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https://github.com/jaffarkeikei/mynd

Mynd is a universal memory layer for AI that automatically captures your digital context and streams it securely to any AI via Model Context Protocol (MCP). Your AIs finally remember everything about you - your decisions, preferences, history, and patterns - while your data never leaves your device.
https://github.com/jaffarkeikei/mynd

chromadb claude-api gemini-api mcp-server python shell sqldb

Last synced: about 2 months ago
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Mynd is a universal memory layer for AI that automatically captures your digital context and streams it securely to any AI via Model Context Protocol (MCP). Your AIs finally remember everything about you - your decisions, preferences, history, and patterns - while your data never leaves your device.

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README 

          
# Mynd



**_Give every AI a photographic memory of YOUR life - securely, locally, forever_**



> **The Problem:** Every AI conversation starts from zero. ChatGPT doesn't remember what you discussed yesterday. Copilot doesn't know your coding style. Claude forgets your preferences. It's like having digital Alzheimer's.



> **The Solution:** Mynd gives EVERY AI perfect memory of your context - securely, privately, forever.



## What is Mynd?



Mynd is a **universal memory layer** for AI that automatically captures your digital context and streams it securely to any AI via Model Context Protocol (MCP). Your AIs finally remember everything about you - your decisions, preferences, history, and patterns - while your data never leaves your device.



## 🌟 NEW: Beautiful Web Interface



Experience Mynd through our **ChatGPT-like web interface** that visually demonstrates the power of AI memory:



![Mynd Web Interface Demo](https://img.shields.io/badge/Live_Demo-Try_Now-blue?style=for-the-badge)



```bash

# Quick start the web demo

python scripts/start_web_demo.py



# Or manually:

python src/web_app.py

# Open http://localhost:8000

```



### Key Features:

- **🎭 Memory Toggle** - Switch memory ON/OFF to see the dramatic difference

- **🔄 Side-by-Side Comparison** - Compare responses with and without memory

- **📊 Real-time Metrics** - Watch response time, relevance scores, and token usage

- **💬 ChatGPT-like Interface** - Beautiful, familiar, and intuitive

- **🎯 Demo Mode** - Pre-loaded context for instant demonstrations



### See The Difference:

1. Ask: "What was our authentication decision?"

2. Toggle memory OFF and ask again

3. Watch the AI go from "I don't have context" to perfect recall!



## System Architecture



### High-Level Architecture

```mermaid

graph TB

    subgraph "Data Sources"

        Browser["🌐 Browser History"]

        Files["📄 Documents & Code"]

        Clipboard["📋 Clipboard"]

        Git["🔧 Git Repositories"]

    end

    

    subgraph "Mynd Core"

        Capture["📥 Data Capture"]

        Extract["🧠 Semantic Extractor"]

        Privacy["🔒 Privacy Filter"]

        

        subgraph "Storage"

            SQLite["📊 SQLite DB
(Metadata)"]

            ChromaDB["🧠 ChromaDB
(Vectors)"]

        end

        

        MCP["🔗 MCP Server"]

    end

    

    subgraph "AI Clients"

        ChatGPT["💬 ChatGPT"]

        Claude["🤖 Claude"]

        Copilot["👨‍💻 GitHub Copilot"]

        AnyAI["🤖 Any AI Tool"]

    end

    

    Browser --> Capture

    Files --> Capture

    Clipboard --> Capture

    Git --> Capture

    

    Capture --> Extract

    Extract --> Privacy

    Privacy --> SQLite

    Privacy --> ChromaDB

    

    SQLite --> MCP

    ChromaDB --> MCP

    

    MCP -->|"Secure Context"| ChatGPT

    MCP -->|"Secure Context"| Claude

    MCP -->|"Secure Context"| Copilot

    MCP -->|"Secure Context"| AnyAI

    

    style Extract fill:#ff6b6b,stroke:#fff,stroke-width:3px

    style Privacy fill:#4ecdc4,stroke:#333,stroke-width:2px

    style MCP fill:#f39c12,stroke:#333,stroke-width:2px

```



### Data Flow Process

```mermaid

sequenceDiagram

    participant U as User Activity

    participant C as Data Capture

    participant E as Semantic Extractor

    participant P as Privacy Filter

    participant D as Database

    participant V as Vector Store

    participant M as MCP Server

    participant A as AI Client

    

    U->>C: Browser/File/Code Activity

    C->>E: Raw Content

    E->>E: Extract Semantic Meaning

    E->>P: Semantic Events

    P->>P: Remove PII & Sensitive Data

    P->>D: Store Metadata

    P->>V: Store Embeddings

    

    Note over D,V: Local Storage Only

    

    A->>M: Request Context for Query

    M->>V: Semantic Search

    M->>D: Get Related Events

    M->>M: Compress & Optimize

    M->>A: Relevant Context (4000 tokens max)

    

    Note over M,A: MCP Protocol

```



## The Memory Crisis (The $2.3T Problem)



**Every AI interaction wastes massive time on context setup:**



- **73% of AI conversations** repeat information from previous chats

- **2.3 hours daily** lost re-explaining context to AI

- **$2.3 trillion annually** in global productivity loss

- **89% of professionals** frustrated with AI's goldfish memory



**Real Examples:**

- "What was that API decision we made last month?" → *"I don't have context"*

- "Continue our React project" → *"Can you share the codebase?"*

- "Remember my coding style preferences" → *"Please describe them again"*



## Mynd Demo Script (2 Minutes)



```bash

# The Setup (30 seconds)

"Every AI suffers from digital amnesia. Watch this..."



[User asks ChatGPT]: "What was that authentication architecture decision from last month?"

[ChatGPT]: "I don't have access to previous conversations..."



# The Magic (60 seconds)

[Install Mynd]: mynd demo

[Capture context]: "Mynd has been learning your patterns..."



[Same question to ChatGPT + Mynd]:

mynd query "authentication architecture decision"



[Result]: "You decided on JWT with refresh tokens over sessions on March 15th 

because of mobile app requirements. You were concerned about XSS attacks but 

chose client-side storage anyway because your team lacks Redis expertise."



# The Jaw-Drop (30 seconds)

"This context came from:

✅ Your browser research from 6 weeks ago

✅ Code comments you wrote in March  

✅ A design doc you saved locally

✅ All delivered securely via MCP - your data never left your machine"

```



## Quick Start (2 Minutes to Life-Changing AI)



### Component Initialization Flow

```mermaid

graph LR

    subgraph "Setup Process"

        Install["🔧 Install Dependencies"]

        Init["🎯 Initialize Components"]

        Demo["🎬 Create Demo Data"]

        Query["🔍 Test Query"]

    end

    

    Install --> Init

    Init --> Demo

    Demo --> Query

    

    subgraph "Components Initialized"

        DB["📊 SQLite Database"]

        Vector["🧠 Vector Store"]

        Extractor["🔍 Semantic Extractor"]

        CLI["💻 CLI Interface"]

    end

    

    Init --> DB

    Init --> Vector

    Init --> Extractor

    Init --> CLI

```



```bash

# Install Mynd

./install.sh  # or pip install -e .



# Set up demo data

mynd demo



# Test the magic

mynd query "authentication architecture"



# Watch AI get perfect memory of your decisions!

```



## AgentHacks 2025 Categories



### **PRIMARY: Personalization & Memory** 

- ✅ **Learns from user activity**: Continuous semantic capture

- ✅ **Evolves behavior over time**: Memory graph grows and improves

- ✅ **User corrections improve system**: Feedback loop for better context

- ✅ **Personal preference adaptation**: Learns your patterns and style



### **SECONDARY: Interfaces for Human-AI Collaboration**

- ✅ **Revolutionizes AI interaction**: No more context re-explanation

- ✅ **Seamless collaboration**: AI knows your full background

- ✅ **Natural communication**: AI understands your references and history



## Business Model & Market



### Market Size

- **TAM**: $450B (Global productivity software market)

- **SAM**: $67B (AI tools and services) 

- **SOM**: $12B (AI productivity and memory solutions)



### Revenue Model

```mermaid

graph TD

    Personal["🆓 Mynd Personal
FREE Forever
• 30-day memory
• 3 data sources
• Community support"] 

    

    Pro["💎 Mynd Pro
$29/month
• Unlimited memory
• All data sources
• Priority MCP access
• Advanced privacy controls"]

    

    Enterprise["🏢 Mynd Enterprise
$199/user/month
• Team memory sharing
• Compliance controls
• Custom integrations
• White-label deployment"]

    

    Personal --> Pro

    Pro --> Enterprise

    

    style Personal fill:#4ecdc4

    style Pro fill:#f39c12

    style Enterprise fill:#e74c3c

```



## Security & Privacy Architecture



### Privacy-First Data Flow

```mermaid

graph TB

    subgraph "Your Device (Secure Zone)"

        Raw["📝 Raw Data
(Browser, Files, Code)"]

        PII["🔒 PII Detection
(Remove Sensitive Info)"]

        LLM["🧠 Local LLM
(Semantic Extraction)"]

        Encrypt["🔐 Encrypted Storage
(SQLite + ChromaDB)"]

    end

    

    subgraph "External AI (Untrusted)"

        ChatGPT["💬 ChatGPT"]

        Claude["🤖 Claude"] 

        Other["🤖 Other AIs"]

    end

    

    Raw --> PII

    PII --> LLM

    LLM --> Encrypt

    

    Encrypt -->|"Semantic Context Only
(No Raw Data)"| ChatGPT

    Encrypt -->|"Semantic Context Only
(No Raw Data)"| Claude

    Encrypt -->|"Semantic Context Only
(No Raw Data)"| Other

    

    style Raw fill:#ff6b6b,stroke:#333,stroke-width:2px

    style PII fill:#4ecdc4,stroke:#333,stroke-width:2px

    style LLM fill:#f39c12,stroke:#333,stroke-width:2px

    style Encrypt fill:#27ae60,stroke:#333,stroke-width:2px

```



**Privacy Promise**: Your raw data NEVER leaves your device. Only semantic meaning is processed, stored locally, and delivered via encrypted MCP.



## Success Metrics & Validation



### Technical Milestones ✅

- [x] Core semantic extraction engine (Local LLM + privacy filters)

- [x] Local encrypted storage (ChromaDB + SQLite)

- [x] MCP server architecture with capability tokens

- [x] Browser history and document capture framework

- [x] CLI interface with full functionality



### Demo Readiness ✅ 

- [x] 2-minute live demo script prepared

- [x] Real context database with semantic events

- [x] Multiple query examples working

- [x] Clear before/after comparison ready



## Join the Memory Revolution



Mynd isn't just a hackathon project - it's the future of AI interaction. We're building the memory layer that every AI desperately needs.



**For Developers**: Finally, coding AI that knows your entire project history

**For Knowledge Workers**: AI assistants that remember every decision and context  

**For Everyone**: The end of explaining the same thing to AI over and over



---