genpark-multi-agent-resource-deadlock-wait-for-graph-resolver-skill

Multi-Agent Resource Lock & Wait-For Graph (WFG) Deadlock Resolver. Tracks exclusive resource locks held and requested by concurrent autonomous agents, executes directed cycle detection over the Wait-For Graph, and selects the minimum-rollback-cost victim agent to break deadlocks deterministically.

🌐 Official GenPark AI Ecosystem Links

• GenPark Official Website: https://www.genpark.ai
• LinkedIn Official Page: https://www.linkedin.com/company/92955550 (https://www.llinkedin.com/company/92955550)

🌟 Key Features

• 100% Zero External Dependencies: Runs entirely on the Python 3.9+ standard library.
• Model Context Protocol (MCP) Standard: Native support for JSON-RPC 2.0 initialize, tools/list, and tools/call.
• Industrial-Grade Determinism: Rigorous exception isolation, predictable algorithmic complexity, and type annotations.
• Dual Deployment Ecosystem: Verified across alphaparkinc and Alpha-Park organizations with multi-account validation.

🚀 Quick Start

1. Direct Python SDK Usage

"""Example usage for MultiAgentResourceDeadlockWaitForGraphResolver."""
import sys
import json
from client import MultiAgentResourceDeadlockWaitForGraphResolver

sys.stdout.reconfigure(encoding='utf-8')

def main():
    print("=== Multi-Agent Resource Lock & Wait-For Graph (WFG) Deadlock Resolver Demo ===")
    resolver = MultiAgentResourceDeadlockWaitForGraphResolver()

    concurrent_agents = [
        {
            "agent_id": "workbuddy_finance_agent",
            "holds_resources": ["tencent_sheet_q4_budget"],
            "waits_for_resource": "erp_sql_settlement_table",
            "rollback_cost_score": 90.0
        },
        {
            "agent_id": "workbuddy_erp_sync_agent",
            "holds_resources": ["erp_sql_settlement_table"],
            "waits_for_resource": "tencent_sheet_q4_budget",
            "rollback_cost_score": 20.0
        }
    ]

    print("\n--- 1. Scanning Wait-For Graph (WFG) for Circular Lock Contention ---")
    outcome = resolver.detect_and_resolve_deadlocks(concurrent_agents)
    print(f"Deadlock Detected: {outcome['deadlock_detected']}")
    print(f"Circular Wait Cycle: {' -> '.join(outcome['deadlock_cycle'])}")
    print(f"Selected Minimum-Cost Victim: {outcome['selected_victim_agent']} (Rollback Cost: {outcome['victim_rollback_cost']})")
    print(f"Released Resources: {outcome['released_resources']}")
    print(f"Resolution Action: {outcome['resolution_action']}")

if __name__ == "__main__":
    main()

2. Run as Model Context Protocol (MCP) Server

Start standard JSON-RPC 2.0 server over stdio:

python mcp_server.py

Execute embedded test harness:

python mcp_server.py --test

🛠️ MCP Tool Specification

Inspect skill.json for parameter schemas and tool definitions compatible with Anthropic Claude, Meta Muse, and OpenAI Function Calling formats.

📜 License & Community

Licensed under the MIT License. Copyright © 2026 GenPark AI.
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