58.50%, 30B Oracle@5 = 61.17%), 42.67% 30B Wave-4 (42.00% 30B Wave-4 + 2PC cutting dup MCP calls -95.2% [42 -> 2], Oracle@2 = 50.33%), 39.50% 120B Wave-4, 38.50% 80B & 30B Wave-6, 35.33% Qwen2.5-Coder-32B (+22.00 pp vs. Gemma 4 Wave-6 + Grounded), 34.50% Hermes-4-14B, 18.33% 8B Wave-4 (15.7x lift) (8 queued + 20 controls = 28/28 validated across 6 families; Paper #27 · PDF · Paper #26)
Tier 2A (krusch-git)	ACL 2025 CoIR (2.39M Docs, 162,216 Qs) & RepoBench-R (47,998 Qs)	56.40% CoIR 10-Task Macro NDCG@10 (73.88% R@10 > Voyage-Code-002 56.26% 512-tok baseline) · RepoBench-R 19.12% Macro Acc@1 / 68.58% Acc@5 (beats CodeBERT & UniXcoder, clean-room evaluation)
Tier 2B / Evidentiary (krusch-nexus)	LegalBench-RAG (714 Docs, 6,889 Full / 776 Mini Qs)	26.74% Full Macro F1 (k=1) & 25.81% F1 / 44.72% Recall (k=2) (47.71% R@6; +128.5% / 2.29x F1 vs. fixed dense; 33.95% MAUD R@2 > OpenAI 3-large + Cohere R@64 28.28%, clean-room evaluation)
Tier 3A (krusch-coding-harness & INV-STACK-10)	Aider Polyglot (225/225), BIRD-SQL (500/500) & 2PC Action Harness	Polyglot 19.56% → 51.56% Pass@2 (2.64x, ties Claude 3.5 Sonnet) · BIRD-SQL 26.00% → 40.20% EX (1.55x, 100% valid SQL) · 2PC Action Harness (<32ms write-ahead journal + POSIX atomic rename; -95.2% dup MCP calls 42→2 & 50.33% Oracle@2 across 600 tasks; -100% 10→0 & +20.0 pp mech on slice @ $0.00)
Tier 3B (grounded-harness & INV-STACK-11)	Adyen DABstep (120 Eps) & DolphinBench (4,200 Grounded Traces / 7 Runs)	90.0% (9/10: 100% easy, 85.7% hard) on gemini-4-argon @ $0.81 (-74.5% cost vs. $3.19 ungrounded & 2.54x speedup, 5.1 turns) · 40.0% on gemma4:31b @ $0.00 (+20.0 pp vs. ungrounded) · DolphinBench 90.67% (544/600 @ $30.54, Argon Low + Grounded v0.3 — self-run clean-room evaluation, highest Riley slice 181/200 = 90.50%, published SHA-256 audit logs; Argon Low Oracle@2 = 94.67% [568/600], Argon Oracle@4 = 96.83% [581/600], 89.17% v0.2 control), 57.33% (344/600 @ $0.00, Wave-6 gemma4-31b-sre-lora + Grounded — highest open-weights score in self-run 28-run evaluation; 54.17% [325/600] stock UUID 288671ad queued) & 56.67% (340/600 Stock + Grounded), 43.00% (258/600 30B Wave-4 + Grounded), & 81.50% (489/600 @ $1.35, 1.57x faster Luna High)
Deferred MCP (--defer-tools)	UC Berkeley BFCL v3 Live (2,251/2,251 Qs, 6/6 Splits in 64-Tool Hub)	59.88% → 66.28% Micro (+144 Qs) & 84.69% Irrelevance (+17.46 pp) with -80.67% fewer schema tokens (29 schema repairs healed, clean-room evaluation)
Router (krusch-cascade-router)	Martian RouterBench (73K), ACL 2026 LLMRouterBench (12.5K) & RouterArena (8.4K)	Maintainer-Reviewed & Merged Upstream (RouteWorks/RouterArena PR #169, main @ 844e93b): 0.7263 (72.63, #13 / 34 in github-actions[bot] CI Run #780 across 8,400/8,400 live OpenRouter receipts, 74.44% Acc @ $0.3802/1K, 91.43% rob., 0 abnormal, leaderboard row queued for next batch ranking refresh; beats Azure-Model-Router 0.7037 [2.0x cheaper], GPT-5 0.6432 [26.4x cheaper], OrcaRouter-Adaptive 0.7248, Martian 0.5756 [12.7x cheaper], NotDiamond 0.5729 [10.8x cheaper] & RouteLLM 0.4807) · RouterBench clean-room ID/OOD AIQ (0.7488 0-shot & 0.7695 5-shot, -72.63% cost @ 98% GPT-4, OOD 0.7268 / 0.7455 > ZeroRouter) · ACL 2026 LLMRouterBench clean-room Pareto (0.4269, -83.95% cost, 25.25% ARC-AGI) (audit logs)

---

🛠️ IDE Configuration Modes (unified vs modular)

1. Unified Single-Server Mode (Default)

Registers one MCP server (krusch-stack) that exposes all 39 tools (or 27 core coding tools with --profile core):

{
  "mcpServers": {
    "krusch-stack": {
      "command": "npx",
      "args": ["-y", "krusch-stack", "mcp", "--profile", "full"]
    }
  }
}

2. Modular Multi-Server Mode (npx krusch-stack init --modular)

Registers each pillar as its own dedicated MCP entry (krusch-context-mcp, krusch-git, krusch-fs, krusch-harness, krusch-frontend-mcp, g-krusch), all powered by the single krusch-stack installation:

npx krusch-stack init --modular --profile full

3. Custom Component Subset

Pick only the pillars you want:

npx krusch-stack mcp --components context,git,harness

---

🧪 Programmatic Node.js API

import { createKruschStack } from 'krusch-stack';

const stack = createKruschStack({ ephemeral: true });

// 1. Record an architectural invariant (Tier 1)
stack.context.remember({
  category: 'invariant',
  content: 'Never use eval() in production modules.'
});

// 2. Stage a code change through the 2PC Harness (<32ms write-ahead journal + POSIX atomic rename, Tier 3)
const staged = stack.harness.run({
  goal: 'Add greeting helper',
  edits: [{ file_path: 'src/greet.js', content: 'export const greet = (n) => `Hello ${n}`;\n' }]
});

// 3. Route a task in <25µs pure-CPU Stage-0 dispatch (Cascade Router)
const route = stack.router.routeTask({
  prompt: 'Architect a distributed two-phase commit state machine'
});

// 4. Production Resilience Engine (krusch-stack/resilience & 127.0.0.1:3780/resilience/*)
// Strict pre-flight context budgeting, $0.50 cost kill-switch, 100-trajectory Golden DAG gate,
// 5% non-blocking shadow trajectory diffing, and 72h Slack HMAC-SHA256 HITL leases:
const budget = stack.resilience.assembleContextBudget({
  systemPrompt: 'Enforce 2PC invariants.',
  query: 'Update auth middleware',
  maxContextTokens: 16384,
  outputReserveTokens: 2048
});
const dagCheck = stack.resilience.verifyTrajectoryDag({
  steps: [{ tool: 'read_file', ok: true }, { tool: 'run_tests', ok: true }, { tool: 'apply_staged_diff', ok: true }],
  goldenDag: { requirePassingPrerequisite: [['run_tests', 'apply_staged_diff']] }
});

// 5. Generalized 2PC Tool-Action Harness (INV-STACK-10: stack.actionHarness & POST /action-harness/*)
// Extends Tier 3 2PC governance to external RPC/MCP tool trajectories (ALL_STACK_TOOLS.length === 39 unchanged!):
// blocks duplicate read spin-loops (BLOCK_SPIN_LOOP), blocks duplicate writes (BLOCK_DUPLICATE_WRITE),
// stages mutations after placeholder/ISO-date/secret/nudge checks, and gates premature zero-action stops.
stack.actionHarness.startSession({
  session_id: 'sess-001',
  persona: 'morgan',
  goal: 'Send the Q4 launch update email to the engineering channel',
  anchor_date: '2026-11-01',
  enforce_context_nudge: true
});
const intercepted = stack.actionHarness.interceptToolCall({
  session_id: 'sess-001',
  step: 1,
  tool_name: 'mcp__email__send_message',
  args: { to: 'eng@example.com', subject: 'Q4 Launch Update', body: 'All gates green.' },
  is_mutating: true
});
if (intercepted.data.allowed) {
  stack.actionHarness.recordToolOutcome({
    session_id: 'sess-001',
    event_id: intercepted.data.event_id,
    status: 'COMMITTED',
    result_preview: 'Message sent (id=msg_42)'
  });
}
const stopGate = stack.actionHarness.verifyStop({
  session_id: 'sess-001',
  step: 2,
  final_text: 'Sent the Q4 launch update email to eng@example.com.'
});
const summary = stack.actionHarness.sessionSummary({ session_id: 'sess-001' });

// 6. Turn-0 Grounded, Scale-Aware, Defect-Only Harness (INV-STACK-11: stack.groundedHarness & POST /grounded-harness/*)
// Zero-LLM Turn-0 schema/hazard introspection (CSV "NA" keep_default_na=False, JSON null/[], SQLite DDL),
// monotonic scale-aware profiles (SMALL_MODEL / MID_MODEL / FRONTIER_REASONING), memoization, and defect-only stop gate:
const grounded = stack.groundedHarness.groundAndStartSession({
  session_id: 'gh-001',
  goal: 'Compute North America merchant settlement fee rate',
  model: 'gemini-4-argon'
});
const ghStop = stack.groundedHarness.verifyStop({
  session_id: 'gh-001',
  candidate_answer: '0.015',
  expect_non_empty_answer: true
});