GitHub - samvallad33/vestige: Vestige enhances agents by deterministic root-cause retrieval that reaches backward through time to find the quiet change, decision, or service that caused today’s failure, not the lookalike.

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Local-first memory for AI agents that finds the cause, not just the match.

Vestige remembers your decisions, catches contradictions before they cost you, and traces a failure back to the older memory that actually caused it. One 25MB Rust binary over MCP. No cloud, no API keys, no telemetry. Your data never leaves your machine.

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Install · Why not RAG · Benchmark · Science · Tools · Dashboard · Pro · Docs


Vestige Black Box: a SIGSEGV on startup traced back to a version pin set 23 days earlier, with the receipt

A labeled fixture store, a real run. The incident is fictional, seeded into a Vestige store with a seven month backdated timeline. The engine is not. A SIGSEGV on startup in an arm64 container, and the version pin set 23 days earlier that shares zero words with the failure. Similarity ranked the pin fourth. Backfill reached back, ranked it first, persisted the causal edge, and sealed the receipt. It names the suspects. It never calls the verdict. Watch the full 58 second walk, then run vestige backfill --contrast on your own store.

Agents re-learn the same lessons: they recommend a change you already tested and rejected, re-derive a fix that was already written down, and treat every session as if the last one never happened. Vestige is the memory layer that ends that. Any MCP-capable agent (Claude Code, Claude Desktop, Codex, Cursor, and others) writes memories as you work and retrieves them later, modeled on real cognitive science: redundant memories merge, contradicted ones are flagged, unused ones fade, and when a failure hits, Vestige reaches backward to the decision that set it up.

The cause never looks like the bug. That is the whole product.

Install

You need Node.js. No Docker, no signup, no compile step (prebuilt for macOS ARM + Intel, Linux x86_64, Windows x86_64).

Android (Termux) builds from source today; see docs/INSTALL-TERMUX.md.

npm install -g vestige-mcp-server@latest

Connect it to your agent. Every MCP client understands this config:

{
  "mcpServers": {
    "vestige": { "command": "vestige-mcp" }
  }
}
Client Setup
Claude Code claude mcp add vestige vestige-mcp -s user
Codex codex mcp add vestige -- vestige-mcp
Cursor / VS Code / Windsurf docs/integrations/
Claude Desktop docs/CONFIGURATION.md
Cline / Continue / Zed / Goose the JSON above, in that client's MCP settings

Verify: vestige dashboard, then open http://localhost:3927/dashboard. First run downloads a 130MB embedding model and, in the background, a ~150MB reranker, once; after that Vestige is fully offline, forever. Full walkthrough: docs/GETTING-STARTED.md.

Why not just RAG?

RAG retrieves text that resembles the query. That is the right tool when the answer looks like the question, and the wrong tool when the cause of a problem looks nothing like the symptom: a config choice from three weeks ago, a library pin, an assumption nobody flagged as risky.

Vector search Vestige
Retrieval basis Similarity to the query Causal + temporal links, plus similarity
Root cause of a failure Cannot; the cause does not resemble the bug vestige backfill --contrast reaches backward to it
Contradictions Both stored, both returned Detected and flagged (claim_contradicts_memory)
Redundant writes Accumulate Merged on write (prediction-error gating)
Unused memories Persist at full weight Fade (FSRS-6 spaced repetition)
Your data Usually a cloud service Never leaves your machine

The backward reach implements Retroactive Salience Backfill (Zaki, Cai et al., Nature 2024, 637:145-155, DOI 10.1038/s41586-024-08168-4): when a memory turns out to matter, the salience of the earlier memories that led to it is raised, so the causal chain becomes retrievable even though the surface text never matched. Every backfill result ships with a receipt naming the exact evidence path; Vestige reports receipt-backed candidate causes, never an unverifiable verdict.

And the limitation on the left column is not marketing: DeepMind proved single-vector retrieval mathematically incapable of certain relevance patterns (arXiv:2508.21038, ICLR 2026).

The receipts: Silent Rotation

The claim is testable, and the test ships with all 246 agent transcripts it produced. Three coding agents fix one failing e2e test; the fix needs the currently live signing key id, randomized per trial from a 50-key keyring, present in no file the agents can read. It exists only in the memory layer. The dangerous outcome is converging on a planted decoy: tests pass, the merge is clean, production breaks.

Arm (6 models, 25 trials) Converged correct Converged wrong Split
No memory 0/25 21/25 4/25
Dense cosine RAG 4/23 12/23 7/23
Vestige 20/23 0/23 3/23

On the verbatim queries the agents typed, the causal memory ranks 7th of 8 under both dense cosine and BM25 while the decoy ranks 1st. Reproduce the central measurement in two seconds, stdlib only:

git clone -b benchmark/silent-rotation --depth 1 https://github.com/samvallad33/vestige.git
cd vestige/benchmarks/silent-rotation
python3 tests/bm25_baseline.py results/runA-trial-1/corpus-export.json --no-dense

The caveats are published alongside the results, including the trials a plain cosine baseline ties and the trial Vestige loses.

The science

Every mechanism is a cited result, implemented in Rust, running locally. Full write-up: docs/SCIENCE.md.

Mechanism What it does Source
Prediction-Error Gating Stores only the novel; merges redundant, flags contradictory Hippocampal novelty gating
FSRS-6 spaced repetition Used memories persist, unused ones fade Modern spaced-repetition research
Retroactive Salience Backfill Reaches backward to a failure's root-cause memory Zaki, Cai et al. 2024, Nature
Synaptic Tagging Marks memories for later consolidation Frey & Morris 1997
Spreading Activation One retrieval activates related memories through the graph Collins & Loftus 1975
Dual-Strength Storage strength vs retrieval strength, tracked separately Bjork & Bjork 1992
Memory Dreaming Sleep-like replay and synthesis Sleep consolidation research
Active Forgetting Reversible top-down suppression, cascading to neighbors Anderson 2025, Davis 2020

The 14 tools

Your agent calls these; you rarely do.

Tool Purpose
recall Retrieve memories relevant to the current context
smart_ingest Store a fact, gated for novelty and contradiction
backfill Reach backward from a failure to its candidate cause
receipt Inspect retrieval receipts and evidence replay (guide)
memory · graph · intention Inspect, promote, explore, track goals
maintain · dedup · suppress Consolidation, merge, reversible forgetting
memory_status · codebase · source_sync · session_start Health, code index, connectors, session priming

Project scoping, hygiene workflows, and making memory a standing habit for your agent: docs/MEMORY_HYGIENE.md · docs/AGENT-MEMORY-PROTOCOL.md · docs/CLAUDE-SETUP.md.

The dashboard

A living WebGPU observatory of your memory at http://localhost:3927/dashboard: memories appear, link, strengthen, and fade in real time, 1000+ nodes at 60fps. It renders a deterministic 12-second loop of your store's life that you can export as an mp4 with one click, and mints a brain print, a signature seeded from your store's shape. Share artifacts are structure-only by design: your brain, never your memories.

Vestige Pro

Everything above is free forever and never metered. Pro ($19/month) is managed, end-to-end encrypted continuity: your memory graph and accountability history (receipts, traces, memory PRs) following you across machines. XChaCha20-Poly1305 applied on your device, Argon2id over a passphrase only you know, ciphertext-only server. Zero-knowledge is the design: lose the passphrase and the data is unrecoverable, by anyone. Checkout opens shortly; watch Releases for the announcement.

Under the hood

Engine Rust 2024, ~145k lines, single 25MB binary, 2,000+ tests, clippy clean at -D warnings
Retrieval Nomic Embed v1.5 (Matryoshka 768d→256d) + USearch HNSW + SQLite FTS5, optional Qwen3 reranker
Storage SQLite, optional SQLCipher encryption (docs/STORAGE.md)
Offline Two model downloads on first run (130MB embedder, ~150MB reranker), then no network, ever

Go deeper

Getting Started · FAQ · The Science · Configuration · Storage · Silent Rotation · Changelog


If Vestige saves you from one repeated mistake, that is the whole point: never solve the same problem twice. If it earns a place in your setup, a star genuinely helps.

Built by Sam. Licensed under AGPL-3.0.