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

Comprehensive Analysis: Multiple analysis categories and perspectives

Pattern Recognition: Identifies patterns in agent behavior

Performance Tracking: Monitors success rates and execution times

Self-Improvement: Identifies opportunities for system improvement

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

Placeholder Methods: Many analysis methods are placeholders

Limited Historical Data: Basic historical analysis only

No Predictive Analysis: Doesn't predict future issues

Single System: Analyzes single system only

No Real-Time: Analysis is retrospective

Recommended Improvements

Implement Placeholders: Complete all analysis methods

Historical Database: Store metrics over time

Predictive Models: ML-based predictions

Real-Time Analysis: Live analysis capabilities

Cross-System: Analyze multiple systems

Visualization: Charts and graphs

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

Complete Implementation: Implement all placeholder methods

Historical Database: Long-term metric storage

ML Integration: Machine learning for predictions

Real-Time Dashboard: Live analysis visualization

Automated Improvements: Auto-apply identified improvements

Cross-Agent Analysis: Analyze agent interactions

Benchmarking: Compare against benchmarks

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