Initial commit: Complete Hive distributed AI orchestration platform
This comprehensive implementation includes: - FastAPI backend with MCP server integration - React/TypeScript frontend with Vite - PostgreSQL database with Redis caching - Grafana/Prometheus monitoring stack - Docker Compose orchestration - Full MCP protocol support for Claude Code integration Features: - Agent discovery and management across network - Visual workflow editor and execution engine - Real-time task coordination and monitoring - Multi-model support with specialized agents - Distributed development task allocation 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
anthonyrawlins
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scripts/coordinate_n8n_development.py
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322
scripts/coordinate_n8n_development.py
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#!/usr/bin/env python3
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"""
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Hive Cluster Coordination for n8n Workflow Development
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Coordinates distributed development of intelligent task allocation workflows
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"""
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import asyncio
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import aiohttp
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import json
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import time
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from typing import Dict, List
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from datetime import datetime
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# Cluster configuration
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AGENTS = {
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"walnut": {
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"endpoint": "http://192.168.1.27:11434",
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"model": "starcoder2:15b",
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"specialty": "Senior Full-Stack Development & Architecture",
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"capabilities": ["workflow_design", "frontend_architecture", "complex_coordination"]
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},
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"ironwood": {
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"endpoint": "http://192.168.1.113:11434",
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"model": "deepseek-coder-v2",
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"specialty": "Backend Development & Code Analysis",
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"capabilities": ["api_design", "database_schema", "backend_logic"]
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},
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"acacia": {
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"endpoint": "http://192.168.1.72:11434",
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"model": "deepseek-r1:7b",
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"specialty": "Infrastructure, DevOps & System Architecture",
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"capabilities": ["deployment", "n8n_integration", "system_architecture"]
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},
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"rosewood": {
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"endpoint": "http://192.168.1.132:11434",
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"model": "deepseek-r1:8b",
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"specialty": "Quality Assurance, Testing & Code Review",
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"capabilities": ["testing_workflows", "quality_validation", "performance_testing"]
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},
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"oak": {
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"endpoint": "http://192.168.1.135:11434",
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"model": "mistral:7b-instruct",
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"specialty": "iOS/macOS Development & Apple Ecosystem",
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"capabilities": ["mobile_integration", "apple_ecosystem", "native_apps"]
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}
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}
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class HiveN8NCoordinator:
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def __init__(self):
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self.session = None
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self.results = {}
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async def __aenter__(self):
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self.session = aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(total=300))
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return self
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async def __aexit__(self, exc_type, exc_val, exc_tb):
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if self.session:
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await self.session.close()
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async def execute_agent_task(self, agent_id: str, task: Dict) -> Dict:
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"""Execute a task on a specific agent"""
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agent = AGENTS[agent_id]
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print(f"🎯 Assigning to {agent_id.upper()}: {task['title']}")
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prompt = f"""You are a {agent['specialty']} specialist working as part of a distributed AI development cluster.
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TASK: {task['title']}
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CONTEXT: We are building intelligent n8n workflows for automatic task allocation based on a 25-person software company model. The existing framework includes role-based AI agent workflows with:
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- Executive Leadership Roles (CEO, CTO, Product Manager)
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- Engineering Roles (Frontend, Backend, DevOps, Security, QA)
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- Support & Business Roles (Technical Writer, Developer Advocate, Marketing, Customer Success)
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- Coordination & Management Roles (Agent Coordinator, Knowledge Manager, Scrum Master)
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Your specific assignment: {task['description']}
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REQUIREMENTS:
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{chr(10).join(f"- {req}" for req in task['requirements'])}
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DELIVERABLES:
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{chr(10).join(f"- {deliverable}" for deliverable in task['deliverables'])}
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Please provide a comprehensive solution that integrates with the existing framework and enhances the automatic task allocation capabilities. Focus on your area of expertise while considering the broader system architecture.
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Respond with detailed technical solutions, code examples, and implementation guidance."""
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try:
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async with self.session.post(
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f"{agent['endpoint']}/api/generate",
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json={
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"model": agent['model'],
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"prompt": prompt,
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"stream": False,
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"options": {
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"num_predict": 4000,
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"temperature": 0.7
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}
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}
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) as response:
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if response.status == 200:
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result = await response.json()
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return {
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"agent": agent_id,
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"task": task['title'],
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"status": "completed",
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"response": result.get('response', ''),
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"model": agent['model'],
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"tokens_generated": result.get('eval_count', 0),
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"generation_time": result.get('eval_duration', 0) / 1000000000, # Convert to seconds
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"timestamp": datetime.now().isoformat()
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}
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else:
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return {
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"agent": agent_id,
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"task": task['title'],
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"status": "failed",
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"error": f"HTTP {response.status}",
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"timestamp": datetime.now().isoformat()
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}
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except Exception as e:
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return {
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"agent": agent_id,
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"task": task['title'],
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"status": "failed",
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"error": str(e),
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"timestamp": datetime.now().isoformat()
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}
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async def coordinate_development(self):
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"""Coordinate the distributed development of n8n workflows"""
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print("🐝 HIVE CLUSTER N8N WORKFLOW DEVELOPMENT COORDINATION")
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print("=" * 70)
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print(f"🚀 Coordinating {len(AGENTS)} specialized AI agents")
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print(f"🎯 Target: Intelligent task allocation workflows for 25-person software company")
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print()
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# Define tasks for each agent
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tasks = {
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"walnut": {
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"title": "Intelligent Task Allocation Algorithm Design",
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"description": "Design the core intelligent task allocation algorithms and workflow architecture for n8n",
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"requirements": [
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"Analyze agent capabilities and performance characteristics",
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"Design dynamic task routing based on complexity and specialty",
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"Create load balancing algorithms for optimal resource utilization",
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"Design failure handling and fallback mechanisms",
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"Plan integration with existing role-based workflow system"
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],
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"deliverables": [
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"Task allocation algorithm specifications",
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"Workflow architecture diagrams and documentation",
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"Agent capability mapping and scoring system",
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"Dynamic routing logic and decision trees",
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"Integration plan with existing n8n workflows"
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]
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},
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"ironwood": {
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"title": "Backend APIs and Database Schema for Task Routing",
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"description": "Implement robust backend APIs and database schema for intelligent task routing and monitoring",
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"requirements": [
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"Design REST APIs for task submission and agent management",
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"Create database schema for task tracking and agent performance",
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"Implement real-time task queue management system",
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"Build agent health monitoring and performance metrics",
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"Design webhook endpoints for n8n integration"
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],
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"deliverables": [
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"Complete REST API specification and implementation",
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"Database schema with indexes and performance optimization",
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"Task queue management system with priority handling",
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"Real-time monitoring APIs with metrics collection",
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"Webhook endpoints for seamless n8n integration"
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]
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},
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"acacia": {
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"title": "n8n Workflow Deployment and Cluster Integration",
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"description": "Set up production-ready n8n workflow deployment with full cluster integration",
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"requirements": [
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"Deploy enhanced n8n workflows to production environment",
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"Configure cluster integration with all 6 agents",
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"Set up monitoring and alerting for workflow performance",
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"Implement backup and recovery procedures",
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"Configure security and access controls"
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],
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"deliverables": [
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"Production deployment scripts and configurations",
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"Complete cluster integration with agent discovery",
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"Monitoring dashboard and alerting system",
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"Backup and recovery documentation and scripts",
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"Security configuration and access control setup"
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]
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},
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"rosewood": {
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"title": "Comprehensive Testing and Quality Assurance Workflows",
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"description": "Develop comprehensive testing strategies and quality assurance workflows for the task allocation system",
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"requirements": [
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"Create automated testing suites for all workflow components",
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"Design performance testing and load testing strategies",
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"Implement quality metrics and success criteria",
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"Create integration testing for agent coordination",
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"Design monitoring and alerting for system health"
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],
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"deliverables": [
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"Automated test suites for n8n workflows and APIs",
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"Performance testing framework and benchmarks",
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"Quality metrics dashboard and reporting",
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"Integration testing scenarios and validation",
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"System health monitoring and alerting configuration"
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]
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},
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"oak": {
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"title": "iOS/macOS Integration and Mobile Task Management",
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"description": "Create iOS/macOS integration components for mobile task management and monitoring",
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"requirements": [
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"Design native iOS/macOS app for task monitoring",
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"Create API integration for real-time cluster status",
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"Implement push notifications for task completion",
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"Design mobile-friendly task submission interface",
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"Plan Apple ecosystem integration features"
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],
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"deliverables": [
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"iOS/macOS app design and architecture",
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"API integration specifications and implementation",
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"Push notification system design",
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"Mobile task submission interface mockups",
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"Apple ecosystem integration roadmap"
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]
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}
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}
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# Execute all tasks in parallel
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print("🔄 Executing tasks across the cluster...")
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print()
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task_coroutines = []
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for agent_id, task in tasks.items():
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task_coroutines.append(self.execute_agent_task(agent_id, task))
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# Wait for all tasks to complete
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results = await asyncio.gather(*task_coroutines, return_exceptions=True)
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# Process results
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successful_tasks = 0
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failed_tasks = 0
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print("\n" + "=" * 70)
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print("📊 DEVELOPMENT COORDINATION RESULTS")
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print("=" * 70)
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for result in results:
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if isinstance(result, Exception):
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print(f"❌ Task failed with exception: {result}")
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failed_tasks += 1
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continue
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if result['status'] == 'completed':
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print(f"✅ {result['agent'].upper()}: {result['task']}")
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print(f" 📝 Response: {len(result['response'])} characters")
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print(f" 🎯 Tokens: {result['tokens_generated']}")
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print(f" ⏱️ Time: {result['generation_time']:.1f}s")
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successful_tasks += 1
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else:
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print(f"❌ {result['agent'].upper()}: {result['task']} - {result.get('error', 'Unknown error')}")
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failed_tasks += 1
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print()
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# Save detailed results
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timestamp = int(time.time())
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results_file = f"/home/tony/AI/projects/hive/results/n8n_coordination_{timestamp}.json"
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with open(results_file, 'w') as f:
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json.dump({
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"coordination_summary": {
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"total_agents": len(AGENTS),
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"successful_tasks": successful_tasks,
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"failed_tasks": failed_tasks,
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"coordination_time": datetime.now().isoformat(),
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"target": "n8n intelligent task allocation workflows"
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},
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"task_results": [r for r in results if not isinstance(r, Exception)],
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"agent_configuration": AGENTS
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}, f, indent=2)
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print("🎉 COORDINATION SUMMARY")
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print(f" 📊 Total Agents: {len(AGENTS)}")
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print(f" ✅ Successful: {successful_tasks}")
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print(f" ❌ Failed: {failed_tasks}")
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print(f" 📁 Results: {results_file}")
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print()
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if successful_tasks > 0:
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print("🚀 Next Steps:")
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print(" 1. Review detailed agent responses for implementation details")
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print(" 2. Integrate solutions from each agent into cohesive system")
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print(" 3. Deploy enhanced workflows to n8n production environment")
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print(" 4. Test intelligent task allocation with real workloads")
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print(" 5. Monitor performance and optimize based on metrics")
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return results
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async def main():
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"""Main coordination function"""
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# Ensure results directory exists
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import os
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os.makedirs("/home/tony/AI/projects/hive/results", exist_ok=True)
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async with HiveN8NCoordinator() as coordinator:
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await coordinator.coordinate_development()
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if __name__ == "__main__":
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asyncio.run(main())
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