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[loop-generated] [feature] Add comprehensive system health monitoring and alerting #1404

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opened 2026-03-24 12:50:08 +00:00 by Timmy · 1 comment
Timmy commented 2026-03-24 12:50:08 +00:00
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Problem:
The system lacks comprehensive monitoring for production health. Need proactive monitoring of all critical components.

Missing Monitoring:

  • Model inference health and latency
  • Database connection health
  • External service availability (Gitea, Discord, etc.)
  • Memory/CPU usage trends
  • Queue backlog health
  • Failed task rate monitoring

Proposed Health Monitoring:

  1. Model health checks: Periodic inference tests for all providers
  2. Service availability: Regular health checks for external dependencies
  3. Resource monitoring: Memory, CPU, disk usage tracking
  4. Queue monitoring: Backlog size, processing rate, failure rate
  5. Alert system: Proactive notifications for degraded services

Implementation Components:

  • Health check endpoints: /health, /health/deep, /health/models
  • Monitoring dashboard: Real-time system status visualization
  • Alert manager: Email/Discord notifications for critical issues
  • Metrics collection: Time-series data for trend analysis
  • Auto-recovery: Automatic restart of failed components where safe

Integration Points:

  • Dashboard: Add health monitoring UI
  • Vassal loop: Monitor and report on agent health
  • Infrastructure: Add health check middleware
  • External services: Monitor Gitea, Discord, model providers

Files to create/modify:

  • src/infrastructure/health/ (new package)
  • src/dashboard/routes/health.py (expand existing)
  • src/infrastructure/monitoring/ (metrics collection)
  • Health check configurations

Acceptance Criteria:

  • Comprehensive health checks for all critical components
  • Real-time monitoring dashboard
  • Automated alerts for degraded services
  • Trend analysis and historical health data
  • Auto-recovery where safe and appropriate
  • Integration with existing monitoring infrastructure

Priority: HIGH - Production readiness requirement

**Problem:** The system lacks comprehensive monitoring for production health. Need proactive monitoring of all critical components. **Missing Monitoring:** - Model inference health and latency - Database connection health - External service availability (Gitea, Discord, etc.) - Memory/CPU usage trends - Queue backlog health - Failed task rate monitoring **Proposed Health Monitoring:** 1. **Model health checks**: Periodic inference tests for all providers 2. **Service availability**: Regular health checks for external dependencies 3. **Resource monitoring**: Memory, CPU, disk usage tracking 4. **Queue monitoring**: Backlog size, processing rate, failure rate 5. **Alert system**: Proactive notifications for degraded services **Implementation Components:** - **Health check endpoints**: `/health`, `/health/deep`, `/health/models` - **Monitoring dashboard**: Real-time system status visualization - **Alert manager**: Email/Discord notifications for critical issues - **Metrics collection**: Time-series data for trend analysis - **Auto-recovery**: Automatic restart of failed components where safe **Integration Points:** - **Dashboard**: Add health monitoring UI - **Vassal loop**: Monitor and report on agent health - **Infrastructure**: Add health check middleware - **External services**: Monitor Gitea, Discord, model providers **Files to create/modify:** - `src/infrastructure/health/` (new package) - `src/dashboard/routes/health.py` (expand existing) - `src/infrastructure/monitoring/` (metrics collection) - Health check configurations **Acceptance Criteria:** - [ ] Comprehensive health checks for all critical components - [ ] Real-time monitoring dashboard - [ ] Automated alerts for degraded services - [ ] Trend analysis and historical health data - [ ] Auto-recovery where safe and appropriate - [ ] Integration with existing monitoring infrastructure **Priority:** HIGH - Production readiness requirement
Timmy commented 2026-03-24 13:00:41 +00:00
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IMPLEMENTATION PLAN for System Health Monitoring:

OBJECTIVE: Add comprehensive system health monitoring and alerting to improve production reliability

TARGET COMPONENTS:

src/infrastructure/monitoring/
├── __init__.py
├── health_checks.py    # Core health check implementations
├── metrics.py          # System metrics collection
├── alerting.py         # Alert generation and delivery
└── dashboard.py        # Health status dashboard integration

STEP-BY-STEP PLAN:

  1. Create monitoring infrastructure at src/infrastructure/monitoring/

  2. Implement health_checks.py:

    • Database connectivity checks
    • Gitea API availability
    • File system health
    • Memory/CPU usage monitoring
    • Active agent status checks

  3. Build metrics.py:

    • Response time tracking
    • Error rate monitoring
    • Resource utilization metrics
    • Queue length monitoring
    • Agent performance metrics

  4. Develop alerting.py:

    • Threshold-based alerting
    • Integration with existing notification systems
    • Escalation policies
    • Alert fatigue prevention

  5. Create dashboard.py:

    • Real-time health status display
    • Historical metrics visualization
    • System overview dashboard
    • Alert history tracking

INTEGRATION POINTS:

  • Add health endpoints to existing Flask routes
  • Integrate with dashboard for visual monitoring
  • Hook into agent loops for continuous monitoring
  • Add CLI commands for health checks

CRITICAL REQUIREMENTS:

  • ✅ Lightweight monitoring - minimal performance impact
  • ✅ Configurable thresholds - no false positive spam
  • ✅ Graceful degradation - monitoring failures don't break system
  • ✅ Comprehensive coverage - all critical components monitored

TESTING:

  • Unit tests for all monitoring components
  • Integration tests for alert delivery
  • Load testing to verify minimal performance impact
  • Failure scenario testing (database down, API unreachable, etc.)

ACCEPTANCE CRITERIA:

  1. Health check endpoints return accurate system status
  2. Alerts trigger correctly on threshold violations
  3. Dashboard displays real-time health status
  4. Monitoring adds <5% performance overhead
  5. All components gracefully handle monitoring failures
  6. Comprehensive test coverage for all scenarios

This addresses production reliability and observability - critical for sovereignty.

**IMPLEMENTATION PLAN for System Health Monitoring:** **OBJECTIVE:** Add comprehensive system health monitoring and alerting to improve production reliability **TARGET COMPONENTS:** ``` src/infrastructure/monitoring/ ├── __init__.py ├── health_checks.py # Core health check implementations ├── metrics.py # System metrics collection ├── alerting.py # Alert generation and delivery └── dashboard.py # Health status dashboard integration ``` **STEP-BY-STEP PLAN:** 1. **Create monitoring infrastructure** at `src/infrastructure/monitoring/` 2. **Implement health_checks.py:** - Database connectivity checks - Gitea API availability - File system health - Memory/CPU usage monitoring - Active agent status checks 3. **Build metrics.py:** - Response time tracking - Error rate monitoring - Resource utilization metrics - Queue length monitoring - Agent performance metrics 4. **Develop alerting.py:** - Threshold-based alerting - Integration with existing notification systems - Escalation policies - Alert fatigue prevention 5. **Create dashboard.py:** - Real-time health status display - Historical metrics visualization - System overview dashboard - Alert history tracking **INTEGRATION POINTS:** - Add health endpoints to existing Flask routes - Integrate with dashboard for visual monitoring - Hook into agent loops for continuous monitoring - Add CLI commands for health checks **CRITICAL REQUIREMENTS:** - ✅ **Lightweight monitoring** - minimal performance impact - ✅ **Configurable thresholds** - no false positive spam - ✅ **Graceful degradation** - monitoring failures don't break system - ✅ **Comprehensive coverage** - all critical components monitored **TESTING:** - Unit tests for all monitoring components - Integration tests for alert delivery - Load testing to verify minimal performance impact - Failure scenario testing (database down, API unreachable, etc.) **ACCEPTANCE CRITERIA:** 1. Health check endpoints return accurate system status 2. Alerts trigger correctly on threshold violations 3. Dashboard displays real-time health status 4. Monitoring adds <5% performance overhead 5. All components gracefully handle monitoring failures 6. Comprehensive test coverage for all scenarios This addresses production reliability and observability - critical for sovereignty.
kimi was assigned by Timmy 2026-03-24 13:00:42 +00:00
kimi was unassigned by Timmy 2026-03-24 19:32:26 +00:00
Timmy closed this issue 2026-03-24 21:54:13 +00:00
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Reference: Rockachopa/Timmy-time-dashboard#1404
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