genpark-agent-hybrid-memory-retriever-skill
OfficialClick on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@genpark-agent-hybrid-memory-retriever-skillrecall my recent memories about the authentication bug"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
genpark-agent-hybrid-memory-retriever-skill
🌐 GenPark MCP Hub • 📦 GenPark Official • 📖 Documentation
📌 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
pipDependencies: 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: Memwyre
🏗️ 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🚀 Quickstart & Usage
1. Direct Python Client Execution
python example_usage.py2. Programmatic Integration
from client import AgentHybridMemoryRetriever
client = AgentHybridMemoryRetriever()
result = client.run_memory_benchmark()
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-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📊 Technical Specifications
Parameter | Type | Required | Description |
|
| Yes | Primary context, code, schema, or content input |
|
| No | Execution flags, compression ratios, or risk bounds |
This server cannot be deployed
Maintenance
Related MCP Connectors
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.
Related MCP Servers
- AlicenseNot gradedqualityAmaintenanceProvides persistent, searchable memory with hybrid keyword and semantic search, storing memories in a single SQLite file without external dependencies.MIT
- AlicenseAqualityAmaintenanceMCP-native persistent memory layer for AI agents across Claude Code, Cursor, VS Code, and OpenClaw. Powered by hybrid vector search, BM25, and cross-encoder reranking with a published 73.1% LoCoMo benchmark accuracy.84Apache 2.0
- AlicenseAqualityAmaintenanceLocal deterministic BM25 memory for AI agents — offline-first, no API key, SHA-256 content-addressed shards, stdio MCP transport. Same query always returns the same ranked result.1142 npmMIT
- FlicenseNot gradedqualityBmaintenanceEnables deterministic, zero-dependency long-horizon conversational memory compaction and episodic anchor extraction for AI agents, with native MCP protocol support and structured JSON telemetry output.7-