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.
genpark-agent-hybrid-memory-retriever-skill
Production-Grade AI Agent Infrastructure Skill • 100% Standard Library Python • Native Model Context Protocol (MCP)
🌐 GenPark MCP Hub • 📦 GenPark Official • 📖 Documentation
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.
Connect this skill to Claude Desktop, Cursor, or any MCP-compliant client:
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
From the project README.
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