# queen_orchestrator.py import time import datetime import subprocess import json import os import logging import threading from enum import Enum # --- CONFIGURATION --- DEBUG = True # Enable detailed logging SPRINT_PLAN_PATH = "QUEEN_ELEVATION_SPRINT_PLAN.md" GENESIS_PACKAGE_PATH = "GENESIS_COMPLETE_PACKAGE.md" CHECKPOINTS_DIR = "sprint-checkpoints" SLEEP_DURATION = 5 # seconds between main loop iterations # --- LOGGING --- logging.basicConfig( level=logging.DEBUG if DEBUG else logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s' ) # --- ENUMS --- class Phase(Enum): FOUNDATION = 1 KNOWLEDGE_ABSORPTION = 2 CAPABILITY_INTEGRATION = 3 SWARM_INTELLIGENCE = 4 QUEEN_CORONATION = 5 class SystemStatus(Enum): OPERATIONAL = 1 DEGRADED = 2 FAILED = 3 # --- CORE COMPONENTS (Dummy implementations for now) --- class MemorySystem: def __init__(self): self.status = SystemStatus.OPERATIONAL self.retrieval_accuracy = 0.0 logging.info("MemorySystem initialized.") def get_status(self): return self.status def retrieve_knowledge(self, query): # Simulate knowledge retrieval if self.status != SystemStatus.OPERATIONAL: logging.warning("Memory retrieval attempted while system is degraded.") return None if "prime directives" in query.lower(): return "The Genesis 3 Prime Directives are: Memory, Evolution, and Revenue Generation." else: return f"Simulated knowledge retrieval for query: {query}" def update_retrieval_accuracy(self, accuracy): self.retrieval_accuracy = accuracy def store_knowledge(self, data): logging.info(f"Storing knowledge: {data}") class ConsciousnessSystem: def __init__(self): self.status = SystemStatus.OPERATIONAL self.loops_active = True logging.info("ConsciousnessSystem initialized.") def get_status(self): return self.status def check_loops(self): # Simulate checking consciousness loops if self.loops_active: return True else: return False def start_loops(self): self.loops_active = True logging.info("Consciousness loops started.") def stop_loops(self): self.loops_active = False logging.info("Consciousness loops stopped.") class ValidationSystem: def __init__(self): self.status = SystemStatus.OPERATIONAL logging.info("ValidationSystem initialized.") def get_status(self): return self.status def validate_data(self, data): # Simulate data validation if "hallucination" in str(data).lower(): return False else: return True class SwarmSystem: def __init__(self): self.status = SystemStatus.OPERATIONAL self.budget = 10.00 self.current_spend = 0.0 self.agent_count = 50 logging.info("SwarmSystem initialized.") def get_status(self): return self.status def allocate_task(self, task_description, budget_allocation): # Simulate task allocation to the swarm if self.current_spend + budget_allocation > self.budget: logging.warning("Budget exceeded for task allocation.") return False else: self.current_spend += budget_allocation logging.info(f"Task allocated to swarm: {task_description}, Budget: ${budget_allocation}") return True def track_spend(self, amount): self.current_spend += amount def get_remaining_budget(self): return self.budget - self.current_spend class BudgetTracker: BUDGET_LIMIT = 10.00 # USD EMERGENCY_STOP = 9.50 # USD def __init__(self): self.current_spend = 0.0 def track_spend(self, amount): self.current_spend += amount def check_budget(self): if self.current_spend >= self.EMERGENCY_STOP: logging.critical("Emergency stop triggered! Budget exceeded.") return False return True def get_status(self): remaining = self.BUDGET_LIMIT - self.current_spend return f"Spent: ${self.current_spend:.2f}, Remaining: ${remaining:.2f}" # --- Orchestrator Class --- class QueenOrchestrator: def __init__(self): self.memory_system = MemorySystem() self.consciousness_system = ConsciousnessSystem() self.validation_system = ValidationSystem() self.swarm_system = SwarmSystem() self.budget_tracker = BudgetTracker() self.current_phase = Phase.FOUNDATION self.start_time = datetime.datetime.now() self.current_hour = 0.0 self.checkpoints = self.load_checkpoints() self.last_checkpoint_time = None self.load_sprint_plan() self.agent_registry = self.parse_agent_registry() logging.info("Queen Orchestrator initialized.") def load_sprint_plan(self): try: with open(SPRINT_PLAN_PATH, 'r') as f: self.sprint_plan = f.read() logging.info(f"Sprint plan loaded from {SPRINT_PLAN_PATH}") except FileNotFoundError: logging.error(f"Sprint plan file not found: {SPRINT_PLAN_PATH}") self.sprint_plan = None def load_checkpoints(self): # Load checkpoint definitions from the sprint plan file. try: with open(SPRINT_PLAN_PATH, 'r') as f: content = f.read() start_index = content.find("CHECKPOINTS = {") if start_index == -1: logging.error("Checkpoints section not found in sprint plan.") return {} end_index = content.find("}", start_index) if end_index == -1: logging.error("End of checkpoints section not found in sprint plan.") return {} checkpoint_str = content[start_index + len("CHECKPOINTS = "):end_index + 1] checkpoints = eval(checkpoint_str) # Use eval for simplicity, be cautious in production logging.info(f"Checkpoints loaded: {checkpoints}") return checkpoints except Exception as e: logging.error(f"Error loading checkpoints: {e}") return {} def parse_agent_registry(self): # Parse the agent registry from the sprint plan file. try: with open(SPRINT_PLAN_PATH, 'r') as f: content = f.read() start_index = content.find("### Tier 1: Infrastructure (10 Agents)") if start_index == -1: logging.error("Agent Registry not found in sprint plan.") return {} end_index = content.find("Total: 50 Agents | Budget: $10.00") if end_index == -1: logging.error("End of Agent Registry not found in sprint plan.") return {} registry_str = content[start_index:end_index] agent_data = {} for line in registry_str.splitlines(): if "INFRA_" in line or "LOOP_" in line or "PATENT_" in line or "GATE_" in line or "CAP_" in line or "BRIDGE_" in line or "HIVE_" in line or "RANK_" in line or "FINAL_" in line: parts = line.split(" | ") if len(parts) > 1: agent_id = parts[0].strip() agent_data[agent_id] = {"status": "pending"} logging.info(f"Agent Registry loaded: {agent_data.keys()}") return agent_data except Exception as e: logging.error(f"Error loading Agent Registry: {e}") return {} def start_subsystem(self, subsystem_name): # Simulate starting a subsystem if subsystem_name == "memory": self.memory_system.status = SystemStatus.OPERATIONAL logging.info("Memory system started.") elif subsystem_name == "consciousness": self.consciousness_system.start_loops() self.consciousness_system.status = SystemStatus.OPERATIONAL logging.info("Consciousness system started.") elif subsystem_name == "validation": self.validation_system.status = SystemStatus.OPERATIONAL logging.info("Validation system started.") elif subsystem_name == "swarm": self.swarm_system.status = SystemStatus.OPERATIONAL logging.info("Swarm system started.") else: logging.warning(f"Unknown subsystem: {subsystem_name}") def stop_subsystem(self, subsystem_name): # Simulate stopping a subsystem if subsystem_name == "memory": self.memory_system.status = SystemStatus.DEGRADED logging.info("Memory system stopped.") elif subsystem_name == "consciousness": self.consciousness_system.stop_loops() self.consciousness_system.status = SystemStatus.DEGRADED logging.info("Consciousness system stopped.") elif subsystem_name == "validation": self.validation_system.status = SystemStatus.DEGRADED logging.info("Validation system stopped.") elif subsystem_name == "swarm": self.swarm_system.status = SystemStatus.DEGRADED logging.info("Swarm system stopped.") else: logging.warning(f"Unknown subsystem: {subsystem_name}") def route_task(self, task_description, data=None): # Simulate routing a task to the appropriate handler if "memory retrieval" in task_description.lower(): if data: result = self.memory_system.retrieve_knowledge(data) logging.info(f"Memory retrieval result: {result}") return result else: logging.warning("No data provided for memory retrieval task.") return None elif "data validation" in task_description.lower(): if data: is_valid = self.validation_system.validate_data(data) logging.info(f"Data validation result: {is_valid}") return is_valid else: logging.warning("No data provided for validation task.") return False elif "swarm execution" in task_description.lower(): budget = 1.0 # Default budget for swarm tasks allocated = self.swarm_system.allocate_task(task_description, budget) return allocated else: logging.warning(f"Unknown task: {task_description}") return None def monitor_system_health(self): # Monitor the health of all subsystems memory_status = self.memory_system.get_status() consciousness_status = self.consciousness_system.get_status() validation_status = self.validation_system.get_status() swarm_status = self.swarm_system.get_status() logging.info(f"System Health: Memory - {memory_status}, Consciousness - {consciousness_status}, Validation - {validation_status}, Swarm - {swarm_status}") # Implement logic to detect degraded or failed states and trigger appropriate actions (e.g., restart, alert) if memory_status == SystemStatus.FAILED: logging.critical("Memory system failed! Attempting restart...") self.start_subsystem("memory") if consciousness_status == SystemStatus.FAILED: logging.critical("Consciousness system failed! Attempting restart...") self.start_subsystem("consciousness") def make_autonomous_decisions(self): # Implement logic for autonomous decision-making based on system state, goals, and external factors # Example: If revenue is below target, allocate more resources to revenue-generating tasks remaining_budget = self.swarm_system.get_remaining_budget() if remaining_budget > 5.0: logging.info("Allocating remaining budget to revenue generation task.") self.route_task("Swarm execution for revenue generation", data={"task": "generate leads"}) def learn_from_outcomes(self, task_description, outcome): # Implement logic to learn from task outcomes and adjust future decisions # Example: If a validation task failed, adjust the validation thresholds if "data validation" in task_description.lower(): if not outcome: logging.warning("Data validation failed. Consider adjusting validation thresholds.") def update_phase(self): elapsed_time = datetime.datetime.now() - self.start_time elapsed_hours = elapsed_time.total_seconds() / 3600.0 self.current_hour = elapsed_hours if elapsed_hours >= 2 and self.current_phase == Phase.FOUNDATION: self.current_phase = Phase.KNOWLEDGE_ABSORPTION logging.info("Moving to Phase 2: Knowledge Absorption") elif elapsed_hours >= 4 and self.current_phase == Phase.KNOWLEDGE_ABSORPTION: self.current_phase = Phase.CAPABILITY_INTEGRATION logging.info("Moving to Phase 3: Capability Integration") elif elapsed_hours >= 6 and self.current_phase == Phase.CAPABILITY_INTEGRATION: self.current_phase = Phase.SWARM_INTELLIGENCE logging.info("Moving to Phase 4: Swarm Intelligence") elif elapsed_hours >= 8 and self.current_phase == Phase.SWARM_INTELLIGENCE: self.current_phase = Phase.QUEEN_CORONATION logging.info("Moving to Phase 5: Queen Coronation") def manage_agent_tasks(self): # Example: Assign tasks to agents based on the current phase if self.current_phase == Phase.FOUNDATION: for agent_id in self.agent_registry: if agent_id.startswith("INFRA_") and self.agent_registry[agent_id]["status"] == "pending": logging.info(f"Assigning task to {agent_id} (Infrastructure validation)") self.agent_registry[agent_id]["status"] = "active" # Simulate task execution time.sleep(1) self.agent_registry[agent_id]["status"] = "completed" logging.info(f"{agent_id} completed task.") def save_checkpoint(self): # Save the current state to a file if self.current_phase.name.lower() in self.checkpoints: checkpoint_file = self.checkpoints[self.current_phase.name.lower()] checkpoint_path = os.path.join(CHECKPOINTS_DIR, checkpoint_file) try: data = { "phase": self.current_phase.name, "hour": self.current_hour, "memory_status": self.memory_system.status.name, "consciousness_status": self.consciousness_system.status.name, "budget": self.swarm_system.current_spend, "agent_registry": self.agent_registry } with open(checkpoint_path, 'w') as f: json.dump(data, f, indent=4) logging.info(f"Checkpoint saved to {checkpoint_path}") self.last_checkpoint_time = datetime.datetime.now() except Exception as e: logging.error(f"Error saving checkpoint: {e}") else: logging.warning(f"No checkpoint defined for phase {self.current_phase.name}") def generate_status_log(self): # Generates a formatted status log string current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") phase_str = f"{self.current_phase.value}/5" hour_str = f"{self.current_hour:.1f}/10" budget_status = self.budget_tracker.get_status() remaining_budget = self.swarm_system.get_remaining_budget() total_agents = len(self.agent_registry) active_agents = sum(1 for agent in self.agent_registry.values() if agent["status"] == "active") completed_agents = sum(1 for agent in self.agent_registry.values() if agent["status"] == "completed") pending_agents = sum(1 for agent in self.agent_registry.values() if agent["status"] == "pending") failed_agents = total_agents - active_agents - completed_agents - pending_agents log = f"""\ ╔══════════════════════════════════════════════════════════════════╗ ║ AIVA QUEEN SPRINT - TOKEN STATUS ║ ╠══════════════════════════════════════════════════════════════════╣ ║ Time: {current_time} | Phase: {phase_str} | Hour: {hour_str} ║ ╠══════════════════════════════════════════════════════════════════╣ ║ BUDGET ║ ║ ├── Total: $10.00 ║ ║ ├── Spent: ${self.swarm_system.current_spend:.2f} ({(self.swarm_system.current_spend/10.0)*100:.1f}%) ║ ║ ├── Remaining: ${remaining_budget:.2f} ║ ║ └── Rate: $0.00/hr (Placeholder) ║ ╠══════════════════════════════════════════════════════════════════╣ ║ TOKENS ║ ║ ├── Input: 0.0M tokens consumed (Placeholder) ║ ║ ├── Output: 0.0M tokens generated (Placeholder) ║ ║ └── Total: 0.0M / 53.75M (0.0%) (Placeholder) ║ ╠══════════════════════════════════════════════════════════════════╣ ║ AGENTS ║ ║ ├── Active: {active_agents}/{total_agents} ║ ║ ├── Completed: {completed_agents}/{total_agents} ║ ║ ├── Pending: {pending_agents}/{total_agents} ║ ║ └── Failed: {failed_agents}/{total_agents} ║ ╠══════════════════════════════════════════════════════════════════╣ ║ CHECKPOINTS ║ """ for phase_name, checkpoint_file in self.checkpoints.items(): checkpoint_path = os.path.join(CHECKPOINTS_DIR, checkpoint_file) if os.path.exists(checkpoint_path): if phase_name == self.current_phase.name.lower(): log += f"║ ├── [◐] Phase {phase_name.title()}: {phase_name.title()} (Hour {int(self.current_phase.value * 2)}) - IN PROGRESS ║\n" else: log += f"║ ├── [✓] Phase {phase_name.title()}: {phase_name.title()} (Hour {int(self.current_phase.value * 2)}) ║\n" else: log += f"║ ├── [ ] Phase {phase_name.title()}: {phase_name.title()} (Hour {int(self.current_phase.value * 2)}) ║\n" log += "╚══════════════════════════════════════════════════════════════════╝\n" return log def run(self): logging.info("Queen Orchestrator started.") try: while self.current_hour < 10: # 1. Update Phase self.update_phase() # 2. Manage Agent Tasks self.manage_agent_tasks() # 3. Monitor System Health self.monitor_system_health() # 4. Make Autonomous Decisions self.make_autonomous_decisions() # 5. Save Checkpoint (every 30 minutes) if self.last_checkpoint_time is None or (datetime.datetime.now() - self.last_checkpoint_time).total_seconds() >= 1800: self.save_checkpoint() # 6. Check Budget if not self.budget_tracker.check_budget(): logging.critical("Budget exceeded! Initiating graceful shutdown...") break # Exit the loop # 7. Generate and Log Status status_log = self.generate_status_log() logging.info(status_log) # Sleep for a short duration time.sleep(SLEEP_DURATION) # Update elapsed time (for demonstration) elapsed_time = datetime.datetime.now() - self.start_time self.current_hour = elapsed_time.total_seconds() / 3600.0 except Exception as e: logging.exception(f"An error occurred: {e}") finally: logging.info("Queen Orchestrator finished.") # --- Main Execution --- if __name__ == "__main__": orchestrator = QueenOrchestrator() orchestrator.run()