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Monitoring System Example

This guide provides a professional, step-by-step walkthrough for using the MonitoringSystem in the agenticaiframework package to log events and record metrics.
It is intended for developers who want to track agent activities, performance, and operational metrics in real-time.

Prerequisites & Configuration

  • Installation: Ensure agenticaiframework is installed and accessible in your Python environment.
  • No additional configuration is required for this example.
  • Python Version: Compatible with Python 3.8+.

Code

from agenticaiframework.monitoring import MonitoringSystem

if __name__ == "__main__":
    monitor = MonitoringSystem()

    # Log events
    monitor.log_event("AgentStarted", {"agent_name": "ExampleAgent"})
    monitor.log_event("TaskCompleted", {"task_name": "AdditionTask", "status": "success"})

    # Record metrics
    monitor.record_metric("ResponseTime", 1.23)
    monitor.record_metric("Accuracy", 0.98)

    # Display logged data
    print("Logged Events:", monitor.events)
    print("Logged Metrics:", monitor.metrics)

Step-by-Step Execution

  1. Import the Class
    Import MonitoringSystem from agenticaiframework.monitoring.

  2. Instantiate the Monitoring System
    Create an instance of MonitoringSystem to manage event logging and metric recording.

  3. Log Events
    Use log_event to record significant occurrences, passing:

  4. event_type: A string describing the event.

  5. details: A dictionary with event-specific data.

  6. Record Metrics
    Use record_metric to store performance or operational metrics, passing:

  7. metric_name: The name of the metric.

  8. value: The metric's value.

  9. Inspect Logged Data
    Access events and metrics attributes to review stored information.

Best Practice: Use consistent naming conventions for events and metrics to simplify analysis and reporting.

Expected Input

No user input is required; the script uses hardcoded values for demonstration purposes. In production, events and metrics could be generated dynamically from agent activities, API calls, or system monitoring hooks.

Expected Output

[YYYY-MM-DD HH:MM:SS] [MonitoringSystem] Event logged: AgentStarted - {'agent_name': 'ExampleAgent'}
[YYYY-MM-DD HH:MM:SS] [MonitoringSystem] Event logged: TaskCompleted - {'task_name': 'AdditionTask', 'status': 'success'}
[YYYY-MM-DD HH:MM:SS] [MonitoringSystem] Metric recorded: ResponseTime = 1.23
[YYYY-MM-DD HH:MM:SS] [MonitoringSystem] Metric recorded: Accuracy = 0.98
Logged Events: [{'type': 'AgentStarted', 'details': {'agent_name': 'ExampleAgent'}, 'timestamp': <timestamp>}, {'type': 'TaskCompleted', 'details': {'task_name': 'AdditionTask', 'status': 'success'}, 'timestamp': <timestamp>}]
Logged Metrics: {'ResponseTime': 1.23, 'Accuracy': 0.98}

How to Run

Run the example from the project root:

python examples/monitoring_example.py

If installed as a package, you can also run it from anywhere:

python -m examples.monitoring_example

Tip: Integrate MonitoringSystem into your agents to automatically log key lifecycle events and performance metrics.