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vestige

Cognitive memory for AI agents โ€” FSRS-6 spaced repetition, 29 brain modules, 3D dashboard, single 22MB Rust binary. MCP server for Claude, Cursor, VS Code, Xcode, JetBrains.

Why this rank:Strong adoptionRecent releaseHealthy release cadence

Description

Cognitive memory for AI agents โ€” FSRS-6 spaced repetition, 29 brain modules, 3D dashboard, single 22MB Rust binary. MCP server for Claude, Cursor, VS Code, Xcode, JetBrains.

README

Vestige

The cognitive engine that gives AI a brain.

GitHub starsRelease Tests License MCP Compatible

Your AI forgets everything between sessions. Vestige fixes that.

Built on 130 years of memory research โ€” FSRS-6 spaced repetition, prediction error gating, synaptic tagging, spreading activation, memory dreaming โ€” all running in a single Rust binary with a 3D neural visualization dashboard. 100% local. Zero cloud.

Quick Start | Dashboard | How It Works | Tools | Docs


What's New in v2.0 "Cognitive Leap"

  • 3D Memory Dashboard โ€” SvelteKit + Three.js neural visualization with real-time WebSocket events, bloom post-processing, force-directed graph layout. Watch your AI's mind in real-time.
  • WebSocket Event Bus โ€” Every cognitive operation broadcasts events: memory creation, search, dreaming, consolidation, retention decay
  • HyDE Query Expansion โ€” Template-based Hypothetical Document Embeddings for dramatically improved search quality on conceptual queries
  • Nomic v2 MoE (experimental) โ€” fastembed 5.11 with optional Nomic Embed Text v2 MoE (475M params, 8 experts) + Metal GPU acceleration. Default: v1.5 (8192 token context)
  • Command Palette โ€” Cmd+K navigation, keyboard shortcuts, responsive mobile layout, PWA installable
  • FSRS Decay Visualization โ€” SVG retention curves with predicted decay at 1d/7d/30d, endangered memory alerts
  • 29 cognitive modules โ€” 1,238 tests, 79,600+ LOC

Quick Start

# 1. Install (macOS Apple Silicon)
curl -L https://github.com/samvallad33/vestige/releases/latest/download/vestige-mcp-aarch64-apple-darwin.tar.gz | tar -xz
sudo mv vestige-mcp vestige vestige-restore /usr/local/bin/

# 2. Connect to Claude Code
claude mcp add vestige vestige-mcp -s user

# Or connect to Codex
codex mcp add vestige -- /usr/local/bin/vestige-mcp

# 3. Test it
# "Remember that I prefer TypeScript over JavaScript"
# ...new session...
# "What are my coding preferences?"
# โ†’ "You prefer TypeScript over JavaScript."
Other platforms & install methods

macOS (Intel):

curl -L https://github.com/samvallad33/vestige/releases/latest/download/vestige-mcp-x86_64-apple-darwin.tar.gz | tar -xz
sudo mv vestige-mcp vestige vestige-restore /usr/local/bin/

Linux (x86_64):

curl -L https://github.com/samvallad33/vestige/releases/latest/download/vestige-mcp-x86_64-unknown-linux-gnu.tar.gz | tar -xz
sudo mv vestige-mcp vestige vestige-restore /usr/local/bin/

Windows: Download from Releases

npm:

npm install -g vestige-mcp-server

Build from source (requires Rust 1.91+):

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

Works Everywhere

Vestige speaks MCP โ€” the universal protocol for AI tools. One brain, every IDE.

IDE Setup
Claude Code claude mcp add vestige vestige-mcp -s user
Codex Integration guide
Claude Desktop 2-min setup
Xcode 26.3 Integration guide
Cursor Integration guide
VS Code (Copilot) Integration guide
JetBrains Integration guide
Windsurf Integration guide

๐Ÿง  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

The dashboard runs automatically at http://localhost:3927/dashboard when the MCP server starts.


Architecture

โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
โ”‚  SvelteKit Dashboard (apps/dashboard)                โ”‚
โ”‚  Three.js 3D Graph ยท WebGL + Bloom ยท Real-time WS   โ”‚
โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค
โ”‚  Axum HTTP + WebSocket Server (port 3927)            โ”‚
โ”‚  15 REST endpoints ยท WS event broadcast              โ”‚
โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค
โ”‚  MCP Server (stdio JSON-RPC)                         โ”‚
โ”‚  23 tools ยท 29 cognitive modules                     โ”‚
โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค
โ”‚  Cognitive Engine                                    โ”‚
โ”‚  โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”       โ”‚
โ”‚  โ”‚ FSRS-6   โ”‚ โ”‚ Spreadingโ”‚ โ”‚ Prediction    โ”‚       โ”‚
โ”‚  โ”‚ Schedulerโ”‚ โ”‚ Activationโ”‚ โ”‚ Error Gating  โ”‚       โ”‚
โ”‚  โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜       โ”‚
โ”‚  โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”       โ”‚
โ”‚  โ”‚ Memory   โ”‚ โ”‚ Synaptic โ”‚ โ”‚ Hippocampal   โ”‚       โ”‚
โ”‚  โ”‚ Dreamer  โ”‚ โ”‚ Tagging  โ”‚ โ”‚ Index         โ”‚       โ”‚
โ”‚  โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜       โ”‚
โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค
โ”‚  Storage Layer                                       โ”‚
โ”‚  SQLite + FTS5 ยท USearch HNSW ยท Nomic Embed v1.5    โ”‚
โ”‚  Optional: Nomic v2 MoE ยท Qwen3 Reranker ยท Metal   โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜

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 โ€” only stores what's surprising or new
Retrieval Nearest-neighbor 7-stage pipeline โ€” HyDE expansion + reranking + spreading activation
Decay Nothing expires FSRS-6 โ€” memories fade naturally, context stays lean
Duplicates Manual dedup Self-healing โ€” auto-merges "likes dark mode" + "prefers dark themes"
Importance All equal 4-channel scoring โ€” novelty, arousal, reward, attention
Sleep No consolidation Memory dreaming โ€” replays, connects, synthesizes insights
Health No visibility Retention dashboard โ€” distributions, trends, recommendations
Visualization None 3D neural graph โ€” real-time WebSocket-powered Three.js
Privacy Usually cloud 100% local โ€” your data never leaves your machine

๐Ÿ”ฌ The Cognitive Science Stack

This isn't a key-value store with an embedding model bolted on. Vestige implements real neuroscience:

Prediction Error Gating โ€” 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 โ€” 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) โ€” 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 โ€” A memory that seemed trivial this morning can be retroactively tagged as critical tonight. Based on Frey & Morris, 1997.

Spreading Activation โ€” 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.

Dual-Strength Model โ€” Every memory has storage strength (encoding quality) and retrieval strength (accessibility). A deeply stored memory can be temporarily hard to retrieve โ€” just like real forgetting. Based on Bjork & Bjork, 1992.

Memory Dreaming โ€” 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 framework.

Waking SWR Tagging โ€” Promoted memories get sharp-wave ripple tags for preferential replay during dream consolidation. 70/30 tagged-to-random ratio. Based on Buzsaki, 2015.

Autonomic Regulation โ€” Self-regulating memory health. Auto-promotes frequently accessed memories. Auto-GCs low-retention memories. Consolidation triggers on 6h staleness or 2h active use.

Full science documentation ->


๐Ÿ›  23 MCP Tools

Context Packets

Tool What It Does
session_context One-call session init โ€” replaces 5 calls with token-budgeted context, automation triggers, expandable IDs

Core Memory

Tool What It Does
search 7-stage cognitive search โ€” 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, delete, check state, promote (thumbs up), demote (thumbs down)
codebase Remember code patterns and architectural decisions per-project
intention Prospective memory โ€” "remind me to X when Y happens"

Cognitive Engine

Tool What It Does
dream Memory consolidation โ€” replays memories, discovers connections, synthesizes insights, persists graph
explore_connections Graph traversal โ€” reasoning chains, associations, bridges between memories
predict Proactive retrieval โ€” predicts what you'll need next based on context and activity

Autonomic

Tool What It Does
memory_health Retention dashboard โ€” distribution, trends, recommendations
memory_graph Knowledge graph export โ€” 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 export, garbage collection
restore Restore from JSON backup

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.

Make Your AI Use Vestige Automatically

Add this to your CLAUDE.md:

## Memory

At the start of every session:
1. Search Vestige for user preferences and project context
2. Save bug fixes, decisions, and patterns without being asked
3. Create reminders when the user mentions deadlines
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

Full CLAUDE.md templates ->


Technical Details

Metric Value
Language Rust 2024 edition (MSRV 1.91)
Codebase 79,600+ lines, 1,238 tests
Binary size ~20MB
Embeddings Nomic Embed Text v1.5 (768d โ†’ 256d Matryoshka, 8192 context)
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 29 stateful (16 neuroscience, 11 advanced, 2 search)
First run Downloads embedding model (~130MB), then fully offline
Platforms macOS (ARM/Intel), Linux (x86_64), Windows

Optional Features

# Metal GPU acceleration (Apple Silicon โ€” faster embedding inference)
cargo build --release -p vestige-mcp --features metal

# Nomic Embed Text v2 MoE (475M params, 305M active, 8 experts)
cargo build --release -p vestige-mcp --features nomic-v2

# Qwen3 Reranker (Candle backend, high-precision cross-encoder)
cargo build --release -p vestige-mcp --features qwen3-reranker

# SQLCipher encryption
cargo build --release -p vestige-mcp --no-default-features --features encryption,embeddings,vector-search

CLI

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 dashboard                # Open 3D dashboard in browser

Documentation

Document Contents
FAQ 30+ common questions answered
Science The neuroscience behind every feature
Storage Modes Global, per-project, multi-instance
CLAUDE.md Setup Templates for proactive memory
Configuration CLI commands, environment variables
Integrations Codex, Xcode, Cursor, VS Code, JetBrains, Windsurf
Changelog Version history

Troubleshooting

"Command not found" after installation

Ensure vestige-mcp is in your PATH:

which vestige-mcp
# Or use the full path:
claude mcp add vestige /usr/local/bin/vestige-mcp -s user
Embedding model download fails

First run downloads ~130MB from Hugging Face. If behind a proxy:

export HTTPS_PROXY=your-proxy:port

Cache: macOS ~/Library/Caches/com.vestige.core/fastembed | Linux ~/.cache/vestige/fastembed

Dashboard not loading

The dashboard starts automatically on port 3927 when the MCP server runs. Check:

curl http://localhost:3927/api/health
# Should return {"status":"healthy",...}

More troubleshooting ->


Contributing

Issues and PRs welcome. See CONTRIBUTING.md.

License

AGPL-3.0 โ€” free to use, modify, and self-host. If you offer Vestige as a network service, you must open-source your modifications.


Built by @samvallad33
79,600+ lines of Rust ยท 29 cognitive modules ยท 130 years of memory research ยท one 22MB binary

Release History

VersionChangesUrgencyDate
v2.1.23v2.1.23 hardens the Sanhedrin launch path so Receipt Lock is portable, observable, and precise enough for broader use. ### Added - Model-agnostic Sanhedrin backend presets for OpenAI-compatible servers, laptops, Ollama, MLX, vLLM, llama.cpp, hosted APIs, and Anthropic via LiteLLM. - Fail-open telemetry in `fail-open.jsonl`, plus a dashboard telemetry API and 7-day ambient dashboard counters. - Receipt schema documentation covering receipt artifacts, appeals, command ledgers, fail-open logs, comHigh5/28/2026
v2.1.22 v2.1.22 makes the optional Sanhedrin hook quieter and more accountable by turning draft judgment into local, appealable receipts instead of opaque vetoes. ### Added - **Receipt Lock** blocks unsupported verification claims such as "tests passed" unless the current transcript contains a matching successful test, build, lint, or typecheck command receipt. - **Veto receipts** are written to `~/.vestige/sanhedrin/latest.json` and `latest.html` with Claim -> Verdict -> Precedent -> Fix -> ApHigh5/25/2026
v2.1.2## Honest Memory v2.1.2 focuses on operational trust: exact search stays exact, purge really removes content, contradictions are directly inspectable, and the update flow no longer depends on copied curl commands. ### Added - **Concrete search mode** โ€” `search` now auto-detects literal queries such as quoted strings, env vars, UUIDs, paths, and code identifiers. Those queries take a keyword/literal path that skips HyDE, semantic fusion, FSRS reweighting, retrieval competition, and spreading aHigh5/7/2026
v2.1.1 v2.1.1 focuses on user-controlled portability: exact storage archives, merge-safe file sync, pluggable sync backends, and explicit hook opt-ins. ### Added - **Exact portable archives** โ€” `vestige portable-export` / `vestige portable-import` preserve raw Vestige storage rows: memory IDs, FSRS state, graph edges, suppression state, audit rows, sessions, intentions, and embedding blobs. - **Merge-safe imports** โ€” `vestige portable-import --merge` can merge into non-empty databases. It applies `sHigh5/1/2026
v2.1.0## [2.1.0] - 2026-04-27 โ€” "Cognitive Sandwich Goes Local" The Sanhedrin Executioner โ€” Vestige's veto layer for Claude Code responses โ€” now runs entirely on a local MLX model (`mlx-community/Qwen3.6-35B-A3B-4bit`). Zero API cost per Claude turn, fully offline, no Anthropic round-trip on the critical path. Combined with four pre-cognitive UserPromptSubmit hooks (synthesis-preflight, cwd-state-injector, vestige-pulse-daemon, preflight-swarm), Vestige now ships a complete "Cognitive Sandwich" โ€” VesHigh4/27/2026
v2.0.8# Vestige v2.0.8 "Pulse" The Pulse release wires the dashboard through to the cognitive engine. Eight new dashboard surfaces expose `deep_reference`, `find_duplicates`, `dream`, FSRS scheduling, 4-channel importance, spreading activation, contradiction arcs, and cross-project pattern transfer โ€” every one of them was MCP-only before. **Intel Mac is back** on the supported list (Microsoft deprecated x86_64 macOS ONNX Runtime prebuilts; we link dynamically against Homebrew `onnxruntime` instead). High4/23/2026
v2.0.7Hygiene release plus two UI gap closures. No breaking changes, no schema migration affecting user data beyond V11 dropping two verified-unused tables. ## โœจ Added - **`POST /api/memories/{id}/suppress`** โ€” Dashboard users can now trigger top-down inhibitory control (Anderson 2025 SIF + Davis Rac1 cascade) without dropping to raw MCP. Optional `{"reason": "..."}`. Each call compounds. Emits `MemorySuppressed` so the 3D graph plays the violet implosion. - **`POST /api/memories/{id}/unsuppress`** High4/20/2026
v2.0.6Polish release aimed at new-user happiness. v2.0.5's cognitive stack was already shipping; v2.0.6 makes it *feel* alive in the dashboard and stays out of your way on the prompt side. ## โœจ Added ### Dashboard reacts to six live events (was: one) Before v2.0.6, the 3D graph was silent on six real cognitive events โ€” the live feed showed them firing but the graph stayed motionless, which made the dashboard feel broken during real work. Now every event has a visual reaction driven by the existing High4/18/2026
v2.0.5**The first AI memory system that can actively forget.** Vestige now treats forgetting as a first-class, neuroscientifically-grounded primitive. New `suppress` MCP tool applies top-down inhibitory control over retrieval โ€” each call compounds a penalty (up to 80%), a background Rac1 worker fades co-activated neighbors over 72h, and it's reversible within a 24h labile window. **Never deletes** โ€” the memory is inhibited, not erased. Based on [Anderson et al. 2025](https://www.nature.com/articles/High4/14/2026
v2.0.4## v2.0.4 โ€” "Deep Reference" The biggest feature release since v2.0.0. Vestige now **reasons** across memories, not just retrieves them. ### New: `deep_reference` โ€” Cognitive Reasoning Engine An 8-stage pipeline that builds a **pre-built reasoning chain** the AI validates โ€” no LLM call needed: 1. Broad retrieval + cross-encoder reranking 2. Spreading activation expansion (finds connected memories search misses) 3. FSRS-6 trust scoring (retention ร— stability ร— reps รท lapses) 4. Intent classifHigh4/9/2026
v2.0.3## What's New ### Live Memory Materialization Memories now **materialize in real-time** in the 3D graph when created via MCP tools. When you tell Claude to "remember something", the node spawns live with spectacular visual effects: - **Rainbow burst** โ€” 120 particles with HSL cycling on node creation - **Double shockwave** โ€” expanding rings with hue shift - **Ripple wave** โ€” cascading pulse through nearby nodes - **Edge growth animation** โ€” new connections grow from source to target - **ImplosLow3/3/2026
v2.0.1## What's Fixed ### Critical - **npm install completely broken** โ€” `postinstall.js` pointed to binary version `1.1.3` (doesn't exist). Now correctly downloads `2.0.1` binaries for all platforms. - **Wrong npm package name** โ€” README and error messages referenced `vestige-mcp` (someone else's Algorand package). Corrected to `vestige-mcp-server`. ### Security - **CSP WebSocket wildcard** โ€” `connect-src ws: wss:` allowed any webpage to open WebSocket connections to the dashboard. Now restricted tLow3/2/2026
v2.0.0# Vestige v2.0.0 โ€” "Cognitive Leap" The biggest release in Vestige history. A complete visual and cognitive overhaul. ## Highlights - **3D Memory Dashboard** โ€” SvelteKit 2 + Three.js visualization at `localhost:3927/dashboard`. 7 interactive pages (Graph, Memories, Timeline, Feed, Explore, Intentions, Stats). Memories pulse on access, burst particles on creation, golden flash lines on connection discovery. - **WebSocket Event Bus** โ€” `tokio::broadcast` channel with 16 event types. Every cogniLow2/22/2026
v1.9.1## What's New in v1.9.1 ### Autonomic Features - **Retention Target System** โ€” auto-GC low-retention memories during consolidation (`VESTIGE_RETENTION_TARGET` env var, default 0.8) - **Auto-Promote on Repeated Access** โ€” memories accessed 3+ times in 24h get frequency-dependent potentiation - **Waking SWR Tagging** โ€” promoted memories get preferential 70/30 dream replay - **Improved Consolidation Scheduler** โ€” triggers on 6h staleness or 2h active use ### New MCP Tools (21 total) - **`memory_hLow2/21/2026
v1.7.0## What's New ### Tool Consolidation: 23 โ†’ 18 Tools - `ingest` โ†’ `smart_ingest` (single + batch mode via `items` param) - `session_checkpoint` โ†’ `smart_ingest` batch mode - `promote_memory` / `demote_memory` โ†’ `memory(action="promote"/"demote")` - `health_check` / `stats` โ†’ `system_status` - All deprecated tools still work via redirects ### Automation Triggers in `system_status` `system_status` now returns an `automationTriggers` object: ```json { "lastDreamTimestamp": "2026-02-21T03:43:54Z"Low2/21/2026
v1.6.0## What's New Four internal optimizations that make Vestige dramatically faster and leaner: ### 1. F16 Vector Quantization USearch HNSW index now stores vectors in half-precision (F16) instead of F32. **2x storage savings** with near-zero recall loss. ### 2. Matryoshka 256-dim Truncation Embeddings truncated from 768 โ†’ 256 dimensions using Matryoshka Representation Learning. **3x embedding storage savings** with only ~2% quality loss on MTEB. Old 768-dim embeddings auto-migrate on load. ### Low2/19/2026
v1.5.0## What's New ### Cognitive Engine โ€” 28 Stateful Modules Every tool call now flows through a full cognitive pipeline. 28 modules persist across calls as stateful instances: **Neuroscience (15):** ActivationNetwork, SynapticTaggingSystem, HippocampalIndex, ContextMatcher, AccessibilityCalculator, CompetitionManager, StateUpdateService, ImportanceSignals, NoveltySignal, ArousalSignal, RewardSignal, AttentionSignal, PredictiveMemory, ProspectiveMemory, IntentionParser **Advanced (11):** ImportaLow2/19/2026
v1.3.0## What's New ### 3 New MCP Tools (16 โ†’ 19 total) | Tool | Description | |------|-------------| | `importance_score` | 4-channel neuroscience importance scoring (novelty, arousal, reward, attention). Wraps the 2,400-line ImportanceSignals engine as an MCP tool. | | `session_checkpoint` | Batch save up to 20 items in one call, each routed through Prediction Error Gating. Use at session end or before context compaction. | | `find_duplicates` | Cosine similarity on stored embeddings with union-fiLow2/12/2026
v1.1.2## Fixed - Embedding model cache now uses platform-appropriate directories instead of polluting project folders: - **macOS**: `~/Library/Caches/com.vestige.core/fastembed` - **Linux**: `~/.cache/vestige/fastembed` - **Windows**: `%LOCALAPPDATA%\vestige\cache\fastembed` - Can still override with `FASTEMBED_CACHE_PATH` environment variable ## Upgrade Download the binary for your platform below, or build from source: ```bash git pull && cargo build --release ```Low1/27/2026
v1.1.1## Bug Fixes - **Fix UTF-8 string slicing bugs** - Prevented potential panics on non-ASCII text in keyword search, natural language parsing, and git commit parsing - **Fix silent errors in MCP protocol** - Clients now receive error responses instead of hanging when serialization fails - **Fix feature flag bug** - Embedding initialization now works correctly in MCP mode - **Fix protocol version negotiation** - Server now accepts older client protocol versions ## Cleanup - Security review: LOW Low1/27/2026
v1.1.0**Full Changelog**: https://github.com/samvallad33/vestige/compare/v1.0.0...v1.1.0Low1/26/2026

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