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Sam Valladares 8c18231a20 docs: keep README focused on v2.1.23
2026-05-28 13:34:09 -05:00

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<div align="center">
# Vestige
### Local cognitive memory for MCP-compatible AI agents.
[![GitHub stars](https://img.shields.io/github/stars/samvallad33/vestige?style=social)](https://github.com/samvallad33/vestige)
[![Release](https://img.shields.io/github/v/release/samvallad33/vestige)](https://github.com/samvallad33/vestige/releases/latest)
[![Tests](https://img.shields.io/badge/tests-passing-brightgreen)](https://github.com/samvallad33/vestige/actions)
[![License](https://img.shields.io/badge/license-AGPL--3.0-blue)](LICENSE)
[![MCP Compatible](https://img.shields.io/badge/MCP-compatible-green)](https://modelcontextprotocol.io)
**Your agent forgets project decisions between sessions. Vestige gives it local, inspectable memory.**
Built on proven memory and retrieval ideas — FSRS-6 spaced repetition, prediction error gating, synaptic tagging, spreading activation, and memory consolidation — all running in a single Rust binary with a local dashboard. 100% local. Zero cloud.
[Quick Start](#quick-start) | [Dashboard](#-3d-memory-dashboard) | [How It Works](#-the-cognitive-science-stack) | [Tools](#-25-mcp-tools) | [Docs](docs/)
</div>
---
## What's New in v2.1.23 "Receipt Lock Hardening"
v2.1.23 turns the Sanhedrin Receipt Lock launch into something more portable,
observable, and harder to spoof.
- **Model-agnostic Sanhedrin presets.** Sanhedrin no longer guesses a large default verifier. Users choose any OpenAI-compatible endpoint/model, or start from custom, small laptop, Ollama, MLX, vLLM, llama.cpp, hosted API, or LiteLLM presets.
- **Sharper Receipt Lock.** Verification claims inside code fences, quotes, blockquotes, or explicitly hedged "let me verify" language no longer trigger false vetoes, while actual "tests passed" claims still require command receipts.
- **Safer command receipts.** Transcript command evidence now prefers structured tool-use receipts; loose JSON scanning is opt-in only.
- **Visible fail-open telemetry.** Timeouts, unavailable model endpoints, and malformed verdicts are logged locally and surfaced in the dashboard's 7-day Sanhedrin stats.
- **Durable evidence boundary.** Staged evidence remains useful context, but it cannot satisfy durable support or contradiction requirements by itself.
- **Safer batch writes.** `smart_ingest` batch mode now keeps caller-separated items separate by default and returns merge previews when an existing memory is mutated.
- **Opt-in NVIDIA acceleration path.** Qwen3 embedding builds expose CUDA/cuDNN feature flags for contributors and users with CUDA-capable hosts.
---
## Quick Start
```bash
# 1. Install
npm install -g vestige-mcp-server@latest
# 2. Connect to any MCP-compatible agent
# Claude Code
claude mcp add vestige vestige-mcp -s user
# Codex
codex mcp add vestige -- vestige-mcp
# 3. Test it
# "Remember that I prefer TypeScript over JavaScript"
# ...new session...
# "What are my coding preferences?"
# → "You prefer TypeScript over JavaScript."
```
<details>
<summary>Other platforms & install methods</summary>
**Updating an existing install:**
```bash
vestige update
```
`vestige update` updates only the Vestige binaries by default. Use
`vestige update --sandwich-companion` if you also want to refresh optional Claude
Code Cognitive Sandwich companion files.
**macOS/Linux manual binary install:**
```bash
vestige update --install-dir /usr/local/bin
```
**macOS (Intel):** Microsoft is discontinuing x86_64 macOS prebuilts after ONNX Runtime v1.23.0, so Vestige's Intel Mac build links dynamically against a Homebrew-installed ONNX Runtime via the `ort-dynamic` feature. Install with:
```bash
brew install onnxruntime
npm install -g vestige-mcp-server@latest
echo 'export ORT_DYLIB_PATH="'"$(brew --prefix onnxruntime)"'/lib/libonnxruntime.dylib"' >> ~/.zshrc
source ~/.zshrc
claude mcp add vestige vestige-mcp -s user
```
Full Intel Mac guide (build-from-source + troubleshooting): [`docs/INSTALL-INTEL-MAC.md`](docs/INSTALL-INTEL-MAC.md).
**Windows + Claude Desktop (recommended):**
Fully quit Claude Desktop from the system tray, then install or update Vestige from PowerShell:
```powershell
npm install -g vestige-mcp-server@latest
vestige-mcp --version
```
Open `%APPDATA%\Claude\claude_desktop_config.json` and point Claude Desktop at the installed MCP command:
```json
{
"mcpServers": {
"vestige": {
"command": "vestige-mcp"
}
}
}
```
If Claude Desktop cannot find `vestige-mcp`, run `where vestige-mcp` in PowerShell and use the exact `.cmd` path it prints as `command`. Example: `"C:\\Users\\you\\AppData\\Roaming\\npm\\vestige-mcp.cmd"`. Reopen Claude Desktop after saving. Future binary updates use `vestige update`; optional Claude Code companion files require `vestige update --sandwich-companion`.
**Windows source build:** Prebuilt binaries ship but `usearch 2.24.0` hit an MSVC compile break ([usearch#746](https://github.com/unum-cloud/usearch/issues/746)); we've pinned `=2.23.0` until upstream fixes it. Source builds work with:
```bash
git clone https://github.com/samvallad33/vestige && cd vestige
cargo build --release -p vestige-mcp
```
**npm:**
```bash
npm install -g vestige-mcp-server
```
**Build from source (requires Rust 1.91+):**
```bash
git clone https://github.com/samvallad33/vestige && cd vestige
cargo build --release -p vestige-mcp
# Optional: enable Metal GPU acceleration on Apple Silicon
cargo build --release -p vestige-mcp --features metal
```
</details>
---
## Works Everywhere
Vestige speaks MCP, so any client that can register a stdio MCP server can use it.
| IDE | Setup |
|-----|-------|
| **Claude Code** | `claude mcp add vestige vestige-mcp -s user` |
| **Codex** | [Integration guide](docs/integrations/codex.md) |
| **Claude Desktop** | [2-min setup](docs/CONFIGURATION.md#claude-desktop-macos) |
| **Xcode 26.3** | [Integration guide](docs/integrations/xcode.md) |
| **Cursor** | [Integration guide](docs/integrations/cursor.md) |
| **VS Code (Copilot)** | [Integration guide](docs/integrations/vscode.md) |
| **JetBrains** | [Integration guide](docs/integrations/jetbrains.md) |
| **Windsurf** | [Integration guide](docs/integrations/windsurf.md) |
---
## 🧠 3D Memory Dashboard
Vestige v2.0 ships with a real-time 3D visualization of your AI's memory. Every memory is a glowing node in 3D space. Watch connections form, memories pulse when accessed, and the entire graph come alive during dream consolidation.
**Features:**
- Force-directed 3D graph with 1000+ nodes at 60fps
- Bloom post-processing for cinematic neural network aesthetic
- Real-time WebSocket events: memories pulse on access, burst on creation, fade on decay
- Dream visualization: graph enters purple dream mode, replayed memories light up sequentially
- FSRS retention curves: see predicted memory decay at 1d, 7d, 30d
- Command palette (`Cmd+K`), keyboard shortcuts, responsive mobile layout
- Installable as PWA for quick access
**Tech:** SvelteKit 2 + Svelte 5 + Three.js + Tailwind CSS 4 + WebSocket
Run `vestige dashboard` to open `http://localhost:3927/dashboard`, or set `VESTIGE_DASHBOARD_ENABLED=true` to start it with the MCP server.
---
## Architecture
```
┌─────────────────────────────────────────────────────┐
│ SvelteKit Dashboard (apps/dashboard) │
│ Three.js 3D Graph · WebGL + Bloom · Real-time WS │
├─────────────────────────────────────────────────────┤
│ Axum HTTP + WebSocket Server (port 3927) │
│ 15 REST endpoints · WS event broadcast │
├─────────────────────────────────────────────────────┤
│ MCP Server (stdio JSON-RPC) │
│ 25 tools · 30 cognitive modules │
├─────────────────────────────────────────────────────┤
│ Cognitive Engine │
│ ┌──────────┐ ┌──────────┐ ┌───────────────┐ │
│ │ FSRS-6 │ │ Spreading│ │ Prediction │ │
│ │ Scheduler│ │ Activation│ │ Error Gating │ │
│ └──────────┘ └──────────┘ └───────────────┘ │
│ ┌──────────┐ ┌──────────┐ ┌───────────────┐ │
│ │ Memory │ │ Synaptic │ │ Hippocampal │ │
│ │ Dreamer │ │ Tagging │ │ Index │ │
│ └──────────┘ └──────────┘ └───────────────┘ │
├─────────────────────────────────────────────────────┤
│ Storage Layer │
│ SQLite + FTS5 · USearch HNSW · Nomic Embed v1.5 │
│ Optional: Nomic v2 MoE · Qwen3 Reranker · Metal │
└─────────────────────────────────────────────────────┘
```
---
## Why Not Just Use RAG?
RAG is a dumb bucket. Vestige is an active organ.
| | RAG / Vector Store | Vestige |
|---|---|---|
| **Storage** | Store everything | **Prediction Error Gating** — only stores what's surprising or new |
| **Retrieval** | Nearest-neighbor | **7-stage pipeline** — HyDE expansion + reranking + spreading activation |
| **Decay** | Nothing expires | **FSRS-6** — memories fade naturally, context stays lean |
| **Forgetting** *(v2.0.5)* | Delete only | **`suppress` tool** — compounding top-down inhibition, neighbor cascade, reversible 24h |
| **Duplicates** | Manual dedup | **Self-healing** — auto-merges "likes dark mode" + "prefers dark themes" |
| **Importance** | All equal | **4-channel scoring** — novelty, arousal, reward, attention |
| **Sleep** | No consolidation | **Memory dreaming** — replays, connects, synthesizes insights |
| **Health** | No visibility | **Retention dashboard** — distributions, trends, recommendations |
| **Visualization** | None | **3D neural graph** — real-time WebSocket-powered Three.js |
| **Privacy** | Usually cloud | **100% local** — your data never leaves your machine |
---
## 🔬 The Cognitive Science Stack
This isn't a key-value store with an embedding model bolted on. Vestige implements real neuroscience:
**Prediction Error Gating** — The hippocampal bouncer. When new information arrives, Vestige compares it against existing memories. Redundant? Merged. Contradictory? Superseded. Novel? Stored with high synaptic tag priority.
**FSRS-6 Spaced Repetition** — 21 parameters governing the mathematics of forgetting. Frequently-used memories stay strong. Unused memories naturally decay. Your context window stays clean.
**HyDE Query Expansion** *(v2.0)* — Template-based Hypothetical Document Embeddings. Expands queries into 3-5 semantic variants, embeds all variants, and searches with the centroid embedding for dramatically better recall on conceptual queries.
**Synaptic Tagging** — A memory that seemed trivial this morning can be retroactively tagged as critical tonight. Based on [Frey & Morris, 1997](https://doi.org/10.1038/385533a0).
**Spreading Activation** — Search for "auth bug" and find the related JWT library update from last week. Memories form a graph, not a flat list. Based on [Collins & Loftus, 1975](https://doi.org/10.1037/0033-295X.82.6.407).
**Dual-Strength Model** — Every memory has storage strength (encoding quality) and retrieval strength (accessibility). A deeply stored memory can be temporarily hard to retrieve — just like real forgetting. Based on [Bjork & Bjork, 1992](https://doi.org/10.1016/S0079-7421(08)60016-9).
**Memory Dreaming** — Like sleep consolidation. Replays recent memories to discover hidden connections, strengthen important patterns, and synthesize insights. Dream-discovered connections persist to a graph database. Based on the [Active Dreaming Memory](https://engrxiv.org/preprint/download/5919/9826/8234) framework.
**Waking SWR Tagging** — Promoted memories get sharp-wave ripple tags for preferential replay during dream consolidation. 70/30 tagged-to-random ratio. Based on [Buzsaki, 2015](https://doi.org/10.1038/nn.3963).
**Autonomic Regulation** — Self-regulating memory health. Auto-promotes frequently accessed memories. Auto-GCs low-retention memories. Consolidation triggers on 6h staleness or 2h active use.
**Active Forgetting** *(v2.0.5)* — Top-down inhibitory control via the `suppress` tool. Other memory systems implement passive decay — the Ebbinghaus 1885 "use it or lose it" curve, sometimes with trust-weighted strength factors. Vestige v2.0.5 also implements *active* top-down suppression: each `suppress` call compounds (Suppression-Induced Forgetting, Anderson 2025), a background Rac1 cascade worker fades co-activated neighbors across the connection graph (Cervantes-Sandoval & Davis 2020), and a 24-hour labile window allows reversal (Nader reconsolidation semantics on a pragmatic axis). The memory persists — it's **inhibited, not erased**. Explicitly distinct from Anderson 1994 retrieval-induced forgetting (bottom-up, passive competition during retrieval), which is a separate, older primitive that several other memory systems implement. Based on [Anderson et al., 2025](https://www.nature.com/articles/s41583-025-00929-y) and [Cervantes-Sandoval et al., 2020](https://pmc.ncbi.nlm.nih.gov/articles/PMC7477079/). First shipped AI memory system with this primitive.
[Full science documentation ->](docs/SCIENCE.md)
---
## 🛠 25 MCP Tools
### Context Packets
| Tool | What It Does |
|------|-------------|
| `session_context` | **One-call session init** — replaces 5 calls with token-budgeted context, automation triggers, expandable IDs |
### Core Memory
| Tool | What It Does |
|------|-------------|
| `search` | Concrete literal search for exact identifiers, or 7-stage cognitive search — HyDE expansion + keyword + semantic + reranking + temporal + competition + spreading activation |
| `smart_ingest` | Intelligent storage with CREATE/UPDATE/SUPERSEDE via Prediction Error Gating. Batch mode for session-end saves |
| `memory` | Get, purge content/embeddings, check state, promote (thumbs up), demote (thumbs down), edit |
| `codebase` | Remember code patterns and architectural decisions per-project |
| `intention` | Prospective memory — "remind me to X when Y happens" |
### Cognitive Engine
| Tool | What It Does |
|------|-------------|
| `dream` | Memory consolidation — replays memories, discovers connections, synthesizes insights, persists graph |
| `explore_connections` | Graph traversal — reasoning chains, associations, bridges between memories |
| `predict` | Proactive retrieval — predicts what you'll need next based on context and activity |
### Autonomic
| Tool | What It Does |
|------|-------------|
| `memory_health` | Retention dashboard — distribution, trends, recommendations |
| `memory_graph` | Knowledge graph export — force-directed layout, up to 200 nodes |
### Scoring & Dedup
| Tool | What It Does |
|------|-------------|
| `importance_score` | 4-channel neuroscience scoring (novelty, arousal, reward, attention) |
| `find_duplicates` | Detect and merge redundant memories via cosine similarity |
### Maintenance
| Tool | What It Does |
|------|-------------|
| `system_status` | Combined health + stats + cognitive state + recommendations |
| `consolidate` | Run FSRS-6 decay cycle (also auto-runs every 6 hours) |
| `memory_timeline` | Browse chronologically, grouped by day |
| `memory_changelog` | Audit trail of state transitions |
| `backup` / `export` / `gc` | Database backup, JSON/JSONL/portable export, garbage collection |
| `restore` | Restore from JSON backup or portable archive |
### Deep Reference (v2.0.4)
| Tool | What It Does |
|------|-------------|
| `deep_reference` | **Cognitive reasoning across memories.** 8-stage pipeline: FSRS-6 trust scoring, intent classification, spreading activation, temporal supersession, contradiction analysis, relation assessment, dream insight integration, and algorithmic reasoning chain generation. Returns trust-scored evidence with a pre-built reasoning scaffold. |
| `cross_reference` | Backward-compatible alias for `deep_reference`. |
| `contradictions` | **Honest memory inspection.** Scans a topic or recent memories for trust-weighted disagreements using the same local contradiction logic as `deep_reference`. |
### Active Forgetting (v2.0.5)
| Tool | What It Does |
|------|-------------|
| `suppress` | **Top-down active forgetting** — neuroscience-grounded inhibitory control over retrieval. Distinct from `memory(action="purge")`, which permanently removes content/embeddings. Each suppression compounds a retrieval-score penalty (Anderson 2025 SIF), and a background Rac1 cascade worker fades co-activated neighbors over 72h (Davis 2020). Reversible within a 24-hour labile window via `reverse: true`. **The memory persists** — it is inhibited, not erased. |
---
## Make Your AI Use Vestige Automatically
Registering the MCP server exposes tools; the agent still needs an instruction
that tells it when to call memory. Use the agent-neutral protocol, then adapt it
to your client-specific instruction file.
| You Say | AI Does |
|---------|---------|
| "Remember this" | Saves immediately |
| "I prefer..." / "I always..." | Saves as preference |
| "Remind me..." | Creates a future trigger |
| "This is important" | Saves + promotes |
[Agent memory protocol ->](docs/AGENT-MEMORY-PROTOCOL.md) · [Claude Code template ->](docs/CLAUDE-SETUP.md)
---
## Technical Details
| Metric | Value |
|--------|-------|
| **Language** | Rust 2024 edition (MSRV 1.91) |
| **Codebase** | 80,000+ lines with Rust core/MCP/e2e, dashboard, and hook coverage |
| **Binary size** | ~20MB |
| **Embeddings** | Nomic Embed Text v1.5 by default (768d -> 256d Matryoshka, 8192 context); Qwen3 0.6B optional |
| **Vector search** | USearch HNSW (20x faster than FAISS) |
| **Reranker** | Jina Reranker v1 Turbo (38M params, +15-20% precision) |
| **Storage** | SQLite + FTS5 (optional SQLCipher encryption) |
| **Dashboard** | SvelteKit 2 + Svelte 5 + Three.js + Tailwind CSS 4 |
| **Transport** | MCP stdio (JSON-RPC 2.0) + WebSocket |
| **Cognitive modules** | 30 stateful (17 neuroscience, 11 advanced, 2 search) |
| **First run** | Downloads embedding model (~130MB), then fully offline |
| **Platforms** | macOS ARM + Intel + Linux x86_64 + Windows x86_64 (all prebuilt). Intel Mac needs `brew install onnxruntime` — see [install guide](docs/INSTALL-INTEL-MAC.md). |
### Optional Features
```bash
# Qwen3 embeddings (Candle backend; add metal on Apple Silicon)
cargo build --release -p vestige-mcp --features qwen3-embeddings,metal
VESTIGE_EMBEDDING_MODEL=qwen3-0.6b vestige consolidate
```
### Building with CUDA support (NVIDIA hosts - Windows / Linux)
The `cuda` feature routes Qwen3 embedding through NVIDIA GPUs via
`candle-core/cuda`. On a host with the CUDA toolkit installed and a supported
NVIDIA runtime, this drops Qwen3-Embedding inference from CPU-bound to GPU-bound
for batched workloads.
```bash
# Linux / Windows + CUDA toolkit (12.x or 13.x)
cargo build --release -p vestige-mcp --features qwen3-embeddings,cuda
# Optional cuDNN acceleration on top of CUDA
cargo build --release -p vestige-mcp --features qwen3-embeddings,cudnn
VESTIGE_EMBEDDING_MODEL=qwen3-0.6b vestige consolidate
```
**Prerequisites:**
- NVIDIA driver + CUDA toolkit (12.x or 13.x). Verify with `nvcc --version`.
- A C++ host compiler that `nvcc` can drive (Linux: `gcc`; Windows: MSVC /
`cl.exe` from a recent Visual Studio Build Tools install).
**Windows + MSVC + CUDA 13.x build note.** Recent CCCL headers shipped with
CUDA 13.x require the modern preprocessor. Without it, the `candle-kernels`
`.cu` compile pass can fail at `cuda/include/cuda/std/__cccl/compiler.h`. Set
this env var before `cargo build` to pass `/Zc:preprocessor` through `nvcc`:
```powershell
# PowerShell
$env:NVCC_PREPEND_FLAGS = '-Xcompiler="/Zc:preprocessor"'
cargo build --release -p vestige-mcp --features qwen3-embeddings,cuda
```
```cmd
:: cmd.exe
set NVCC_PREPEND_FLAGS=-Xcompiler="/Zc:preprocessor"
cargo build --release -p vestige-mcp --features qwen3-embeddings,cuda
```
Linux + CUDA 13.x builds with `gcc` do not need the equivalent flag.
**Verifying GPU is actually used.** With CUDA-enabled builds, run
`VESTIGE_EMBEDDING_MODEL=qwen3-0.6b vestige consolidate` on a corpus of 1000+
memories and watch `nvidia-smi`; embedding passes should pin a single GPU while
the run is active.
---
## CLI
```bash
vestige stats # Memory statistics
vestige stats --tagging # Retention distribution
vestige stats --states # Cognitive state breakdown
vestige health # System health check
vestige consolidate # Run memory maintenance
vestige restore <file> # Restore from backup
vestige portable-export <file> # Exact cross-device archive
vestige portable-import <file> # Import archive into an empty database
vestige portable-import <file> --merge # Merge archive into this database
vestige sync <file> # Pull/merge/push via file backend
vestige dashboard # Open 3D dashboard in browser
```
---
## Documentation
| Document | Contents |
|----------|----------|
| [FAQ](docs/FAQ.md) | 30+ common questions answered |
| [Science](docs/SCIENCE.md) | The neuroscience behind every feature |
| [Storage Modes](docs/STORAGE.md) | Global, per-project, multi-instance |
| [CLAUDE.md Setup](docs/CLAUDE-SETUP.md) | Templates for proactive memory |
| [Configuration](docs/CONFIGURATION.md) | CLI commands, environment variables |
| [Integrations](docs/integrations/) | Codex, Xcode, Cursor, VS Code, JetBrains, Windsurf |
| [Changelog](CHANGELOG.md) | Version history |
---
## Troubleshooting
<details>
<summary>"Command not found" after installation</summary>
Ensure `vestige-mcp` is in your PATH:
```bash
which vestige-mcp
# Or use the full path:
claude mcp add vestige /usr/local/bin/vestige-mcp -s user
```
</details>
<details>
<summary>Embedding model download fails</summary>
First run downloads ~130MB from Hugging Face. If behind a proxy:
```bash
export HTTPS_PROXY=your-proxy:port
```
Cache: platform user cache directory first, then `./.fastembed_cache` as a fallback. Override with `FASTEMBED_CACHE_PATH`.
</details>
<details>
<summary>Dashboard not loading</summary>
Run `vestige dashboard` or set `VESTIGE_DASHBOARD_ENABLED=true`, then check:
```bash
curl http://localhost:3927/api/health
# Should return {"status":"healthy",...}
```
</details>
[More troubleshooting ->](docs/FAQ.md#troubleshooting)
---
## Contributing
Issues and PRs welcome. See [CONTRIBUTING.md](CONTRIBUTING.md).
## License
AGPL-3.0 — free to use, modify, and self-host. If you offer Vestige as a network service, you must open-source your modifications.
---
<p align="center">
<i>Built by <a href="https://github.com/samvallad33">@samvallad33</a></i><br>
<sub>80,000+ lines of Rust · 30 cognitive modules · 130 years of memory research · one 22MB binary</sub>
</p>
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