System Analyzer Tool Documentation

Overview

The SystemAnalyzerTool performs holistic analysis of the mindX system state, including codebase structure, performance metrics, resource usage, and improvement backlogs. It uses LLM-powered analysis to generate actionable insights and improvement suggestions.

File: tools/system_analyzer_tool.py Class: SystemAnalyzerTool Version: 1.0.0 Status: ✅ Active

Architecture

Design Principles

1. Holistic Analysis: Analyzes entire system state
2. LLM-Powered: Uses LLM for intelligent analysis
3. Data Integration: Integrates multiple data sources
4. Actionable Insights: Generates concrete improvement suggestions
5. Fallback Support: Works without LLM if needed

Core Components

class SystemAnalyzerTool:
    - belief_system: BeliefSystem - Shared belief system
    - llm_handler: LLMHandlerInterface - LLM for analysis
    - coordinator_ref: CoordinatorAgent - System state access
    - performance_monitor: PerformanceMonitor - Performance metrics
    - resource_monitor: ResourceMonitor - Resource usage

Usage

Basic Analysis

from tools.system_analyzer_tool import SystemAnalyzerTool
from core.belief_system import BeliefSystem
from llm.llm_interface import LLMHandlerInterface
from orchestration.coordinator_agent import CoordinatorAgent

tool = SystemAnalyzerTool(
    belief_system=belief_system,
    llm_handler=llm_handler,
    coordinator_ref=coordinator
)
Perform analysis
result = await tool.execute(analysis_focus_hint="performance optimization")

Focused Analysis

# Analyze specific area
result = await tool.analyze_system_for_improvements(
    analysis_focus_hint="memory management"
)

Response Format

Success Response

{
    "improvement_suggestions": [
        {
            "target_component_path": str,
            "suggestion": str,
            "justification": str,
            "priority": int  # 1-10
        }
    ]
}

Error Response

{
    "error": str,
    "improvement_suggestions": []
}

Data Sources

1. Performance Metrics

From PerformanceMonitor:

• Execution times
• Success rates
• Error frequencies
• Throughput metrics

2. Resource Usage

From ResourceMonitor:

• CPU usage
• Memory usage
• Disk usage
• Network usage

3. Improvement Backlog

From Coordinator:

• Top 10 backlog items
• Pending improvements
• Prioritized tasks

4. Campaign History

From Coordinator:

• Last 5 campaigns
• Campaign results
• Historical patterns

Features

1. LLM-Powered Analysis

Uses LLM to:

• Synthesize system data
• Identify patterns
• Generate insights
• Prioritize improvements

2. Fallback Support

If LLM unavailable:

• Returns basic suggestions
• Focuses on critical issues
• Provides actionable recommendations

3. Focused Analysis

Can focus on specific areas:

• Performance optimization
• Memory management
• Security improvements
• Code quality

Limitations

Current Limitations

1. LLM Dependency: Requires LLM for best results
2. Limited Data: Only uses coordinator data
3. No Historical: No trend analysis
4. Basic Fallback: Simple fallback suggestions
5. No Validation: Doesn't validate suggestions

Recommended Improvements

1. Enhanced Data Sources: More data sources
2. Historical Analysis: Trend analysis
3. Better Fallback: Improved fallback logic
4. Suggestion Validation: Validate suggestions
5. Multi-Model: Use multiple LLM models
6. Real-Time: Continuous analysis
7. Visualization: Charts and graphs

Integration

With Coordinator Agent

Accesses system state:

self.performance_monitor = self.coordinator_ref.performance_monitor
self.resource_monitor = self.coordinator_ref.resource_monitor

With LLM Handler

Uses LLM for analysis:

response_str = await self.llm_handler.generate_text(
    prompt,
    model=self.llm_handler.model_name_for_api,
    max_tokens=2000,
    temperature=0.2,
    json_mode=True
)

Examples

Performance Analysis

result = await tool.analyze_system_for_improvements(
    analysis_focus_hint="performance optimization"
)

for suggestion in result["improvement_suggestions"]:
    print(f"Priority {suggestion['priority']}: {suggestion['suggestion']}")

Technical Details

Dependencies

• core.belief_system.BeliefSystem: Belief system
• llm.llm_interface.LLMHandlerInterface: LLM handler
• orchestration.coordinator_agent.CoordinatorAgent: System access
• llm.model_selector.ModelSelector: Model selection (optional)

LLM Prompt Structure

prompt = (
    "You are a Senior Systems Architect AI...\n"
    f"System State Snapshot:\n

json\n{system_state}\n``\n\n" "Analysis Task:\n" "1. Synthesize data...\n" "2. Propose improvements...\n" "3. Provide priority...\n" ) ``

Future Enhancements

1. Multi-Source Data: More data sources
2. Historical Trends: Trend analysis
3. ML Integration: ML-based predictions
4. Real-Time Analysis: Continuous monitoring
5. Visualization: Charts and dashboards
6. Validation Framework: Validate suggestions
7. Automated Implementation: Auto-implement suggestions

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