Memory Analysis Tool Documentation

Overview

The MemoryAnalysisTool provides comprehensive analysis of agent memory logs to identify patterns, performance metrics, and improvement opportunities. It enables mindX to perform self-improvement through data-driven analysis of its own behavior.

File: tools/memory_analysis_tool.py Class: MemoryAnalysisTool Version: 1.0.0 Status: ✅ Active

Architecture

Design Principles

1. Comprehensive Analysis: Multiple analysis categories and perspectives
2. Pattern Recognition: Identifies patterns in agent behavior
3. Performance Tracking: Monitors success rates and execution times
4. Self-Improvement: Identifies opportunities for system improvement
5. Data-Driven: Based on actual memory logs and execution data

Core Components

class MemoryAnalysisTool(BaseTool):
    - memory_agent: MemoryAgent - For accessing memory logs
    - analysis_categories: Dict - Analysis category definitions
    - config: Config - Configuration

Available Actions

1. analyze_agent_performance

Analyzes performance metrics for specific agent or all agents.

Parameters:

• agent_id (str, optional): Specific agent to analyze (default: all)
• days_back (int, optional): Days to analyze (default: 7)

Returns:

{
    "timestamp": str,
    "analysis_type": "agent_performance",
    "days_back": int,
    "agents_analyzed": List[Dict],
    "aggregate_insights": Dict
}

2. analyze_system_patterns

Analyzes system-wide patterns and interactions.

Parameters:

• days_back (int, optional): Days to analyze (default: 7)

Returns: System pattern analysis

3. identify_improvement_opportunities

Identifies specific improvement opportunities.

Parameters:

• focus_area (str, optional): Focus area (default: "all")

Returns: Improvement opportunities with priority matrix

4. generate_self_improvement_report

Generates comprehensive self-improvement report.

Parameters:

• target_agent (str, optional): Target agent (default: "bdi_agent")

Returns: Complete improvement report

5. analyze_agent_collaboration

Analyzes collaboration patterns between agents.

Parameters: Varies

Returns: Collaboration analysis

6. track_evolution_progress

Tracks evolution and improvement progress.

Parameters: Varies

Returns: Evolution tracking data

7. analyze_memory_patterns

Analyzes memory usage patterns.

Parameters: Varies

Returns: Memory pattern analysis

Usage

Analyze Agent Performance

from tools.memory_analysis_tool import MemoryAnalysisTool
from agents.memory_agent import MemoryAgent

tool = MemoryAnalysisTool(memory_agent=memory_agent)
Analyze specific agent
success, result = await tool.execute(
    action="analyze_agent_performance",
    agent_id="bdi_agent_mastermind",
    days_back=30
)

Analyze all agents
success, result = await tool.execute(
    action="analyze_agent_performance",
    days_back=7
)

Generate Improvement Report

# Generate comprehensive report
success, report = await tool.execute(
    action="generate_self_improvement_report",
    target_agent="bdi_agent"
)

if success:
    print(f"Executive Summary: {report['executive_summary']}")
    print(f"Recommendations: {report['recommendations']}")

Identify Opportunities

# Find improvement opportunities
success, opportunities = await tool.execute(
    action="identify_improvement_opportunities",
    focus_area="performance"
)

Analysis Categories

Performance

• Success rates
• Execution times
• Error patterns

Behavior

• Decision patterns
• Goal completion
• Tool usage

Collaboration

• Agent interactions
• Coordination patterns
• Communication efficiency

Evolution

• Improvement trends
• Capability growth
• Adaptation patterns

System Health

• Resource usage
• Error frequency
• Recovery patterns

Features

1. Success Rate Analysis

Analyzes operation success rates:

• Overall success rate
• Success by process type
• Failure reasons
• Trend analysis

2. Error Pattern Detection

Identifies error patterns:

• Error frequency
• Error categories
• Error trends
• Common error sequences
• Recovery patterns

3. Performance Metrics

Tracks performance:

• Execution times
• Operation frequency
• Time distribution
• Process sequences

4. Improvement Trends

Analyzes improvement over time:

• Success rate trends
• Error reduction
• Capability improvements
• Learning indicators

Limitations

Current Limitations

1. Placeholder Methods: Many analysis methods are placeholders
2. Limited Historical Data: Basic historical analysis only
3. No Predictive Analysis: Doesn't predict future issues
4. Single System: Analyzes single system only
5. No Real-Time: Analysis is retrospective

Recommended Improvements

1. Implement Placeholders: Complete all analysis methods
2. Historical Database: Store metrics over time
3. Predictive Models: ML-based predictions
4. Real-Time Analysis: Live analysis capabilities
5. Cross-System: Analyze multiple systems
6. Visualization: Charts and graphs
7. Automated Actions: Auto-apply improvements

Integration

With Memory Agent

Uses MemoryAgent to access memory logs:

memories = await self._get_agent_memories(agent_id, days_back)

With Other Tools

Can be used with:

• System Health Tool: Correlate health with performance
• Strategic Analysis Tool: Include in strategic decisions
• Audit and Improve Tool: Guide improvement efforts

Examples

Complete Analysis Workflow

# 1. Analyze performance
success, perf = await tool.execute("analyze_agent_performance", days_back=30)

2. Identify opportunities
success, opps = await tool.execute("identify_improvement_opportunities")

3. Generate report
success, report = await tool.execute("generate_self_improvement_report")

Technical Details

Memory Access

Reads from MemoryAgent STM (Short-Term Memory):

stm_path = self.memory_agent.stm_path / agent_id
for day_dir in stm_path.iterdir():
    for memory_file in day_dir.glob("*.memory.json"):
        # Load and analyze memory

Analysis Methods

• _analyze_success_rates(): Calculate success metrics
• _analyze_error_patterns(): Identify error patterns
• _analyze_execution_patterns(): Analyze execution
• _analyze_improvement_trends(): Track improvements

Future Enhancements

1. Complete Implementation: Implement all placeholder methods
2. Historical Database: Long-term metric storage
3. ML Integration: Machine learning for predictions
4. Real-Time Dashboard: Live analysis visualization
5. Automated Improvements: Auto-apply identified improvements
6. Cross-Agent Analysis: Analyze agent interactions
7. Benchmarking: Compare against benchmarks

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