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docs: comprehensive README rewrite
- Add one-liner CLI install (claude mcp add vestige vestige-mcp) - Document first-run network requirement (~130MB model download) - Add data storage locations and backup instructions - Reorganize all 29 tools into clear tables - Add troubleshooting section - Honest framing of neuroscience claims Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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README.md
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<p align="center">
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<pre>
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██╗ ██╗███████╗███████╗████████╗██╗ ██████╗ ███████╗
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██║ ██║██╔════╝██╔════╝╚══██╔══╝██║██╔════╝ ██╔════╝
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██║ ██║█████╗ ███████╗ ██║ ██║██║ ███╗█████╗
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╚██╗ ██╔╝██╔══╝ ╚════██║ ██║ ██║██║ ██║██╔══╝
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╚████╔╝ ███████╗███████║ ██║ ██║╚██████╔╝███████╗
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╚═══╝ ╚══════╝╚══════╝ ╚═╝ ╚═╝ ╚═════╝ ╚══════╝
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</pre>
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</p>
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# Vestige
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<h1 align="center">Vestige</h1>
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**Memory that fades like yours does.**
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<p align="center">
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<strong>Memory traces that fade like yours do</strong>
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</p>
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The only MCP memory server built on cognitive science. FSRS-6 spaced repetition, spreading activation, synaptic tagging—all running 100% local.
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<p align="center">
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The only AI memory system built on real cognitive science.<br/>
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FSRS-6 spaced repetition. Prediction Error Gating. Context-dependent recall.<br/>
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29 MCP tools. 100% local. 100% free.
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</p>
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<p align="center">
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<a href="#quick-start-2-minutes">Quick Start</a> |
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<a href="#all-29-tools">All 29 Tools</a> |
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<a href="#the-science">The Science</a>
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</p>
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<p align="center">
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<a href="https://github.com/samvallad33/vestige/releases"><img src="https://img.shields.io/github/v/release/samvallad33/vestige?style=flat-square" alt="Release"></a>
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<a href="https://github.com/samvallad33/vestige/blob/main/LICENSE"><img src="https://img.shields.io/badge/license-MIT-blue?style=flat-square" alt="License"></a>
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<a href="https://github.com/samvallad33/vestige/actions"><img src="https://img.shields.io/github/actions/workflow/status/samvallad33/vestige/release.yml?style=flat-square" alt="Build"></a>
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</p>
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[](https://github.com/samvallad33/vestige)
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[](LICENSE)
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[](https://modelcontextprotocol.io)
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---
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## Quick Start (2 minutes)
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## Why Vestige?
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### Step 1: Build & Install
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| Problem | How Vestige Solves It |
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|---------|----------------------|
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| AI forgets everything between sessions | Persistent memory with intelligent retrieval |
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| RAG dumps irrelevant context | **Prediction Error Gating** auto-decides CREATE/UPDATE/SUPERSEDE |
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| Memory bloat eats your token budget | **FSRS-6 decay** naturally fades unused memories |
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| No idea what AI "knows" | `recall`, `semantic_search`, `hybrid_search` let you query |
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| Context pollution confuses the model | **29 atomic tools** > 1 overloaded tool with 15 parameters |
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---
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## Quick Start
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### 1. Install
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```bash
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git clone https://github.com/samvallad33/vestige
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cd vestige
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cargo build --release
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sudo cp target/release/vestige-mcp /usr/local/bin/
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```
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That's it. Vestige is now globally available.
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Add to your PATH:
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```bash
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# macOS/Linux
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sudo cp target/release/vestige-mcp /usr/local/bin/
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### Step 2: Add to Claude Code
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# Or add to ~/.bashrc / ~/.zshrc
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export PATH="$PATH:/path/to/vestige/target/release"
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```
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Add to your `~/.claude/settings.json`:
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### 2. Configure Claude
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**Option A: One-liner (Recommended)**
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```bash
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claude mcp add vestige vestige-mcp
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```
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**Option B: Manual Config**
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<details>
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<summary>Claude Code (~/.claude/settings.json)</summary>
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```json
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{
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@ -61,220 +62,273 @@ Add to your `~/.claude/settings.json`:
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}
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}
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```
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</details>
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### Step 3: Add to Claude Desktop (Optional)
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**macOS:** `~/Library/Application Support/Claude/claude_desktop_config.json`
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**Windows:** `%APPDATA%\Claude\claude_desktop_config.json`
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<details>
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<summary>Claude Desktop (macOS)</summary>
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Add to `~/Library/Application Support/Claude/claude_desktop_config.json`:
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```json
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{
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"mcpServers": {
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"vestige": {
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"command": "vestige-mcp",
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"args": [],
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"env": {
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"VESTIGE_DATA_DIR": "~/.vestige"
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}
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"command": "vestige-mcp"
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}
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}
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}
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```
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</details>
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### Step 4: Restart & Verify
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<details>
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<summary>Claude Desktop (Windows)</summary>
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Restart Claude Code/Desktop. You should see 29 Vestige tools available.
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Add to `%APPDATA%\Claude\claude_desktop_config.json`:
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```json
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{
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"mcpServers": {
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"vestige": {
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"command": "vestige-mcp"
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}
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}
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}
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```
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</details>
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### 3. Restart Claude
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Restart Claude Code or Desktop. You should see **29 Vestige tools** available.
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### 4. Test It
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Ask Claude:
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> "Remember that I prefer TypeScript over JavaScript"
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Then in a new session:
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> "What are my coding preferences?"
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It remembers.
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---
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## Why Vestige?
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## Important Notes
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| Feature | What It Does |
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|---------|--------------|
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| **Prediction Error Gating** | Auto-decides create/update/supersede based on similarity |
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| **FSRS-6 Spaced Repetition** | Full 21-parameter algorithm trained on millions of reviews |
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| **Retroactive Importance** | Mark something important, past 9 hours of memories strengthen |
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| **Context-Dependent Recall** | Retrieval matches encoding context (Tulving 1973) |
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| **Memory States** | Active/Dormant/Silent/Unavailable accessibility model |
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| **100% Local** | No API keys, no cloud, no data leaves your machine |
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### First-Run Network Requirement
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Vestige downloads the **Nomic Embed Text v1.5** model (~130MB) from Hugging Face on first use for semantic search.
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**All subsequent runs are fully offline.**
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Model cache location:
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- macOS/Linux: `~/.cache/huggingface/`
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- Windows: `%USERPROFILE%\.cache\huggingface\`
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### Data Storage & Backup
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All memories are stored locally in SQLite:
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| Platform | Database Location |
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|----------|------------------|
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| macOS | `~/Library/Application Support/com.vestige.core/vestige.db` |
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| Linux | `~/.local/share/vestige/core/vestige.db` |
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| Windows | `%APPDATA%\vestige\core\vestige.db` |
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**There is no cloud sync or automatic backup.** Your memories live on your machine.
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To back up manually:
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```bash
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# macOS
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cp ~/Library/Application\ Support/com.vestige.core/vestige.db ~/vestige-backup.db
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# Linux
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cp ~/.local/share/vestige/core/vestige.db ~/vestige-backup.db
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```
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> For most users, losing memories isn't catastrophic—you just start fresh. But if you've built valuable context, periodic backups are recommended.
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---
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## All 29 Tools
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### Core Memory (8 tools)
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### Core Memory
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| Tool | Description |
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|------|-------------|
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| `ingest` | Store a new memory |
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| `smart_ingest` | **Prediction Error Gating** - auto-decides CREATE/UPDATE/SUPERSEDE based on semantic similarity to existing memories |
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| `recall` | Semantic search with keyword matching |
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| `semantic_search` | Pure embedding-based similarity search |
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| `hybrid_search` | BM25 + semantic + RRF fusion (best retrieval quality) |
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| `get_knowledge` | Retrieve a specific memory by ID |
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| `ingest` | Add new knowledge to memory |
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| `smart_ingest` | **Intelligent ingestion** with Prediction Error Gating—auto-decides CREATE/UPDATE/SUPERSEDE |
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| `recall` | Search by keywords, ranked by retention strength |
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| `semantic_search` | Find conceptually related content via embeddings |
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| `hybrid_search` | Combined keyword + semantic with RRF fusion |
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| `get_knowledge` | Retrieve specific memory by ID |
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| `delete_knowledge` | Remove a memory |
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| `mark_reviewed` | FSRS review with 1-4 rating (strengthens memory) |
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### Feedback System (3 tools)
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| `mark_reviewed` | FSRS review with rating (1=Again, 2=Hard, 3=Good, 4=Easy) |
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### Feedback System
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| Tool | Description |
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|------|-------------|
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| `promote_memory` | Thumbs up - memory led to good outcome, increase retrieval strength |
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| `demote_memory` | Thumbs down - memory was wrong/unhelpful, decrease retrieval strength |
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| `request_feedback` | Ask user if a memory was helpful after using it |
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### Stats & Maintenance (3 tools)
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| `promote_memory` | Thumbs up—memory led to good outcome |
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| `demote_memory` | Thumbs down—memory was wrong or unhelpful |
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| `request_feedback` | Ask user if a memory was helpful |
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### Stats & Maintenance
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| Tool | Description |
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|------|-------------|
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| `get_stats` | Memory system statistics (total nodes, retention, embeddings) |
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| `get_stats` | Memory system statistics |
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| `health_check` | System health status |
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| `run_consolidation` | Trigger memory consolidation cycle (decay, promote, embed) |
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### Codebase Memory (3 tools)
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| `run_consolidation` | Trigger decay cycle, generate embeddings |
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### Codebase Memory
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| Tool | Description |
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|------|-------------|
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| `remember_pattern` | Store a code pattern or convention |
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| `remember_decision` | Store an architectural decision with rationale |
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| `get_codebase_context` | Retrieve patterns and decisions for current project |
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### Prospective Memory (5 tools)
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| `remember_pattern` | Save code patterns/conventions |
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| `remember_decision` | Save architectural decisions with rationale |
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| `get_codebase_context` | Retrieve patterns/decisions for current project |
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### Prospective Memory (Intentions)
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| Tool | Description |
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|------|-------------|
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| `set_intention` | Remember to do something in the future (time/context/event triggers) |
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| `check_intentions` | Check if any intentions should trigger based on current context |
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| `complete_intention` | Mark an intention as fulfilled |
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| `set_intention` | "Remind me to X when Y" |
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| `check_intentions` | Check triggered intentions for current context |
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| `complete_intention` | Mark intention as fulfilled |
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| `snooze_intention` | Delay an intention |
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| `list_intentions` | List all active intentions |
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### Neuroscience (7 tools)
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| `list_intentions` | View all intentions |
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### Neuroscience Layer
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| Tool | Description |
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|------|-------------|
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| `get_memory_state` | Check cognitive state (Active/Dormant/Silent/Unavailable) |
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| `list_by_state` | List memories filtered by cognitive state |
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| `state_stats` | Distribution of memories across states |
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| `get_memory_state` | Check if memory is Active/Dormant/Silent/Unavailable |
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| `list_by_state` | List memories grouped by cognitive state |
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| `state_stats` | Distribution of memory states |
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| `trigger_importance` | Retroactively strengthen recent memories (Synaptic Tagging) |
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| `find_tagged` | Find memories with high retention (tagged/strengthened) |
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| `find_tagged` | Find high-retention memories |
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| `tagging_stats` | Synaptic tagging statistics |
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| `match_context` | Context-dependent retrieval (Tulving's Encoding Specificity) |
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| `match_context` | Context-dependent retrieval (Encoding Specificity) |
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---
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## Prediction Error Gating
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## How It Works
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The `smart_ingest` tool implements neuroscience-inspired memory gating:
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### Prediction Error Gating
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When you call `smart_ingest`, Vestige compares new content against existing memories:
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| Similarity | Action | Why |
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|------------|--------|-----|
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| > 0.92 | **REINFORCE** existing | Almost identical—just strengthen |
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| > 0.75 | **UPDATE** existing | Related—merge the information |
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| < 0.75 | **CREATE** new | Novel—add as new memory |
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This prevents duplicate memories and keeps your knowledge base clean.
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### FSRS-6 Spaced Repetition
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Memories decay over time following the **Ebbinghaus forgetting curve**:
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```
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New memory: "The API uses JWT tokens"
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↓
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[Prediction Error Gate]
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↓
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┌────────────────────────────────────────────┐
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│ Found similar: "API uses OAuth" │
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│ Similarity: 0.82 | Prediction Error: 0.18 │
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│ │
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│ Decision: UPDATE existing memory │
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│ (Not creating duplicate) │
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└────────────────────────────────────────────┘
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Retention = e^(-time/stability)
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```
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**Thresholds:**
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- `>0.92` similarity → **Reinforce** (near-identical, just strengthen)
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- `>0.75` similarity → **Update/Merge** (related, combine information)
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- `<0.75` similarity → **Create** (sufficiently different, new memory)
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- Demoted memory + similar new → **Supersede** (replace bad with good)
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- Memories you access stay strong
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- Memories you ignore fade naturally
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- No manual cleanup required
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This solves the "bad vs good similar memory" problem automatically.
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FSRS-6 uses 21 parameters optimized on millions of Anki reviews—30% more efficient than SM-2.
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### Memory States
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Based on accessibility, memories exist in four states:
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| State | Description |
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|-------|-------------|
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| **Active** | High retention, immediately retrievable |
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| **Dormant** | Medium retention, retrievable with effort |
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| **Silent** | Low retention, rarely surfaces |
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| **Unavailable** | Below threshold, effectively forgotten |
|
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---
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## The Science
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### FSRS-6 Algorithm (2024)
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Vestige implements concepts from memory research:
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Free Spaced Repetition Scheduler version 6. Trained on 700M+ reviews:
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| Feature | Inspired By | Reference |
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|---------|-------------|-----------|
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| Spaced repetition | FSRS-6 algorithm | [Piotr Wozniak, 2022](https://github.com/open-spaced-repetition/fsrs4anki) |
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| Storage vs Retrieval strength | Bjork's New Theory of Disuse | [Bjork & Bjork, 1992](https://psycnet.apa.org/record/1992-97586-004) |
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| Retroactive importance | Synaptic Tagging & Capture | [Frey & Morris, 1997](https://www.nature.com/articles/385533a0) |
|
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| Context-dependent retrieval | Encoding Specificity Principle | [Tulving & Thomson, 1973](https://psycnet.apa.org/record/1973-31800-001) |
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| Forgetting curve | Ebbinghaus decay function | [Ebbinghaus, 1885](https://en.wikipedia.org/wiki/Forgetting_curve) |
|
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|
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```rust
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const FSRS_WEIGHTS: [f64; 21] = [
|
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0.40255, 1.18385, 3.173, 15.69105, 7.1949,
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0.5345, 1.4604, 0.0046, 1.54575, 0.1192,
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1.01925, 1.9395, 0.11, 0.29605, 2.2698,
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0.2315, 2.9898, 0.51655, 0.6621, 0.1, 0.5
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];
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```
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20-30% better retention than SM-2 (what Anki uses).
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### Bjork & Bjork Dual-Strength Model (1992)
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Memories have two independent strengths:
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- **Storage Strength**: How well encoded (never decreases)
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- **Retrieval Strength**: How accessible now (decays with time)
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Key insight: difficult retrievals increase storage strength more than easy ones.
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### Synaptic Tagging & Capture (Frey & Morris 1997)
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When something important happens, it retroactively strengthens memories from the past several hours. Vestige implements this with a 9-hour capture window.
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### Encoding Specificity Principle (Tulving 1973)
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Memory retrieval is most effective when the retrieval context matches the encoding context. The `match_context` tool scores memories by context similarity.
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### Ebbinghaus Forgetting Curve (1885)
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Memory retention decays exponentially: `R = e^(-t/S)`
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Where R = retrievability, t = time, S = stability.
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> **Note**: These are *simplified models inspired by* cognitive science research, designed to be practical for AI memory management. They are not literal implementations of neural biochemistry.
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|
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---
|
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|
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## Configuration
|
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|
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### Environment Variables
|
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Environment variables:
|
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|
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| Variable | Description | Default |
|
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|----------|-------------|---------|
|
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| `VESTIGE_DATA_DIR` | Data storage directory | `~/.vestige` |
|
||||
| `VESTIGE_LOG_LEVEL` | Log verbosity (`error`, `warn`, `info`, `debug`, `trace`) | `info` |
|
||||
| Variable | Default | Description |
|
||||
|----------|---------|-------------|
|
||||
| `VESTIGE_DATA_DIR` | Platform default | Custom database location |
|
||||
| `VESTIGE_LOG_LEVEL` | `info` | Logging verbosity |
|
||||
| `RUST_LOG` | - | Detailed tracing output |
|
||||
|
||||
### Data Location
|
||||
Command-line options:
|
||||
```bash
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||||
vestige-mcp --data-dir /custom/path
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||||
vestige-mcp --help
|
||||
```
|
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|
||||
All data is stored in `~/.vestige/` by default:
|
||||
- `vestige.db` - SQLite database with FTS5
|
||||
- `embeddings/` - Local embedding cache
|
||||
---
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### "Command not found" after installation
|
||||
|
||||
Make sure `vestige-mcp` is in your PATH:
|
||||
```bash
|
||||
which vestige-mcp
|
||||
# Should output: /usr/local/bin/vestige-mcp
|
||||
```
|
||||
|
||||
If not found:
|
||||
```bash
|
||||
# Use full path in Claude config
|
||||
claude mcp add vestige /full/path/to/vestige-mcp
|
||||
```
|
||||
|
||||
### Model download fails
|
||||
|
||||
First run requires internet to download the embedding model. If behind a proxy:
|
||||
```bash
|
||||
export HTTPS_PROXY=your-proxy:port
|
||||
```
|
||||
|
||||
### "Tools not showing" in Claude
|
||||
|
||||
1. Check config file syntax (valid JSON)
|
||||
2. Restart Claude completely (not just reload)
|
||||
3. Check logs: `tail -f ~/.claude/logs/mcp.log`
|
||||
|
||||
### Database locked errors
|
||||
|
||||
Vestige uses SQLite with WAL mode. If you see lock errors:
|
||||
```bash
|
||||
# Only one instance should run at a time
|
||||
pkill vestige-mcp
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Development
|
||||
|
||||
### Prerequisites
|
||||
|
||||
- Rust 1.75+
|
||||
|
||||
### Building
|
||||
|
||||
```bash
|
||||
git clone https://github.com/samvallad33/vestige
|
||||
cd vestige
|
||||
cargo build --release
|
||||
```
|
||||
# Run tests
|
||||
cargo test --all-features
|
||||
|
||||
### Testing
|
||||
# Run with logging
|
||||
RUST_LOG=debug cargo run --release
|
||||
|
||||
```bash
|
||||
cargo test --workspace
|
||||
```
|
||||
|
||||
### Installing Locally
|
||||
|
||||
```bash
|
||||
sudo cp target/release/vestige-mcp /usr/local/bin/
|
||||
# Build optimized binary
|
||||
cargo build --release --all-features
|
||||
```
|
||||
|
||||
---
|
||||
|
|
@ -288,43 +342,22 @@ cargo build --release
|
|||
sudo cp target/release/vestige-mcp /usr/local/bin/
|
||||
```
|
||||
|
||||
Restart Claude Code/Desktop to pick up changes.
|
||||
|
||||
---
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### "vestige-mcp: command not found"
|
||||
|
||||
Make sure you copied the binary:
|
||||
```bash
|
||||
sudo cp target/release/vestige-mcp /usr/local/bin/
|
||||
```
|
||||
|
||||
### "No tools showing in Claude"
|
||||
|
||||
1. Check your config file syntax (valid JSON)
|
||||
2. Restart Claude Code/Desktop completely
|
||||
3. Check logs: `VESTIGE_LOG_LEVEL=debug vestige-mcp`
|
||||
|
||||
### "Embeddings not generating"
|
||||
|
||||
First run downloads the embedding model (~100MB). Check your internet connection and wait for download to complete.
|
||||
|
||||
---
|
||||
|
||||
## Contributing
|
||||
|
||||
Contributions welcome! Please open an issue or submit a pull request.
|
||||
Then restart Claude.
|
||||
|
||||
---
|
||||
|
||||
## License
|
||||
|
||||
MIT OR Apache-2.0
|
||||
MIT OR Apache-2.0 (dual-licensed)
|
||||
|
||||
---
|
||||
|
||||
## Contributing
|
||||
|
||||
Issues and PRs welcome! See [CONTRIBUTING.md](CONTRIBUTING.md).
|
||||
|
||||
---
|
||||
|
||||
<p align="center">
|
||||
<sub>Built with cognitive science and Rust.</sub>
|
||||
<i>Built by <a href="https://github.com/samvallad33">@samvallad33</a></i>
|
||||
</p>
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue