docs: add VitePress documentation site

- 22 content pages across Guide, Concepts, and Reference sections
- Custom indigo/cyan theme with Lucide icons and Mermaid diagrams
- GitHub Actions workflow for GitHub Pages deployment
- Live preview: https://mempalace-docs.netlify.app/

.github/workflows/deploy-docs.yml

@@ -0,0 +1,52 @@
+name: Deploy Docs
+
+on:
+  push:
+    branches: [main]
+    paths:
+      - 'website/**'
+  workflow_dispatch:
+
+permissions:
+  contents: read
+  pages: write
+  id-token: write
+
+concurrency:
+  group: pages
+  cancel-in-progress: false
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - uses: oven-sh/setup-bun@v2
+        with:
+          bun-version: latest
+
+      - name: Install dependencies
+        working-directory: website
+        run: bun install
+
+      - name: Build docs
+        working-directory: website
+        run: bun run docs:build
+
+      - uses: actions/upload-pages-artifact@v3
+        with:
+          path: website/.vitepress/dist
+
+  deploy:
+    environment:
+      name: github-pages
+      url: ${{ steps.deployment.outputs.page_url }}
+    needs: build
+    runs-on: ubuntu-latest
+    steps:
+      - name: Deploy to GitHub Pages
+        id: deployment
+        uses: actions/deploy-pages@v4

website/.gitignore

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+# dependencies (bun install)
+node_modules
+
+# output
+out
+dist
+.vitepress/dist
+.vitepress/.temp
+*.tgz
+
+# code coverage
+coverage
+*.lcov
+
+# logs
+logs
+*.log
+report.[0-9]_.[0-9]_.[0-9]_.[0-9]_.json
+
+# dotenv environment variable files
+.env
+.env.development.local
+.env.test.local
+.env.production.local
+.env.local
+
+# caches
+.eslintcache
+.cache
+*.tsbuildinfo
+
+# IntelliJ based IDEs
+.idea
+
+# Finder (MacOS) folder config
+.DS_Store

website/.vitepress/config.mts

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+import { defineConfig } from 'vitepress'
+import { withMermaid } from 'vitepress-plugin-mermaid'
+
+const docsBase = process.env.DOCS_BASE || '/mempalace/'
+
+export default withMermaid(
+  defineConfig({
+    title: 'MemPalace',
+    description: 'Give your AI a memory. Local-first storage and retrieval for AI workflows, with benchmark results and MCP tooling.',
+    base: docsBase,
+
+    head: [
+      ['link', { rel: 'icon', href: `${docsBase}mempalace_logo.png` }],
+      ['link', { rel: 'preconnect', href: 'https://fonts.googleapis.com' }],
+      ['link', { rel: 'preconnect', href: 'https://fonts.gstatic.com', crossorigin: '' }],
+      ['link', { href: 'https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap', rel: 'stylesheet' }],
+      ['meta', { property: 'og:title', content: 'MemPalace — AI Memory System' }],
+      ['meta', { property: 'og:description', content: '96.6% LongMemEval recall. Zero API calls. Local, free, open source.' }],
+      ['meta', { property: 'og:image', content: `${docsBase}mempalace_logo.png` }],
+    ],
+
+    themeConfig: {
+      logo: '/mempalace_logo.png',
+      siteTitle: 'MemPalace',
+
+      nav: [
+        { text: 'Guide', link: '/guide/getting-started' },
+        { text: 'Concepts', link: '/concepts/the-palace' },
+        { text: 'Reference', link: '/reference/cli' },
+      ],
+
+      sidebar: {
+        '/guide/': [
+          {
+            text: 'Guide',
+            items: [
+              { text: 'Getting Started', link: '/guide/getting-started' },
+              { text: 'Mining Your Data', link: '/guide/mining' },
+              { text: 'Searching Memories', link: '/guide/searching' },
+              { text: 'MCP Integration', link: '/guide/mcp-integration' },
+              { text: 'Claude Code Plugin', link: '/guide/claude-code' },
+              { text: 'Gemini CLI', link: '/guide/gemini-cli' },
+              { text: 'Local Models', link: '/guide/local-models' },
+              { text: 'Auto-Save Hooks', link: '/guide/hooks' },
+              { text: 'Configuration', link: '/guide/configuration' },
+            ],
+          },
+        ],
+        '/concepts/': [
+          {
+            text: 'Concepts',
+            items: [
+              { text: 'The Palace', link: '/concepts/the-palace' },
+              { text: 'Memory Stack', link: '/concepts/memory-stack' },
+              { text: 'AAAK Dialect', link: '/concepts/aaak-dialect' },
+              { text: 'Knowledge Graph', link: '/concepts/knowledge-graph' },
+              { text: 'Specialist Agents', link: '/concepts/agents' },
+              { text: 'Contradiction Detection', link: '/concepts/contradiction-detection' },
+            ],
+          },
+        ],
+        '/reference/': [
+          {
+            text: 'Reference',
+            items: [
+              { text: 'CLI Commands', link: '/reference/cli' },
+              { text: 'MCP Tools', link: '/reference/mcp-tools' },
+              { text: 'Python API', link: '/reference/python-api' },
+              { text: 'API Reference', link: '/reference/api-reference' },
+              { text: 'Module Map', link: '/reference/modules' },
+              { text: 'Benchmarks', link: '/reference/benchmarks' },
+              { text: 'Contributing', link: '/reference/contributing' },
+            ],
+          },
+        ],
+      },
+
+      socialLinks: [
+        { icon: 'github', link: 'https://github.com/milla-jovovich/mempalace' },
+        { icon: 'discord', link: 'https://discord.com/invite/ycTQQCu6kn' },
+      ],
+
+      search: {
+        provider: 'local',
+      },
+
+      footer: {
+        message: 'Released under the MIT License.',
+        copyright: 'Copyright © 2026 MemPalace contributors',
+      },
+
+      editLink: {
+        pattern: 'https://github.com/milla-jovovich/mempalace/edit/main/website/:path',
+        text: 'Edit this page on GitHub',
+      },
+    },
+
+    mermaid: {
+      theme: 'dark',
+    },
+  })
+)

website/.vitepress/theme/index.ts

@@ -0,0 +1,6 @@
+import DefaultTheme from 'vitepress/theme'
+import './style.css'
+
+export default {
+  extends: DefaultTheme,
+}

website/.vitepress/theme/style.css

@@ -0,0 +1,215 @@
+/* ── MemPalace Custom Theme ──────────────────────────────────────────── */
+/* Deep indigo / cyan palette — evoking architectural grandeur          */
+
+:root {
+  /* Brand palette */
+  --mp-indigo: #4f46e5;
+  --mp-indigo-light: #6366f1;
+  --mp-indigo-dark: #3730a3;
+  --mp-cyan: #06b6d4;
+  --mp-cyan-light: #22d3ee;
+  --mp-purple: #8b5cf6;
+  --mp-purple-light: #a78bfa;
+  --mp-emerald: #10b981;
+  --mp-amber: #f59e0b;
+
+  /* VitePress overrides */
+  --vp-c-brand-1: var(--mp-indigo);
+  --vp-c-brand-2: var(--mp-indigo-light);
+  --vp-c-brand-3: var(--mp-purple);
+  --vp-c-brand-soft: rgba(79, 70, 229, 0.14);
+
+  --vp-font-family-base: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
+  --vp-font-family-mono: 'JetBrains Mono', 'Fira Code', monospace;
+
+  /* Home hero gradient */
+  --vp-home-hero-name-color: transparent;
+  --vp-home-hero-name-background: linear-gradient(135deg, var(--mp-indigo) 0%, var(--mp-cyan) 50%, var(--mp-purple) 100%);
+  --vp-home-hero-image-background-image: linear-gradient(135deg, rgba(79, 70, 229, 0.25) 0%, rgba(6, 182, 212, 0.25) 50%, rgba(139, 92, 246, 0.15) 100%);
+  --vp-home-hero-image-filter: blur(56px);
+
+  /* Button colors */
+  --vp-button-brand-border: transparent;
+  --vp-button-brand-text: #ffffff;
+  --vp-button-brand-bg: var(--mp-indigo);
+  --vp-button-brand-hover-border: transparent;
+  --vp-button-brand-hover-text: #ffffff;
+  --vp-button-brand-hover-bg: var(--mp-indigo-light);
+
+  --vp-button-alt-border: rgba(79, 70, 229, 0.25);
+  --vp-button-alt-text: var(--mp-indigo);
+  --vp-button-alt-bg: rgba(79, 70, 229, 0.08);
+  --vp-button-alt-hover-border: rgba(79, 70, 229, 0.4);
+  --vp-button-alt-hover-text: var(--mp-indigo-dark);
+  --vp-button-alt-hover-bg: rgba(79, 70, 229, 0.14);
+}
+
+/* Dark mode overrides */
+.dark {
+  --vp-c-brand-1: var(--mp-cyan-light);
+  --vp-c-brand-2: var(--mp-cyan);
+  --vp-c-brand-3: var(--mp-purple-light);
+  --vp-c-brand-soft: rgba(6, 182, 212, 0.14);
+
+  --vp-button-brand-bg: var(--mp-indigo-light);
+  --vp-button-brand-hover-bg: var(--mp-indigo);
+
+  --vp-button-alt-border: rgba(34, 211, 238, 0.25);
+  --vp-button-alt-text: var(--mp-cyan-light);
+  --vp-button-alt-bg: rgba(34, 211, 238, 0.08);
+  --vp-button-alt-hover-border: rgba(34, 211, 238, 0.4);
+  --vp-button-alt-hover-text: var(--mp-cyan);
+  --vp-button-alt-hover-bg: rgba(34, 211, 238, 0.14);
+
+  --vp-home-hero-image-background-image: linear-gradient(135deg, rgba(99, 102, 241, 0.3) 0%, rgba(6, 182, 212, 0.3) 50%, rgba(139, 92, 246, 0.2) 100%);
+}
+
+/* ── Hero section ───────────────────────────────────────────────────── */
+
+.VPHero .VPImage {
+  max-width: 180px;
+  border-radius: 20px;
+}
+
+.VPHero .name {
+  font-weight: 700 !important;
+}
+
+.VPHero .text {
+  font-weight: 500;
+  background: linear-gradient(135deg, var(--vp-c-text-1) 0%, var(--mp-indigo-light) 100%);
+  -webkit-background-clip: text;
+  -webkit-text-fill-color: transparent;
+  background-clip: text;
+}
+
+.dark .VPHero .text {
+  background: linear-gradient(135deg, var(--vp-c-text-1) 0%, var(--mp-cyan-light) 100%);
+  -webkit-background-clip: text;
+  -webkit-text-fill-color: transparent;
+  background-clip: text;
+}
+
+/* ── Feature cards ──────────────────────────────────────────────────── */
+
+.VPFeature {
+  transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1);
+  border: 1px solid var(--vp-c-divider);
+}
+
+.VPFeature:hover {
+  transform: translateY(-4px);
+  border-color: var(--mp-indigo);
+  box-shadow: 0 12px 40px rgba(79, 70, 229, 0.12);
+}
+
+.dark .VPFeature:hover {
+  border-color: var(--mp-cyan);
+  box-shadow: 0 12px 40px rgba(6, 182, 212, 0.12);
+}
+
+.VPFeature .title {
+  font-weight: 600;
+}
+
+/* ── Sidebar ────────────────────────────────────────────────────────── */
+
+.VPSidebar .VPSidebarItem .text {
+  transition: color 0.2s ease;
+}
+
+.VPSidebar .VPSidebarItem.is-active .text {
+  color: var(--mp-indigo) !important;
+  font-weight: 600;
+}
+
+.dark .VPSidebar .VPSidebarItem.is-active .text {
+  color: var(--mp-cyan-light) !important;
+}
+
+/* ── Code blocks ────────────────────────────────────────────────────── */
+
+.vp-doc div[class*='language-'] {
+  border: 1px solid var(--vp-c-divider);
+  border-radius: 8px;
+}
+
+.dark .vp-doc div[class*='language-'] {
+  border-color: rgba(6, 182, 212, 0.15);
+}
+
+/* ── Custom containers ──────────────────────────────────────────────── */
+
+.vp-doc .custom-block.tip {
+  border-color: var(--mp-cyan);
+}
+
+.vp-doc .custom-block.warning {
+  border-color: var(--mp-amber);
+}
+
+.vp-doc .custom-block.info {
+  border-color: var(--mp-indigo);
+}
+
+/* ── Tables ─────────────────────────────────────────────────────────── */
+
+.vp-doc table {
+  border-radius: 8px;
+  overflow: hidden;
+}
+
+.vp-doc th {
+  background: rgba(79, 70, 229, 0.06);
+  font-weight: 600;
+}
+
+.dark .vp-doc th {
+  background: rgba(6, 182, 212, 0.08);
+}
+
+/* ── Nav ────────────────────────────────────────────────────────────── */
+
+.VPNavBar .VPNavBarTitle .title {
+  font-weight: 700;
+  letter-spacing: -0.01em;
+}
+
+/* ── Footer ─────────────────────────────────────────────────────────── */
+
+.VPFooter {
+  border-top: 1px solid var(--vp-c-divider);
+}
+
+/* ── Scrollbar ──────────────────────────────────────────────────────── */
+
+::-webkit-scrollbar {
+  width: 6px;
+}
+
+::-webkit-scrollbar-thumb {
+  background: var(--vp-c-divider);
+  border-radius: 3px;
+}
+
+::-webkit-scrollbar-thumb:hover {
+  background: var(--mp-indigo);
+}
+
+.dark ::-webkit-scrollbar-thumb:hover {
+  background: var(--mp-cyan);
+}
+
+/* ── Smooth transitions ─────────────────────────────────────────────── */
+
+a, .VPLink {
+  transition: color 0.2s ease;
+}
+
+.VPButton {
+  transition: all 0.25s cubic-bezier(0.4, 0, 0.2, 1);
+}
+
+.VPButton:hover {
+  transform: translateY(-1px);
+}

website/bun.lock

@@ -0,0 +1,633 @@
+{
+  "lockfileVersion": 1,
+  "configVersion": 1,
+  "workspaces": {
+    "": {
+      "name": "website",
+      "devDependencies": {
+        "@lucide/vue": "^1.8.0",
+        "mermaid": "^11.14.0",
+        "vitepress": "^1.6.4",
+        "vitepress-plugin-mermaid": "^2.0.17",
+        "vue": "^3.5.32",
+      },
+    },
+  },
+  "packages": {
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+  }
+}

website/concepts/aaak-dialect.md

@@ -0,0 +1,125 @@
+# AAAK Dialect
+
+AAAK is an experimental lossy abbreviation system designed to pack repeated entities and relationships into fewer tokens at scale. It is readable by any LLM — Claude, GPT, Gemini, Llama, Mistral — without a decoder.
+
+::: warning Experimental
+AAAK is a separate compression layer, **not the storage default**. The 96.6% benchmark score comes from raw verbatim mode. AAAK mode currently scores 84.2% R@5 — a 12.4 point regression. We're iterating.
+:::
+
+## What AAAK Is
+
+- **Lossy, not lossless.** Uses regex-based abbreviation, not reversible compression.
+- **A structured summary format.** Extracts entities, topics, key sentences, emotions, and flags from plain text.
+- **Readable by any LLM.** No decoder needed — models read it naturally.
+- **Designed for scale.** Saves tokens when the same entities appear hundreds of times.
+
+## What AAAK Is Not
+
+- **Not lossless compression.** The original text cannot be reconstructed.
+- **Not efficient at small scale.** Short text already tokenizes efficiently — AAAK overhead costs more than it saves.
+- **Not the default storage format.** MemPalace stores raw verbatim text in ChromaDB.
+
+## Format
+
+```
+Header:   FILE_NUM|PRIMARY_ENTITY|DATE|TITLE
+Zettel:   ZID:ENTITIES|topic_keywords|"key_quote"|WEIGHT|EMOTIONS|FLAGS
+Tunnel:   T:ZID<->ZID|label
+Arc:      ARC:emotion->emotion->emotion
+```
+
+### Entity Codes
+
+Three-letter uppercase codes: `ALC=Alice`, `KAI=Kai`, `MAX=Max`.
+
+### Emotion Codes
+
+| Code | Meaning | Code | Meaning |
+|------|---------|------|---------|
+| `vul` | vulnerability | `joy` | joy |
+| `fear` | fear | `trust` | trust |
+| `grief` | grief | `wonder` | wonder |
+| `rage` | rage | `love` | love |
+| `hope` | hope | `despair` | despair |
+| `peace` | peace | `humor` | humor |
+| `tender` | tenderness | `raw` | raw honesty |
+| `doubt` | self-doubt | `relief` | relief |
+| `anx` | anxiety | `exhaust` | exhaustion |
+
+### Flags
+
+| Flag | Meaning |
+|------|---------|
+| `ORIGIN` | Origin moment (birth of something) |
+| `CORE` | Core belief or identity pillar |
+| `SENSITIVE` | Handle with absolute care |
+| `PIVOT` | Emotional turning point |
+| `GENESIS` | Led directly to something existing |
+| `DECISION` | Explicit decision or choice |
+| `TECHNICAL` | Technical architecture detail |
+
+## Example
+
+**Input:**
+```
+We decided to use GraphQL instead of REST because the frontend team needs
+flexible queries. Kai recommended it after researching both options. The team
+was excited about the schema-first approach.
+```
+
+**AAAK output:**
+```
+0:KAI|graphql_rest_decided|"decided to use GraphQL instead of REST"|determ+excite|DECISION+TECHNICAL
+```
+
+## Usage
+
+### Compress drawers
+
+```bash
+# Preview compression
+mempalace compress --wing myapp --dry-run
+
+# Compress and store
+mempalace compress --wing myapp
+```
+
+### With entity config
+
+```bash
+mempalace compress --wing myapp --config entities.json
+```
+
+Entity config format:
+```json
+{
+  "entities": {"Alice": "ALC", "Bob": "BOB"},
+  "skip_names": ["Gandalf", "Sherlock"]
+}
+```
+
+### Python API
+
+```python
+from mempalace.dialect import Dialect
+
+# Basic compression
+dialect = Dialect()
+compressed = dialect.compress("We decided to use GraphQL...")
+
+# With entity mappings
+dialect = Dialect(entities={"Alice": "ALC", "Kai": "KAI"})
+compressed = dialect.compress(text, metadata={"wing": "myapp", "room": "arch"})
+
+# From config file
+dialect = Dialect.from_config("entities.json")
+```
+
+## When to Use AAAK
+
+AAAK is most useful when:
+- You have **many repeated entities** across thousands of sessions
+- You need to **compress context** for local models with small windows
+- You want **structured summaries** pointing back to verbatim drawers
+
+For most users, raw verbatim mode is the better default.

website/concepts/agents.md

@@ -0,0 +1,61 @@
+# Specialist Agents
+
+MemPalace currently supports **agent diaries** through MCP tools. The practical model is simple: give an agent a stable name, and write/read diary entries under that agent's wing.
+
+::: warning Current Scope
+This page documents the diary workflow that exists today. MemPalace does **not** currently ship an agent registry, `~/.mempalace/agents/*.json`, or a `mempalace_list_agents` tool.
+:::
+
+## What Agents Do
+
+Each agent:
+
+- **Has a focus** — what it pays attention to
+- **Keeps a diary** — entries persist across sessions
+- **Can read recent history** — useful for patterns, continuity, and follow-up work
+
+## Agent Diary
+
+The diary is a lightweight memory stream for one named agent: observations, findings, decisions, and recurring patterns.
+
+### Writing Entries
+
+```text
+MCP tool: mempalace_diary_write
+  arguments: {
+    "agent_name": "reviewer",
+    "entry": "PR#42|auth.bypass.found|missing.middleware.check|pattern:3rd.time.this.quarter|★★★★"
+  }
+```
+
+### Reading History
+
+```text
+MCP tool: mempalace_diary_read
+  arguments: { "agent_name": "reviewer", "last_n": 10 }
+  → returns last 10 findings, compressed in AAAK
+```
+
+### MCP Tools
+
+| Tool | Description |
+|------|-------------|
+| `mempalace_diary_write` | Write an AAAK diary entry |
+| `mempalace_diary_read` | Read recent diary entries |
+
+## How It Works
+
+Each named agent maps to its own wing in the palace:
+- `wing_reviewer` — the reviewer's diary, findings, patterns
+- `wing_architect` — the architect's decisions, tradeoffs
+- `wing_ops` — the ops agent's incidents, deploys
+
+All entries go into a `diary` room within the wing, tagged with topic, timestamp, and agent name.
+
+## Specialization
+
+Separate diary streams let you keep different working contexts apart. A reviewer can keep bug patterns, an architect can keep decisions, and an ops agent can keep incident notes without mixing them into one shared log.
+
+::: tip
+If you use multiple specialist prompts or toolchains, keep the agent names stable so each one writes back to the same diary wing over time.
+:::

website/concepts/contradiction-detection.md

@@ -0,0 +1,33 @@
+# Contradiction Detection
+
+::: warning Experimental
+Contradiction detection is a planned capability, not a shipped end-to-end feature in the current MCP workflow. The examples below show the intended behavior rather than a fully integrated command path.
+:::
+
+## What It Does
+
+Checks assertions against entity facts in the knowledge graph. When enabled, it catches contradictions like:
+
+```
+Input:  "Soren finished the auth migration"
+Output: 🔴 AUTH-MIGRATION: attribution conflict — Maya was assigned, not Soren
+
+Input:  "Kai has been here 2 years"
+Output: 🟡 KAI: wrong_tenure — records show 3 years (started 2023-04)
+
+Input:  "The sprint ends Friday"
+Output: 🟡 SPRINT: stale_date — current sprint ends Thursday (updated 2 days ago)
+```
+
+## How It Works
+
+Facts are checked against the knowledge graph:
+- **Attribution conflicts** — the wrong person credited for a task
+- **Temporal errors** — wrong dates, tenures, or durations
+- **Stale information** — facts that have been superseded
+
+Ages, dates, and tenures are calculated dynamically from the entity's recorded facts — not hardcoded.
+
+## Status
+
+The current codebase includes the temporal knowledge graph primitives needed for this direction, but not a complete contradiction-checking tool exposed through the CLI or MCP server.

website/concepts/knowledge-graph.md

@@ -0,0 +1,91 @@
+# Knowledge Graph
+
+MemPalace includes a temporal entity-relationship graph — like Zep's Graphiti, but SQLite instead of Neo4j. Local and free.
+
+## What It Stores
+
+Entity-relationship triples with temporal validity:
+
+```
+Subject → Predicate → Object [valid_from → valid_to]
+```
+
+Facts have time windows. When something stops being true, you invalidate it — and historical queries still find it.
+
+## Usage
+
+### Python API
+
+```python
+from mempalace.knowledge_graph import KnowledgeGraph
+
+kg = KnowledgeGraph()
+
+# Add facts
+kg.add_triple("Kai", "works_on", "Orion", valid_from="2025-06-01")
+kg.add_triple("Maya", "assigned_to", "auth-migration", valid_from="2026-01-15")
+kg.add_triple("Maya", "completed", "auth-migration", valid_from="2026-02-01")
+
+# Query: everything about Kai
+kg.query_entity("Kai")
+# → [Kai → works_on → Orion (current), Kai → recommended → Clerk (2026-01)]
+
+# Query: what was true in January?
+kg.query_entity("Maya", as_of="2026-01-20")
+# → [Maya → assigned_to → auth-migration (active)]
+
+# Timeline
+kg.timeline("Orion")
+# → chronological story of the project
+```
+
+### Invalidating Facts
+
+When something stops being true:
+
+```python
+kg.invalidate("Kai", "works_on", "Orion", ended="2026-03-01")
+```
+
+Now queries for Kai's current work won't return Orion. Historical queries still will.
+
+### MCP Tools
+
+Through the MCP server, the knowledge graph is available as tools:
+
+| Tool | Description |
+|------|-------------|
+| `mempalace_kg_query` | Query entity relationships with time filtering |
+| `mempalace_kg_add` | Add facts |
+| `mempalace_kg_invalidate` | Mark facts as ended |
+| `mempalace_kg_timeline` | Chronological entity story |
+| `mempalace_kg_stats` | Graph overview |
+
+## Storage
+
+The knowledge graph uses SQLite with two tables:
+
+**`entities`** — people, projects, tools, concepts:
+- `id` — lowercase normalized name
+- `name` — display name
+- `type` — person, project, tool, concept, etc.
+- `properties` — JSON blob for extra metadata
+
+**`triples`** — relationships between entities:
+- `subject` → `predicate` → `object`
+- `valid_from` — when this became true
+- `valid_to` — when it stopped being true (NULL = still current)
+- `confidence` — 0.0 to 1.0
+- `source_closet` — link back to the verbatim memory
+
+Database location: `~/.mempalace/knowledge_graph.sqlite3`
+
+## Comparison
+
+| Feature | MemPalace | Zep (Graphiti) |
+|---------|-----------|----------------|
+| Storage | SQLite (local) | Neo4j (cloud) |
+| Cost | Free | $25/mo+ |
+| Temporal validity | Yes | Yes |
+| Self-hosted | Always | Enterprise only |
+| Privacy | Everything local | SOC 2, HIPAA |

website/concepts/memory-stack.md

@@ -0,0 +1,104 @@
+# Memory Stack
+
+MemPalace uses a 4-layer memory stack. Each layer loads progressively more data only when needed.
+
+## The Layers
+
+| Layer | What | Size | When |
+|-------|------|------|------|
+| **L0** | Identity — who is this AI? | ~50-100 tokens | Always loaded |
+| **L1** | Essential Story — top moments | ~500-800 tokens | Always loaded |
+| **L2** | Room Recall — filtered retrieval | ~200–500 each | When topic comes up |
+| **L3** | Deep Search — full semantic query | Variable | When explicitly asked |
+
+In the current implementation, a typical wake-up is roughly **~600-900 tokens** for L0 + L1. Searches only fire when needed.
+
+## Layer 0: Identity
+
+A plain text file at `~/.mempalace/identity.txt`. Always loaded as the AI's self-concept.
+
+```text
+I am Atlas, a personal AI assistant for Alice.
+Traits: warm, direct, remembers everything.
+People: Alice (creator), Bob (Alice's partner).
+Project: A journaling app that helps people process emotions.
+```
+
+~50 tokens. Tells the AI who it is and who it works with.
+
+## Layer 1: Essential Story
+
+Auto-generated from the highest-importance drawers in the palace. Groups by room, picks the top moments, and keeps the output bounded.
+
+The generation process:
+1. Reads all drawers from ChromaDB
+2. Scores each by importance/emotional weight
+3. Takes the top 15 moments
+4. Groups by room for readability
+5. Truncates to fit within 3,200 characters
+
+```
+## L1 — ESSENTIAL STORY
+
+[auth-migration]
+  - Team decided to migrate from Auth0 to Clerk — pricing + DX  (session_2026-01-15.md)
+  - Kai debugged the OAuth token refresh issue  (session_2026-01-20.md)
+
+[deploy-process]
+  - Switched to blue-green deploys after the January outage  (session_2026-02-01.md)
+```
+
+## Layer 2: On-Demand Recall
+
+Loaded when a specific topic or wing comes up in conversation. Retrieves drawers filtered by wing and/or room — typically ~200–500 tokens.
+
+```python
+stack = MemoryStack()
+stack.recall(wing="driftwood", room="auth")
+# → returns recent drawers about auth in the driftwood project
+```
+
+## Layer 3: Deep Search
+
+Full semantic search against the entire palace. This is what fires when you or the AI explicitly asks a question.
+
+```python
+stack.search("why did we switch to GraphQL")
+# → returns top-5 matching drawers with similarity scores
+```
+
+## Wake-Up Budget
+
+The point of the stack is bounded startup context, not a fixed universal token count. The exact size depends on your identity file and what Layer 1 selects, but the implementation keeps wake-up meaningfully smaller than loading the full corpus into the prompt.
+
+## Using the Stack
+
+### CLI
+
+```bash
+# Wake-up context (L0 + L1)
+mempalace wake-up
+
+# Project-specific wake-up
+mempalace wake-up --wing driftwood
+```
+
+### Python API
+
+```python
+from mempalace.layers import MemoryStack
+
+stack = MemoryStack()
+
+# L0 + L1: wake-up (~600-900 tokens in typical use)
+print(stack.wake_up())
+
+# L2: on-demand recall
+print(stack.recall(wing="myapp"))
+
+# L3: deep search
+print(stack.search("pricing change"))
+
+# Status
+print(stack.status())
+```

website/concepts/the-palace.md

@@ -0,0 +1,110 @@
+# The Palace
+
+Ancient Greek orators memorized entire speeches by placing ideas in rooms of an imaginary building. Walk through the building, find the idea. MemPalace applies the same principle to AI memory.
+
+## Structure
+
+Your conversations are organized into a navigable hierarchy:
+
+```mermaid
+graph TD
+    subgraph wing_person["WING: Person"]
+        direction TB
+        RA["Room A"] -- hall --> RB["Room B"]
+        RA --> CA["Closet"]
+        CA --> DA["Drawer (verbatim)"]
+    end
+
+    subgraph wing_project["WING: Project"]
+        direction TB
+        RA2["Room A"] -- hall --> RC["Room C"]
+        RA2 --> CA2["Closet"]
+        CA2 --> DA2["Drawer (verbatim)"]
+    end
+
+    RA -.->|tunnel| RA2
+
+    style wing_person fill:#1e1b4b,stroke:#4f46e5,color:#e0e7ff
+    style wing_project fill:#164e63,stroke:#06b6d4,color:#cffafe
+    style RA fill:#312e81,stroke:#6366f1,color:#e0e7ff
+    style RB fill:#312e81,stroke:#6366f1,color:#e0e7ff
+    style CA fill:#3b0764,stroke:#8b5cf6,color:#f3e8ff
+    style DA fill:#0f766e,stroke:#14b8a6,color:#ccfbf1
+    style RA2 fill:#155e75,stroke:#22d3ee,color:#cffafe
+    style RC fill:#155e75,stroke:#22d3ee,color:#cffafe
+    style CA2 fill:#3b0764,stroke:#8b5cf6,color:#f3e8ff
+    style DA2 fill:#0f766e,stroke:#14b8a6,color:#ccfbf1
+```
+
+## Components
+
+### Wings
+
+A person or project. As many as you need.
+
+Every project, person, or topic gets its own wing in the palace. Wings are the top-level organizational unit.
+
+### Rooms
+
+Specific topics within a wing. Examples: `auth-migration`, `graphql-switch`, `ci-pipeline`.
+
+Rooms are named ideas. They're auto-detected from your folder structure during `mempalace init`, and you can create additional rooms manually.
+
+### Halls
+
+Halls are the conceptual categories that describe how related memories connect *within* a wing:
+
+- `hall_facts` — decisions made, choices locked in
+- `hall_events` — sessions, milestones, debugging
+- `hall_discoveries` — breakthroughs, new insights
+- `hall_preferences` — habits, likes, opinions
+- `hall_advice` — recommendations and solutions
+
+### Tunnels
+
+Connections *between* wings. When the same room appears in different wings, the graph layer can treat that as a cross-wing connection.
+
+```
+wing_kai       / hall_events / auth-migration  → "Kai debugged the OAuth token refresh"
+wing_driftwood / hall_facts  / auth-migration  → "team decided to migrate auth to Clerk"
+wing_priya     / hall_advice / auth-migration  → "Priya approved Clerk over Auth0"
+```
+
+Same room. Three wings. The graph can use that shared room name as a bridge.
+
+### Closets
+
+Closets are the summary layer in the broader MemPalace vocabulary: compact notes that point back to the original content. In the current implementation, the main persisted storage path is still the underlying drawer text plus metadata.
+
+### Drawers
+
+The original stored text chunks. This is the primary retrieval layer used by the current search and benchmark flows.
+
+## Why Structure Matters
+
+Tested on 22,000+ real conversation memories:
+
+| Search scope | R@10 | Improvement |
+|-------------|------|-------------|
+| All closets | 60.9% | baseline |
+| Within wing | 73.1% | +12% |
+| Wing + hall | 84.8% | +24% |
+| Wing + room | 94.8% | +34% |
+
+The practical point is that structure improves retrieval. In the project benchmarks, narrowing the search scope by wing and room outperformed searching the entire corpus at once.
+
+## Navigation
+
+The palace supports graph traversal across wings:
+
+```text
+MCP tool: mempalace_traverse
+  arguments: { "start_room": "auth-migration" }
+  → discovers rooms in wing_kai, wing_driftwood, wing_priya
+
+MCP tool: mempalace_find_tunnels
+  arguments: { "wing_a": "wing_code", "wing_b": "wing_team" }
+  → auth-migration, deploy-process, ci-pipeline
+```
+
+This is the navigation story: shared room structure gives the model more than one way to reach relevant context.

website/guide/claude-code.md

@@ -0,0 +1,38 @@
+# Claude Code Plugin
+
+The recommended way to use MemPalace with Claude Code — native marketplace install.
+
+## Installation
+
+```bash
+claude plugin marketplace add milla-jovovich/mempalace
+claude plugin install --scope user mempalace
+```
+
+Restart Claude Code, then type `/skills` to verify "mempalace" appears.
+
+## How It Works
+
+With the plugin installed, Claude Code automatically:
+- Starts the MemPalace MCP server on launch
+- Has access to all 19 tools
+- Learns the AAAK dialect and memory protocol from the `mempalace_status` response
+- Searches the palace before answering questions about past work
+
+No manual configuration needed. Just ask:
+
+> *"What did we decide about auth last month?"*
+
+## Alternative: Manual MCP
+
+If you prefer manual setup over the marketplace plugin:
+
+```bash
+claude mcp add mempalace -- python -m mempalace.mcp_server
+```
+
+Both approaches give identical functionality. The plugin approach handles server lifecycle automatically.
+
+## Hooks
+
+Set up [auto-save hooks](/guide/hooks) to ensure memories are saved automatically during long conversations.

website/guide/configuration.md

@@ -0,0 +1,85 @@
+# Configuration
+
+## Global Config
+
+Located at `~/.mempalace/config.json`:
+
+```json
+{
+  "palace_path": "/custom/path/to/palace",
+  "collection_name": "mempalace_drawers",
+  "people_map": {"Kai": "KAI", "Priya": "PRI"}
+}
+```
+
+| Key | Default | Description |
+|-----|---------|-------------|
+| `palace_path` | `~/.mempalace/palace` | Where ChromaDB stores your drawers |
+| `collection_name` | `mempalace_drawers` | ChromaDB collection name |
+| `people_map` | `{}` | Entity name → AAAK code mappings |
+
+## Project Config
+
+Generated by `mempalace init` in your project directory:
+
+### `mempalace.yaml`
+
+```yaml
+wing: myproject
+rooms:
+  - backend
+  - frontend
+  - decisions
+palace_path: ~/.mempalace/palace
+```
+
+### `entities.json`
+
+```json
+{
+  "Kai": "KAI",
+  "Priya": "PRI"
+}
+```
+
+Wings are auto-detected during `mempalace init` from:
+- Directory names → project wings
+- Detected people in file content → person wings
+- Explicit `--wing` flag on mine commands
+
+## Identity
+
+Located at `~/.mempalace/identity.txt`. Plain text. Becomes Layer 0 — loaded every session.
+
+```text
+I am Atlas, a personal AI assistant for Alice.
+Traits: warm, direct, remembers everything.
+People: Alice (creator), Bob (Alice's partner).
+Project: A journaling app that helps people process emotions.
+```
+
+::: tip
+Write your identity file in first person from the AI's perspective. This becomes the AI's self-concept on wake-up.
+:::
+
+## Palace Path Override
+
+All commands accept `--palace <path>` to override the default location:
+
+```bash
+mempalace search "query" --palace /tmp/test-palace
+mempalace mine ~/data/ --palace /tmp/test-palace
+```
+
+The MCP server also accepts `--palace`:
+
+```bash
+python -m mempalace.mcp_server --palace /custom/palace
+```
+
+## Environment Variables
+
+| Variable | Description |
+|----------|-------------|
+| `MEMPALACE_PALACE_PATH` | Override palace path (same as `--palace`) |
+| `MEMPAL_DIR` | Directory for auto-mining in hooks |

website/guide/gemini-cli.md

@@ -0,0 +1,96 @@
+# Gemini CLI
+
+MemPalace works natively with [Gemini CLI](https://github.com/google/gemini-cli), which handles the MCP server and save hooks automatically.
+
+## Prerequisites
+
+- Python 3.9+
+- Gemini CLI installed and configured
+
+## Installation
+
+```bash
+# Clone the repository
+git clone https://github.com/milla-jovovich/mempalace.git
+cd mempalace
+
+# Create a virtual environment
+python3 -m venv .venv
+
+# Install dependencies
+.venv/bin/pip install -e .
+```
+
+## Initialize the Palace
+
+```bash
+.venv/bin/python3 -m mempalace init .
+```
+
+### Identity and Project Configuration (Optional)
+
+You can optionally create or edit:
+
+- **`~/.mempalace/identity.txt`** — plain text describing your role and focus
+- **`./mempalace.yaml`** — per-project MemPalace configuration created by `mempalace init`
+- **`./entities.json`** — per-project entity mappings used by AAAK compression
+
+## Connect to Gemini CLI
+
+Register MemPalace as an MCP server:
+
+```bash
+gemini mcp add mempalace /absolute/path/to/mempalace/.venv/bin/python3 \
+  -m mempalace.mcp_server --scope user
+```
+
+::: warning
+Use the **absolute path** to the Python binary to ensure it works from any directory.
+:::
+
+## Enable Auto-Saving
+
+Add a `PreCompress` hook to `~/.gemini/settings.json`:
+
+```json
+{
+  "hooks": {
+    "PreCompress": [
+      {
+        "matcher": "*",
+        "hooks": [
+          {
+            "type": "command",
+            "command": "/absolute/path/to/mempalace/hooks/mempal_precompact_hook.sh"
+          }
+        ]
+      }
+    ]
+  }
+}
+```
+
+Make sure the hook scripts are executable:
+```bash
+chmod +x hooks/*.sh
+```
+
+## Usage
+
+Once connected, Gemini CLI will automatically:
+- Start the MemPalace server on launch
+- Use `mempalace_search` to find relevant past discussions
+- Use the `PreCompress` hook to save memories before context compression
+
+### Manual Mining
+
+Mine existing code or docs:
+```bash
+.venv/bin/python3 -m mempalace mine /path/to/your/project
+```
+
+### Verification
+
+In a Gemini CLI session:
+- `/mcp list` — verify `mempalace` is `CONNECTED`
+- `/hooks panel` — verify the `PreCompress` hook is active

website/guide/getting-started.md

@@ -0,0 +1,82 @@
+# Getting Started
+
+## Installation
+
+Install MemPalace from PyPI:
+
+```bash
+pip install mempalace
+```
+
+### Requirements
+
+- Python 3.9+
+- `chromadb>=0.5.0` (installed automatically)
+- `pyyaml>=6.0` (installed automatically)
+
+No API key required for the core local workflow. After installation, the main storage and retrieval path runs locally.
+
+### From Source
+
+```bash
+git clone https://github.com/milla-jovovich/mempalace.git
+cd mempalace
+pip install -e ".[dev]"
+```
+
+## Quick Start
+
+Three steps: **init**, **mine**, **search**.
+
+### 1. Initialize Your Palace
+
+```bash
+mempalace init ~/projects/myapp
+```
+
+This scans your project directory and:
+- Detects people and projects from file content
+- Creates rooms from your folder structure
+- Sets up `~/.mempalace/` config directory
+
+### 2. Mine Your Data
+
+```bash
+# Mine project files (code, docs, notes)
+mempalace mine ~/projects/myapp
+
+# Mine conversation exports (Claude, ChatGPT, Slack)
+mempalace mine ~/chats/ --mode convos
+
+# Mine with auto-classification into memory types
+mempalace mine ~/chats/ --mode convos --extract general
+```
+
+Two mining modes plus one extraction strategy:
+- **projects** — code and docs, auto-detected rooms
+- **convos** — conversation exports, chunked by exchange pair
+- **general extraction** — an `--extract general` option for conversation mining that classifies content into decisions, preferences, milestones, problems, and emotional context
+
+### 3. Search
+
+```bash
+mempalace search "why did we switch to GraphQL"
+```
+
+That gives you a working local memory index.
+
+## What Happens Next
+
+After the one-time setup, you don't run MemPalace commands manually. Your AI uses it for you through [MCP integration](/guide/mcp-integration) or a [Claude Code plugin](/guide/claude-code).
+
+Ask your AI anything:
+
+> *"What did we decide about auth last month?"*
+
+It calls `mempalace_search` automatically, gets verbatim results, and answers you. You never type `mempalace search` again.
+
+## Next Steps
+
+- [Mining Your Data](/guide/mining) — deep dive into mining modes
+- [MCP Integration](/guide/mcp-integration) — connect to Claude, ChatGPT, Cursor, Gemini
+- [The Palace](/concepts/the-palace) — understand wings, rooms, halls, and tunnels

website/guide/hooks.md

@@ -0,0 +1,116 @@
+# Auto-Save Hooks
+
+Two hooks for Claude Code and Codex that automatically save memories during work. No manual "save" commands needed.
+
+## What They Do
+
+| Hook | When It Fires | What Happens |
+|------|--------------|-------------|
+| **Save Hook** | Every 15 human messages | Blocks the AI, tells it to save key topics/decisions/quotes to the palace |
+| **PreCompact Hook** | Right before context compaction | Emergency save — forces the AI to save everything before losing context |
+
+The AI does the actual filing — it knows the conversation context, so it classifies memories into the right wings/halls/closets. The hooks just tell it **when** to save.
+
+## Install — Claude Code
+
+Add to `.claude/settings.local.json`:
+
+```json
+{
+  "hooks": {
+    "Stop": [{
+      "matcher": "*",
+      "hooks": [{
+        "type": "command",
+        "command": "/absolute/path/to/hooks/mempal_save_hook.sh",
+        "timeout": 30
+      }]
+    }],
+    "PreCompact": [{
+      "hooks": [{
+        "type": "command",
+        "command": "/absolute/path/to/hooks/mempal_precompact_hook.sh",
+        "timeout": 30
+      }]
+    }]
+  }
+}
+```
+
+Make them executable:
+```bash
+chmod +x hooks/mempal_save_hook.sh hooks/mempal_precompact_hook.sh
+```
+
+## Install — Codex CLI
+
+Add to `.codex/hooks.json`:
+
+```json
+{
+  "Stop": [{
+    "type": "command",
+    "command": "/absolute/path/to/hooks/mempal_save_hook.sh",
+    "timeout": 30
+  }],
+  "PreCompact": [{
+    "type": "command",
+    "command": "/absolute/path/to/hooks/mempal_precompact_hook.sh",
+    "timeout": 30
+  }]
+}
+```
+
+## Configuration
+
+Edit `mempal_save_hook.sh` to change:
+
+- **`SAVE_INTERVAL=15`** — How many messages between saves. Lower = more frequent, higher = less interruption.
+- **`STATE_DIR`** — Where hook state is stored (defaults to `~/.mempalace/hook_state/`)
+- **`MEMPAL_DIR`** — Optional. Set to a conversations directory to auto-run `mempalace mine` on each save trigger.
+
+## How It Works
+
+### Save Hook (Stop event)
+
+```
+User sends message → AI responds → Stop hook fires
+                                          ↓
+                                  Count human messages in transcript
+                                          ↓
+                            ┌── < 15 since last save → let AI stop
+                            │
+                            └── ≥ 15 since last save → block + save
+                                                            ↓
+                                                    AI saves to palace
+                                                            ↓
+                                                    AI stops (flag set)
+```
+
+The `stop_hook_active` flag prevents infinite loops.
+
+### PreCompact Hook
+
+```
+Context window full → PreCompact fires → ALWAYS blocks → AI saves → Compaction proceeds
+```
+
+No counting needed — compaction always warrants a save.
+
+## Debugging
+
+```bash
+cat ~/.mempalace/hook_state/hook.log
+```
+
+Example output:
+```
+[14:30:15] Session abc123: 12 exchanges, 12 since last save
+[14:35:22] Session abc123: 15 exchanges, 15 since last save
+[14:35:22] TRIGGERING SAVE at exchange 15
+[14:40:01] Session abc123: 18 exchanges, 3 since last save
+```
+
+## Cost
+
+**Zero extra tokens.** The hooks are bash scripts that run locally. They don't call any API. The only "cost" is a few seconds of the AI organizing memories at each checkpoint.

website/guide/local-models.md

@@ -0,0 +1,70 @@
+# Local Models
+
+MemPalace works with any local LLM — Llama, Mistral, or any offline model. Since local models generally don't speak MCP yet, there are two approaches.
+
+## Wake-Up Command
+
+Load your world into the model's context:
+
+```bash
+mempalace wake-up > context.txt
+# Paste context.txt into your local model's system prompt
+```
+
+This gives your local model a bounded wake-up context, typically around **~600-900 tokens** in the current implementation. It includes:
+- **Layer 0**: Your identity — who you are, what you work on
+- **Layer 1**: Top moments from the palace — key decisions, recent work
+
+For project-specific context:
+```bash
+mempalace wake-up --wing driftwood > context.txt
+```
+
+## CLI Search
+
+Query on demand, feed results into your prompt:
+
+```bash
+mempalace search "auth decisions" > results.txt
+# Include results.txt in your prompt
+```
+
+## Python API
+
+For programmatic integration with your local model pipeline:
+
+```python
+from mempalace.searcher import search_memories
+
+results = search_memories(
+    "auth decisions",
+    palace_path="~/.mempalace/palace",
+)
+
+# Format results for your model's context
+context = "\n".join(
+    f"[{r['wing']}/{r['room']}] {r['text']}"
+    for r in results["results"]
+)
+
+# Inject into your local model's prompt
+prompt = f"Context from memory:\n{context}\n\nUser: What did we decide about auth?"
+```
+
+## AAAK for Compression
+
+Use [AAAK dialect](/concepts/aaak-dialect) to compress wake-up context further:
+
+```bash
+mempalace compress --wing myapp --dry-run
+```
+
+AAAK is readable by any LLM that reads text — Claude, GPT, Gemini, Llama, Mistral — without a decoder.
+
+## Full Offline Stack
+
+The core memory stack can run offline:
+- **ChromaDB** on your machine — vector storage and search
+- **Local model** on your machine — reasoning and responses
+- **AAAK** for compression — optional, no cloud dependency
+- **Optional reranking or external model integrations** may introduce cloud calls, depending on how you configure the system

website/guide/mcp-integration.md

@@ -0,0 +1,92 @@
+# MCP Integration
+
+MemPalace provides 19 tools through the [Model Context Protocol (MCP)](https://modelcontextprotocol.io/), giving any MCP-compatible AI full read/write access to your palace.
+
+## Setup
+
+### Manual Connection
+
+```bash
+claude mcp add mempalace -- python -m mempalace.mcp_server
+```
+
+### With Custom Palace Path
+
+```bash
+claude mcp add mempalace -- python -m mempalace.mcp_server --palace /path/to/palace
+```
+
+Now your AI has all 19 tools available. Ask it anything:
+
+> *"What did we decide about auth last month?"*
+
+Claude calls `mempalace_search` automatically, gets verbatim results, and answers you.
+
+## Compatible Tools
+
+MemPalace works with any tool that supports MCP:
+
+- **Claude Code** — native via plugin or manual MCP
+- **ChatGPT** — via MCP bridge
+- **Cursor** — native MCP support
+- **Gemini CLI** — see [Gemini CLI guide](/guide/gemini-cli)
+
+## Memory Protocol
+
+When the AI first calls `mempalace_status`, it receives the **Memory Protocol** — a behavior guide that teaches it to:
+
+1. **On wake-up**: Call `mempalace_status` to load the palace overview
+2. **Before responding** about any person, project, or past event: search first, never guess
+3. **If unsure**: Say "let me check" and query the palace
+4. **After each session**: Write diary entries to record what happened
+5. **When facts change**: Invalidate old facts, add new ones
+
+This protocol is what turns storage into memory — the AI knows to verify before speaking.
+
+## Tool Overview
+
+### Palace (read)
+
+| Tool | What |
+|------|------|
+| `mempalace_status` | Palace overview + AAAK spec + memory protocol |
+| `mempalace_list_wings` | Wings with counts |
+| `mempalace_list_rooms` | Rooms within a wing |
+| `mempalace_get_taxonomy` | Full wing → room → count tree |
+| `mempalace_search` | Semantic search with wing/room filters |
+| `mempalace_check_duplicate` | Check before filing |
+| `mempalace_get_aaak_spec` | AAAK dialect reference |
+
+### Palace (write)
+
+| Tool | What |
+|------|------|
+| `mempalace_add_drawer` | File verbatim content |
+| `mempalace_delete_drawer` | Remove by ID |
+
+### Knowledge Graph
+
+| Tool | What |
+|------|------|
+| `mempalace_kg_query` | Entity relationships with time filtering |
+| `mempalace_kg_add` | Add facts |
+| `mempalace_kg_invalidate` | Mark facts as ended |
+| `mempalace_kg_timeline` | Chronological entity story |
+| `mempalace_kg_stats` | Graph overview |
+
+### Navigation
+
+| Tool | What |
+|------|------|
+| `mempalace_traverse` | Walk the graph from a room across wings |
+| `mempalace_find_tunnels` | Find rooms bridging two wings |
+| `mempalace_graph_stats` | Graph connectivity overview |
+
+### Agent Diary
+
+| Tool | What |
+|------|------|
+| `mempalace_diary_write` | Write AAAK diary entry |
+| `mempalace_diary_read` | Read recent diary entries |
+
+For detailed schemas and parameters, see [MCP Tools Reference](/reference/mcp-tools).

website/guide/mining.md

@@ -0,0 +1,134 @@
+# Mining Your Data
+
+MemPalace ingests your data by **mining** — scanning files and filing their content as verbatim drawers in the palace.
+
+## Mining Modes
+
+### Projects Mode (default)
+
+Scans code, docs, and notes. Respects `.gitignore` by default.
+
+```bash
+mempalace mine ~/projects/myapp
+```
+
+Each file becomes a drawer, tagged with a wing (project name) and room (topic). Rooms are auto-detected from your folder structure during `mempalace init`.
+
+Options:
+```bash
+# Override wing name
+mempalace mine ~/projects/myapp --wing myapp
+
+# Ignore .gitignore rules
+mempalace mine ~/projects/myapp --no-gitignore
+
+# Include specific ignored paths
+mempalace mine ~/projects/myapp --include-ignored dist,build
+
+# Limit number of files
+mempalace mine ~/projects/myapp --limit 100
+
+# Preview without filing
+mempalace mine ~/projects/myapp --dry-run
+```
+
+### Conversations Mode
+
+Indexes conversation exports from Claude, ChatGPT, Slack, and other tools. Chunks by exchange pair (human + assistant turns).
+
+```bash
+mempalace mine ~/chats/ --mode convos
+```
+
+Supports five chat formats automatically:
+- Claude JSON exports
+- ChatGPT exports
+- Slack exports
+- Markdown conversations
+- Plain text transcripts
+
+### General Extraction
+
+Auto-classifies conversation content into five memory types:
+
+```bash
+mempalace mine ~/chats/ --mode convos --extract general
+```
+
+Memory types:
+- **Decisions** — choices made, options rejected
+- **Preferences** — habits, likes, opinions
+- **Milestones** — sessions completed, goals reached
+- **Problems** — bugs, blockers, issues encountered
+- **Emotional context** — reactions, concerns, excitement
+
+## Splitting Mega-Files
+
+Some transcript exports concatenate multiple sessions into one huge file. Split them first:
+
+```bash
+# Preview what would be split
+mempalace split ~/chats/ --dry-run
+
+# Split files with 2+ sessions (default)
+mempalace split ~/chats/
+
+# Only split files with 3+ sessions
+mempalace split ~/chats/ --min-sessions 3
+
+# Output to a different directory
+mempalace split ~/chats/ --output-dir ~/chats-split/
+```
+
+::: tip
+Always run `mempalace split` before mining conversation files. It's a no-op if files don't need splitting.
+:::
+
+## Multi-Project Setup
+
+Mine each project into its own wing:
+
+```bash
+mempalace mine ~/chats/orion/  --mode convos --wing orion
+mempalace mine ~/chats/nova/   --mode convos --wing nova
+mempalace mine ~/chats/helios/ --mode convos --wing helios
+```
+
+Six months later:
+```bash
+# Project-specific search
+mempalace search "database decision" --wing orion
+
+# Cross-project search
+mempalace search "rate limiting approach"
+# → finds your approach in Orion AND Nova, shows the differences
+```
+
+## Team Usage
+
+Mine Slack exports and AI conversations for team history:
+
+```bash
+mempalace mine ~/exports/slack/ --mode convos --wing driftwood
+mempalace mine ~/.claude/projects/ --mode convos
+```
+
+Then search across people and projects:
+```bash
+mempalace search "Soren sprint" --wing driftwood
+# → 14 closets: OAuth refactor, dark mode, component library migration
+```
+
+## Agent Tag
+
+Every drawer is tagged with the agent that filed it:
+
+```bash
+# Default agent name
+mempalace mine ~/data/ --agent mempalace
+
+# Custom agent name
+mempalace mine ~/data/ --agent reviewer
+```
+
+This is used by [Specialist Agents](/concepts/agents) to partition memories.

website/guide/searching.md

@@ -0,0 +1,107 @@
+# Searching Memories
+
+MemPalace uses ChromaDB's semantic vector search to find relevant memories. When you search, you get **verbatim text** — the exact words, never summaries.
+
+## CLI Search
+
+```bash
+# Search everything
+mempalace search "why did we switch to GraphQL"
+
+# Filter by wing (project)
+mempalace search "database decision" --wing myapp
+
+# Filter by room (topic)
+mempalace search "auth decisions" --room auth-migration
+
+# Filter by both
+mempalace search "pricing" --wing driftwood --room costs
+
+# More results
+mempalace search "deploy process" --results 10
+```
+
+## How Search Works
+
+1. Your query is embedded using ChromaDB's default model (`all-MiniLM-L6-v2`)
+2. The embedding is compared against all drawers using cosine similarity
+3. Optional wing/room filters narrow the search scope
+4. Results are returned with similarity scores and source metadata
+
+### Why Structure Matters
+
+Tested on 22,000+ real conversation memories:
+
+```
+Search all closets:          60.9%  R@10
+Search within wing:          73.1%  (+12%)
+Search wing + hall:          84.8%  (+24%)
+Search wing + room:          94.8%  (+34%)
+```
+
+Wings and rooms aren't cosmetic — they're a **34% retrieval improvement**.
+
+## Programmatic Search
+
+Use the Python API for integration:
+
+```python
+from mempalace.searcher import search_memories
+
+results = search_memories(
+    query="auth decisions",
+    palace_path="~/.mempalace/palace",
+    wing="myapp",
+    room="auth",
+    n_results=5,
+)
+
+for hit in results["results"]:
+    print(f"[{hit['similarity']}] {hit['wing']}/{hit['room']}")
+    print(f"  {hit['text'][:200]}")
+```
+
+The `search_memories()` function returns a dict:
+
+```python
+{
+    "query": "auth decisions",
+    "filters": {"wing": "myapp", "room": "auth"},
+    "results": [
+        {
+            "text": "We decided to migrate auth to Clerk because...",
+            "wing": "myapp",
+            "room": "auth-migration",
+            "source_file": "session_2026-01-15.md",
+            "similarity": 0.892,
+        },
+        # ...
+    ],
+}
+```
+
+## MCP Search
+
+When connected via MCP, your AI searches automatically:
+
+> *"What did we decide about auth last month?"*
+
+The AI calls `mempalace_search` behind the scenes. You never type a search command.
+
+See [MCP Integration](/guide/mcp-integration) for setup.
+
+## Wake-Up Context
+
+Instead of searching, you can load a compact context of your world:
+
+```bash
+# Load identity + top memories (~600-900 tokens in typical use)
+mempalace wake-up
+
+# Project-specific context
+mempalace wake-up --wing driftwood
+```
+
+This loads Layer 0 (identity) and Layer 1 (essential story) as bounded startup context before the first retrieval call.
+
+See [Memory Stack](/concepts/memory-stack) for details on the 4-layer architecture.

website/index.md

@@ -0,0 +1,87 @@
+---
+layout: home
+
+hero:
+  name: MemPalace
+  text: Give your AI a memory.
+  tagline: "96.6% recall on LongMemEval in raw mode. Local-first, open source, and usable without an API key."
+  image:
+    src: /mempalace_logo.png
+    alt: MemPalace
+  actions:
+    - theme: brand
+      text: Get Started
+      link: /guide/getting-started
+    - theme: alt
+      text: Architecture →
+      link: /concepts/the-palace
+    - theme: alt
+      text: GitHub ↗
+      link: https://github.com/milla-jovovich/mempalace
+
+features:
+  - icon:
+      src: /icons/file-text.svg
+      alt: Verbatim Storage
+    title: Verbatim Storage
+    details: Store source text directly instead of extracting facts up front. The raw benchmark result comes from retrieving verbatim content.
+  - icon:
+      src: /icons/building-2.svg
+      alt: Palace Structure
+    title: Palace Structure
+    details: Wings and rooms give retrieval useful structure. In the project benchmarks, narrowing search scope outperformed flat search.
+  - icon:
+      src: /icons/search.svg
+      alt: Semantic Search
+    title: Semantic Search
+    details: ChromaDB-powered vector search lets the model retrieve past discussions by topic, project, or room.
+  - icon:
+      src: /icons/git-merge.svg
+      alt: Knowledge Graph
+    title: Knowledge Graph
+    details: Temporal entity-relationship triples in SQLite. Facts can be added, queried, and invalidated over time.
+  - icon:
+      src: /icons/wrench.svg
+      alt: 19 MCP Tools
+    title: 19 MCP Tools
+    details: MCP tools expose search, filing, knowledge graph, graph navigation, and diary operations to compatible clients.
+  - icon:
+      src: /icons/shield-check.svg
+      alt: Zero Cloud
+    title: Zero Cloud
+    details: Core storage and retrieval run locally on ChromaDB and SQLite. Optional reranking features can add an API dependency.
+---
+
+<style>
+:root {
+  --vp-home-hero-name-color: transparent;
+  --vp-home-hero-name-background: linear-gradient(
+    135deg,
+    #4f46e5 0%,
+    #06b6d4 50%,
+    #8b5cf6 100%
+  );
+}
+</style>
+
+<div style="max-width: 688px; margin: 0 auto; padding: 48px 24px 0;">
+
+## Verbatim Retrieval First
+
+MemPalace starts from a simple premise: **store the source text and retrieve it well**. The benchmarked raw mode does not require an LLM extraction step.
+
+| System | LongMemEval R@5 | API Required | Cost |
+|--------|----------------|--------------|------|
+| **MemPalace (hybrid)** | **100%** | Optional | Free |
+| Supermemory ASMR | ~99% | Yes | — |
+| **MemPalace (raw)** | **96.6%** | **None** | **Free** |
+| Mastra | 94.87% | Yes | API costs |
+| Mem0 | ~85% | Yes | $19–249/mo |
+
+The raw 96.6% LongMemEval result is the baseline story: strong recall without requiring an API key or an LLM in the retrieval pipeline.
+
+<div style="text-align: center; padding-top: 16px;">
+  <a href="./reference/benchmarks" style="color: var(--vp-c-brand-1); font-weight: 500;">Full benchmark results →</a>
+</div>
+
+</div>

website/package.json

@@ -0,0 +1,17 @@
+{
+  "name": "mempalace-docs",
+  "private": true,
+  "type": "module",
+  "scripts": {
+    "docs:dev": "vitepress dev",
+    "docs:build": "vitepress build",
+    "docs:preview": "vitepress preview"
+  },
+  "devDependencies": {
+    "@lucide/vue": "^1.8.0",
+    "mermaid": "^11.14.0",
+    "vitepress": "^1.6.4",
+    "vitepress-plugin-mermaid": "^2.0.17",
+    "vue": "^3.5.32"
+  }
+}

website/public/icons/building-2.svg

@@ -0,0 +1,21 @@
+<!-- @license lucide-static v0.468.0 - ISC -->
+<svg
+  class="lucide lucide-building-2"
+  xmlns="http://www.w3.org/2000/svg"
+  width="24"
+  height="24"
+  viewBox="0 0 24 24"
+  fill="none"
+  stroke="#06b6d4"
+  stroke-width="2"
+  stroke-linecap="round"
+  stroke-linejoin="round"
+>
+  <path d="M6 22V4a2 2 0 0 1 2-2h8a2 2 0 0 1 2 2v18Z" />
+  <path d="M6 12H4a2 2 0 0 0-2 2v6a2 2 0 0 0 2 2h2" />
+  <path d="M18 9h2a2 2 0 0 1 2 2v9a2 2 0 0 1-2 2h-2" />
+  <path d="M10 6h4" />
+  <path d="M10 10h4" />
+  <path d="M10 14h4" />
+  <path d="M10 18h4" />
+</svg>

website/public/icons/file-text.svg

@@ -0,0 +1,19 @@
+<!-- @license lucide-static v0.468.0 - ISC -->
+<svg
+  class="lucide lucide-file-text"
+  xmlns="http://www.w3.org/2000/svg"
+  width="24"
+  height="24"
+  viewBox="0 0 24 24"
+  fill="none"
+  stroke="#06b6d4"
+  stroke-width="2"
+  stroke-linecap="round"
+  stroke-linejoin="round"
+>
+  <path d="M15 2H6a2 2 0 0 0-2 2v16a2 2 0 0 0 2 2h12a2 2 0 0 0 2-2V7Z" />
+  <path d="M14 2v4a2 2 0 0 0 2 2h4" />
+  <path d="M10 9H8" />
+  <path d="M16 13H8" />
+  <path d="M16 17H8" />
+</svg>

website/public/icons/git-merge.svg

@@ -0,0 +1,17 @@
+<!-- @license lucide-static v0.468.0 - ISC -->
+<svg
+  class="lucide lucide-git-merge"
+  xmlns="http://www.w3.org/2000/svg"
+  width="24"
+  height="24"
+  viewBox="0 0 24 24"
+  fill="none"
+  stroke="#06b6d4"
+  stroke-width="2"
+  stroke-linecap="round"
+  stroke-linejoin="round"
+>
+  <circle cx="18" cy="18" r="3" />
+  <circle cx="6" cy="6" r="3" />
+  <path d="M6 21V9a9 9 0 0 0 9 9" />
+</svg>

website/public/icons/search.svg

@@ -0,0 +1,16 @@
+<!-- @license lucide-static v0.468.0 - ISC -->
+<svg
+  class="lucide lucide-search"
+  xmlns="http://www.w3.org/2000/svg"
+  width="24"
+  height="24"
+  viewBox="0 0 24 24"
+  fill="none"
+  stroke="#06b6d4"
+  stroke-width="2"
+  stroke-linecap="round"
+  stroke-linejoin="round"
+>
+  <circle cx="11" cy="11" r="8" />
+  <path d="m21 21-4.3-4.3" />
+</svg>

website/public/icons/shield-check.svg

@@ -0,0 +1,16 @@
+<!-- @license lucide-static v0.468.0 - ISC -->
+<svg
+  class="lucide lucide-shield-check"
+  xmlns="http://www.w3.org/2000/svg"
+  width="24"
+  height="24"
+  viewBox="0 0 24 24"
+  fill="none"
+  stroke="#06b6d4"
+  stroke-width="2"
+  stroke-linecap="round"
+  stroke-linejoin="round"
+>
+  <path d="M20 13c0 5-3.5 7.5-7.66 8.95a1 1 0 0 1-.67-.01C7.5 20.5 4 18 4 13V6a1 1 0 0 1 1-1c2 0 4.5-1.2 6.24-2.72a1.17 1.17 0 0 1 1.52 0C14.51 3.81 17 5 19 5a1 1 0 0 1 1 1z" />
+  <path d="m9 12 2 2 4-4" />
+</svg>

website/public/icons/wrench.svg

@@ -0,0 +1,15 @@
+<!-- @license lucide-static v0.468.0 - ISC -->
+<svg
+  class="lucide lucide-wrench"
+  xmlns="http://www.w3.org/2000/svg"
+  width="24"
+  height="24"
+  viewBox="0 0 24 24"
+  fill="none"
+  stroke="#06b6d4"
+  stroke-width="2"
+  stroke-linecap="round"
+  stroke-linejoin="round"
+>
+  <path d="M14.7 6.3a1 1 0 0 0 0 1.4l1.6 1.6a1 1 0 0 0 1.4 0l3.77-3.77a6 6 0 0 1-7.94 7.94l-6.91 6.91a2.12 2.12 0 0 1-3-3l6.91-6.91a6 6 0 0 1 7.94-7.94l-3.76 3.76z" />
+</svg>

website/reference/api-reference.md

@@ -0,0 +1,246 @@
+# API Reference
+
+Comprehensive parameter-level documentation for all public Python APIs.
+
+## `mempalace.searcher`
+
+### `search(query, palace_path, wing=None, room=None, n_results=5)`
+
+CLI-oriented search that prints results to stdout.
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `query` | `str` | — | Search query text |
+| `palace_path` | `str` | — | Path to ChromaDB palace directory |
+| `wing` | `str` | `None` | Filter by wing name |
+| `room` | `str` | `None` | Filter by room name |
+| `n_results` | `int` | `5` | Maximum number of results |
+
+**Raises:** `SearchError` if palace not found or query fails.
+
+---
+
+### `search_memories(query, palace_path, wing=None, room=None, n_results=5) → dict`
+
+Programmatic search returning a dict. Used by the MCP server.
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `query` | `str` | — | Search query text |
+| `palace_path` | `str` | — | Path to ChromaDB palace directory |
+| `wing` | `str` | `None` | Filter by wing name |
+| `room` | `str` | `None` | Filter by room name |
+| `n_results` | `int` | `5` | Maximum number of results |
+
+**Returns:**
+```python
+{
+    "query": str,
+    "filters": {"wing": str | None, "room": str | None},
+    "results": [
+        {
+            "text": str,           # verbatim drawer content
+            "wing": str,           # wing name
+            "room": str,           # room name
+            "source_file": str,    # original file basename
+            "similarity": float,   # 0.0 to 1.0
+        }
+    ]
+}
+```
+
+On error: `{"error": str, "hint": str}`
+
+---
+
+## `mempalace.layers`
+
+### `class Layer0(identity_path=None)`
+
+Identity layer (~50 tokens). Reads from `~/.mempalace/identity.txt`.
+
+| Method | Returns | Description |
+|--------|---------|-------------|
+| `render()` | `str` | Identity text or default message |
+| `token_estimate()` | `int` | Approximate token count (`len(text) // 4`) |
+
+---
+
+### `class Layer1(palace_path=None, wing=None)`
+
+Essential story layer (~500–800 tokens). Auto-generated from top drawers.
+
+| Attribute | Type | Description |
+|-----------|------|-------------|
+| `MAX_DRAWERS` | `int` | Max moments in wake-up (15) |
+| `MAX_CHARS` | `int` | Hard cap on L1 text (3200) |
+
+| Method | Returns | Description |
+|--------|---------|-------------|
+| `generate()` | `str` | Compact L1 text grouped by room |
+
+---
+
+### `class Layer2(palace_path=None)`
+
+On-demand retrieval layer (~200–500 tokens per call).
+
+| Method | Parameters | Returns |
+|--------|-----------|---------|
+| `retrieve(wing, room, n_results=10)` | Wing/room filters | Formatted drawer text |
+
+---
+
+### `class Layer3(palace_path=None)`
+
+Deep semantic search layer (unlimited depth).
+
+| Method | Parameters | Returns |
+|--------|-----------|---------|
+| `search(query, wing=None, room=None, n_results=5)` | Query + optional filters | Formatted result text |
+| `search_raw(query, wing=None, room=None, n_results=5)` | Query + optional filters | List of result dicts |
+
+---
+
+### `class MemoryStack(palace_path=None, identity_path=None)`
+
+Unified 4-layer interface.
+
+| Method | Parameters | Returns | Description |
+|--------|-----------|---------|-------------|
+| `wake_up(wing=None)` | Optional wing | `str` | L0 + L1 context (~170–900 tokens) |
+| `recall(wing, room, n_results=10)` | Filters | `str` | L2 on-demand retrieval |
+| `search(query, wing, room, n_results=5)` | Query + filters | `str` | L3 deep search |
+| `status()` | — | `dict` | All layer status info |
+
+---
+
+## `mempalace.knowledge_graph`
+
+### `class KnowledgeGraph(db_path=None)`
+
+Default path: `~/.mempalace/knowledge_graph.sqlite3`
+
+#### Write Methods
+
+| Method | Parameters | Returns | Description |
+|--------|-----------|---------|-------------|
+| `add_entity(name, entity_type='unknown', properties=None)` | Name, type, props dict | `str` (entity ID) | Add or update entity node |
+| `add_triple(subject, predicate, obj, valid_from, valid_to, confidence, source_closet, source_file)` | See below | `str` (triple ID) | Add relationship triple |
+| `invalidate(subject, predicate, obj, ended=None)` | Entity names, end date | — | Mark relationship as ended |
+
+**`add_triple` parameters:**
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `subject` | `str` | — | Source entity name |
+| `predicate` | `str` | — | Relationship type |
+| `obj` | `str` | — | Target entity name |
+| `valid_from` | `str` | `None` | Start date (YYYY-MM-DD) |
+| `valid_to` | `str` | `None` | End date |
+| `confidence` | `float` | `1.0` | Confidence score 0.0–1.0 |
+| `source_closet` | `str` | `None` | Link to verbatim memory |
+| `source_file` | `str` | `None` | Original source file |
+
+#### Query Methods
+
+| Method | Parameters | Returns |
+|--------|-----------|---------|
+| `query_entity(name, as_of=None, direction='outgoing')` | Entity name, date filter, direction | `list[dict]` |
+| `query_relationship(predicate, as_of=None)` | Relationship type, date filter | `list[dict]` |
+| `timeline(entity_name=None)` | Optional entity filter | `list[dict]` |
+| `stats()` | — | `dict` with entities, triples, predicates |
+| `seed_from_entity_facts(entity_facts)` | Dict of entity facts | — |
+
+**`query_entity` direction values:** `"outgoing"` (entity→?), `"incoming"` (?→entity), `"both"`
+
+---
+
+## `mempalace.palace_graph`
+
+### `build_graph(col=None, config=None) → (nodes, edges)`
+
+Build the palace graph from ChromaDB metadata.
+
+**Returns:**
+- `nodes`: `dict` of `{room: {wings: list, halls: list, count: int, dates: list}}`
+- `edges`: `list` of `{room, wing_a, wing_b, hall, count}`
+
+---
+
+### `traverse(start_room, col=None, config=None, max_hops=2) → list`
+
+BFS graph traversal from a room across wings.
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `start_room` | `str` | — | Room slug to start from |
+| `max_hops` | `int` | `2` | Max connection depth |
+
+**Returns:** `[{room, wings, halls, count, hop, connected_via}]` (max 50)
+
+---
+
+### `find_tunnels(wing_a=None, wing_b=None, col=None, config=None) → list`
+
+Find rooms spanning multiple wings.
+
+**Returns:** `[{room, wings, halls, count, recent}]` (max 50)
+
+---
+
+### `graph_stats(col=None, config=None) → dict`
+
+**Returns:** `{total_rooms, tunnel_rooms, total_edges, rooms_per_wing, top_tunnels}`
+
+---
+
+## `mempalace.dialect`
+
+### `class Dialect(entities=None, skip_names=None)`
+
+| Parameter | Type | Description |
+|-----------|------|-------------|
+| `entities` | `dict[str, str]` | Full name → 3-letter code mapping |
+| `skip_names` | `list[str]` | Names to skip (fictional characters, etc.) |
+
+#### Class Methods
+
+| Method | Parameters | Returns |
+|--------|-----------|---------|
+| `from_config(config_path)` | JSON file path | `Dialect` instance |
+
+#### Instance Methods
+
+| Method | Parameters | Returns | Description |
+|--------|-----------|---------|-------------|
+| `compress(text, metadata=None)` | Plain text + optional metadata dict | `str` | AAAK-formatted summary |
+| `encode_entity(name)` | Entity name | `str \| None` | 3-letter entity code |
+| `encode_emotions(emotions)` | List of emotion strings | `str` | Compact emotion codes |
+| `compress_file(path, output=None)` | Zettel JSON path | `str` | Compress zettel file |
+| `compress_all(dir, output=None)` | Zettel directory | `str` | Compress all zettels |
+| `save_config(path)` | Output path | — | Save entity mappings |
+| `compression_stats(original, compressed)` | Both texts | `dict` | Compression ratio stats |
+
+#### Static Methods
+
+| Method | Parameters | Returns |
+|--------|-----------|---------|
+| `count_tokens(text)` | Any text | `int` |
+
+---
+
+## `mempalace.config`
+
+### `class MempalaceConfig()`
+
+Reads from `~/.mempalace/config.json` and environment variables.
+
+| Property | Type | Default | Description |
+|----------|------|---------|-------------|
+| `palace_path` | `str` | `~/.mempalace/palace` | ChromaDB storage path |
+| `collection_name` | `str` | `mempalace_drawers` | ChromaDB collection name |
+
+| Method | Description |
+|--------|-------------|
+| `init()` | Create config directory and default files |

website/reference/benchmarks.md

@@ -0,0 +1,95 @@
+# Benchmarks
+
+Curated summary of MemPalace benchmark results. For the full 725-line progression with every experiment, see [`benchmarks/BENCHMARKS.md`](https://github.com/milla-jovovich/mempalace/blob/main/benchmarks/BENCHMARKS.md) in the repository.
+
+## The Core Finding
+
+MemPalace's benchmarked raw baseline stores the source text and searches it with ChromaDB's default embeddings. No extraction layer or summarization step is required for that baseline.
+
+**And it scores 96.6% on LongMemEval.**
+
+## LongMemEval Results
+
+| Mode | R@5 | LLM Required | Cost/query |
+|------|-----|-------------|------------|
+| Raw ChromaDB | **96.6%** | None | $0 |
+| Hybrid v3 + rerank | 99.4% | Haiku | ~$0.001 |
+| Palace + rerank | 99.4% | Haiku | ~$0.001 |
+| **Hybrid v4 + rerank** | **100%** | Haiku | ~$0.001 |
+
+The 96.6% raw score requires no API key, no cloud, and no LLM at any stage. The 100% result uses optional Haiku reranking.
+
+### Per-Category Breakdown (Raw, 96.6%)
+
+| Question Type | R@5 | Count |
+|---------------|-----|-------|
+| Knowledge update | 99.0% | 78 |
+| Multi-session | 98.5% | 133 |
+| Temporal reasoning | 96.2% | 133 |
+| Single-session user | 95.7% | 70 |
+| Single-session preference | 93.3% | 30 |
+| Single-session assistant | 92.9% | 56 |
+
+### Held-Out Validation
+
+**98.4% R@5** on 450 questions that hybrid_v4 was never tuned on — confirming the improvements generalize.
+
+## Comparison vs Published Systems
+
+| System | LongMemEval R@5 | API Required | Cost |
+|--------|----------------|--------------|------|
+| **MemPalace (hybrid)** | **100%** | Optional | Free |
+| Supermemory ASMR | ~99% | Yes | — |
+| **MemPalace (raw)** | **96.6%** | **None** | **Free** |
+| Mastra | 94.87% | Yes | API costs |
+| Hindsight | 91.4% | Yes | API costs |
+| Mem0 | ~85% | Yes | $19–249/mo |
+
+## Other Benchmarks
+
+### ConvoMem (Salesforce, 75K+ QA pairs)
+
+| System | Score |
+|--------|-------|
+| **MemPalace** | **92.9%** |
+| Gemini (long context) | 70–82% |
+| Block extraction | 57–71% |
+| Mem0 (RAG) | 30–45% |
+
+On this benchmark, MemPalace materially outperforms the Mem0 result cited in the comparison table.
+
+### LoCoMo (1,986 multi-hop QA pairs)
+
+| Mode | R@10 | LLM |
+|------|------|-----|
+| Hybrid v5 + Sonnet rerank (top-50) | **100%** | Sonnet |
+| bge-large + Haiku rerank (top-15) | 96.3% | Haiku |
+| Hybrid v5 (top-10, no rerank) | **88.9%** | None |
+| Session, no rerank (top-10) | 60.3% | None |
+
+### MemBench (ACL 2025, 8,500 items)
+
+**80.3% R@5** overall. Strongest categories: aggregative (99.3%), comparative (98.4%), lowlevel_rec (99.8%).
+
+## Reproducing Results
+
+All benchmarks are reproducible with public datasets:
+
+```bash
+git clone https://github.com/milla-jovovich/mempalace.git
+cd mempalace
+pip install chromadb pyyaml
+
+# Download LongMemEval data
+curl -fsSL -o /tmp/longmemeval_s_cleaned.json \
+  https://huggingface.co/datasets/xiaowu0162/longmemeval-cleaned/resolve/main/longmemeval_s_cleaned.json
+
+# Run raw baseline (96.6%, no API key needed)
+python benchmarks/longmemeval_bench.py /tmp/longmemeval_s_cleaned.json
+```
+
+::: tip
+Results are deterministic. Same data + same script = same result every time. Every result JSONL file contains every question, every retrieved document, every score.
+:::
+
+For complete reproduction instructions, benchmark integrity notes, and the full score progression, see the [full benchmark documentation](https://github.com/milla-jovovich/mempalace/blob/main/benchmarks/BENCHMARKS.md).

website/reference/cli.md

@@ -0,0 +1,156 @@
+# CLI Commands
+
+All commands accept `--palace <path>` to override the default palace location.
+
+## `mempalace init`
+
+Detect rooms from your folder structure and set up the palace.
+
+```bash
+mempalace init <dir>
+mempalace init <dir> --yes  # non-interactive mode
+```
+
+| Option | Description |
+|--------|-------------|
+| `<dir>` | Project directory to scan |
+| `--yes` | Auto-accept all detected entities |
+
+What it does:
+1. Scans for people and projects in file content
+2. Detects rooms from folder structure
+3. Creates `~/.mempalace/` config directory
+4. Saves detected entities to `<dir>/entities.json`
+
+## `mempalace mine`
+
+Mine files into the palace.
+
+```bash
+mempalace mine <dir>
+mempalace mine <dir> --mode convos
+mempalace mine <dir> --mode convos --extract general
+mempalace mine <dir> --wing myapp
+```
+
+| Option | Default | Description |
+|--------|---------|-------------|
+| `<dir>` | — | Directory to mine |
+| `--mode` | `projects` | `projects` for code/docs, `convos` for chat exports |
+| `--wing` | directory name | Wing name override |
+| `--agent` | `mempalace` | Agent name tag |
+| `--limit` | `0` (all) | Max files to process |
+| `--dry-run` | — | Preview without filing |
+| `--extract` | `exchange` | `exchange` or `general` (for convos mode) |
+| `--no-gitignore` | — | Don't respect .gitignore |
+| `--include-ignored` | — | Always scan these paths even if ignored |
+
+## `mempalace search`
+
+Find anything by semantic search.
+
+```bash
+mempalace search "query"
+mempalace search "query" --wing myapp
+mempalace search "query" --wing myapp --room auth
+mempalace search "query" --results 10
+```
+
+| Option | Default | Description |
+|--------|---------|-------------|
+| `"query"` | — | What to search for |
+| `--wing` | all | Filter by wing |
+| `--room` | all | Filter by room |
+| `--results` | `5` | Number of results |
+
+## `mempalace split`
+
+Split concatenated transcript mega-files into per-session files.
+
+```bash
+mempalace split <dir>
+mempalace split <dir> --dry-run
+mempalace split <dir> --min-sessions 3
+mempalace split <dir> --output-dir ~/split-output/
+```
+
+| Option | Default | Description |
+|--------|---------|-------------|
+| `<dir>` | — | Directory with transcript files |
+| `--output-dir` | same dir | Write split files here |
+| `--dry-run` | — | Preview without writing |
+| `--min-sessions` | `2` | Only split files with N+ sessions |
+
+## `mempalace wake-up`
+
+Show L0 + L1 wake-up context (~170–900 tokens).
+
+```bash
+mempalace wake-up
+mempalace wake-up --wing driftwood
+```
+
+| Option | Description |
+|--------|-------------|
+| `--wing` | Project-specific wake-up |
+
+## `mempalace compress`
+
+Compress drawers using AAAK Dialect.
+
+```bash
+mempalace compress --wing myapp
+mempalace compress --wing myapp --dry-run
+mempalace compress --config entities.json
+```
+
+| Option | Description |
+|--------|-------------|
+| `--wing` | Wing to compress (default: all) |
+| `--dry-run` | Preview without storing |
+| `--config` | Entity config JSON file |
+
+## `mempalace status`
+
+Show what's been filed — drawer count, wing/room breakdown.
+
+```bash
+mempalace status
+```
+
+## `mempalace repair`
+
+Rebuild palace vector index from stored data. Fixes segfaults after database corruption.
+
+```bash
+mempalace repair
+```
+
+Creates a backup at `<palace_path>.backup` before rebuilding.
+
+## `mempalace hook`
+
+Run hook logic for Claude Code / Codex integration.
+
+```bash
+mempalace hook run --hook stop --harness claude-code
+mempalace hook run --hook precompact --harness claude-code
+mempalace hook run --hook session-start --harness codex
+```
+
+| Option | Values | Description |
+|--------|--------|-------------|
+| `--hook` | `session-start`, `stop`, `precompact` | Hook name |
+| `--harness` | `claude-code`, `codex` | Harness type |
+
+## `mempalace instructions`
+
+Output skill instructions to stdout.
+
+```bash
+mempalace instructions init
+mempalace instructions search
+mempalace instructions mine
+mempalace instructions help
+mempalace instructions status
+```

website/reference/contributing.md

@@ -0,0 +1,81 @@
+# Contributing
+
+PRs welcome. MemPalace is open source and we welcome contributions of all sizes — from typo fixes to new features.
+
+## Getting Started
+
+```bash
+git clone https://github.com/milla-jovovich/mempalace.git
+cd mempalace
+pip install -e ".[dev]"
+```
+
+## Running Tests
+
+```bash
+pytest tests/ -v
+```
+
+All tests must pass before submitting a PR. Tests should run without API keys or network access.
+
+## Running Benchmarks
+
+```bash
+# Quick test (20 questions, ~30 seconds)
+python benchmarks/longmemeval_bench.py /path/to/longmemeval_s_cleaned.json --limit 20
+
+# Full benchmark (500 questions, ~5 minutes)
+python benchmarks/longmemeval_bench.py /path/to/longmemeval_s_cleaned.json
+```
+
+See [Benchmarks](/reference/benchmarks) for data download instructions.
+
+## PR Guidelines
+
+1. Fork the repo and create a feature branch: `git checkout -b feat/my-thing`
+2. Write your code
+3. Add or update tests if applicable
+4. Run `pytest tests/ -v` — everything must pass
+5. Commit with clear [conventional commits](https://www.conventionalcommits.org/):
+   - `feat: add Notion export format`
+   - `fix: handle empty transcript files`
+   - `docs: update MCP tool descriptions`
+   - `bench: add LoCoMo turn-level metrics`
+6. Push to your fork and open a PR against `main`
+
+## Code Style
+
+- **Formatting**: [Ruff](https://docs.astral.sh/ruff/) with 100-char line limit
+- **Naming**: `snake_case` for functions/variables, `PascalCase` for classes
+- **Docstrings**: on all modules and public functions
+- **Type hints**: where they improve readability
+- **Dependencies**: minimize — ChromaDB + PyYAML only. Don't add new deps without discussion.
+
+## Good First Issues
+
+Check the [Issues](https://github.com/milla-jovovich/mempalace/issues) tab:
+
+- **New chat formats** — add import support for Cursor, Copilot, or other AI tool exports
+- **Room detection** — improve pattern matching in `room_detector_local.py`
+- **Tests** — increase coverage, especially for `knowledge_graph.py` and `palace_graph.py`
+- **Entity detection** — better name disambiguation in `entity_detector.py`
+- **Docs** — improve examples, add tutorials
+
+## Architecture Decisions
+
+If you're planning a significant change, open an issue first. Key principles:
+
+- **Verbatim first** — never summarize user content. Store exact words.
+- **Local first** — everything runs on the user's machine. No cloud dependencies.
+- **Zero API by default** — core features must work without any API key.
+- **Palace structure matters** — wings, halls, and rooms aren't cosmetic — they drive a 34% retrieval improvement.
+
+## Community
+
+- [Discord](https://discord.com/invite/ycTQQCu6kn)
+- [GitHub Issues](https://github.com/milla-jovovich/mempalace/issues) — bug reports and feature requests
+- [GitHub Discussions](https://github.com/milla-jovovich/mempalace/discussions) — questions and ideas
+
+## License
+
+MIT — your contributions will be released under the same license.

website/reference/mcp-tools.md

@@ -0,0 +1,249 @@
+# MCP Tools Reference
+
+Detailed parameter schemas for all 19 MCP tools.
+
+## Palace — Read Tools
+
+### `mempalace_status`
+
+Palace overview: total drawers, wing and room counts, AAAK spec, and memory protocol.
+
+**Parameters:** None
+
+**Returns:** `{ total_drawers, wings, rooms, palace_path, protocol, aaak_dialect }`
+
+---
+
+### `mempalace_list_wings`
+
+List all wings with drawer counts.
+
+**Parameters:** None
+
+**Returns:** `{ wings: { "wing_name": count } }`
+
+---
+
+### `mempalace_list_rooms`
+
+List rooms within a wing (or all rooms if no wing given).
+
+| Parameter | Type | Required | Description |
+|-----------|------|----------|-------------|
+| `wing` | string | No | Wing to list rooms for |
+
+**Returns:** `{ wing, rooms: { "room_name": count } }`
+
+---
+
+### `mempalace_get_taxonomy`
+
+Full wing → room → drawer count tree.
+
+**Parameters:** None
+
+**Returns:** `{ taxonomy: { "wing": { "room": count } } }`
+
+---
+
+### `mempalace_search`
+
+Semantic search. Returns verbatim drawer content with similarity scores.
+
+| Parameter | Type | Required | Description |
+|-----------|------|----------|-------------|
+| `query` | string | **Yes** | What to search for |
+| `limit` | integer | No | Max results (default: 5) |
+| `wing` | string | No | Filter by wing |
+| `room` | string | No | Filter by room |
+
+**Returns:** `{ query, filters, results: [{ text, wing, room, source_file, similarity }] }`
+
+---
+
+### `mempalace_check_duplicate`
+
+Check if content already exists in the palace before filing.
+
+| Parameter | Type | Required | Description |
+|-----------|------|----------|-------------|
+| `content` | string | **Yes** | Content to check |
+| `threshold` | number | No | Similarity threshold 0–1 (default: 0.9) |
+
+**Returns:** `{ is_duplicate, matches: [{ id, wing, room, similarity, content }] }`
+
+---
+
+### `mempalace_get_aaak_spec`
+
+Returns the AAAK dialect specification.
+
+**Parameters:** None
+
+**Returns:** `{ aaak_spec: "..." }`
+
+---
+
+## Palace — Write Tools
+
+### `mempalace_add_drawer`
+
+File verbatim content into the palace. Identical content (same deterministic drawer ID) is silently skipped. For similarity-based duplicate detection before filing, use `mempalace_check_duplicate`.
+
+| Parameter | Type | Required | Description |
+|-----------|------|----------|-------------|
+| `wing` | string | **Yes** | Wing (project name) |
+| `room` | string | **Yes** | Room (aspect: backend, decisions, etc.) |
+| `content` | string | **Yes** | Verbatim content to store |
+| `source_file` | string | No | Where this came from |
+| `added_by` | string | No | Who is filing (default: "mcp") |
+
+**Returns:** `{ success, drawer_id, wing, room }`
+
+---
+
+### `mempalace_delete_drawer`
+
+Delete a drawer by ID. Irreversible.
+
+| Parameter | Type | Required | Description |
+|-----------|------|----------|-------------|
+| `drawer_id` | string | **Yes** | ID of the drawer to delete |
+
+**Returns:** `{ success, drawer_id }`
+
+---
+
+## Knowledge Graph Tools
+
+### `mempalace_kg_query`
+
+Query entity relationships with time filtering.
+
+| Parameter | Type | Required | Description |
+|-----------|------|----------|-------------|
+| `entity` | string | **Yes** | Entity to query (e.g. "Max", "MyProject") |
+| `as_of` | string | No | Date filter — only facts valid at this date (YYYY-MM-DD) |
+| `direction` | string | No | `outgoing`, `incoming`, or `both` (default: `both`) |
+
+**Returns:** `{ entity, as_of, facts: [{ direction, subject, predicate, object, valid_from, valid_to, current }], count }`
+
+---
+
+### `mempalace_kg_add`
+
+Add a fact to the knowledge graph.
+
+| Parameter | Type | Required | Description |
+|-----------|------|----------|-------------|
+| `subject` | string | **Yes** | The entity doing/being something |
+| `predicate` | string | **Yes** | Relationship type (e.g. "loves", "works_on") |
+| `object` | string | **Yes** | The entity being connected to |
+| `valid_from` | string | No | When this became true (YYYY-MM-DD) |
+| `source_closet` | string | No | Closet ID where this fact appears |
+
+**Returns:** `{ success, triple_id, fact }`
+
+---
+
+### `mempalace_kg_invalidate`
+
+Mark a fact as no longer true.
+
+| Parameter | Type | Required | Description |
+|-----------|------|----------|-------------|
+| `subject` | string | **Yes** | Entity |
+| `predicate` | string | **Yes** | Relationship |
+| `object` | string | **Yes** | Connected entity |
+| `ended` | string | No | When it stopped being true (default: today) |
+
+**Returns:** `{ success, fact, ended }`
+
+---
+
+### `mempalace_kg_timeline`
+
+Chronological timeline of facts.
+
+| Parameter | Type | Required | Description |
+|-----------|------|----------|-------------|
+| `entity` | string | No | Entity to get timeline for (omit for full timeline) |
+
+**Returns:** `{ entity, timeline: [{ subject, predicate, object, valid_from, valid_to, current }], count }`
+
+---
+
+### `mempalace_kg_stats`
+
+Knowledge graph overview.
+
+**Parameters:** None
+
+**Returns:** `{ entities, triples, current_facts, expired_facts, relationship_types }`
+
+---
+
+## Navigation Tools
+
+### `mempalace_traverse`
+
+Walk the palace graph from a room. Find connected ideas across wings.
+
+| Parameter | Type | Required | Description |
+|-----------|------|----------|-------------|
+| `start_room` | string | **Yes** | Room to start from |
+| `max_hops` | integer | No | How many connections to follow (default: 2) |
+
+**Returns:** `[{ room, wings, halls, count, hop, connected_via }]`
+
+---
+
+### `mempalace_find_tunnels`
+
+Find rooms that bridge two wings.
+
+| Parameter | Type | Required | Description |
+|-----------|------|----------|-------------|
+| `wing_a` | string | No | First wing |
+| `wing_b` | string | No | Second wing |
+
+**Returns:** `[{ room, wings, halls, count, recent }]`
+
+---
+
+### `mempalace_graph_stats`
+
+Palace graph overview: nodes, tunnels, edges, connectivity.
+
+**Parameters:** None
+
+**Returns:** `{ total_rooms, tunnel_rooms, total_edges, rooms_per_wing, top_tunnels }`
+
+---
+
+## Agent Diary Tools
+
+### `mempalace_diary_write`
+
+Write to your personal agent diary.
+
+| Parameter | Type | Required | Description |
+|-----------|------|----------|-------------|
+| `agent_name` | string | **Yes** | Your name — each agent gets its own wing |
+| `entry` | string | **Yes** | Diary entry (in AAAK format recommended) |
+| `topic` | string | No | Topic tag (default: "general") |
+
+**Returns:** `{ success, entry_id, agent, topic, timestamp }`
+
+---
+
+### `mempalace_diary_read`
+
+Read recent diary entries.
+
+| Parameter | Type | Required | Description |
+|-----------|------|----------|-------------|
+| `agent_name` | string | **Yes** | Your name |
+| `last_n` | integer | No | Number of recent entries (default: 10) |
+
+**Returns:** `{ agent, entries: [{ date, timestamp, topic, content }], total, showing }`

website/reference/modules.md

@@ -0,0 +1,129 @@
+# Module Map
+
+Complete source file reference for the MemPalace codebase.
+
+## Project Structure
+
+```
+mempalace/
+├── README.md                  ← project documentation
+├── mempalace/                 ← core package
+│   ├── cli.py                 ← CLI entry point
+│   ├── mcp_server.py          ← MCP server (19 tools)
+│   ├── knowledge_graph.py     ← temporal entity graph
+│   ├── palace_graph.py        ← room navigation graph
+│   ├── dialect.py             ← AAAK compression
+│   ├── miner.py               ← project file ingest
+│   ├── convo_miner.py         ← conversation ingest
+│   ├── searcher.py            ← semantic search
+│   ├── layers.py              ← 4-layer memory stack
+│   ├── onboarding.py          ← guided setup
+│   ├── config.py              ← configuration loading
+│   ├── normalize.py           ← chat format converter
+│   ├── entity_detector.py     ← auto-detect people/projects
+│   ├── entity_registry.py     ← entity code registry
+│   ├── room_detector_local.py ← room detection from directories
+│   ├── general_extractor.py   ← 5-type memory extraction
+│   ├── split_mega_files.py    ← transcript splitting
+│   ├── spellcheck.py          ← optional spell checking
+│   ├── hooks_cli.py           ← hook logic
+│   ├── instructions_cli.py    ← skill instructions
+│   └── version.py             ← version string
+├── benchmarks/                ← reproducible benchmark runners
+│   ├── BENCHMARKS.md          ← full results + methodology
+│   ├── longmemeval_bench.py   ← LongMemEval runner
+│   ├── locomo_bench.py        ← LoCoMo runner
+│   ├── membench_bench.py      ← MemBench runner
+│   └── convomem_bench.py      ← ConvoMem runner
+├── hooks/                     ← Claude Code auto-save hooks
+│   ├── mempal_save_hook.sh    ← save every N messages
+│   └── mempal_precompact_hook.sh ← emergency save
+├── examples/                  ← usage examples
+│   ├── basic_mining.py
+│   ├── convo_import.py
+│   ├── mcp_setup.md
+│   └── gemini_cli_setup.md
+├── tests/                     ← test suite
+├── assets/                    ← logo + brand
+└── pyproject.toml             ← package config
+```
+
+## Core Modules
+
+### `cli.py` — CLI Entry Point
+
+Argparse-based CLI with subcommands: `init`, `mine`, `split`, `search`, `compress`, `wake-up`, `repair`, `status`, `hook`, `instructions`. Dispatches to the corresponding module.
+
+### `mcp_server.py` — MCP Server
+
+JSON-RPC over stdin/stdout. Implements the MCP protocol with 19 tools covering palace read/write, knowledge graph, navigation, and agent diary operations. Includes the Memory Protocol and AAAK Spec in status responses.
+
+### `searcher.py` — Semantic Search
+
+Two functions: `search()` for CLI output and `search_memories()` for programmatic use. Both query ChromaDB with optional wing/room filters and return verbatim drawer content with similarity scores.
+
+### `layers.py` — Memory Stack
+
+Four classes (`Layer0` through `Layer3`) and the unified `MemoryStack`. Layer 0 reads identity, Layer 1 auto-generates from top drawers, Layer 2 does filtered retrieval, Layer 3 does semantic search.
+
+### `knowledge_graph.py` — Temporal KG
+
+SQLite-backed entity-relationship graph with temporal validity windows. Supports add, invalidate, query, timeline, and stats. Auto-creates entities on triple insertion.
+
+### `palace_graph.py` — Navigation Graph
+
+Builds a graph from ChromaDB metadata where nodes = rooms and edges = tunnels (rooms spanning multiple wings). Supports BFS traversal and tunnel finding.
+
+### `dialect.py` — AAAK Compression
+
+Lossy abbreviation system with entity encoding, emotion detection, topic extraction, and flag identification. Works on both plain text and structured zettel data.
+
+## Ingest Modules
+
+### `miner.py` — Project Ingest
+
+Scans project directories for code and doc files. Respects `.gitignore`. Files content as drawers tagged with wing/room metadata.
+
+### `convo_miner.py` — Conversation Ingest
+
+Imports conversation exports (Claude, ChatGPT, Slack, Markdown, plaintext). Chunks by exchange pair. Supports `exchange` and `general` extraction modes.
+
+### `normalize.py` — Format Converter
+
+Converts 5 chat formats to a standard transcript format before mining.
+
+### `general_extractor.py` — Memory Type Extraction
+
+Classifies conversation content into decisions, preferences, milestones, problems, and emotional context.
+
+## Detection Modules
+
+### `entity_detector.py` — Entity Detection
+
+Scans file content to auto-detect people and projects using regex patterns and heuristics.
+
+### `entity_registry.py` — Entity Registry
+
+Manages entity name → code mappings for AAAK dialect.
+
+### `room_detector_local.py` — Room Detection
+
+Detects rooms from folder structure during `mempalace init`.
+
+## Utility Modules
+
+### `config.py` — Configuration
+
+Loads settings from `~/.mempalace/config.json` and environment variables.
+
+### `split_mega_files.py` — Transcript Splitting
+
+Splits concatenated transcripts into per-session files based on session boundary detection.
+
+### `onboarding.py` — Guided Setup
+
+Interactive setup wizard for `mempalace init`. Generates AAAK bootstrap and wing config.
+
+### `spellcheck.py` — Spell Checking
+
+Optional spell checking utility (requires `autocorrect` package).

website/reference/python-api.md

@@ -0,0 +1,121 @@
+# Python API
+
+High-level overview of the key Python interfaces you'd use to integrate MemPalace into your application.
+
+## Search
+
+The primary way to query the palace programmatically.
+
+```python
+from mempalace.searcher import search_memories
+
+results = search_memories(
+    query="why did we switch to GraphQL",
+    wing="myapp",          # optional filter
+    room="architecture",   # optional filter
+    n_results=5,
+)
+
+# Results structure:
+# {
+#     "query": "...",
+#     "filters": {"wing": "myapp", "room": "architecture"},
+#     "results": [
+#         {"text": "...", "wing": "...", "room": "...", "source_file": "...", "similarity": 0.89}
+#     ]
+# }
+```
+
+## Memory Stack
+
+The 4-layer memory system with a unified interface.
+
+```python
+from mempalace.layers import MemoryStack
+
+stack = MemoryStack()  # uses default paths from MempalaceConfig
+
+# Wake-up: L0 (identity) + L1 (essential story)
+context = stack.wake_up(wing="myapp")  # ~170-900 tokens
+
+# On-demand: L2 retrieval
+recall = stack.recall(wing="myapp", room="auth", n_results=10)
+
+# Deep search: L3 semantic search
+results = stack.search("pricing change", wing="myapp")
+
+# Status
+status = stack.status()
+```
+
+## Knowledge Graph
+
+Temporal entity-relationship graph built on SQLite.
+
+```python
+from mempalace.knowledge_graph import KnowledgeGraph
+
+kg = KnowledgeGraph()  # uses default path: ~/.mempalace/knowledge_graph.sqlite3
+
+# Write
+kg.add_entity("Kai", entity_type="person")
+kg.add_triple("Kai", "works_on", "Orion", valid_from="2025-06-01")
+kg.invalidate("Kai", "works_on", "Orion", ended="2026-03-01")
+
+# Read
+facts = kg.query_entity("Kai", as_of="2026-01-15", direction="both")
+relationships = kg.query_relationship("works_on")
+timeline = kg.timeline("Orion")
+stats = kg.stats()
+```
+
+## Palace Graph
+
+Room-based navigation graph built from ChromaDB metadata.
+
+```python
+from mempalace.palace_graph import build_graph, traverse, find_tunnels, graph_stats
+
+# Build the graph
+nodes, edges = build_graph()
+
+# Navigate
+path = traverse("auth-migration", max_hops=2)
+tunnels = find_tunnels(wing_a="wing_code", wing_b="wing_team")
+stats = graph_stats()
+```
+
+## AAAK Dialect
+
+Lossy compression for token density at scale.
+
+```python
+from mempalace.dialect import Dialect
+
+# Basic
+dialect = Dialect()
+text = "We decided to use GraphQL because REST was too chatty for our dashboard."
+compressed = dialect.compress(text)
+
+# With entity mappings
+dialect = Dialect(entities={"Alice": "ALC", "Bob": "BOB"})
+compressed = dialect.compress(text, metadata={"wing": "myapp"})
+
+# From config
+dialect = Dialect.from_config("entities.json")
+
+# Stats
+stats = dialect.compression_stats(text, compressed)
+```
+
+## Configuration
+
+```python
+from mempalace.config import MempalaceConfig
+
+config = MempalaceConfig()
+print(config.palace_path)       # ~/.mempalace/palace
+print(config.collection_name)   # mempalace_drawers
+```
+
+For detailed parameter documentation, see [API Reference](/reference/api-reference).