holographic-memory
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@holographic-memorystore that I prefer Go over Python"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
🧠 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 | Зачем голографическая память | |
2 | Как это работает | |
3 | Математическая модель | |
4 | Инструменты MCP | |
5 | Архитектура | |
6 | Редакции и цены | |
7 | Установка | |
8 | Технологический стек | |
9 | Дорожная карта | |
10 | Документация | |
11 | 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
rof addressX, 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 a structured memory (fact + context + emotional valence + importance + tags) as a superposed hypervector. |
| Retrieve a de-noised "meaning cloud" from a vague or emotional cue. |
| Detect when a new fact collides with an existing belief; return the conflict + confidence. |
| "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 .-> CLOUD6. 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 --> BizLocal (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-memoryOr 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
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."
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