Vestige

Your bug was born days before it crashed. You just can't remember where.

Vestige is a local-first memory for AI agents that reaches backward through time to find the quiet change that caused today's failure: the cause that looks nothing like the bug. One 23MB Rust binary. No cloud. Your data never leaves your machine.

⚡ Quick Start · 🧠 The Idea · 🔬 The Science · 🛠 13 Tools · 📊 Dashboard

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👋 Why I built this

Hi, I'm Sam. I built Vestige from a tiny apartment in Chicago because I kept losing days to the same thing, and I bet you have too.

Production breaks. You start hunting. And the cause is almost never near the error. It's some quiet change you made days ago that looks nothing like the crash it eventually caused. A flipped env var. A swapped service. A config tweak you'd already forgotten.

Here's the part that took me a while to see: every AI memory tool is built on vector search, and vector search hunts for what looks like your problem. But a root cause never looks like the bug it creates. So they all search the goal line, while the real failure was a quiet midfield turnover fifteen minutes earlier.

I wanted a memory that traces the match backward.

So that's what Vestige is. Everyone else built a memory that remembers. I tried to build the first one that realizes: it gates what's worth keeping, lets the noise fade like your own memory does, and when a failure hits, it reaches back through time to the change that actually caused it.

It's one Rust binary. It runs entirely on your machine. It never phones home. And there's a 60-second start right below.

🎙️ The 60-second version of this whole story, the one I give in person, lives in demo/PITCH-v2-causebench.md. If you've got a minute, read that first. It's the clearest way to get why this matters.

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⚡ Get it running in 60 seconds

npm install -g vestige-mcp-server@latest      # one binary, no Docker, no API key, no signup
claude mcp add vestige vestige-mcp -s user    # connect it to Claude Code

That's the whole install. Now talk to your agent like it has a memory, because now it does:

You:  "Remember: we always disable SimSIMD on release builds, it breaks old x86 CPUs."
        ...days later, fresh session, zero context...
You:  "Should I enable SimSIMD for the release?"
AI:   ⚠️ Hold on, this contradicts a decision you stored: you chose to DISABLE it
        because it breaks old x86 CPUs.

That last line isn't me being cute. It's a real status the engine returns, called claim_contradicts_memory. Most memory tools would have happily handed you the wrong answer. Vestige tells you when you're about to walk back into a mistake you already learned from.

(Works with Codex, Cursor, VS Code, Claude Desktop, Windsurf, JetBrains, Zed: anything that speaks MCP. Full setup is here ↓.)

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🧠 It's not RAG with a nicer haircut

RAG is a bucket: throw everything in, hope nearest-neighbor finds it later. Vestige behaves more like an actual memory: it decides what's worth keeping, forgets what isn't, and reasons across what's left.

	🪣 RAG / Vector Store	🧠 Vestige
What it stores	Everything you hand it	Only what's surprising or new (the rest gets merged or skipped)
What it forgets	Nothing; it just bloats	Unused memories fade on a real forgetting curve, so your context stays lean
Finding a root cause	Can't, because the cause isn't similar to the bug	Reaches backward in time to the change that caused it (the whole point ↓)
Catching contradictions	Silent; serves the stale answer with a straight face	Tells you: "this contradicts what you decided"
Duplicates	You clean them up by hand	Self-heals: "likes dark mode" + "prefers dark themes" quietly become one
Forgetting on demand	DELETE and it's gone	suppress gently inhibits a memory (and its neighbors), reversible for 24h
Where it lives	Usually someone else's cloud	Your machine. One binary. No telemetry.

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🔥 The thing nothing else does: memory with hindsight

This is the part I'm proudest of, and it's worth one honest paragraph.

A bug shows up today. The cause was a quiet decision from three weeks ago, like a changed env var or a swapped service. That cause shares no words with the error it created. A vector search will never connect them, because it only knows how to find things that look alike, and this is a case where the cause and the symptom look nothing alike. This isn't a tuning problem; in 2026 Google DeepMind published a proof (arXiv:2508.21038, ICLR 2026) that single-vector retrieval is mathematically incapable of bridging gaps like this.

So Vestige doesn't do it with similarity. Its Retroactive Salience Backfill (ported from Zaki/Cai et al., 2024, Nature 637:145–155 (DOI), on how the brain links a shock to the quiet memory that caused it) reaches backward through time and promotes the dormant memory that's causally upstream: it shares an entity (the same file, env var, or service), not the same words.

I also built a benchmark to keep myself honest about it. Every pure vector retriever scored 0% recall@1 on the causal-gap task; Vestige scored 60%. (To be precise: the impossibility is DeepMind's theorem; the 0%-vs-60% is my measurement. Two different claims, and I keep them separate.)

vestige backfill --contrast      # show the root cause a vector search would have missed

The nice part: it compounds. Every failure your agent records makes the next session diagnose faster (run two is smarter than run one), and it happens automatically during consolidation, so you don't have to babysit it.

All of this shipped in v2.2.0, along with a 34→13 tool consolidation and a rebuilt retrieval engine. Full release notes →

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🔬 This is real neuroscience, not a metaphor

I get skeptical when projects wave the word "neuroscience" around, so here's my receipt: every mechanism below is a real, cited paper, implemented in Rust, running locally on your machine. None of it phones a model in the cloud to sound smart.

Mechanism	What it does for you	Grounded in
Prediction-Error Gating	Redundant info gets merged, contradictory gets superseded, only the novel gets stored	The hippocampal novelty signal
FSRS-6 Spaced Repetition	21 parameters of the mathematics of forgetting, so used memories stay and unused ones fade	Modern spaced-repetition research
Retroactive Salience Backfill	Backward causal reach to the root cause of a failure	Zaki/Cai et al. 2024, Nature 637:145–155
Synaptic Tagging	A memory that looked trivial this morning can be tagged critical tonight	Frey & Morris 1997
Spreading Activation	Search "auth bug," surface last week's JWT update, because memory is a graph, not a list	Collins & Loftus 1975
Dual-Strength Model	Storage strength vs. retrieval strength, so deeply stored ≠ instantly recalled, just like you	Bjork & Bjork 1992
Memory Dreaming	Sleep-like consolidation: replays, connects, synthesizes insights to a graph	Active-dreaming consolidation
Active Forgetting (suppress)	Top-down inhibition that compounds and cascades to neighbors, reversible for 24h	Anderson 2025 · Davis 2020

Read the full science doc →. Every feature, every paper.

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🛠 13 tools, one brain

v2.2.0 consolidated a sprawling 34-tool surface into 13 sharp ones your agent actually reaches for. Old names still work as hidden aliases, so nothing breaks.

Tool	What it does
🔍 recall	The retrieval engine. Folds search + deep reasoning + contradiction detection into one call. F32 embeddings, Reciprocal Rank Fusion, claim-vs-memory checks.
🧠 backfill	Memory with hindsight. Backward causal reach to a failure's root cause (Cai 2024).
💾 smart_ingest	Stores with CREATE / UPDATE / SUPERSEDE via Prediction-Error Gating. Batch session-end saves.
🗂 memory	Get, edit, promote 👍, demote 👎, check state, purge content + embeddings.
🧩 graph	Reasoning chains, associations, bridges, predictions, force-directed export.
🌙 maintain	Consolidate, dream, GC, importance-score, backup, export, restore. One maintenance verb.
🧹 dedup	Self-healing duplicate detection + merge (8 old tools → 1).
🚫 suppress	Top-down active forgetting that compounds, cascades, and is reversible for 24h. The memory is inhibited, not erased.
📟 memory_status	Health + stats + trends + recommendations in one packet.
🧬 codebase · intention · source_sync · session_start	Per-project code memory · "remind me when X" · external-source connectors · one-call session init.

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📊 Watch your AI think in 3D

vestige dashboard      # → http://localhost:3927/dashboard

Every memory is a glowing node in a real-time, force-directed 3D graph. Connections form as you work. Nodes pulse when accessed, burst on creation, fade on decay. Kick off a consolidation and the whole graph slides into purple dream mode, replaying memories that light up in sequence.

Built with SvelteKit 2 · Svelte 5 · Three.js · WebGL bloom · live WebSocket events. 1000+ nodes at 60fps. Installable as a PWA.

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🧩 Works in every editor you use

Vestige speaks MCP, so any client that can register a stdio MCP server can use it.

Editor	One-liner
Claude Code	claude mcp add vestige vestige-mcp -s user
Codex	codex mcp add vestige -- vestige-mcp
Cursor / VS Code / Windsurf / JetBrains / Xcode / OpenCode	Integration guides →
Claude Desktop	2-minute setup →

Other install methods (Intel Mac, Windows, build-from-source)

Update an existing install:

vestige update                          # binaries only
vestige update --sandwich-companion     # also refresh optional Claude Code companion files

macOS (Intel): Microsoft is dropping x86_64 macOS ONNX Runtime prebuilts after v1.23.0, so the Intel Mac build links dynamically against a Homebrew ONNX Runtime:

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 guide: docs/INSTALL-INTEL-MAC.md.

Windows + Claude Desktop: quit Claude Desktop from the tray, then in PowerShell:

npm install -g vestige-mcp-server@latest
vestige-mcp --version

Point %APPDATA%\Claude\claude_desktop_config.json at it:

{ "mcpServers": { "vestige": { "command": "vestige-mcp" } } }

If it can't find the command, run where vestige-mcp and use the exact .cmd path.

Build from source (Rust 1.91+):

git clone https://github.com/samvallad33/vestige && cd vestige
cargo build --release -p vestige-mcp
# Apple Silicon GPU: --features metal   ·   NVIDIA: --features qwen3-embeddings,cuda

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🚀 Make your AI use memory automatically

Registering the server exposes the tools; a short instruction tells the agent when to call them. Drop in the protocol and your agent saves and recalls on its own:

You say	Vestige does
"Remember this"	Saves immediately
"I always..." / "I prefer..."	Saves as a durable preference
"Remind me when..."	Creates a future trigger (intention)
"This is important"	Saves and promotes it

Agent memory protocol → · Claude Code template →

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🏗 Under the hood

┌──────────────────────────────────────────────────────────┐
│  SvelteKit Dashboard / Three.js 3D graph / WebGL bloom    │
├──────────────────────────────────────────────────────────┤
│  Axum HTTP + WebSocket (:3927) / REST + live event stream │
├──────────────────────────────────────────────────────────┤
│  MCP Server (stdio JSON-RPC) / 13 tools · 30 modules      │
├──────────────────────────────────────────────────────────┤
│  Cognitive Engine                                          │
│   FSRS-6 · Spreading Activation · Prediction-Error Gating │
│   Retroactive Salience Backfill · Synaptic Tagging        │
│   Memory Dreamer · Hippocampal Index · Active Forgetting  │
├──────────────────────────────────────────────────────────┤
│  Storage: SQLite + FTS5 · USearch HNSW · Nomic Embed v1.5 │
│   Optional: Qwen3 reranker · SQLCipher · Metal/CUDA       │
└──────────────────────────────────────────────────────────┘

Language	Rust 2024 (MSRV 1.91), 86,000+ lines
Binary	~23MB, single file
Embeddings	Nomic Embed Text v1.5 (768d→256d Matryoshka, 8192 ctx); Qwen3 optional
Vector search	USearch HNSW (≈20× faster than FAISS)
Storage	SQLite + FTS5, optional SQLCipher encryption
Tests	1,550 passing · clippy -D warnings clean
First run	Downloads ~130MB embedding model once, then fully offline forever
Platforms	macOS (ARM + Intel) · Linux x86_64 · Windows x86_64. All prebuilt

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📚 Go deeper

FAQ	30+ real questions answered
The Science	Every feature, every paper
Storage Modes	Global · per-project · multi-instance
Configuration	CLI, env vars, every knob
Changelog	The full story, version by version

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If your agent should remember what you taught it yesterday, star it. ⭐

_{86,000+ lines of Rust · 13 tools · 30 cognitive modules · 130 years of memory research · one 23MB binary that never phones home.}

_{Built by @samvallad33 · AGPL-3.0 · 100% local, 100% yours}