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KruschStack (krusch-stack)

License: MIT Node.js >= 22 MCP Compatible DolphinBench #1 Overall #1 Open-Weights ($0.00)

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.

🏆 Official DolphinBench (19 Runs / 11,400 Official gpt-5.6-sol Traces Across 6 Model Families, 13,539 Sessions): Pairing Gemini 4 Argon (Low Thinking) with our byte-identical 0o444 frozen $0.00 SovereignMemoryStore (node:sqlite Zero-LLM Raw-RAG + 4-Way RRF) achieves #1 Overall at 91.17% (547/600 @ $24.54, 35.38s median)—+20.50 pp (+123 tasks) above Official #1 Hermes + GPT 5.6 Luna + Mem0 (70.67% @ $96.21) at ~3.9x (~4x) lower cost (alongside model-matched GPT 5.6 Luna (High) 81.33% [488/600 @ $1.35, +10.67 pp & 71.0x cheaper than #1 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, $\Delta\Delta = +3.17\text{ pp}, p = 0.0320$; 97.00% [582/600] Oracle@19, 4.19x LBF=4+ multiplier; Queued UUIDs c047b063-822c-49fb-8b36-27c3763d6ca7 & 29389157-b01b-42b5-8193-17d9a496f580). At $0.00 total cost, Apache-2.0 Google Gemma 4 31B (30.7B dense Q4_K_M, Queued UUID 288671ad-afe0-40a3-93af-b834c9bbd306) achieves #1 Open-Weights at 54.17% (325/600 Thinking ON) & 52.00% (312/600 Thinking OFF, Oracle@2 = 61.50%), 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 full-600 30B Wave-4 + 2PC cuts duplicate MCP calls -95.2% (42 -> 2, Oracle@2 = 50.33%) and lifts $0.00 open-weights Oracle@11 to 80.00% (480/600) (8 queued archives on dolphinbench.ai, 19/19 submission.validate passed). Read Paper #27 — Return on Memory (PDF) and Paper #26 — What DolphinBench Taught Us 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 L1 heuristic fast-path -> L2 Local MoE -> L2.5 120B -> L3 Referee  │
└────────────────────────────────────────────────────────────────────────────┘
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 3: 2PC Staged Harness krusch-coding-harness krusch_run, krusch_task_status, krusch_diff, krusch_apply_diff, krusch_explain, krusch_reject, krusch_abort (+ stack.actionHarness & POST /action-harness/* INV-STACK-10) 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 <25µs
Companion Runtime Bricks toolkit (@krusch/toolkit & krusch_toolkit) 35 Node/TS/Workers & Python Lego bricks + krusch-lego CLI backing get_canonical_template (lego-*) & INV-LEGO-01 App Runtime (ESM / Py 3.11+)

📊 Verified Third-Party Benchmarks Across Pillars

📄 Full Cross-Stack Comparison, Honest Gaps & Reproduction Gates: Read BENCHMARKS.md for the complete comparison against open-source wrapper stacks and commercial cloud stacks (past.dev, Mem0, Honcho, NotDiamond, Martian, OpenAI + Cohere) across 317,500+ queries and multi-turn episodes, our 75/75 SHA-256 clean-room verification gates across 11 external suites (36 raw JSON audit logs), open/queued upstream submissions (DolphinBench 8 queued across 19 validated runs / 11,400 traces, RouterArena PR #169, withmartian/routerbench), and where frontier cloud models still lead.

Stack Pillar Official External Benchmark Verified Result (Sovereign Hybrid & Hermetic Open-Weights)
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
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
Agentic Memory & Execution Official DolphinBench (600/600, 19 Runs / 11,400 Traces, 13,539 Sessions) #1 Overall 91.17% (547/600 @ $24.54, 35.38s) Gemini 4 Argon (Low) + 89.17% (535/600 @ $24.57) Medium + model-matched GPT 5.6 Luna (High) 81.33% (488/600 @ $1.35, 71.0x cheaper than #1 Mem0) & Luna (Low) 76.67% (460/600 @ $1.10) over byte-identical 0o444 frozen $0.00 SovereignMemoryStore (+20.50 pp / +123 tasks > #1 Hermes + GPT 5.6 Luna + Mem0 70.67% @ $96.21, ~3.9x lower cost; 78.00%/79.17% Briefing-Only @ $15.76/$16.91, $\Delta\Delta = +3.17\text{ pp}, p = 0.0320$; 97.00% [582/600] Oracle@19, 4.19x LBF=4+; UUID c047b063-822c-49fb-8b36-27c3763d6ca7) · #1 Open-Weights 54.17% (325/600 @ $0.00, UUID 288671ad-afe0-40a3-93af-b834c9bbd306) Google Gemma 4 31B (52.00% Thinking OFF, Oracle@2 = 61.50%, Oracle@11 = 80.00% [480/600]), 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 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, 35.33% Qwen2.5-Coder-32B (+18.83 pp domain gap, $p = 1.35 \times 10^{-16}$), 34.50% Hermes-4-14B, 18.33% 8B Wave-4 (15.7x lift) (8 queued on dolphinbench.ai 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% #1 CoIR 10-Task NDCG@10 (73.88% R@10 > Voyage-Code-002 56.26%) · RepoBench-R 19.12% Macro Acc@1 / 68.58% Acc@5 (beats CodeBERT & UniXcoder)
Tier 2B / Evidentiary (krusch-nexus) LegalBench-RAG (714 Docs, 6,889 Full / 776 Mini Qs) 26.74% 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%)
Tier 3 (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 (-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)
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)
Router (krusch-cascade-router) Martian RouterBench (73K), ACL 2026 LLMRouterBench (12.5K) & RouterArena (8.4K) #1 RouterBench ID/OOD AIQ (0.7488 0-shot & 0.7695 5-shot, -72.63% cost @ 98% GPT-4, #1 OOD 0.7268 / 0.7455 > ZeroRouter) · #1 LLMRouterBench Pareto (0.4269, -83.95% cost, 25.25% ARC-AGI) · 0.7263 (72.63, #13 / 34) RouterArena Official CI (74.44% Acc @ $0.3802/1K, 91.43% rob., 0 abnormal, 8,400/8,400 OpenRouter receipts; 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, PR #169 open) (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 (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 (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' });

🌐 HTTP Gateway Action-Harness Routes (npx krusch-stack serve, default :3780)

When running npx krusch-stack serve (or via Docker Compose on http://127.0.0.1:3780), the Generalized 2PC Tool-Action Harness (INV-STACK-10) is exposed over HTTP alongside /health, /tools, /call, and /resilience/*—operating at the outer orchestration boundary so ALL_STACK_TOOLS.length === 39 remains strictly unchanged:

  • POST /action-harness/start → stack.actionHarness.startSession(body)
  • POST /action-harness/intercept → stack.actionHarness.interceptToolCall(body) (ALLOW_READ, ALLOW_MUTATION, BLOCK_SPIN_LOOP, BLOCK_READ_STREAK, BLOCK_DUPLICATE_WRITE, HOLD_FOR_REVISION)
  • POST /action-harness/outcome → stack.actionHarness.recordToolOutcome(body)
  • POST /action-harness/verify-stop → stack.actionHarness.verifyStop(body) (ALLOW_STOP vs. CONTINUE_VERIFY)
  • POST /action-harness/summary → stack.actionHarness.sessionSummary(body)

📖 Architecture & Benchmark Whitepapers: Read Paper #27 — Return on Memory (#1 Official DolphinBench 91.17%) (PDF), Paper #26 — What DolphinBench Taught Us About Local AI (#1 Open-Weights 54.17% Gemma 4 31B @ $0.00), and Paper #23 — The 15 Production Agentic Systems.


🤗 Open-Weights Models, SFT+DPO Dataset & Benchmarks (Trained Through krusch-stack)

We use krusch-stack to generate AST-grounded 11-phase code audit trajectories, unit-test-verified (npm test / pytest, exit 0) bug remediations, @krusch/toolkit Lego-First idioms, and multi-turn MCP/workspace tool-calling + DPO calibration pairs, then distill them back into open-weights Wave-4 adapters across L1.5, L2, and L2.5:

Resource Hugging Face / GitHub Cascade Role / Base Model
Jean SRE Daemon (jean-sre) kruschdev/Jean-SRE-by-kruschdev Autonomous 24/7 11-Phase Code Auditor, Test-Gated Remediator & Distillation Harness
SFT + DPO Dataset (18,131 rows) krush420/sovereign-code-audit-sft 7 Zero-Leak Splits: code_audit_11phase_sft (15,290), elitist_120b_teacher (2,308), argon_golden_remediations (401), agentic_tool_calling_sft (64), lego_and_referee_calibration_sft (38), agentic_tool_calling_dpo (18), referee_calibration_dpo (12)
8B Edge Agent & Referee LoRA (L1.5) krush420/llama3-hermes3-8b-sre-lora NousResearch/Hermes-3-Llama-3.1-8B (Built with Meta Llama 3 · BF16 LoRA + DPO, 18.33% [110/600] = 15.7x DolphinBench lift)
30B Student QLoRA (L2) krush420/qwen3-coder-30b-sre-lora Qwen/Qwen3-Coder-30B-A3B-Instruct (30.5B total / 3.3B active MoE · 42.67% [256/600] Base & 42.00% + 2PC [Oracle@2 = 50.33%] DolphinBench @ $0.00)
31B Dense Open-Weights Leader (L2) gemma4:31b Google Gemma 4 31B (30.7B 100% active dense Q4_K_M, Apache-2.0 · #1 Open-Weights 54.17% [325/600] ON / 52.00% OFF, Oracle@2 = 61.50% @ $0.00)
120B Teacher QLoRA (L2.5) krush420/gpt-oss-120b-sre-lora openai/gpt-oss-120b (116.8B total / 5.1B active MXFP4 MoE · 39.50% [237/600] DolphinBench @ $0.00)
Hermetic Benchmarks (75/75 Gates) krush420/sovereign-g4-benchmarks · Arena 317,581+ queries/episodes across 11 suites (#1 Official DolphinBench 91.17% Argon Low @ $24.54, model-matched GPT 5.6 Luna 81.33% @ $1.35 [71.0x cheaper than Mem0], + #1 Open-Weights 54.17% Gemma 4 31B @ $0.00 across 19-run 11,400-trace 6-family matrix; 97.00% Oracle@19, 80.00% Oracle@11 open-weights), 36 SHA-256 raw JSON audit logs, and 14 viewer splits

📜 License

MIT © Kevin Ruschman (kruschdev)

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