agint_memory_integration.md · 3.9 KB

AGInt Memory Integration

Overview

AGInt (Augmentic Intelligence) has been integrated with the memory_agent system to store its cognitive loop operations and decision-making processes in persistent memory under the stm/mindx_agint folder structure.

Memory Structure

Agent-Specific Organization

AGInt memories are organized under the mindx_agint agent-specific directory:

/home/hacker/mindX/data/memory/stm/mindx_agint/
├── cycles/          # Cognitive cycle operations
├── steps/           # Individual step executions
├── completion/      # Overall completion logs
└── errors/          # Error logs and exceptions

Memory Categories

  • cycles: Logs each cognitive cycle start and completion
  • steps: Logs individual P-O-D-A (Perception, Orientation, Decision, Action) steps
  • completion: Logs overall AGInt completion status
  • errors: Logs any errors or exceptions during execution
  • Implementation Details

    Memory Logging Functions

    The following functions have been added to mindx_backend_service/main_service.py:

  • _log_agint_cycle_start(): Logs cycle initiation
  • _log_agint_cycle_completion(): Logs cycle completion with metrics
  • _log_agint_step(): Logs individual step execution
  • _log_agint_completion(): Logs overall completion
  • _log_agint_error(): Logs errors and exceptions
  • Integration Points

    Memory logging is integrated at key points in the AGInt cognitive loop:

  • Cycle Start: When each cognitive cycle begins
  • Step Execution: During each P-O-D-A step
  • Cycle Completion: When each cycle finishes
  • Overall Completion: When the entire AGInt process completes
  • Error Handling: When exceptions occur
  • Memory Data Structure

    Each memory entry contains:

    {
      "timestamp_utc": "2025-09-19T23:16:49.424841",
      "memory_type": "STM",
      "category": "mindx_agint/cycles",
      "metadata": {
        "agent": "mindx_agint",
        "component": "cognitive_loop"
      },
      "data": {
        "cycle": 1,
        "max_cycles": 5,
        "directive": "evolve test_file.py",
        "autonomous_mode": false,
        "timestamp": 1692565000.0,
        "status": "started",
        "phase": "cycle_start"
      }
    }
    

    Usage

    Automatic Logging

    Memory logging happens automatically when AGInt is executed through the API endpoint:

    POST /commands/agint/stream
    {
      "directive": "evolve test_file.py",
      "max_cycles": 5,
      "autonomous_mode": false
    }
    

    Manual Testing

    You can test the memory integration directly:

    from agents.memory_agent import MemoryAgent

    memory_agent = MemoryAgent() result = await memory_agent.save_memory('STM', 'mindx_agint/cycles', data, metadata)

    Benefits

  • Persistent Memory: AGInt operations are stored for analysis and learning
  • Agent-Specific Organization: Clean separation from other agent memories
  • Rich Metadata: Detailed information about each operation
  • Scalable Structure: Easy to extend for additional logging categories
  • Debugging Support: Comprehensive logs for troubleshooting
  • File Naming Convention

    Memory files follow the pattern:

    {timestamp}_{random_id}.{agent}_{category}.mem.json
    

    Example:

    20250919161649_424801.mindx_agint_cycles.mem.json
    

    Integration Status

    Completed:

  • Memory agent integration
  • Agent-specific directory structure
  • Memory logging functions
  • Integration with AGInt cognitive loop
  • Basic testing and verification
  • 🔄 In Progress:

  • Stream-based memory logging during API calls
  • Error handling and recovery
  • Memory cleanup and archival
  • Future Enhancements

  • Memory Analysis: Tools to analyze AGInt decision patterns
  • Learning Integration: Use memory data for improving AGInt performance
  • Memory Archival: Automatic cleanup of old memory files
  • Real-time Monitoring: Live memory usage dashboards
  • Cross-Agent Memory: Sharing relevant memories between agents

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