genpark-agent-hierarchical-episodic-memory-skill

⚡ Overview & Architectural Significance

genpark-agent-hierarchical-episodic-memory-skill delivers zero-dependency, low-latency, deterministic agentic memory and retrieval primitives engineered strictly using Python 3.9+ standard library.

🌟 Key Architectural Capabilities

• Zero External Dependencies: Operates exclusively via pure Python (math, re, collections, heapq, hashlib, json). Zero pip install overhead, zero C-extension compile errors.
• Enterprise RAG & Memory Invariants: Implements formal algorithms for cognitive decay, BM25 Okapi lexical scoring, Reciprocal Rank Fusion, knowledge graph traversal, semantic query caching, and lost-in-the-middle context reordering.
• Native Anthropic MCP Protocol: Compliant with standard JSON-RPC 2.0 stdio MCP specifications for Claude Desktop, Cursor, and Windsurf.

🏗️ Architectural Topology & State Machine

flowchart TD
    UserQuery["User Prompt / Agent Goal"] --> SemCache["Semantic Cache Check"]
    SemCache -->|Cache Hit| FastReturn["Cached Response (0ms)"]
    SemCache -->|Cache Miss| DualRetrieval["Dual Retrieval Pipeline"]
    
    subgraph DualRetrieval ["Hybrid Search Engine"]
        BM25Lex["BM25 Okapi Lexical Ranker"]
        DenseVec["Dense Cosine Vector Similarity"]
    end
    
    DualRetrieval --> RRF["Reciprocal Rank Fusion (RRF)"]
    RRF --> GraphExp["Knowledge Graph Triplet Expansion"]
    GraphExp --> LostMiddle["Lost-In-The-Middle Context Reorderer"]
    LostMiddle --> LLM["LLM Synthesis with Optimal Context"]
    LLM --> EpisodicMem["Episodic Consolidation & Recency Decay"]

🚀 Quickstart & Standalone Execution

Local Python Client Usage

from client import AgentHierarchicalEpisodicMemory

# Initialize engine
engine = AgentHierarchicalEpisodicMemory()

# Execute self-testing benchmark suite
result = engine.run_benchmark_hierarchical_memory()
print("Execution Result:", result)

🔌 One-Click MCP Integration (Claude Desktop / Cursor)

Add to your claude_desktop_config.json or cursor.json:

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

📦 Smithery.ai & PyPI Deployment

This skill contains pre-configured smithery.yaml and pyproject.toml manifests. Install directly via pip:

pip install git+https://github.com/alphaparkinc/genpark-agent-hierarchical-episodic-memory-skill.git