Genpark Agent Hybrid Memory Retriever Skill

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📌 Overview & Capability

genpark-agent-hybrid-memory-retriever-skill is a deterministic, high-performance, zero-dependency Python tool and native Model Context Protocol (MCP) server designed for autonomous AI agents, multi-agent frameworks (LangGraph, CrewAI, AutoGen, OpenAI Swarm), and developer environments (Cursor, Windsurf, Claude Desktop).

Executive Capability: Production-grade hybrid sparse-dense memory retrieval engine combining BM25 keyword search, semantic n-gram cosine similarity, and recency decay ranking for agent episodic long-term memory.

⚡ Key Highlights

• 🐍 Zero External pip Dependencies: Implemented entirely with pure Python standard library for instant zero-overhead execution.
• 🔌 Native Model Context Protocol (MCP): Plugs directly into any MCP-compliant client via JSON-RPC 2.0 stdio.
• ⚡ Sub-Millisecond Execution: Slashes token burn and latency by resolving routine agent tasks deterministically without frontier LLM round-trips.
• 🛡️ Production-Hardened: Comprehensive error handling, boundary validation, and telemetry.

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🏗️ Architecture

graph LR
    Agent([🤖 Autonomous Agent / IDE]) -->|MCP Protocol / JSON-RPC| Server[⚡ genpark-agent-hybrid-memory-retriever-skill Server]
    Server --> Core[🧠 Deterministic Processing Core]
    Core --> Out[📊 Actionable Result & Telemetry]
    Out --> Agent

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🚀 Quickstart & Usage

1. Direct Python Client Execution

python example_usage.py

2. Programmatic Integration

from client import AgentHybridMemoryRetriever

client = AgentHybridMemoryRetriever()
result = client.run_memory_benchmark()
print(result)

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🔌 Model Context Protocol (MCP) Setup

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

claude_desktop_config.json

{
  "mcpServers": {
    "genpark-agent-hybrid-memory-retriever-skill": {
      "command": "python",
      "args": ["/path/to/genpark-agent-hybrid-memory-retriever-skill/mcp_server.py"]
    }
  }
}

Direct MCP Testing

python mcp_server.py --test

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📊 Technical Specifications

Parameter	Type	Required	Description
payload	string / dict	Yes	Primary context, code, schema, or content input
options	dict	No	Execution flags, compression ratios, or risk bounds

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