Local-first memory for AI agents that reaches backward to find a failure's root cause.
io.github.samvallad33/vestige - MCP Server
Vestige is a local-first memory for AI agents that reaches backward through time to find the quiet changes that caused today's failure. The MCP server exposes model-context capabilities to support long-term memory, failure tracing, and root-cause analysis without cloud reliance. Data remains on-device.
🛠️ Key Features
Local-first memory for AI agents with backward tracing
Root-cause analysis of failures over time
MCP-focused server for model-context orchestration
Security: data stays on the user’s machine
Rust-based, lightweight footprint (23MB binary)
Embeddings, FSRS, and SQLite-backed storage
🚀 Use Cases
Debugging AI failure modes by tracing historical changes
Maintaining persistent model-context memories for agents
Local inference workflows with memory recall
Long-term-memory management for cognitive-science research
⚡ Developer Benefits
Self-contained, cloud-free deployment
Clear MCP-server integration for model-context pipelines
Efficient on-device storage and retrieval
Open topics relevant to MCP, memory, and tooling
⚠️ Limitations
Local-only by design; cloud-based collaboration not provided
Readme excerpt implies ongoing readme and docs updates
Specific MCP endpoints and schemas not detailed in data provided
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.
Autonomous agents are currently bleeding enterprise budgets via prompt bloat and context window amnesia.
I take on a limited number of technical advisory retainers and consulting projects for AI developer tool startups, multi-agent frameworks, and enterprise engineering teams looking to optimize their context economics.
Core Specializations:
Context Optimization & Filtering: Implementing local Prediction Error Gating to strip out redundant tool runtime noise and drop token overhead by 40%–60%.
Causal Agent Memory Design: Structuring local SQLite graph architectures using Retroactive Salience Backfilling to eliminate agent amnesia during heavy, multi-file code execution.
Air-Gapped AI Governance: Designing zero-knowledge, high-performance Rust memory scaffolding that runs entirely on local metal to protect proprietary enterprise IP.
For architectural reviews, integration advisory, or founding infrastructure roles, reach out directly at: sam@vestige.sh
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).
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:
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
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.
