1. Conduid
  2. AI
  3. Empirehacks 2026
MCP server · AI

Empirehacks 2026

Mnemosyne — Agent-Agnostic Knowledge Management Layer based on DIKW Model. EmpireHacks 2026 Track 3.

Unclaimed last commit 7 months ago ai
34Low

Scored 7 months ago · breakdown

About Empirehacks 2026

Empirehacks 2026 is an MCP server published by ChangXiang-SCU in the AI category: mnemosyne — Agent-Agnostic Knowledge Management Layer based on DIKW Model. EmpireHacks 2026 Track 3.

The repository has 2 stars and 0 forks, with the last commit 7 months ago. Six months or more without a commit doesn't mean the server is broken, but check the open issues before depending on it in production.

Install

From source
git clone https://github.com/ChangXiang-SCU/empirehacks-2026

This server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.

Ask AI

✦

Ask AI about Empirehacks 2026

Powered by Claude · Grounded in docs

I know everything about Empirehacks 2026. Ask me about installation, configuration, usage, or troubleshooting.

Security checks

  • ✓README presentThe repository ships documentation.

5 checks have not been run yet.

README

🏛️ Mnemosyne

Persistent Memory for AI Agents

EmpireHacks 2026 Track 3 Node.js React MCP

Your AI tools generate valuable knowledge every session — then forget everything. Mnemosyne remembers.

Getting Started · Architecture · How It Works · API Reference · Contributing


🎯 The Problem

Every time you close an AI session, all context is lost:

  • Claude Code doesn't remember what you researched yesterday
  • Codex can't access insights from your Claude sessions
  • Valuable decisions, patterns, and lessons are buried in chat logs nobody revisits
  • You repeat yourself across sessions, re-explaining context every time

The more you use AI tools, the more knowledge you lose.

💡 The Solution

Mnemosyne is an agent-agnostic knowledge management layer that sits between your AI tools and creates persistent, cross-platform memory.

It automatically:

  1. Captures raw conversation data from Claude Code and Codex via file watchers & hooks
  2. Transforms it through a DIKW pipeline (Data → Information → Knowledge → Wisdom)
  3. Visualizes everything as an interactive knowledge graph
  4. Feeds it back into your AI tools via MCP (Model Context Protocol)

The more you use AI tools, the smarter they become.


✨ Key Features

🔄 Auto-Capture Engine

File watchers monitor ~/.claude/projects/ and ~/.codex/sessions/ in real-time. When a session completes, Mnemosyne automatically ingests the conversation — no manual export needed.

🧠 DIKW Transform Pipeline

Raw AI conversations are transformed through four levels using LLM-powered reflection:

┌─────────┐     ┌─────────────┐     ┌───────────┐     ┌────────┐
│  Data   │ ──▶ │ Information │ ──▶ │ Knowledge │ ──▶ │ Wisdom │
│ (raw)   │     │ (context)   │     │ (patterns)│     │ (meta) │
└─────────┘     └─────────────┘     └───────────┘     └────────┘
  Tool calls,      What happened,      Reusable          When & why
  file excerpts    why it matters       techniques        to apply them

🌐 Cross-Platform Knowledge Graph

Interactive D3.js force-directed graph with color-coded DIKW nodes. Pan, zoom, click to explore connections across all your AI sessions.

🔌 MCP Feedback Loop

Mnemosyne exposes an MCP server that feeds accumulated knowledge back into Claude Desktop, Claude Code, and any MCP-compatible client. Your AI assistant gains persistent memory.

🛠️ Platform Hub

Sync skills, MCP servers, and configurations between Claude Code and Codex with one click. Right-click context menus for quick actions.


🏗️ Architecture

flowchart TB
    subgraph AI_Platforms["AI Platforms"]
        CC["Claude Code"]
        CX["Codex"]
        CD["Claude Desktop"]
    end

    subgraph Capture["Auto-Capture Layer"]
        FW["File Watchers\n~/.claude/ ~/.codex/"]
        HK["Session Hooks\npost-tool-use, stop"]
    end

    subgraph Core["Mnemosyne Core"]
        SRV["Express + WebSocket\nServer"]
        DIKW["DIKW Transform\nEngine"]
        DB[("SQLite\nDatabase")]
        REC["Recommendation\nEngine"]
    end

    subgraph Interface["User Interface"]
        KG["Knowledge Graph\nD3.js Visualization"]
        PH["Platform Hub\nSync & Manage"]
        INB["Inbox\nNew Sessions"]
    end

    subgraph Feedback["MCP Feedback Loop"]
        MCP["MCP Server\n(stdio)"]
    end

    CC -->|session files| FW
    CX -->|session files| FW
    CC -->|hooks| HK
    FW --> SRV
    HK --> SRV
    SRV --> DIKW
    DIKW --> DB
    DB --> REC
    SRV --> KG
    SRV --> PH
    SRV --> INB
    DB --> MCP
    MCP -->|knowledge context| CD
    MCP -->|knowledge context| CC

    style AI_Platforms fill:#1a1a2e,stroke:#667eea,color:#fff
    style Capture fill:#16213e,stroke:#f39c12,color:#fff
    style Core fill:#0f3460,stroke:#e94560,color:#fff
    style Interface fill:#1a1a2e,stroke:#00d2ff,color:#fff
    style Feedback fill:#16213e,stroke:#a29bfe,color:#fff

🔬 How It Works

1. Session Capture

Mnemosyne monitors AI tool directories using chokidar file watchers:

// Watches for new/modified session files
watch('~/.claude/projects/**/*.jsonl')  // Claude Code sessions
watch('~/.codex/sessions/**/*.jsonl')   // Codex sessions

When a session file changes, the watcher automatically parses tool calls, responses, and metadata into Data nodes.

2. DIKW Transformation

The engine processes Data nodes through three LLM-powered reflection stages:

Stage Input Output Example
D → I Raw tool calls & responses Contextual summary "User researched WebAssembly edge computing trends"
I → K Information nodes Reusable patterns "When evaluating new tech, compare: community size, benchmark data, production adoption"
K → W Knowledge nodes Strategic judgment "Prioritize technologies with both strong benchmarks AND growing community — performance alone isn't enough"

3. Knowledge Graph

All nodes and connections are visualized in a real-time D3.js force-directed graph:

  • 🟣 Data — Raw session artifacts
  • 🔵 Information — Contextualized insights
  • 🟢 Knowledge — Reusable patterns & techniques
  • 🟡 Wisdom — Meta-level judgment & strategy

4. MCP Feedback

The MCP server exposes three resources to any connected AI client:

mnemosyne://knowledge-nodes    → All Knowledge-level insights
mnemosyne://wisdom-nodes       → All Wisdom-level insights  
mnemosyne://graph-summary       → Full graph overview

When Claude Desktop connects, it can access your accumulated knowledge as live context.


🚀 Quick Start

Prerequisites

  • Node.js 18+ and npm
  • Git
  • (Optional) Claude Code and/or Codex installed for auto-capture

Installation

# Clone the repository
git clone https://github.com/ChangXiang-SCU/empirehacks-2026.git
cd empirehacks-2026

# Install dependencies
npm install
cd backend && npm install && cd ..
cd frontend && npm install && cd ..

Run

# Start the backend (port 3001)
npm run backend

# In another terminal, start the frontend (port 5173)
npm run frontend

Open http://localhost:5173 — you'll see the Mnemosyne knowledge graph.

Production Build

npm run build:frontend
npm start
# Frontend is served from backend at http://localhost:3001

Connect to Claude Desktop (MCP)

Add to your Claude Desktop config (claude_desktop_config.json):

{
  "mcpServers": {
    "mnemosyne": {
      "command": "node",
      "args": ["<path-to-repo>/backend/mcp-stdio-server.mjs"]
    }
  }
}

Restart Claude Desktop — you'll see "Add from mnemosyne" in the + menu.


📁 Project Structure

mnemosyne/
├── backend/
│   ├── server.js              # Express + WebSocket + DIKW engine + API
│   ├── mcp-stdio-server.mjs   # MCP server for Claude Desktop integration
│   ├── lib/                   # Modular utilities
│   └── db/                    # SQLite database (auto-created)
├── frontend/
│   ├── src/
│   │   ├── components/
│   │   │   ├── MindPalace.jsx     # D3 force-directed knowledge graph
│   │   │   ├── ConnectPanel.jsx   # Platform Hub (skills, MCPs sync)
│   │   │   ├── NodeCard.jsx       # DIKW node detail view
│   │   │   ├── Sidebar.jsx        # Project filter panel
│   │   │   └── InboxPanel.jsx     # Unclassified session inbox
│   │   └── App.jsx
│   ├── dist/                  # Production build output
│   └── vite.config.js
├── hooks/                     # Claude Code hook scripts
│   ├── post-tool-use.sh       # Captures each tool call
│   ├── stop.sh                # Triggers DIKW reflection on session end
│   └── session-start.sh       # Loads context from knowledge graph
├── demo/                      # Demo seed data
├── launcher.mjs               # Auto-launcher script
├── package.json
├── LICENSE
└── CONTRIBUTING.md

📡 API Reference

REST Endpoints

Method Endpoint Description
GET /api/graph Fetch full DIKW graph (nodes + connections)
GET /api/nodes?type=K Filter nodes by DIKW type
POST /api/import Upload ChatGPT/Claude export (multipart)
POST /api/dikw/transform Trigger DIKW pipeline on Data nodes
POST /api/hook/tool-use Claude Code hook endpoint
GET /api/platform/skills List skills from Claude Code & Codex
GET /api/platform/mcps List MCP servers from both platforms
POST /api/platform/mcps/sync Sync MCP server between platforms

WebSocket Events

Event Direction Description
node:added Server → Client New DIKW node created
connection:added Server → Client New edge in graph
dikw:transform:complete Server → Client Pipeline finished
filewatcher:new-session Server → Client New session detected

MCP Resources

URI Description
mnemosyne://knowledge-nodes All Knowledge-level nodes
mnemosyne://wisdom-nodes All Wisdom-level nodes
mnemosyne://graph-summary Aggregated graph overview

🛤️ Roadmap

  • Auto-capture from Claude Code & Codex
  • DIKW transform pipeline with LLM reflection
  • Interactive knowledge graph (D3.js)
  • MCP server for Claude Desktop integration
  • Platform Hub with cross-platform sync
  • Claude Code hooks (post-tool-use, stop, session-start)
  • ChatGPT export import support
  • Multi-user collaboration
  • Custom DIKW transformation rules
  • Knowledge graph search & semantic query
  • Mobile-friendly responsive UI
  • Plugin system for custom data sources

🤝 Contributing

Contributions are welcome! See CONTRIBUTING.md for guidelines.


📄 License

MIT — free for personal and commercial use.


Built with 🧠 for EmpireHacks 2026 — Track 3: The Sidekick

"The more you use AI tools, the smarter they become."

README copy captured when this server was indexed; it may lag the repository. The original is authoritative.

Questions

About Empirehacks 2026

How do I install Empirehacks 2026?

No package has been published for this server, so install it from source: clone the repository and follow its README.

Is Empirehacks 2026 safe to use with an AI agent?

Its trust score is 34 out of 100 (low). It passes 1 of the 1 static security checks run so far. It has no ConduID identity yet, so agent calls to it are not receipted.

Is Empirehacks 2026 still maintained?

The last commit was 7 months ago. That's long enough that you should check whether the maintainer is responding to issues before depending on it.