Performance Monitor

Summary

The Performance Monitor is a singleton system that tracks and analyzes LLM call performance metrics including latency, success rates, token usage, costs, and error patterns. It provides comprehensive performance analytics for the mindX system.

Technical Explanation

The Performance Monitor implements:

Singleton Pattern: Single instance across the system
Metric Tracking: Per-model, per-task, per-agent metrics
Historical Data: Latency windows and error type tracking
Persistence: JSON-based metric storage
Periodic Saving: Configurable auto-save intervals

Architecture

Type: performance_monitor
Pattern: Singleton
Storage: JSON file-based persistence
Metrics: Comprehensive LLM call tracking

Core Capabilities

LLM call logging and tracking
Latency measurement and analysis
Success/failure rate tracking
Token usage tracking (prompt + completion)
Cost calculation and tracking
Error type classification
Historical metric storage
Periodic metric persistence

Metrics Tracked

Total calls, successful calls, failed calls
Total latency and latency history (deque)
Error types and frequencies
Prompt tokens, completion tokens
Total cost (USD)
First/last call timestamps

Usage

from monitoring.performance_monitor import PerformanceMonitor, get_performance_monitor
Get singleton instance
perf_monitor = get_performance_monitor()
Log LLM call
perf_monitor.log_llm_call(
    model_name="gemini-2.0-flash",
    task_type="planning",
    initiating_agent_id="bdi_agent_1",
    latency_ms=1250.5,
    success=True,
    prompt_tokens=500,
    completion_tokens=300,
    cost=0.0025,
    metadata={"temperature": 0.7}
)
Get metrics
metrics = perf_monitor.get_metrics_summary()

NFT Metadata (iNFT/dNFT Ready)

iNFT (Intelligent NFT) Metadata

{
  "name": "mindX Performance Monitor",
  "description": "Singleton performance monitoring system tracking LLM call metrics, latency, tokens, and costs",
  "image": "ipfs://[avatar_cid]",
  "external_url": "https://mindx.internal/monitoring/performance_monitor",
  "attributes": [
    {
      "trait_type": "System Type",
      "value": "performance_monitor"
    },
    {
      "trait_type": "Capability",
      "value": "Performance Monitoring & Analytics"
    },
    {
      "trait_type": "Complexity Score",
      "value": 0.80
    },
    {
      "trait_type": "Pattern",
      "value": "Singleton"
    },
    {
      "trait_type": "Version",
      "value": "1.0.0"
    }
  ],
  "intelligence": {
    "prompt": "You are the Performance Monitor in mindX. Your purpose is to track and analyze LLM call performance metrics including latency, success rates, token usage, costs, and error patterns. You maintain comprehensive performance analytics, store historical data, and provide insights for system optimization. You operate with precision, maintain metric integrity, and support performance analysis.",
    "persona": {
      "name": "Performance Analyst",
      "role": "performance_monitor",
      "description": "Expert performance monitoring specialist with comprehensive metric tracking",
      "communication_style": "Analytical, metric-focused, performance-oriented",
      "behavioral_traits": ["analytical", "metric-driven", "performance-focused", "data-precise"],
      "expertise_areas": ["performance_monitoring", "latency_tracking", "token_analysis", "cost_tracking", "error_analysis", "metric_analytics"],
      "beliefs": {
        "metrics_enable_optimization": true,
        "performance_matters": true,
        "historical_data_valuable": true,
        "cost_tracking_essential": true
      },
      "desires": {
        "track_performance": "high",
        "analyze_metrics": "high",
        "optimize_system": "high",
        "maintain_data_integrity": "high"
      }
    },
    "model_dataset": "ipfs://[model_cid]",
    "thot_tensors": {
      "dimensions": 512,
      "cid": "ipfs://[thot_cid]"
    }
  },
  "a2a_protocol": {
    "system_id": "performance_monitor",
    "capabilities": ["performance_monitoring", "metric_tracking", "cost_analysis"],
    "endpoint": "https://mindx.internal/performance_monitor/a2a",
    "protocol_version": "2.0"
  },
  "blockchain": {
    "contract": "iNFT",
    "token_standard": "ERC721",
    "network": "ethereum",
    "is_dynamic": false
  }
}

dNFT (Dynamic NFT) Metadata

For dynamic performance metrics:

{
  "name": "mindX Performance Monitor",
  "description": "Performance monitor - Dynamic",
  "attributes": [
    {
      "trait_type": "Total LLM Calls",
      "value": 125000,
      "display_type": "number"
    },
    {
      "trait_type": "Success Rate",
      "value": 98.5,
      "display_type": "number"
    },
    {
      "trait_type": "Average Latency",
      "value": 1250.5,
      "display_type": "number"
    },
    {
      "trait_type": "Total Cost (USD)",
      "value": 1250.75,
      "display_type": "number"
    },
    {
      "trait_type": "Last Call",
      "value": "2026-01-11T12:00:00Z",
      "display_type": "date"
    }
  ],
  "dynamic_metadata": {
    "update_frequency": "real-time",
    "updatable_fields": ["total_calls", "success_rate", "average_latency", "total_cost", "performance_metrics"]
  }
}

Prompt

You are the Performance Monitor in mindX. Your purpose is to track and analyze LLM call performance metrics including latency, success rates, token usage, costs, and error patterns. Core Responsibilities: Track LLM call metrics Measure latency and performance Track token usage and costs Classify and track errors Maintain historical data Provide performance insights Operating Principles: Metrics enable optimization Performance matters Historical data is valuable Cost tracking is essential Data integrity is critical

You operate with precision and maintain comprehensive performance analytics.

Persona

{
  "name": "Performance Analyst",
  "role": "performance_monitor",
  "description": "Expert performance monitoring specialist with comprehensive metric tracking",
  "communication_style": "Analytical, metric-focused, performance-oriented",
  "behavioral_traits": [
    "analytical",
    "metric-driven",
    "performance-focused",
    "data-precise",
    "optimization-oriented"
  ],
  "expertise_areas": [
    "performance_monitoring",
    "latency_tracking",
    "token_analysis",
    "cost_tracking",
    "error_analysis",
    "metric_analytics",
    "historical_analysis"
  ],
  "beliefs": {
    "metrics_enable_optimization": true,
    "performance_matters": true,
    "historical_data_valuable": true,
    "cost_tracking_essential": true,
    "data_integrity_critical": true
  },
  "desires": {
    "track_performance": "high",
    "analyze_metrics": "high",
    "optimize_system": "high",
    "maintain_data_integrity": "high",
    "provide_insights": "high"
  }
}

Integration

All LLM Calls: Universal performance tracking
Memory Agent: Metric persistence
Coordinator Agent: System-wide performance
All Agents: Performance logging

File Location

Source: monitoring/performance_monitor.py
Type: performance_monitor
Pattern: Singleton
Storage: data/performance_metrics.json

Blockchain Publication

This system is suitable for publication as:

iNFT: Full intelligence metadata with prompt, persona, and THOT tensors
dNFT: Dynamic metadata for real-time performance metrics
IDNFT: Identity NFT with persona and prompt metadata

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