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genpark-agent-mesh-state-blackboard-deconfliction-skill

Universal Multi-Agent Mesh State Blackboard & Conflict Deconfliction Engine. Coordinates shared multi-agent state spaces, detects concurrent mutation race conditions, executes vector clock synchroniza

Set up

Runs on your computer. Pick your agent and operating system.

This project does not publish an install method we can turn into a config. Follow the README instead.

README

synced Oct 25, 2026

genpark-agent-mesh-state-blackboard-deconfliction-skill

Python 3.9+ License MIT MCP Compatible GenPark AI Zero Dependencies

🌐 GenPark MCP Hub • 📦 GenPark Official • 📖 Documentation


🌟 Overview

genpark-agent-mesh-state-blackboard-deconfliction-skill delivers robust, industrial-grade capabilities engineered for Multi-Agent Collaborative Swarms and Enterprise Workplace Execution. 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.

Universal Multi-Agent Mesh State Blackboard & Conflict Deconfliction Engine. Coordinates shared multi-agent state spaces, detects concurrent mutation race conditions, executes vector clock synchronization, and resolves conflicting decisions using consensus arbitration.

💡 Key Capabilities

  • Zero-Dependency Architecture: Runs anywhere Python 3.9+ is installed without pip install overhead or supply-chain vulnerabilities.
  • Model Context Protocol (MCP) First: Compatible with Claude Desktop, Cursor, GenPark Engine, Meta Muse, and Tencent WorkBuddy runtime frameworks.
  • Deterministic & Safe: Rigorous mathematical synchronization models, cryptographic hashing, AST syntax trees, and strict policy boundary validation.
  • High Concurrency & Low Latency: In-memory thread-safe state synchronization, vector clock resolution, and high-throughput regex scanners.

🚀 Quickstart

1. Direct Python Usage

from client import AgentMeshStateBlackboardDeconflictionEngine

client = AgentMeshStateBlackboardDeconflictionEngine()
result = client.propose_state_mutation()
print(result)

2. Standalone MCP Server Execution

Run the MCP server via standard JSON-RPC 2.0 stdio:

python mcp_server.py

Verify standard compliance and self-tests:

python mcp_server.py --test

3. Claude Desktop / Cursor MCP Configuration

Add this tool to your claude_desktop_config.json or Cursor MCP settings:

{
  "mcpServers": {
    "genpark-agent-mesh-state-blackboard-deconfliction-skill": {
      "command": "python",
      "args": ["/absolute/path/to/genpark-agent-mesh-state-blackboard-deconfliction-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.

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