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genpark-meta-muse-episodic-memory-graph-mcp

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genpark-meta-muse-episodic-memory-graph-mcp

Python 3.9+ License MIT MCP Compatible GenPark AI Zero Dependencies

🌐 GenPark MCP Hub • 📦 GenPark Official • 📖 Documentation

📌 Overview & Paradigm

genpark-meta-muse-episodic-memory-graph-mcp is a deterministic, zero-dependency Python skill and native Model Context Protocol (MCP) server engineered for next-generation personal AI agents. It distills core architectural principles from Meta (ambient multimodal perception), Muse (continuous episodic memory), Instinct (zero-prompt proactive agency), and Jev (System-1 sub-millisecond typed decision cognition).

Executive Capability: Native Model Context Protocol (MCP) server fusing Meta ambient multimodal perception with Muse continuous episodic memory streams and Ebbinghaus recency decay.

⚡ Key Highlights & Value

  • 🐍 Zero External pip Dependencies: Runs instantaneously on standard Python 3.9+ with zero environment bloat.

  • 🔌 Native Model Context Protocol (MCP): Plugs directly into Claude Desktop, Cursor IDE, Windsurf, and custom agent swarms.

  • 🧠 System-1 Low-Latency Cognition: Slashes unnecessary frontier LLM invocations by routing routine and reflexive decisions at up to 200x faster execution speed.

  • 🛡️ Safety & Privacy Guardrails: Enforces reversible execution checkpoints, strict token budgets, and local-first memory retention.

Related MCP server: genpark-jev-system1-subconscious-decision-skill

🏗️ Architecture & Cognitive Flow

graph LR
    A[👁️ Ambient Perception: Meta / Screen] --> B[🧠 Instinct Proactive Sensor]
    B --> C{⚡ Jev System-1 Decision Layer}
    C -->|Fast Reflex / Cached Tool| D[🛠️ Deterministic Action]
    C -->|Ambiguous / Multi-Hop Plan| E[🤔 System-2 Frontier LLM]
    D --> F[(📜 Muse Episodic Memory Stream)]
    E --> F
    F -->|Decayed Context Briefing| A

🚀 Quickstart & Usage

1. Direct Python Client Execution

python example_usage.py

2. Programmatic Integration

from client import MetaMuseEpisodicMemoryGraph

client = MetaMuseEpisodicMemoryGraph()
result = client.run_benchmark_episodic_stream()
print(result)

🔌 Model Context Protocol (MCP) Setup

Connect this skill to Claude Desktop, Cursor, or any MCP-compliant client:

claude_desktop_config.json

{
  "mcpServers": {
    "genpark-meta-muse-episodic-memory-graph-mcp": {
      "command": "python",
      "args": ["/path/to/genpark-meta-muse-episodic-memory-graph-mcp/mcp_server.py"]
    }
  }
}

Direct MCP Testing

python mcp_server.py --test

📊 Technical Specifications

Parameter

Type

Required

Description

payload

string / dict

Yes

Primary context, state vector, or action candidate

output_format

json / dict

Yes

Standardized schema containing typed decision outputs and telemetry

❓ Frequently Asked Questions (FAQ)

Q1: How does this differ from traditional LLM prompts?

Rather than sending every small interaction to heavy reasoning LLMs, this architecture implements Jev-style System-1 cognition and Instinct proactive sensing to execute fast, deterministic, schema-enforced routing and guardrails.

Q2: What are the memory retention guarantees?

Memory records utilize Muse-style Ebbinghaus forgetting curves with recency decay, contradiction resolution, and user-controlled deletion cascades.

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Maintenance

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