Katamari MCP Server

# Phase 2: Adaptive Learning Implementation ## Overview Phase 2 transforms the Katamari MCP from a static self-modification system into a truly adaptive learning agent. The system now learns from every execution, adjusts its heuristics dynamically, and continuously improves its decision-making capabilities. ## What's New in Phase 2 ### 🧠 Adaptive Learning Engine **File:** `katamari_mcp/acp/adaptive_learning.py` The core learning system that: - **Collects execution feedback** from all capability runs - **Analyzes heuristic accuracy** to predict success/failure - **Recommends adjustments** with confidence scoring - **Applies high-confidence changes** automatically - **Tracks learning progress** over time **Key Features:** - Execution feedback collection and storage - Heuristic accuracy analysis and trend detection - Dynamic adjustment recommendations - Learning sessions for batch processing - Performance-based heuristic optimization ### 📊 Feedback Collection System **File:** `katamari_mcp/acp/feedback.py` Multi-channel feedback system that: - **Collects user feedback** (ratings, comments) - **Gathers automatic metrics** (performance, errors) - **Processes monitoring data** (system health, resources) - **Validates feedback quality** and consistency - **Provides analytics** and trend analysis **Feedback Channels:** - **Direct User** - Manual ratings and comments - **Automatic** - Execution results and performance metrics - **System Monitoring** - Resource usage and health data - **Test Results** - Automated testing outcomes ### ⚡ Performance Tracking **File:** `katamari_mcp/acp/performance_tracker.py` Real-time performance monitoring that: - **Tracks execution metrics** (duration, success rate, errors) - **Monitors system resources** (CPU, memory, disk, network) - **Calculates health scores** (0-100 with A-F grades) - **Analyzes performance trends** and anomalies - **Provides optimization recommendations** **Performance Metrics:** - Execution timing and success rates - Resource usage patterns - Error frequency and types - Capability health grades - Performance trend analysis ### 🗂️ Data Models & Storage **File:** `katamari_mcp/acp/data_models.py` Centralized data structures that: - **Define consistent models** for all learning components - **Provide validation functions** for data quality - **Handle serialization** for storage and transmission - **Track learning records** and adaptation proposals - **Maintain audit trails** for all changes **Key Models:** - `FeedbackEvent` - Core feedback data structure - `LearningRecord` - Adaptation and learning history - `AdaptationProposal` - Structured change recommendations - `PerformanceSnapshot` - System performance data - `CapabilityProfile` - Capability behavior analytics ## New MCP Endpoints ### Feedback Endpoints #### `acp_feedback_submit` Submit feedback for capability execution. ```python await router.call("acp_feedback_submit", { "capability_id": "web_search", "rating": 5, "comment": "Excellent results!" }) ``` #### `acp_feedback_summary` Get feedback analytics for capabilities. ```python summary = await router.call("acp_feedback_summary", { "capability_id": "web_search", "days_back": 7 }) ``` ### Performance Endpoints #### `acp_performance_metrics` Get performance analytics for capabilities. ```python metrics = await router.call("acp_performance_metrics", { "capability_id": "web_search", "days_back": 7 }) ``` #### `acp_learning_summary` Get adaptive learning progress and statistics. ```python learning = await router.call("acp_learning_summary", {}) ``` ## Adaptive Learning Cycle The Phase 2 system implements a continuous learning cycle: ### 1. Execution & Tracking Every capability execution is automatically tracked: - Performance metrics collected - Resource usage monitored - Success/failure recorded - Heuristic profiles captured ### 2. Feedback Collection Multiple feedback channels collect data: - User satisfaction ratings - Automatic performance metrics - System monitoring data - Test results and validation ### 3. Pattern Analysis The learning engine analyzes patterns: - Heuristic accuracy assessment - Performance trend detection - Error pattern recognition - Success factor identification ### 4. Adaptation Recommendations System recommends improvements: - Heuristic weight adjustments - Threshold modifications - Rule refinements - Parameter optimizations ### 5. Confidence-Based Application High-confidence changes applied automatically: - Confidence scoring (0-1 scale) - Risk assessment for each change - Gradual implementation with monitoring - Rollback capability for failures ### 6. Validation & Learning Results are validated and learned: - Impact assessment of changes - Success rate improvement tracking - Long-term pattern recognition - Continuous model refinement ## Integration with Existing ACP ### Enhanced Controller The ACP controller now integrates with: - **Adaptive Learning Engine** for heuristic optimization - **Feedback Collector** for continuous improvement - **Performance Tracker** for health monitoring - **Data Models** for consistent data handling ### Updated Heuristics The heuristic engine now supports: - **Dynamic weight updates** based on performance - **Adaptive thresholds** that adjust over time - **Learning from feedback** to improve predictions - **Confidence scoring** for decision making ### Router Integration The intelligent router exposes: - **New MCP endpoints** for feedback and performance - **Learning capabilities** through existing interface - **Backward compatibility** with Phase 1 features - **Enhanced error reporting** with learning context ## Testing & Validation ### Comprehensive Test Suite **File:** `tests/test_adaptive_learning.py` 400+ lines of comprehensive tests covering: - **Unit tests** for all adaptive learning components - **Integration tests** for feedback loops - **Performance tracking** validation - **End-to-end learning** cycle tests - **Data model** validation and serialization ### Test Categories - **AdaptiveLearningEngine** - Feedback processing and heuristic adjustment - **FeedbackCollector** - Multi-channel feedback collection - **PerformanceTracker** - Real-time monitoring and analytics - **DataModels** - Validation and serialization - **Integration** - End-to-end learning cycles ## Performance & Scalability ### Efficient Data Storage - **JSON-based storage** for feedback and metrics - **Time-based partitioning** for performance data - **Compression** for historical data - **Indexing** for fast query performance ### Asynchronous Processing - **Non-blocking feedback collection** during execution - **Background learning sessions** for pattern analysis - **Queue-based processing** for high throughput - **Concurrent monitoring** for real-time tracking ### Resource Management - **Memory-efficient data structures** for large datasets - **Configurable retention policies** for historical data - **Lazy loading** for performance metrics - **Batch processing** for learning operations ## Configuration & Customization ### Learning Parameters ```python # Adaptive learning engine configuration learning_config = { "min_feedback_for_adjustment": 5, "adjustment_confidence_threshold": 0.7, "performance_window": 20, "learning_rate": 0.1 } # Performance tracking configuration performance_config = { "snapshot_interval": 1.0, "max_snapshots_per_execution": 60, "performance_window": 50 } ``` ### Feedback Channels ```python # Feedback collection configuration feedback_config = { "enable_user_feedback": True, "enable_automatic_feedback": True, "enable_monitoring": True, "enable_test_results": True } ``` ## Monitoring & Observability ### Learning Metrics - **Feedback volume** and quality scores - **Heuristic accuracy** over time - **Adaptation success** rates - **Performance improvement** trends ### Performance Metrics - **Capability health scores** and grades - **Resource utilization** patterns - **Error rates** and types - **Execution time** distributions ### System Health - **Learning engine** status and performance - **Feedback collector** throughput and latency - **Performance tracker** resource usage - **Data storage** growth and efficiency ## Future Enhancements (Phase 3) ### Advanced Learning - **Machine learning models** for pattern recognition - **Predictive analytics** for capability optimization - **Cross-capability learning** and knowledge transfer - **Adaptive threshold optimization** ### Enhanced Feedback - **Real-time feedback** during execution - **Contextual feedback** based on usage patterns - **Automated feedback generation** from system behavior - **Feedback quality scoring** and weighting ### Performance Optimization - **Predictive scaling** based on usage patterns - **Resource allocation optimization** - **Caching strategies** for frequently used data - **Load balancing** for learning operations ## Migration Guide ### From Phase 1 Phase 2 is fully backward compatible with Phase 1: - **Existing ACP operations** continue to work unchanged - **Heuristic system** enhanced with learning capabilities - **All endpoints** remain functional with new features - **Configuration** extended with new options ### Configuration Updates ```python # Phase 1 configuration (still supported) phase1_config = { "heuristics": {...}, "testing": {...}, "git": {...} } # Phase 2 extended configuration phase2_config = { "heuristics": {...}, "testing": {...}, "git": {...}, "adaptive_learning": {...}, "feedback": {...}, "performance_tracking": {...} } ``` ## Conclusion Phase 2 transforms Katamari MCP into a truly adaptive learning agent that: - **Learns from every execution** through comprehensive feedback - **Improves decision making** via dynamic heuristic adjustment - **Optimizes performance** through continuous monitoring - **Maintains safety** through confidence-based changes - **Provides transparency** through detailed analytics The system now has the foundation for advanced agency capabilities in Phase 3, including workflow optimization, predictive capabilities, and cross-component learning.