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neo37

holographic-memory

by neo37

🧠 Holographic Memory — MCP Server (Go)

🌐 Website: holo.ai3d.art  ·  Live (GitHub Pages): neo37.github.io/holographic-memory  ·  Open-source · Privacy-first · $5/mo cloud

The first fully open-source, privacy-first holographic long-term memory for AI agents. Built on Kanerva's Sparse Distributed Memory (SDM) — the associative memory that recent research (2021–2026) proved to be mathematically equivalent to the Attention mechanism inside Transformers (GPT-4, Claude).

Give Claude Desktop, Cursor and any MCP-compatible agent a memory that thinks by association, not by keyword match. Say "I don't like Python" today, ask "what should I write this script in?" next month — and the agent recalls "Go, because you don't like Python." Plain vector RAG can't do that. Interference-based recall can.


Table of Contents / Оглавление

#

English

Русский

1

Why Holographic Memory

Зачем голографическая память

2

How It Works

Как это работает

3

The Math

Математическая модель

4

MCP Tools

Инструменты MCP

5

Architecture

Архитектура

6

Editions & Pricing

Редакции и цены

7

Install

Установка

8

Tech Stack

Технологический стек

9

Roadmap

Дорожная карта

10

Documentation

Документация

11

License

Лицензия


Related MCP server: Memsolus MCP Server

1. Why Holographic Memory

Classic RAG is literal: no keyword overlap → no hit. SDM stores every fact as a high-dimensional binary vector ({0,1}ⁿ, n ≈ 10 000) smeared across many addresses. Recall reconstructs the signal by majority vote over everything inside the activation radius, so it survives noise, partial cues and vague prompts — and it surfaces connections the user only hinted at.

Vector RAG

Holographic Memory (SDM)

Match model

keyword / cosine similarity

associative interference

Vague query

misses

reconstructs from noise

Conflicting facts

silently coexist

flagged as interference

Foundation

ad-hoc embeddings

Kanerva SDM ≈ Transformer Attention

2. How It Works

flowchart LR
    A["Fact:<br/>'User dislikes Python'"] -->|encode| B["Hypervector<br/>{0,1}^10000"]
    B -->|"write into radius r"| C[(Distributed<br/>address cloud)]
    Q["Vague query:<br/>'what language?'"] -->|encode| D["Query vector"]
    D -->|"activate within r"| C
    C -->|"majority-rule read"| E["De-noised recall:<br/>'Use Go — you dislike Python'"]

A fact is not stored in one row — it is superposed across every hard location within a Hamming radius. Reading a noisy or vague cue re-collects those overlapping traces and votes them back into a clean answer.

3. The Math

Implemented in Go, straight from Kanerva's SDM:

  • Distance — Hamming:   d(A, B) = Σᵢ (Aᵢ ⊕ Bᵢ)

  • Write — interference: activate every hard location within radius r of address X, then increment/decrement their counters (wave superposition): Activate(X) = { Y ∈ HardLocations | d(X, Y) ≤ r }

  • Read — associative recall: sum activated cells around query Q, apply the majority rule: Outputᵢ = sign( Σ_{Y ∈ Activate(Q)} CellContents(Y)ᵢ )

This reconstructs a 100%-clean context even from a noisy or partially forgotten query.

4. MCP Tools

Tool

What it does

store_holographic_snapshot

Store a structured memory (fact + context + emotional valence + importance + tags) as a superposed hypervector.

recall_by_association

Retrieve a de-noised "meaning cloud" from a vague or emotional cue.

interference_analysis

Detect when a new fact collides with an existing belief; return the conflict + confidence.

consolidate_and_prune

"Sleep": drop weak associations, reinforce frequently used ones, keep the store fast.

{
  "name": "recall_by_association",
  "arguments": { "query": "the project I worked on when I felt down", "association_depth": 3 }
}
{ "name": "interference_analysis", "arguments": { "new_fact": "I moved to Berlin" } }
// → { "conflict_detected": true, "previous_memory": "User lives in London", "confidence": 0.85 }

5. Architecture

flowchart TB
    subgraph Client["AI Agent — Claude Desktop / Cursor"]
        AG[LLM Agent]
    end
    subgraph Server["Holographic Memory Server (Go)"]
        MCP["MCP handler<br/>(stdio / JSON-RPC)"]
        LIC{"License gate<br/>LOCAL = free"}
        SDM["SDM Engine<br/>encode · write · recall"]
        STORE[("SQLite / binary<br/>association store")]
    end
    CLOUD["☁️ Cloud Sync (Pro $5/mo)<br/>encrypted cross-device"]

    AG <-->|"tools/call"| MCP
    MCP --> LIC --> SDM --> STORE
    SDM -. optional .-> CLOUD

6. Editions & Pricing

This project ships Open-Core: the engine is free and open, convenience is paid.

flowchart LR
    Free["🆓 Local — Free<br/>MIT/Apache-2.0<br/>Full SDM engine · 4 tools<br/>Local SQLite · 100% private"]
    Pro["⭐ Cloud / Pro — $5/mo<br/>Encrypted cross-device sync<br/>Managed hosting + backups<br/>Semantic-cloud viz"]
    Biz["🏢 Business<br/>Dual-licensing<br/>Custom SDM integrations"]
    Free --> Pro --> Biz
  • Local (Free) — runs 100% on your machine; your memories never leave your computer.

  • Cloud / Pro ($5/mo) — same memory in Claude at work and Cursor at home; managed, backed up, and visualized.

  • Business — closed-source embedding rights + bespoke integrations.

7. Install

# One command via Smithery
npx -y @smithery/cli install holographic-memory

Or add it manually to claude_desktop_config.json:

{
  "mcpServers": {
    "holographic-memory": {
      "command": "uvx",
      "args": ["holographic-memory-server"],
      "env": {
        "MEMORY_MODE": "LOCAL",
        "MEMORY_LICENSE_KEY": "optional — only for Cloud/Pro sync"
      }
    }
  }
}

MEMORY_MODE=LOCAL needs no key and is free forever. Set a MEMORY_LICENSE_KEY (get one at holo.ai3d.art) only to unlock encrypted cross-device sync.

8. Tech Stack

  • Go 1.24+ — fast, low RAM, single static binary

  • MCP over stdio (JSON-RPC)

  • Local storage — SQLite / binary association file implementing Kanerva SDM

  • Docker — multi-stage Alpine build

  • Payments — Lemon Squeezy (license keys + subscriptions)

9. Roadmap

Tier

Focus

Status

1

Long-term memory for Claude Desktop / Cursor

🚧 In progress

2

Game engines (Unity / Unreal) — NPC skeletal "muscle memory"

🔭 Planned

B2B

Logs / SIEM anomaly detection (patterns smeared across time)

🔭 Planned

Full timeline & Gantt: see docs/GTM_PLAN.md.

10. Documentation

11. License

Dual-licensed:

  • AGPL-3.0 (free) — personal, self-hosted, and open-source use. If you run a modified version as a network service, AGPL requires you to publish your corresponding source. See LICENSE.

  • Commercial License (paid) — required to embed this software in a closed-source or commercial product, or to run it inside a proprietary service without publishing your source. Get it at holo.ai3d.art. See COMMERCIAL-LICENSE.md.


"Smart long-term memory for Claude that doesn't forget the context of a chat from a week ago."

A
license - permissive license
-
quality - not tested
B
maintenance

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