aurum-recall

aurum-recall

A memory system for AI agents that maintains a human-readable, typed Markdown knowledge base with an always-in-context index, enabling recall, correction, and trust decay through MCP.

Category
Visit Server

README

Aurum Recall

AI-native memory you and your agent can actually read — and navigate.

Two layers, one system:

  1. The Store — sovereign, human-readable, self-curating memory: typed Markdown files + an always-in-context index + [[links]] + trust-decay. A library and an MCP server.
  2. The Lattice (ContextQR) — a visual routing layer over that store: color-coded context tiles, trust borders, and a real scannable root QR. Route before you retrieve.

<p align="center"> <img src="assets/example_map.svg" alt="Aurum Recall lattice — color-coded context routers" width="560">    <img src="assets/example_qr.png" alt="Scannable root QR" width="170"> </p>

The store is where memory lives. The lattice is how an agent flies through it — narrowing to the right branch, respecting privacy and freshness, and pulling only what it needs, before spending tokens on retrieval.


Why

Vector-DB memory is opaque, unownable, and un-auditable — and RAG retrieves text first, with no cheap way to route. Aurum Recall inverts both:

Vector RAG:   Question → embedding search → maybe-relevant chunks → answer
Aurum Recall: Question → route the lattice → narrow the branch → search inside it → verify → answer

You get lower token use, real privacy boundaries, first-class trust/freshness/provenance, and a memory that is your files, in the open, on your terms.

Context windows do not expire. They crystallize into recursive memory tiles. When an agent's context fills, it compresses into a tile; 64 tiles seal into an 8×8 layer; layers hash-chain (Merkle) and recurse. The architecture: CONCEPT.md.


The Store

  • One durable fact per file, typed (user / feedback / project / reference), with a one-line hook. MEMORY.md is the always-loaded index — the working set. Full format: SPEC.md.
  • Zero-dependency core: recall / remember / update / forget / link / compact. Trust decays with age.
  • MCP server — one config line and any MCP agent (Claude Desktop, Claude Code) gets durable, inspectable memory. See QUICKSTART.md.
npm install && npm run build && npm test

The Lattice (ContextQR)

Build a routable visual lattice from a real memory store, render it, and mint the root QR:

node dist/lattice/cli.js from-store <memory-dir>            --out lattice.json
node dist/lattice/cli.js validate  lattice.json
node dist/lattice/cli.js render    lattice.json            --out map.svg
node dist/lattice/cli.js qr        lattice.json            --out root_qr.png
node dist/lattice/cli.js subtree   lattice.json ctx_type_project --out projects.svg
node dist/lattice/cli.js inspect   lattice.json ctx_type_project

Color = context type · border = trust level · brightness = freshness · marker = machine-readable pointer. Only the root is a literal scannable QR; deeper tiles are recursive routers, not nested pixels.

The moat isn't QR codes — it's the combination: visual context routing + context crystallization + recursive 8×8 layers + trust/freshness/privacy metadata + hash-verifiable provenance + agent navigation before retrieval.


Open core

Public (the credibility layer, this repo): the memory store + MCP server + lens, and the lattice — schema, validator, SVG renderer, root QR, CLI, the store→lattice importer, and the concept paper.

Private (the commercial layer, not built in public): the production routing engine, memory & compression heuristics, trust/freshness/privacy scoring logic, persistence, cloud service, and product integrations (Nomad, the AgentX-Ray "Context Navigation" benchmark).

Apache-2.0 · Aurum Nebula LLC · SPEC.md · CONCEPT.md · BUILD_PLAN.md

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured