Skip to main content
Glama

The local-first second brain that Claude remembers. Karpathy's self-compiling wiki × Zettelkasten — fully local, vault-safe, and MCP-native.

MCP server npm CI tests node license

English · 한국어 · 日本語 · 简体中文

🤖 Add to Claude / Cursor · ⬇ Desktop App · ⚡ Quickstart · 🌐 Live Demo

One command to let Claude read your vault:

npx -y stellavault setup    # wires the MCP server into Claude Code / Desktop, Cursor, Windsurf, or VS Code

A second brain that compiles itself. Stellavault fuses two ideas about how knowledge should live and grow:

  • 🧠 Karpathy's self-compiling wiki — drop in anything (a PDF, a YouTube link, a passing thought) and it's auto-extracted into raw/, then compiled into a clean _wiki/ of concepts and backlinks. Your knowledge re-compiles itself as it grows, instead of rotting in a folder.

  • 🕸️ Zettelkasten — atomic notes, [[wikilinks]], and emergent connections, so a web of ideas (not a folder tree) becomes the real structure of how you think.

The result is one local-first knowledge tool — a full markdown editor, a 3D neural graph, hybrid AI search, and spaced-repetition memory decay — shipped as a desktop app, CLI, Obsidian plugin, and MCP server that lets Claude read your entire vault. No cloud, no API keys, and your original files are never modified.

Contents

Highlights · Why Stellavault? · Install · Editor · Pipeline · Intelligence · Search & Ranking · MCP Integration · 3D Visualization · Configuration · Performance · Tech Stack · Security · Troubleshooting

Related MCP server: RAG MCP Server

Highlights

  • 🧠 It compiles itself. Drop in a PDF, a YouTube link, or a half-formed thought — Stellavault extracts it to raw/, then compiles a clean _wiki/ with concepts and backlinks. Knowledge that organizes itself as it grows.

  • 🔍 Search that actually finds it. Hybrid retrieval fuses semantic meaning, exact keywords (BM25), and your own [[wikilinks]] / #tags with weighted RRF, then re-ranks by an FSRS memory model so what you're actually using resurfaces. 50+ languages, fully local, zero API keys.

  • 🌌 Your mind, in 3D. A real-time neural graph (React Three Fiber) — cluster coloring, constellations, heatmaps, a timeline, and a multiverse P2P view. A way to see the shape of what you know.

  • 🤖 Claude reads your entire vault. A first-class MCP server (21 tools): Claude can search, ask, draft, lint, and analyze your knowledge from Claude Code, Claude Desktop, Cursor, Windsurf, or VS Code.

  • It never silently forgets. FSRS memory decay surfaces the real notes you're about to lose — plus gap, contradiction, and duplicate detection across the whole vault.

  • 🔒 Local-first. Vault-safe. Zero keys. Local embeddings, an on-device vector store, and your original files are never modified. Nothing leaves your machine unless you opt in.

Why Stellavault?

Most tools make you choose between writing, searching, and remembering. Stellavault does all three — locally, and in a way Claude can read.

Stellavault

Obsidian

Notion

Plain RAG script

Local-first, works offline

☁️ cloud

⚠️ usually cloud

Semantic search, no API key

⚠️ plugin + key

💰 paid AI

⚠️ needs key

Original files never modified

❌ proprietary

Self-compiling (ingest → wiki)

3D knowledge graph

2D / plugin

Spaced-repetition decay (FSRS)

⚠️ plugin

Gap / contradiction / dup detection

MCP-native (Claude reads your vault)

➖ community

☁️ cloud

NOTE

Not either/or — Stellavault even runsinside Obsidian as a plugin. Keep your editor; add a brain.

Install

TIP

Download → Unzip → Runstellavault.exe (Windows) or stellavault (Linux) → Pick your notes folder → Done.

CLI (for developers)

npm install -g stellavault    # or: npx stellavault
stellavault init              # Interactive setup (3 min): index vault + connect AI clients
stellavault setup             # Connect to Claude Code/Desktop, Cursor, Windsurf, VS Code (one command)
stellavault graph             # Launch 3D graph in browser

Requires Node.js 20+. Run stellavault doctor to diagnose issues.

Obsidian Plugin

  1. Download main.js + manifest.json + styles.css from stellavault-obsidian releases

  2. Place in .obsidian/plugins/stellavault/

  3. Enable in Settings → Community plugins

  4. Start API: npx stellavault graph in your vault folder


Editor

Full-featured markdown editor — on par with Obsidian.

Feature

Status

Bold, Italic, Underline, Strikethrough

Headings 1–6

Bullet, Numbered, Task lists (nested checkboxes)

Tables (create, resize columns, add/remove rows & cols)

Code blocks with syntax highlighting (40+ languages)

Images (URL, clipboard paste, drag & drop)

KaTeX math rendering ($E=mc^2$ inline, $$...$$ display)

/Slash commands (12 block types, fuzzy search)

[[Wikilink]] autocomplete

Split view (vertical + horizontal, Ctrl+\)

Text alignment (left / center / right)

Highlight, Superscript, Subscript

Smart typography (curly quotes, em/en dashes)

Horizontal rules


The Pipeline

Capture ──→ Organize ──→ Distill ──→ Express

Drop anything → auto-extract → raw/ → compile → _wiki/ → draft

Inspired by Karpathy's self-compiling knowledge architecture.

Ingest 14 Formats

Input

How

PDF, DOCX, PPTX, XLSX

stellavault ingest report.pdf

JSON, CSV, XML, YAML, HTML, RTF

stellavault ingest data.json

YouTube

stellavault ingest https://youtu.be/... — transcript + timestamps

URL

stellavault ingest https://... — HTML → markdown

Text

stellavault ingest "quick thought"

Folder

stellavault ingest ./papers/ — batch all files

Desktop / Web UI

Drag & drop files directly

Express: Get Knowledge Out

stellavault draft "AI" --format blog      # Blog post from your vault
stellavault draft "AI" --format outline   # Structured outline
stellavault draft "AI" --ai              # Claude API enhanced ($0.03)

Or use the Express tab in the desktop app — enter a topic, pick a format, and generate a draft grounded in your vault. Save to _drafts/ and edit inline.


Intelligence (What Makes Stellavault Unique)

These features do not exist in Obsidian — even with plugins.

Feature

Command / Desktop

Description

Memory Decay

stellavault decay / Memory tab

FSRS-based — shows which real notes you are forgetting

Knowledge Gaps

stellavault gaps

Detects weak connections between topic clusters

Contradictions

stellavault contradictions

Finds conflicting statements across your vault

Duplicates

stellavault duplicates

Near-identical notes with similarity score

Health Check

stellavault lint

Aggregated vault health score (0–100)

Learning Path

stellavault learn

AI-personalized review recommendations

Daily Brief

Desktop app home screen

Push-type: top decaying notes + stats on app open

Auto-Tagging

Automatic on ingest

Content-based keyword extraction + category rules

Self-Compiling

stellavault compile

raw/ → _wiki/ with extracted concepts + backlinks


Search & Ranking

Hybrid retrieval that fuses multiple signals with weighted Reciprocal Rank Fusion (RRF) — tuned for a personal knowledge vault, fully local, zero API keys:

Signal

What it captures

Default weight

Semantic (dense)

meaning; multilingual (50+ languages)

1.0

BM25 (keyword)

exact terms, code, names

1.0

Entity-linking

your [[wikilinks]], #tags, headings, titles — the curated graph

1.5

FSRS recency

gently surfaces notes you're actively using / forgetting

±10%

  • Entity matching resolves natural-language queries via fuzzy substring + punctuation-normalized matching (Korean / CJK friendly), with a per-document diversity cap so one large note can't flood the top results.

  • Recency reuses the same FSRS memory model as the decay engine (not raw file mtime) — a note you're forgetting resurfaces; a mastered evergreen note isn't buried just for being old.

  • Adaptive rerank (long-running MCP server) further boosts results by your current session context (recent tags / paths).

  • Every weight is tunable per vault or via env vars — see Configuration.


MCP Integration (21 Tools)

stellavault setup            # one command → Claude Code, Claude Desktop, Cursor, Windsurf, VS Code
# or, for Claude Code only:
claude mcp add stellavault -- stellavault serve
{
  "mcpServers": {
    "stellavault": {
      "command": "npx",
      "args": ["-y", "stellavault", "serve"]
    }
  }
}

Listed on the MCP registry as io.github.Evanciel/stellavault (also discoverable via Glama, Smithery, and mcp.so).

Claude can search, ask, draft, lint, and analyze your vault directly. Search runs the full hybrid pipeline — weighted RRF over semantic + BM25 + entity-linking, plus FSRS recency and session-adaptive reranking (see Search & Ranking).

Tool

What it does

search

Weighted RRF (semantic + BM25 + entity) + FSRS recency + adaptive rerank

ask

Vault-grounded Q&A

generate-draft

AI drafts from your knowledge

get-decay-status

Memory decay report (FSRS)

detect-gaps

Knowledge gap analysis

create-knowledge-node

AI creates wiki-quality notes

federated-search

P2P search across vaults

+ 14 more

Documents, topics, decisions, snapshots, export


3D Visualization

  • Neural graph with cluster coloring (React Three Fiber)

  • Cluster view (default) — a large vault folds into a small galaxy of labeled cluster super-nodes; an in-viewer [Cluster | All nodes] toggle switches to the dense raw view without reload, and clicking a super-node drills into that cluster's members

  • Constellation view (MST star patterns)

  • Heatmap overlay + Timeline slider + Decay overlay

  • Multiverse view — your vault as a universe in a P2P network

  • Dark/Light theme

Graph viewer scaling

The 3D graph renderer is bounded by two environment knobs (set them in the environment before launching stellavault graph):

Env var

Default

Applies to

What it caps

GRAPH_NODE_CAP

1500

raw / "All nodes" view

# of (most-recent) notes rendered as individual nodes.

GRAPH_CLUSTER_CAP

3000

cluster view (default)

# of notes folded into the galaxy before clustering.

Why the caps matter. Edge construction is an inline O(n²) all-pairs cosine loop — n·(n-1)/2 pairs, each over 384 dims (see packages/core/src/api/graph-data.ts, the neighbors loop). At cap=3000 that is ~4.5M pairs (the multi-second build the 5-minute server cache absorbs); raising toward 13000 is ~84M pairs = minutes of build plus a multi-GB intermediate array that freezes the single-threaded Express handler (there is no worker). The raw "All nodes" path is the expensive direction (it runs the full cap), so the server clamps the requested ?cap per view (raw ≤ 4000, cluster ≤ 6000, rounded) to bound both build cost and cache cardinality.

Even the default cluster view is cheap to RENDER, not to BUILD. The first uncached fetch of either view runs the scoped-embedding load + the O(n²) cosine pass + k-means + a force settle on a single thread, stalling the handler for seconds. The viewer shows a loading state on the toggle so the tap isn't silently swallowed during that cold build.


Try It Now (Demo Vault)

npx stellavault index --vault ./examples/demo-vault   # Index 10 sample notes
npx stellavault search "vector database"               # Semantic search
npx stellavault graph                                  # 3D graph visualization

The demo vault includes interconnected notes about Vector Databases, Knowledge Graphs, Spaced Repetition, RAG, MCP, and more — perfect for exploring all features instantly.


Getting Started Guide

Desktop App

  1. Download → Unzip → Run

  2. First launch asks you to pick your notes folder

  3. Your notes appear in the sidebar — click to open

  4. Press Ctrl+P for quick file switching

  5. Click ✦ in the title bar for AI panel (semantic search, stats, draft)

  6. Click ◉ for 3D graph

CLI

npm install -g stellavault
stellavault init                          # Setup wizard
stellavault search "machine learning"     # Semantic search
stellavault ingest paper.pdf              # Add knowledge
stellavault graph                         # 3D graph in browser
stellavault brief                         # Morning briefing
stellavault decay                         # What are you forgetting?

Keyboard Shortcuts (Desktop)

Shortcut

Action

Ctrl+P

Quick Switcher (fuzzy file search)

Ctrl+Shift+P

Command Palette (all actions)

Ctrl+S

Save current note

Ctrl+\

Toggle split view

Ctrl+B

Bold

Ctrl+I

Italic

Ctrl+U

Underline

Ctrl+E

Inline code

/

Slash commands (at start of line)

[[

Wikilink autocomplete

Quick Reference

Action

Desktop

CLI

Search notes

Ctrl+P or AI panel

stellavault search "query"

Add a note

+ Note button or drag & drop

stellavault ingest "text"

See 3D graph

◉ button

stellavault graph

Memory decay

AI panel → Memory

stellavault decay

Generate draft

AI panel → Draft

stellavault draft "topic"

Health check

AI panel → Stats

stellavault lint


Configuration

Stellavault reads ./.stellavault.json (or ~/.stellavault.json). Search ranking is fully tunable — sensible defaults work out of the box:

{
  "search": {
    "rrfK": 60,
    "weights": { "semantic": 1.0, "bm25": 1.0, "entity": 1.5 },
    "recencyWeight": 0.2,                          // FSRS recency strength; 0 = off
    "entityAliases": { "k8s": ["kubernetes"] }     // synonym / cross-lingual groups (exact-only)
  }
}

Environment variables override config (parsed with guards):

Env var

Effect

STELLAVAULT_W_SEMANTIC / _BM25 / _ENTITY

per-signal RRF weight (e.g. STELLAVAULT_W_ENTITY=2.0 for aggressive entity surfacing)

STELLAVAULT_RECENCY_WEIGHT

recency strength 01 (0 disables)

STELLAVAULT_DB_PATH

override the index DB location

STELLAVAULT_WATCH

0 to disable the auto-reindex file watcher while serve runs

Note: cross-lingual recall (e.g. a Korean query finding English notes) is handled automatically by the multilingual embedding model — entityAliases is an optional precision boost for the curated entity graph (tags / wikilinks) and abbreviations.


Performance

Tested on synthetic vaults — all operations under 1 second for typical use cases:

Operation

100 docs

500 docs

1000 docs

Store init

15ms

15ms

16ms

Bulk upsert

12ms

102ms

~200ms

Search (BM25)

<1ms

<1ms

<1ms

Get all docs

<1ms

2ms

~4ms

124K dot products

36ms

Run your own benchmarks:

node tests/stress.mjs 500     # Test with 500 synthetic documents

Key optimizations:

  • HNSW graph building — sqlite-vec KNN for 200+ docs (O(n·K·log n) vs O(n²))

  • Pre-normalized vectors: cosine similarity → single dot product

  • Batched embedding loading (500/batch, prevents RAM overflow)

  • Upper-triangle brute-force for small vaults (< 200 docs)

  • O(n) K-Means centroid updates with typed arrays


Tech Stack

Layer

Tech

Desktop

Electron + React + TipTap (15 extensions) + Zustand

Runtime

Node.js 20+ (ESM, TypeScript)

Vector Store

SQLite-vec (local, zero config)

Embedding

MiniLM-L12-v2 (local, 50+ languages, batch processing)

Search

Weighted RRF (semantic + BM25 + entity) + FSRS recency

Math

KaTeX (inline + display)

Code

lowlight / highlight.js (40+ languages)

3D

React Three Fiber + Three.js

AI

MCP (21 tools) + Anthropic SDK

P2P

Hyperswarm (optional, differential privacy)

CI

GitHub Actions (Node 20 + 22)


Security

  • Local-first — no data leaves your machine unless you use --ai

  • Vault files never modified — indexes into SQLite, originals untouched

  • Electron sandbox enabled — renderer runs with reduced OS privileges

  • IPC path validation — all file operations stay inside vault root

  • API auth token — per-session, header-only (X-Stellavault-Token). Token endpoint is same-origin-only

  • CORS allow-listlocalhost / 127.0.0.1 only by default; MCP HTTP transport opt-in

  • SSRF protection — private IPs blocked on URL ingest

  • E2E encryption — AES-256-GCM for cloud sync

Federation (experimental, off by default)

Peer-to-peer semantic search is shipped as an opt-in experimental feature. The default install does not join any swarm and never shares data.

Enable explicitly:

# PowerShell
$env:STELLAVAULT_FEDERATION_EXPERIMENTAL = "1"

# bash / zsh
export STELLAVAULT_FEDERATION_EXPERIMENTAL=1

stellavault federate join

When enabled, federation uses Ed25519 identities with signed envelopes, mutual challenge-response handshake, per-envelope replay nonces, handshake timeout, per-peer rate limiting, and a receive-only sharing default (myNodeLevel=0). Run set-level 1+ in the federation prompt to actually share titles/snippets with peers.

WARNING

Upgrade note (v0.7.4) — federation wire format bumped from v2.0 to v2.1 (envelope-level nonce). v0.7.3 federation nodes are not compatible. Existing ~/.stellavault/federation/sharing.json files are not auto-downgraded to the safer defaults; review your myNodeLevel if you previously opted in.

See SECURITY.md for full details.

Troubleshooting

stellavault doctor    # Check config, vault, DB, model, Node version

Common issues:

  • "Command not found"npm i -g stellavault@latest

  • "API server not found"npx stellavault graph

  • Empty graphstellavault index

  • Slow first run → AI model downloads ~30MB once

Contributing

Issues and pull requests are welcome. See CONTRIBUTING.md to get started, and SECURITY.md to report a vulnerability responsibly.

License

MIT — full source code available for audit.


Found this useful?Star Stellavault — it genuinely helps the project reach more people who think in connected notes.

Built with ✦ for second-brain builders. · Download · Connect Claude · Report an issue

A
license - permissive license
-
quality - not tested
A
maintenance

Maintenance

Maintainers
Response time
1wRelease cycle
12Releases (12mo)
Commit activity

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Servers

View all related MCP servers

Related MCP Connectors

  • Read and write your Fresh Jots notes from Claude, Cursor, and any MCP client.

  • Search your AI chat history (ChatGPT, Claude, Codex) from any MCP client. Remote, private, read-only

  • Shared long-term memory vault for AI agents with 20 MCP tools.

View all MCP Connectors

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Evanciel/stellavault'

If you have feedback or need assistance with the MCP directory API, please join our Discord server