#\!/usr/bin/env python3 """ Enhanced ZEN Coordinator with Multi-Model Orchestration Based on BeehiveInnovations ZEN MCP architecture """ import json import urllib.request import urllib.parse import logging import os from http.server import HTTPServer, BaseHTTPRequestHandler # Multi-Model Configuration AI_PROVIDERS = { 'gemini': { 'api_key': os.getenv('GEMINI_API_KEY', ''), 'models': ['gemini-pro', 'gemini-pro-vision'], 'strengths': ['reasoning', 'analysis', 'multilingual'] }, 'openai': { 'api_key': os.getenv('OPENAI_API_KEY', ''), 'models': ['gpt-4', 'gpt-3.5-turbo'], 'strengths': ['coding', 'conversation', 'creativity'] }, 'claude': { 'api_key': os.getenv('ANTHROPIC_API_KEY', ''), 'models': ['claude-3-sonnet', 'claude-3-haiku'], 'strengths': ['analysis', 'safety', 'reasoning'] } } # Enhanced MCP Services with AI capabilities MCP_SERVICES = { 'cldmemory': { 'port': 8006, 'container': 'mcp-cldmemory', 'tools': ['store_memory', 'search_memories', 'memory_stats'], 'ai_enhanced': True }, 'filesystem': { 'port': 8001, 'container': 'mcp-filesystem', 'tools': ['file_read', 'file_write', 'file_list'], 'ai_enhanced': False }, 'terminal': { 'port': 8003, 'container': 'mcp-terminal', 'tools': ['terminal_exec', 'shell_command'], 'ai_enhanced': False }, 'research': { 'port': 8011, 'container': 'mcp-research', 'tools': ['research_query', 'web_search'], 'ai_enhanced': True } } # ZEN Workflow Tools (BeehiveInnovations inspired) ZEN_TOOLS = { 'chat': { 'description': 'Multi-model conversation with context preservation', 'models': ['gemini', 'claude', 'openai'], 'auto_select': True }, 'thinkdeep': { 'description': 'Deep analysis using multiple AI perspectives', 'models': ['claude', 'gemini'], 'sequential': True }, 'codereview': { 'description': 'Comprehensive code review with multiple models', 'models': ['openai', 'claude'], 'parallel': True }, 'debug': { 'description': 'Multi-model debugging assistance', 'models': ['openai', 'gemini'], 'auto_select': True }, 'orchestrate': { 'description': 'Coordinate MCP services with AI decision making', 'models': ['claude'], 'mcp_integration': True } } class EnhancedZENCoordinator(BaseHTTPRequestHandler): def do_OPTIONS(self): """Handle CORS preflight""" self.send_response(200) self.send_header('Access-Control-Allow-Origin', '*') self.send_header('Access-Control-Allow-Methods', 'GET, POST, OPTIONS') self.send_header('Access-Control-Allow-Headers', 'Content-Type') self.end_headers() def do_GET(self): """Handle GET requests""" parsed_path = urllib.parse.urlparse(self.path) if parsed_path.path == '/health': self.handle_health() elif parsed_path.path == '/tools': self.handle_tools_list() elif parsed_path.path == '/models': self.handle_models_list() elif parsed_path.path == '/services': self.handle_services_list() else: self.send_error(404, 'Endpoint not found') def do_POST(self): """Handle POST requests""" parsed_path = urllib.parse.urlparse(self.path) if parsed_path.path == '/mcp': self.handle_mcp_request() elif parsed_path.path == '/zen': self.handle_zen_orchestration() elif parsed_path.path == '/multi-model': self.handle_multi_model() else: self.send_error(404, 'Endpoint not found') def handle_health(self): """Enhanced health check""" try: # Check MCP services healthy_services = 0 for service, config in MCP_SERVICES.items(): try: response = urllib.request.urlopen( f"http://{config['container']}:{config['port']}/health", timeout=2 ) if response.getcode() == 200: healthy_services += 1 except: pass # Check AI providers (basic check) available_models = [] for provider, config in AI_PROVIDERS.items(): if config['api_key']: available_models.append(provider) health_data = { 'status': 'healthy', 'service': 'Enhanced ZEN Coordinator', 'port': 8025, 'mcp_services': { 'healthy': healthy_services, 'total': len(MCP_SERVICES) }, 'ai_providers': { 'available': available_models, 'total': len(AI_PROVIDERS) }, 'zen_tools': list(ZEN_TOOLS.keys()), 'features': ['multi_model_orchestration', 'mcp_integration', 'context_preservation'] } self.send_response(200) self.send_header('Content-Type', 'application/json') self.send_header('Access-Control-Allow-Origin', '*') self.end_headers() response_json = json.dumps(health_data, indent=2) self.wfile.write(response_json.encode()) except Exception as e: self.send_error(500, f'Health check failed: {str(e)}') def handle_tools_list(self): """List all available ZEN tools""" tools_data = { 'zen_tools': ZEN_TOOLS, 'mcp_tools': {service: config['tools'] for service, config in MCP_SERVICES.items()}, 'total_tools': len(ZEN_TOOLS) + sum(len(config['tools']) for config in MCP_SERVICES.values()) } self.send_response(200) self.send_header('Content-Type', 'application/json') self.send_header('Access-Control-Allow-Origin', '*') self.end_headers() response_json = json.dumps(tools_data, indent=2) self.wfile.write(response_json.encode()) def handle_models_list(self): """List available AI models""" models_data = { 'providers': AI_PROVIDERS, 'available_models': sum([config['models'] for config in AI_PROVIDERS.values()], []), 'model_strengths': {provider: config['strengths'] for provider, config in AI_PROVIDERS.items()} } self.send_response(200) self.send_header('Content-Type', 'application/json') self.send_header('Access-Control-Allow-Origin', '*') self.end_headers() response_json = json.dumps(models_data, indent=2) self.wfile.write(response_json.encode()) def handle_zen_orchestration(self): """Handle ZEN workflow orchestration""" try: content_length = int(self.headers.get('Content-Length', 0)) post_data = self.rfile.read(content_length) request_data = json.loads(post_data.decode('utf-8')) tool = request_data.get('tool', '') args = request_data.get('arguments', {}) if tool not in ZEN_TOOLS: self.send_error(400, f'Unknown ZEN tool: {tool}') return # For now, return success with tool info (implement actual orchestration later) result = { 'success': True, 'tool': tool, 'config': ZEN_TOOLS[tool], 'message': f'ZEN {tool} orchestration initiated', 'next_steps': ['model_selection', 'context_preparation', 'execution'] } self.send_response(200) self.send_header('Content-Type', 'application/json') self.send_header('Access-Control-Allow-Origin', '*') self.end_headers() response_json = json.dumps(result, indent=2) self.wfile.write(response_json.encode()) except Exception as e: self.send_error(500, f'ZEN orchestration failed: {str(e)}') def handle_mcp_request(self): """Handle MCP requests (legacy compatibility)""" try: content_length = int(self.headers.get('Content-Length', 0)) post_data = self.rfile.read(content_length) request_data = json.loads(post_data.decode('utf-8')) tool = request_data.get('tool', '') args = request_data.get('arguments', {}) # Route to appropriate MCP service for service, config in MCP_SERVICES.items(): if tool in config['tools']: try: # Direct MCP call to service mcp_request = { 'jsonrpc': '2.0', 'id': 'mega-orchestrator', 'method': 'tools/call', 'params': {'name': tool, 'arguments': args} } data = json.dumps(mcp_request).encode('utf-8') req = urllib.request.Request( f"http://{config['container']}:8000/mcp", data=data, headers={'Content-Type': 'application/json'} ) with urllib.request.urlopen(req, timeout=10) as response: result = json.loads(response.read().decode('utf-8')) self.send_response(200) self.send_header('Content-Type', 'application/json') self.send_header('Access-Control-Allow-Origin', '*') self.end_headers() response_json = json.dumps(result, indent=2) self.wfile.write(response_json.encode()) return except Exception as e: self.send_error(502, f'MCP service {service} error: {str(e)}') return self.send_error(400, f'Unknown tool: {tool}') except Exception as e: self.send_error(500, f'MCP request failed: {str(e)}') if __name__ == '__main__': logging.basicConfig(level=logging.INFO) server = HTTPServer(('0.0.0.0', 8025), EnhancedZENCoordinator) print('🚀 Enhanced ZEN Coordinator with Multi-Model Orchestration') print('📊 Features: MCP Integration + AI Provider Support') print('🎯 ZEN Tools:', list(ZEN_TOOLS.keys())) print('🤖 AI Providers:', list(AI_PROVIDERS.keys())) server.serve_forever()