Memory Graph Bridge

🌟 Overview

The Memory Graph Bridge is a specialized integration system designed to connect Claude-Mem (observation storage) with Memora (knowledge graph). This bridge enables seamless synchronization, visualization, and analysis of AI memory systems, creating a unified knowledge base that combines the structured logging of Claude-Mem with the graph capabilities of Memora.

🚀 Goal: Bridge the gap between linear observation logs and structured knowledge graphs.

🔗 Ecosystem References

This project serves as the connecting link between two powerful memory systems within the DarKWinGTM Node Network:

System	Role	Function	Status
Claude-Mem	🧠 Short/Long-term Memory	Logs observations, decisions, and task history in a linear, time-based format.
Memora	🕸️ Knowledge Graph	Provides graph-based knowledge representation, typed relationships, and visualization capabilities.

💡 Core Concepts

1. The Gap

• Claude-Mem: Excellent for logging "what happened" (Stream).
• Memora: Powerful for understanding "how things relate" (Graph).
• The Bridge: Connects these two systems to provide both historical context and structural understanding.

2. Solution Architecture

flowchart LR
    subgraph Stream [Claude-Mem]
        direction TB
        Log[Observations Log]
        History[Chat History]
    end

    subgraph Bridge [Sync Bridge]
        Extract[📥 Extract] --> Transform[🔄 Transform]
        Transform --> Link[🔗 Auto-Link]
    end

    subgraph Graph [Memora]
        Knowledge[Knowledge Graph]
        Viz[Visualization]
    end

    Log --> Extract
    Link --> Knowledge
    Knowledge --> Viz

    style Stream fill:#f9f,stroke:#333,stroke-width:2px
    style Graph fill:#bbf,stroke:#333,stroke-width:2px
    style Bridge fill:#dfd,stroke:#333,stroke-width:2px

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🛠️ Implementation Approaches

This project offers two distinct implementation paths tailored to different needs:

Path A: Full Bridge (Production)

• Features: CLI tools, custom Python API, advanced auto-linking, multiple export formats.
• Best for: Production systems, automated pipelines, large-scale graph analysis.

Path B: Minimal Integration (Skill + Hook)

• Features: AI-driven analysis via Claude Code Skill (/sync-memora), hook-based auto-sync.
• Best for: Rapid setup, personal use, prototyping, leveraging AI reasoning for linking.

📂 Repository Structure

memory-graph-bridge/
├── README.md           # This file
├── img/                # Visual assets
│   └── image_*.png     # Concept visualization
└── skills/             # Prototype skills directory
    └── sync-memora/    # Ready-to-use sync skill

🚀 Getting Started

Prerequisites

• Node.js ≥16
• Python ≥3.10
• Claude Code CLI

Quick Setup (Minimal Mode)

1. Install Memora MCP: Follow standard Memora installation instructions.

2. Install Skill:

   mkdir -p ~/.claude/skills/sync-memora
   # Copy skill files from this repo
   cp skills/sync-memora/* ~/.claude/skills/sync-memora/

3. Run Sync: In Claude Code:

   /sync-memora
   

🗺️ Roadmap

• Phase 1: Concept & Design

  • Initial Architecture Design
  • Skill Prototype (/sync-memora)
  • Documentation & Repository Setup

• Phase 2: Core Development

  • Python-based Sync Engine
  • Semantic Analysis Module
  • CLI Tool (mgb)

• Phase 3: Integration

  • Obsidian Plugin
  • Automated Cron Sync
  • Web Visualization Dashboard

📄 License

Distributed under the MIT License. See LICENSE for more information.

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Built with ❤️ by DarKWinGTM