matryca-plumber 1.7.0

Enterprise-grade, local-first autonomous background AI daemon and headless mutation plane for Logseq OG.

Matryca Plumber

Developed by Marco Porcellato · Matryca.ai — open-source local-first maintenance daemon for Logseq OG. The product name is Matryca Plumber (not “Matryca” alone). See docs/BRANDING.md.

v1.5 — Ironclad Release. Agentic Knowledge Management for Logseq OG. An enterprise-grade, local-first background AI daemon with a real-time Sovereign UI control room, a typed CLI, and direct Logseq Markdown AST mutation (no Logseq HTTP API, no auxiliary database). The default experience is autonomous: the daemon and Python workers poll your graph, run structured local-LLM passes, and commit indexes and lint artifacts while you work or sleep. An optional FastMCP stdio sidecar exposes the same headless mutation plane to external clients (for example Claude Desktop) — same graph_dispatch contract, not a separate data path. Heavily inspired by Andrej Karpathy's LLM-Wiki vision. 100% native Logseq AST parity, optimistic concurrency safety, versioned AI authorship stamping.

Matryca Plumber is a 100% headless, sandboxed standalone daemon + CLI that turns your local Logseq graph into a high token-density agentic workspace — no network APIs and no Logseq desktop JSON-RPC. It treats your vault as a tree of blocks, not a flat document store. Logseq OG remains optional: humans and the daemon co-edit the same .md trees on disk.

Matryca Plumber is not a one-shot script — it is an enterprise-grade, local-first background AI daemon for Logseq. It polls your graph on a duty cycle, calls a local LLM (LM Studio or Ollama), appends semantic indexes, runs optional cognitive lint modules, and logs every token transaction — while you edit the same .md files in Logseq or leave the vault idle. Optional MCP-attached sessions reuse the identical mutation plane for interactive queries; they are not required for background operation. Every write path mirrors Logseq's on-disk AST contract: page frontmatter at line 0, block properties contiguous to their parent bullet, namespace filenames encoded exactly like Logseq's Clojure Datalog layer, and optimistic concurrency control that aborts stale writes when you type during inference.

Matryca Plumber turns your local graph into a high token-density agentic workspace by continuously polling your notes, running local LLMs (like LM Studio or Ollama), appending semantic indexes, and healing broken links—all completely offline, while you work or sleep.

Zero Cloud. Zero Data Leaks. 100% Native Logseq AST.

---

⚠️ Important: Clone Your Graph First

Matryca Plumber edits your local .md files directly. While it features safe Optimistic Concurrency Control (OCC) to prevent data loss, we strongly recommend testing it on a clone of your graph first. This allows you to see the AI in action and explore its capabilities without affecting your primary notes.

How to safely clone your graph (crucial if you use Logseq Sync):

1. Make a copy of your entire Logseq graph folder on your computer (e.g., duplicate your MyGraph folder and rename it to MyGraph_Test).
2. Open Logseq, click on your graph name in the top left, and select Add new graph.
3. Choose the new MyGraph_Test directory.
4. If you use Logseq Sync: Do not enable Sync on this test graph. This ensures the AI's test edits remain strictly local and do not propagate to your other devices.
5. Alternatively, for a minimal test graph: a. Create an empty folder and add it as a new graph in Logseq. b. Close Logseq; copy only pages/, journals/, assets/ from production. c. Reopen and Re-index.
6. Point Matryca Plumber's .env configuration (LOGSEQ_GRAPH_PATH) to this test folder.

Once you are comfortable with how Matryca Plumber behaves and have tuned the safety tiers, you can point it to your main graph.

---

🚀 Quick Install & Getting Started

The fastest way to get started is using uv, the blazing-fast Python package manager.

1. Try it instantly (Zero-install)

Run the CLI directly without polluting your system. This only opens the Sovereign UI at http://127.0.0.1:8500 — it does not start graph maintenance until you complete the pre-flight checklist and click Start Engine (or run matryca plumber start separately).

uvx --from matryca-plumber matryca-plumber status

(matryca-plumber status is shorthand for matryca plumber status.)

2. Global Installation (Recommended)

Install the binary to use the matryca command anywhere:

uv tool install matryca-plumber

3. Open the control room (recommended first step)

matryca plumber status
# same as: matryca-plumber status

The browser opens the Sovereign UI. On every fresh visit, a Pre-flight checklist modal appears first (even if you already ran matryca plumber start in a terminal). Dismiss it with Continue to dashboard once the live checks are green, then click Start Engine in the header to launch the maintenance daemon from the UI.

Optional — headless daemon before opening the UI:

matryca plumber start    # background worker only; no browser
matryca plumber status   # UI still shows pre-flight; engine may already show IDLE/RUNNING

4. Set it and forget it (Background Service)

Install it as a LaunchAgent/systemd service so it wakes up with your OS:

matryca service install

---

🧠 What does it actually do?

Unlike generic scripts, Matryca Plumber is a continuous background engine. When paired with a local LLM (Gemma 4-E4b Instruct via LM Studio or Ollama), it provides:

• Semantic Indexing: Automatically generates summaries, suggested tags, and cross-references for your pages.
• Dangling Link Healing: Finds broken [[WikiLinks]] and creates isolated seed pages for them.
• Entity Consolidation: Suggests alias:: properties for overlapping concepts.
• Auto-Split Dense Blocks: Extracts oversized subtrees into new pages to keep your graph fast and readable.
• Claude Desktop Integration (FastMCP): Use Claude to query and mutate your Logseq graph natively. Set MATRYCA_MCP_ENABLED=true in .env (stdio MCP is off by default).

---

🖥️ The Sovereign UI

Matryca Plumber is 100% headless, but it ships with a Sovereign UI Cockpit (matryca plumber status or uvx … matryca-plumber status). It's a local React dashboard running on http://127.0.0.1:8500 that provides:

• Pre-flight checklist (modal on each UI open): operator guidance plus automated readiness checks before Start Engine is enabled.
• Live Graph Telemetry: See exactly what the AI is indexing in real-time.
• Dynamic Impact: Mathematically separates Organic Human Mind (your notes) from Agent Cognition (AI enhancements).
• Zero-Trust Security: Every REST call requires a Bearer token (X-Matryca-Token). Session bootstrap is loopback-only; split rate limits for authenticated vs anonymous API traffic.
• Trust & Safety Drawer: Visually toggle what the AI is allowed to edit (Safe Mode, Augmented Mode, Surgeon Mode).

See SECURITY.md for the full operator hardening matrix (MATRYCA_MCP_ENABLED, graph path allowlist, shared LLM SSRF policy, log redaction).

Pre-flight checklist (what you see in the app)

Matryca Plumber provisions missing runtime files automatically where possible (repo .env from .env.example, matryca-l1/, cache dirs, matryca-wiki.yml). The modal still walks you through setup so nothing surprises you on first run. It is developed by Marco Porcellato at Matryca.ai — the same attribution shown in the Sovereign UI pre-flight wizard.

Operator steps (same text as the UI wizard):

1. Control room connection — If you can read this dashboard, the local API on port 8500 is up. Keep the window open while the engine runs.

2. Logseq graph (test vault first) — Point LOGSEQ_GRAPH_PATH at the root of a Logseq OG vault (the folder that contains pages/). Use a clone for your first run; do not enable Logseq Sync on test graphs. In the UI: Settings (gear) → Logseq Graph Path → absolute path → Save.

3. Local LLM — Start an OpenAI-compatible server (LM Studio, Ollama, etc.). In Settings set the base URL (e.g. http://localhost:1234/v1) and the exact model id, then Refresh models to confirm discovery.

   Matryca Plumber (by Marco Porcellato · Matryca.ai) is built for offline, CPU-only use on a typical 16 GB RAM machine — no cloud subscription or discrete GPU required. The recommended and tested model is Gemma 4-E4b Instruct — set the exact id gemma-4-e4b-it in Settings, then Refresh models. For responsive CPU inference (about 15–30 tokens/second), prefer GGUF weights at Q4_K_M or Q5_K_M. We are actively testing additional open models to improve CPU-only, 16 GB setups; Gemma 4-E4b Instruct is our current default. Avoid large MoE models (e.g. Llama 4 Scout): full weights still require 60GB+ RAM.

4. First-run expectations — Phase 1 catalogs the entire graph (can take a long time on large vaults). Phase 2 processes roughly one LLM-heavy page per poll interval by default.

Live checks (re-run anytime with Re-run checks):

Check	What it validates
Environment file	Repository .env exists (created from .env.example on first boot when possible).
Logseq graph path	LOGSEQ_GRAPH_PATH is set and points at a valid vault root.
L1 session memory	Sibling matryca-l1/ (or configured MATRYCA_L1_PATH / wiki memory_path) is ready.
Local LLM endpoint	LLM_BASE_URL passes SSRF policy and GET /v1/models responds; warns if the configured model id is not listed.

Start Engine stays disabled until every live check is green. If you started the daemon earlier with matryca plumber start, the UI may already show IDLE or RUNNING and Start Engine may be disabled — the pre-flight modal still opens so you can review settings; use Pre-flight in the header to reopen it later.

---

✨ Key Features & Differentiators

• 🤖 100% Local-First & Headless: No Logseq HTTP API required. It edits the .md files directly using atomic file I/O.
• 📐 Exact Logseq AST Compliance: True line-0 page frontmatter, block properties at +2 indent, and exact namespace encoding. Other tools break your graph; Matryca Plumber keeps it pristine.
• 🔐 Optimistic Concurrency Control: It checks st_mtime before any AI inference. If you typed in Logseq while the AI was "thinking", Matryca Plumber aborts its write. No silent data loss.
• 🪟 Windows, macOS & Linux Support: Runs safely in the background everywhere using a robust cross-platform lock (.matryca_plumber_daemon.lock).
• ⚡ Context Acceleration Shield: Obliterates LLM prefill latency on 5,000+ block pages by using hierarchical summaries instead of raw megabyte-class text.

---

🛡️ Trust & Safety Risk Tiers

You are in control. Nothing mutates your prose unless you explicitly enable it in the UI.

Mode	Risk	What it allows
🟢 Safe Mode	Read-only	Semantic routing cache, entity consolidation (alias::), property hygiene — never edits your bullet text.
🟠 Augmented Mode	Side-blocks	Heal Dangling Links, Backpropagate Links (appends foldable context sections) — your original bullets stay intact.
🔴 Surgeon Mode	Inline edits	Inline Semantic Corrections (wraps concepts in [[WikiLinks]]), Auto-Split Dense Blocks — strictly opt-in.

---

⚙️ Configuration Quickstart

Copy .env.example to .env. The only required variable is your graph path:

LOGSEQ_GRAPH_PATH=/absolute/path/to/your/Logseq/graph
MATRYCA_LM_BASE_URL=http://localhost:1234/v1   # LM Studio or Ollama endpoint
MATRYCA_LM_MODEL=gemma-4-e4b-it                # Gemma 4-E4b Instruct — tested default

# Optional: Claude Desktop / Cursor MCP (off by default)
MATRYCA_MCP_ENABLED=true

On first start (daemon, CLI, MCP, or UI), Matryca Plumber automatically creates anything missing for a healthy runtime:

• logs/ (or paths from MATRYCA_PLUMBER_LOG_PATH / MATRYCA_LOGURU_LOG_PATH)
• <parent-of-your-vault>/matryca-l1/ — session rules beside the vault (not inside pages/); optional override via MATRYCA_L1_PATH
• <vault>/.matryca_semantic_cache/, templates/, and matryca-wiki.yml (from matryca-wiki.example.yml when absent)

See docs/openspec/runtime-bootstrap.md for rationale (L1 vs L2, idempotency, and what is intentionally not auto-created).

(See docs/ARCHITECTURE.md for advanced thermal pacing, context compression, and deep linter settings. Copy the full template from .env.example — it documents UI auth, rate limits, graph allowlists, and log redaction.)

---

🧑‍💻 Developer Setup

Want to contribute or run from source?

git clone [https://github.com/MarcoPorcellato/matryca-plumber.git](https://github.com/MarcoPorcellato/matryca-plumber.git)
cd matryca-plumber
make install

# Build the React frontend
cd frontend && npm install && npm run build && cd ..

# Run tests (453 passing, 0 Mypy strict issues)
make check

---

📚 Documentation Map

Document	Description
SYSTEM_PROMPT.md	Agent discipline, made-by:: authorship, OCC rules.
docs/ARCHITECTURE.md	Data planes, Plumber lifecycle, RMW locking rationale.
docs/BRANDING.md	Product name (Matryca Plumber), Matryca.ai attribution, writing rules.
docs/openspec/runtime-bootstrap.md	Startup provisioning: logs, L1, cache, wiki YAML.
docs/openspec/l1-l2-routing.md	L1 memory vs L2 graph routing for agents.
docs/PROJECT_DIARY.md	Maintainer log, phase history, crushed bottlenecks.
CONTRIBUTING.md	Setup, uv commands, make check standards.
SECURITY.md	Vulnerability reporting and .env hardening controls.

License

Apache-2.0 — see LICENSE.