Mai/.planning/phases/04-memory-context-management/04-05-PLAN.md

phase, plan, type, wave, depends_on, files_modified, autonomous, gap_closure, must_haves

phase	plan	type	wave	depends_on	files_modified	autonomous	gap_closure	must_haves
04-memory-context-management	05	execute	1	04-04	src/memory/personality/adaptation.py src/memory/__init__.py src/personality.py	true	true	truths artifacts key_links Personality layers learn from conversation patterns Personality system integrates with existing personality.py path provides min_lines src/memory/personality/adaptation.py Dynamic personality updates 50 path provides exports src/memory/__init__.py Complete MemoryManager with personality learning PersonalityLearner path provides min_lines src/personality.py Updated personality system with memory integration 20 from to via pattern src/memory/personality/adaptation.py src/memory/personality/layer_manager.py layer updates for adaptation layer_manager.update_layer from to via pattern src/memory/__init__.py src/memory/personality/adaptation.py PersonalityLearner integration PersonalityLearner.*update_personality from to via pattern src/personality.py src/memory/personality/layer_manager.py personality overlay application layer_manager.get_active_layers

Complete personality learning integration by implementing missing PersonalityAdaptation class and connecting all personality learning components to the MemoryManager and existing personality system.

Purpose: Close the personality learning integration gap identified in verification Output: Working personality learning system fully integrated with memory and personality systems

<execution_context> @/.opencode/get-shit-done/workflows/execute-plan.md @/.opencode/get-shit-done/templates/summary.md </execution_context>

@.planning/phases/04-memory-context-management/04-CONTEXT.md @.planning/phases/04-memory-context-management/04-RESEARCH.md @.planning/phases/04-memory-context-management/04-memory-context-management-VERIFICATION.md

Reference existing personality components

@src/memory/personality/pattern_extractor.py @src/memory/personality/layer_manager.py @src/resource/personality.py

Reference memory manager

@src/memory/init.py

Task 1: Implement PersonalityAdaptation class src/memory/personality/adaptation.py Create src/memory/personality/adaptation.py with PersonalityAdaptation class to close the missing file gap:

1. PersonalityAdaptation class with time-weighted learning:

   • update_personality_layer(patterns, layer_id, adaptation_rate)
   • calculate_adaptation_rate(conversation_history, user_feedback)
   • apply_stability_controls(proposed_changes, current_state)
   • integrate_user_feedback(feed_data, layer_weights)

2. Time-weighted learning implementation:

   • Recent conversations have less influence (exponential decay)
   • Historical patterns provide stable baseline
   • Prevent rapid personality swings with rate limiting
   • Confidence scoring for pattern reliability

3. Stability controls:

   • Maximum change per update (e.g., 10% weight shift)
   • Cooling period between major adaptations
   • Core value protection (certain aspects never change)
   • Reversion triggers for unwanted changes

4. Integration methods:

   • import_pattern_data(pattern_extractor, conversation_range)
   • export_layer_config(layer_manager, output_format)
   • validate_layer_consistency(layers, core_personality)

5. Configuration and persistence:

   • Learning rate configuration (slow/medium/fast)
   • Adaptation history tracking
   • Rollback capability for problematic changes
   • Integration with existing memory storage

Follow existing error handling patterns from layer_manager.py. Use similar data structures and method signatures for consistency. python -c "from src.memory.personality.adaptation import PersonalityAdaptation; pa = PersonalityAdaptation(); print('PersonalityAdaptation created successfully')" PersonalityAdaptation class provides time-weighted learning with stability controls

Task 2: Integrate personality learning with MemoryManager src/memory/__init__.py Update src/memory/__init__.py to integrate personality learning and export PersonalityLearner:

1. Import PersonalityAdaptation in memory/personality/init.py:

   • Add from .adaptation import PersonalityAdaptation
   • Update all to include PersonalityAdaptation

2. Create PersonalityLearner class in MemoryManager:

   • Combines PatternExtractor, LayerManager, and PersonalityAdaptation
   • Methods: learn_from_conversations(conversation_range), apply_learning(), get_current_personality()
   • Learning triggers: after conversations, periodic updates, manual requests

3. Integration with existing MemoryManager:

   • Add personality_learner attribute to MemoryManager.init
   • Implement learning_workflow() method for coordinated learning
   • Add personality data persistence to existing storage
   • Provide learning controls (enable/disable, rate, triggers)

4. Export PersonalityLearner from memory/init.py:

   • Add PersonalityLearner to all
   • Ensure it's importable as from src.memory import PersonalityLearner

5. Learning workflow integration:

   • Hook into conversation storage for automatic learning triggers
   • Periodic learning schedule (e.g., daily pattern analysis)
   • Integration with existing configuration system
   • Memory usage monitoring for learning processes

Update existing MemoryManager methods to support personality learning without breaking current functionality. Follow the existing pattern of having feature-specific managers within the main MemoryManager. python -c "from src.memory import PersonalityLearner; pl = PersonalityLearner(); print('PersonalityLearner imported successfully')" PersonalityLearner is integrated with MemoryManager and available for import

Task 3: Create src/personality.py with memory integration src/personality.py Create src/personality.py to integrate with memory personality learning system:

1. Core personality system:

   • Import PersonalityLearner from memory system
   • Maintain core personality values (immutable)
   • Apply learned personality layers as overlays
   • Protect core values from learned modifications

2. Integration with existing personality:

   • Import and extend src/resource/personality.py functionality
   • Add memory integration to existing personality methods
   • Hybrid system prompt + behavior configuration
   • Context-aware personality layer activation

3. Personality application methods:

   • get_personality_response(context, user_input) -> enhanced_response
   • apply_personality_layers(base_response, context) -> final_response
   • get_active_layers(conversation_context) -> List[PersonalityLayer]
   • validate_personality_consistency(applied_layers) -> bool

4. Configuration and control:

   • Learning enable/disable flag
   • Layer activation rules
   • Core value protection settings
   • User feedback integration for personality tuning

5. Integration points:

   • Connect to MemoryManager.PersonalityLearner
   • Use existing personality.py from src/resource as base
   • Ensure compatibility with existing conversation systems
   • Provide clear separation between core and learned personality

Follow the pattern established in src/resource/personality.py but extend it with memory learning integration. Ensure core personality values remain protected while allowing learned layers to enhance responses. python -c "from src.personality import get_personality_response; print('Personality system integration working')" src/personality.py integrates with memory learning while protecting core values

After completion, verify: 1. PersonalityAdaptation class exists and implements time-weighted learning 2. PersonalityLearner is integrated into MemoryManager and exportable 3. src/personality.py exists and integrates with memory personality system 4. Personality learning workflow connects all components (PatternExtractor -> LayerManager -> PersonalityAdaptation) 5. Core personality values are protected from learned modifications 6. Learning system can be enabled/disabled through configuration

<success_criteria>

• Personality learning integration gap is completely closed
• All personality components work together as a cohesive system
• Personality layers learn from conversation patterns over time
• Core personality values remain protected while allowing adaptive learning
• Integration follows existing patterns and maintains code consistency
• System is ready for testing and eventual user verification </success_criteria>

After completion, create `.planning/phases/04-memory-context-management/04-05-SUMMARY.md`