This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
🌐 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.
🐍 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.
Related MCP server: vanguard-memory-node
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 --> Agentpython example_usage.pyfrom client import AgentHybridMemoryRetriever
client = AgentHybridMemoryRetriever()
result = client.run_memory_benchmark()
print(result)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"]
}
}
}python mcp_server.py --testParameter | Type | Required | Description |
|
| Yes | Primary context, code, schema, or content input |
|
| No | Execution flags, compression ratios, or risk bounds |
Memory system for AI agents with semantic search. Store and recall memories with ease.
Persistent memory for AI agents. Search, store, and recall across sessions.
Token-efficient MCP memory for Markdown vaults. Tiered search, GraphRAG, AI memories.
Universal persistent memory and knowledge retrieval layer for AI agents and LLMs.
