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- Initialize analytics service on app startup in main.tsx
- Integrate analytics consent management in App.tsx
- Track app lifecycle events (start, screen changes)
- Update Tauri configuration for production build
- Set up proper analytics shutdown on app close
- Ensure analytics is initialized before other services
This completes the analytics integration setup with proper
initialization and lifecycle management.
- Track MCP server additions with configuration method (manual/preset/import)
- Monitor server connections and disconnections with success metrics
- Record server removal events with connection state
- Track MCP tool invocations with source attribution
- Monitor connection errors with retry attempts
- Add performance tracking for server operations
These metrics help understand MCP server usage patterns and
identify connection reliability issues.
- Track prompt submissions with detailed metrics (length, complexity, attachments)
- Monitor session lifecycle (start, stop, duration, engagement)
- Record tool executions with performance and success metrics
- Track checkpoint creation and restoration events
- Implement enhanced session metrics including:
- Time to first message
- Average response time
- Files created/modified/deleted count
- Error frequency and recovery attempts
- Token usage and code generation metrics
- Add session engagement scoring
- Monitor conversation abandonment patterns
- Track agent execution context when applicable
This provides deep insights into user interactions and session
quality for improving the AI coding experience.
- Create AnalyticsErrorBoundary component to catch and track UI errors
- Implement automatic error reporting to analytics on component failures
- Provide customizable fallback UI for error states
- Add withAnalyticsErrorBoundary HOC for easy component wrapping
- Include error recovery functionality with reset capability
- Track component stack information for debugging
This ensures all UI errors are captured and reported for better
application stability monitoring and debugging.
- Add ResourceMonitor for tracking system resource usage (memory, CPU, network)
- Implement API request tracking with performance metrics and error monitoring
- Create usePerformanceMonitor hook for component-level performance tracking
- Add useAsyncPerformanceTracker for async operation monitoring
- Track memory warnings, performance bottlenecks, and network failures
- Support configurable thresholds for resource usage alerts
- Implement periodic sampling with intelligent reporting
These utilities enable proactive performance monitoring to identify
and address bottlenecks before they impact user experience.
