Content
# 🧠 Holographic Memory — MCP Server (Go)
**🌐 [holo.ai3d.art](https://holo.ai3d.art)** · 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](#1-why-holographic-memory) | Зачем голографическая память |
| 2 | [How It Works](#2-how-it-works) | Как это работает |
| 3 | [The Math](#3-the-math) | Математическая модель |
| 4 | [MCP Tools](#4-mcp-tools) | Инструменты MCP |
| 5 | [Architecture](#5-architecture) | Архитектура |
| 6 | [Editions & Pricing](#6-editions--pricing) | Редакции и цены |
| 7 | [Install](#7-install) | Установка |
| 8 | [Tech Stack](#8-tech-stack) | Технологический стек |
| 9 | [Roadmap](#9-roadmap) | Дорожная карта |
| 10 | [Documentation](#10-documentation) | Документация |
| 11 | [License](#11-license) | Лицензия |
---
## 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
```mermaid
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>
```json
{
"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>
```json
{ "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
```mermaid
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.
```mermaid
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
```bash
# One command via Smithery
npx -y @smithery/cli install holographic-memory
```
Or add it manually to `claude_desktop_config.json`:
```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](https://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](docs/GTM_PLAN.md)**.
## 10. Documentation
- 📋 **[Technical Specification (SRS) / Техническое задание](docs/TECHNICAL_SPEC.md)**
- 🚀 **[Go-to-Market Plan & Gantt / План выхода на рынок](docs/GTM_PLAN.md)**
## 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`](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](https://holo.ai3d.art)**. See
[`COMMERCIAL-LICENSE.md`](COMMERCIAL-LICENSE.md).
---
*"Smart long-term memory for Claude that doesn't forget the context of a chat from a week ago."*
