genpark-episodic-memory-consolidation-decay-skill
Click 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-episodic-memory-consolidation-decay-skillconsolidate my frequent memories and decay stale ones"
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-episodic-memory-consolidation-decay-skill
🌐 GenPark MCP Hub • 📦 GenPark Official • 📖 Documentation
🌟 Overview
genpark-episodic-memory-consolidation-decay-skill delivers robust, industrial-grade capabilities for autonomous agents, copilot frameworks, and personal assistant architectures. Built exclusively on the Python standard library with zero external runtime dependencies, it integrates seamlessly as a native Model Context Protocol (MCP) server or an importable Python module.
Biological Sleep-Cycle & Ebbinghaus Forgetting Curve Episodic-to-Semantic Memory Consolidation Engine. Decays stale transient interactions and consolidates frequent episodic events into enduring semantic user persona traits.
💡 Key Capabilities
Zero-Dependency Architecture: Runs anywhere Python 3.9+ is installed without
pip installoverhead or supply-chain vulnerabilities.Model Context Protocol (MCP) First: Compatible with Claude Desktop, Cursor, GenPark Engine, and custom agentic frameworks.
Deterministic & Safe: Designed with strict validation, graceful error handling, and structured telemetry.
High Concurrency & Low Latency: In-memory caching and optimized data structures for real-time agent execution loops.
Related MCP server: cortex-engine
🚀 Quickstart
1. Direct Python Usage
from client import EpisodicMemoryConsolidationDecayEngine
client = EpisodicMemoryConsolidationDecayEngine()
result = client.record_episodic_event()
print(result)2. Standalone MCP Server Execution
Run the MCP server via standard JSON-RPC 2.0 stdio:
python mcp_server.pyVerify standard compliance and self-tests:
python mcp_server.py --test3. Claude Desktop / Cursor MCP Configuration
Add this tool to your claude_desktop_config.json or Cursor MCP settings:
{
"mcpServers": {
"genpark-episodic-memory-consolidation-decay-skill": {
"command": "python",
"args": ["/absolute/path/to/genpark-episodic-memory-consolidation-decay-skill/mcp_server.py"]
}
}
}🛠️ Verification & Testing
Run the included verification suite:
python example_usage.py📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
Developed with ❤️ by the GenPark Autonomous Agent Ecosystem Team.
This server cannot be deployed
Maintenance
Related MCP Connectors
Biological memory for AI agents. Pattern learning, decay, clustering, 8-drive behavior.
Universal memory runtime for AI agents — episodic, semantic, and procedural memory.
Long-term memory for AI agents: semantic facts, episodic events, and procedural workflows
Persistent memory and drift detection for AI agents across session restarts.
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