Refactor cockpit to use DockerTmuxController pattern
Based on claude-code-tools TmuxCLIController, this refactor: - Added DockerTmuxController class for robust tmux session management - Implements send_keys() with configurable delay_enter - Implements capture_pane() for output retrieval - Implements wait_for_prompt() for pattern-based completion detection - Implements wait_for_idle() for content-hash-based idle detection - Implements wait_for_shell_prompt() for shell prompt detection Also includes workflow improvements: - Pre-task git snapshot before agent execution - Post-task commit protocol in agent guidelines Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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admin
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lib/learning_context_patch.py
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254
lib/learning_context_patch.py
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#!/usr/bin/env python3
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"""
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Learning Context Patch - Integrates AutonomousLearningIntegration with SubAgentContextManager
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This module provides initialization code to connect the autonomous learning system
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with the sub-agent context manager in the orchestrator startup sequence.
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Usage in orchestrator startup:
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from sub_agent_context import SubAgentContextManager
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from autonomous_learning_integration import AutonomousLearningIntegration
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from learning_context_patch import initialize_learning_for_orchestrator
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# After creating SubAgentContextManager:
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context_manager = SubAgentContextManager()
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learning = initialize_learning_for_orchestrator(context_manager)
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"""
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import logging
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from typing import Optional, Dict, Any
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from pathlib import Path
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logger = logging.getLogger(__name__)
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def initialize_learning_for_orchestrator(
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context_manager,
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learning_config_path: Optional[Path] = None
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) -> 'AutonomousLearningIntegration':
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"""
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Initialize and connect learning system to sub-agent orchestrator.
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Args:
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context_manager: SubAgentContextManager instance
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learning_config_path: Optional path to learning_config.json
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Returns:
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Initialized AutonomousLearningIntegration instance
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"""
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try:
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from autonomous_learning_integration import AutonomousLearningIntegration
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# Initialize learning system
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if learning_config_path is None:
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learning_config_path = Path("/etc/luzia/learning_config.json")
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learning = AutonomousLearningIntegration(config_path=learning_config_path)
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# Connect context manager
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learning.set_context_manager(context_manager)
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# Register metrics provider callback
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metrics_provider = _create_metrics_provider(context_manager)
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learning.set_metrics_provider(metrics_provider)
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logger.info("Autonomous learning system initialized and connected")
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return learning
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except Exception as e:
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logger.error(f"Failed to initialize learning system: {e}")
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raise
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def create_active_task_stream_listener(
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context_manager,
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learning_system: 'AutonomousLearningIntegration'
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) -> callable:
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"""
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Create listener for active task stream.
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Connects to task execution stream and records tasks for learning analysis.
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Args:
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context_manager: SubAgentContextManager instance
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learning_system: AutonomousLearningIntegration instance
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Returns:
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Callback function for task stream events
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"""
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def on_task_event(event: Dict[str, Any]) -> None:
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"""Handle task stream event"""
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try:
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# Extract task info
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task_info = {
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"task_id": event.get("task_id"),
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"parent_task_id": event.get("parent_task_id"),
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"status": event.get("status"), # success, failed, pending
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"latency": event.get("latency", 0), # milliseconds
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"sub_agents_used": event.get("sub_agents_used", 0),
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"timestamp": event.get("timestamp"),
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"phase": event.get("phase"),
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"success": event.get("status") == "success"
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}
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# Record for learning
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learning_system.record_task(task_info)
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logger.debug(f"Task recorded: {task_info['task_id']} ({task_info['status']})")
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except Exception as e:
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logger.error(f"Error processing task event: {e}")
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return on_task_event
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def _create_metrics_provider(context_manager) -> callable:
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"""
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Create metrics provider callback for learning system.
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Returns context about current sub-agent coordination state.
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Args:
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context_manager: SubAgentContextManager instance
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Returns:
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Callback that returns coordination metrics
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"""
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def get_metrics() -> Dict[str, Any]:
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"""Get current coordination metrics"""
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try:
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import psutil
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# Get system metrics
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cpu_percent = psutil.cpu_percent(interval=0.1)
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memory = psutil.virtual_memory()
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# Get sub-agent count
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total_agents = len(context_manager.active_contexts)
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metrics = {
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"cpu_percent": cpu_percent,
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"memory_mb": memory.available / 1024 / 1024,
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"sub_agent_count": total_agents,
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"active_agents": sum(
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1 for ctx in context_manager.active_contexts.values()
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if ctx and ctx.phase_progression
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),
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"parallel_slots": max(1, total_agents),
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"timestamp": str(__import__('datetime').datetime.utcnow().isoformat())
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}
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return metrics
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except Exception as e:
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logger.error(f"Error getting metrics: {e}")
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return {
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"cpu_percent": 0,
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"memory_mb": 0,
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"sub_agent_count": 0,
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"active_agents": 0,
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"parallel_slots": 1
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}
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return get_metrics
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def patch_sub_agent_context_manager(context_manager) -> None:
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"""
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Add learning-specific methods to SubAgentContextManager.
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Modifies SubAgentContextManager instance to include learning-aware features:
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- Track task execution for learning
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- Record coordination metrics
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- Callback on phase transitions
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Args:
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context_manager: SubAgentContextManager instance to patch
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"""
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original_update_phase = context_manager.update_phase
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def update_phase_with_learning(sub_agent_id: str, phase_name: str, status: str, **kwargs):
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"""Wrapped update_phase that records to learning system"""
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result = original_update_phase(sub_agent_id, phase_name, status, **kwargs)
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# Log phase transition for learning
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if hasattr(context_manager, '_learning_system'):
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learning = context_manager._learning_system
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task_info = {
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"sub_agent_id": sub_agent_id,
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"phase": phase_name,
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"phase_status": status,
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"timestamp": str(__import__('datetime').datetime.utcnow().isoformat())
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}
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learning.record_task(task_info)
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return result
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context_manager.update_phase = update_phase_with_learning
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logger.debug("Patched SubAgentContextManager with learning awareness")
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def start_learning_on_orchestrator_startup(
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context_manager,
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learning_system: 'AutonomousLearningIntegration'
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) -> None:
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"""
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Start autonomous learning system during orchestrator startup.
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Call this during orchestrator initialization to activate the learning cycle.
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Args:
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context_manager: SubAgentContextManager instance
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learning_system: AutonomousLearningIntegration instance
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"""
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try:
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# Attach learning system to context manager for callbacks
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context_manager._learning_system = learning_system
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# Patch context manager for learning awareness
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patch_sub_agent_context_manager(context_manager)
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# Start learning cycle
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learning_system.start_learning()
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logger.info("Autonomous learning cycle started on orchestrator startup")
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except Exception as e:
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logger.error(f"Failed to start learning: {e}")
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raise
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def create_orchestrator_startup_hook():
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"""
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Create a startup hook for use in orchestrator initialization.
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Returns:
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Async function suitable for orchestrator.on_startup()
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"""
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async def startup_hook(orchestrator_state: Dict[str, Any]) -> None:
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"""Initialize learning system during orchestrator startup"""
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try:
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from autonomous_learning_integration import AutonomousLearningIntegration
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# Get or create context manager
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if 'context_manager' not in orchestrator_state:
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from sub_agent_context import SubAgentContextManager
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orchestrator_state['context_manager'] = SubAgentContextManager()
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context_manager = orchestrator_state['context_manager']
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# Initialize learning
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learning = initialize_learning_for_orchestrator(context_manager)
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orchestrator_state['learning_system'] = learning
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# Start learning cycle
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start_learning_on_orchestrator_startup(context_manager, learning)
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logger.info("Learning system initialized on orchestrator startup")
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except Exception as e:
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logger.error(f"Startup hook failed: {e}")
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raise
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return startup_hook
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