🧠 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.

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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

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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.

<details> <summary><b>Example — associative recall</b></summary>

{
  "name": "recall_by_association",
  "arguments": { "query": "the project I worked on when I felt down", "association_depth": 3 }
}

</details>

<details> <summary><b>Example — interference detection</b></summary>

{ "name": "interference_analysis", "arguments": { "new_fact": "I moved to Berlin" } }
// → { "conflict_detected": true, "previous_memory": "User lives in London", "confidence": 0.85 }

</details>

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

• 🌐 Live site — holo.ai3d.art (custom domain). Mirrors: GitHub Pages · GitLab Pages — landing source: index.html
• 📋 Technical Specification (SRS) / Техническое задание
• 🚀 Go-to-Market Plan & Gantt / План выхода на рынок
• 🧩 MVP Status / Статус MVP
• 📖 Project Story / История проекта

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.

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