Logging and Monitoring

Effective logging and monitoring are essential for tracking the behavior of agents, identifying potential issues, and ensuring that the system is operating as expected. Cleo provides robust support for logging and monitoring, allowing users to track task executions, agent status, and other important events in the system.

This guide provides an overview of how to implement logging and monitor the performance of your Cleo-based agents, including various logging levels, aggregation, and visualization techniques.

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Logging Basics

Cleo supports comprehensive logging functionality, allowing you to capture key events and track the execution of tasks in your agents. By integrating with Python's built-in logging module or using third-party logging solutions, you can tailor the logging behavior to meet the needs of your system.

1. Basic Logging Configuration

The logging module in Python provides flexible logging configurations, including log levels, log formats, and output destinations. Cleo's logging system is built on top of this module, providing basic functionality out-of-the-box.

Example: Basic Logging Setup

pythonCopyEditimport logging

# Configure basic logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler("cleo_log.log"),
        logging.StreamHandler()
    ]
)

# Example log messages
logging.info("Agent initialized.")
logging.warning("Task execution delayed.")
logging.error("Task failed due to timeout.")

This configuration ensures that log messages are both written to a log file and displayed on the console. The log messages will contain timestamps, log levels, and the corresponding message.

2. Logging Levels

Logging levels control the verbosity of log messages. Cleo supports all standard logging levels, including:

• DEBUG: Detailed information, typically useful only for diagnosing problems.

• INFO: General information about system operation.

• WARNING: Indications that something unexpected happened, but the system is still working.

• ERROR: A more serious issue that prevented the task from completing.

• CRITICAL: A very serious issue that likely causes the system to crash.

You can adjust the logging level to capture more or less detail based on your needs.

Example: Setting a Specific Log Level

pythonCopyEditlogging.basicConfig(level=logging.DEBUG)  # Show all log messages, including DEBUG-level

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Log Aggregation

In larger distributed systems, it is common to aggregate logs from multiple sources into a central repository. This allows for easier querying, filtering, and analysis of logs.

1. Centralized Logging Systems

To handle log aggregation, Cleo can integrate with centralized logging systems such as ELK Stack (Elasticsearch, Logstash, and Kibana), Graylog, or Fluentd. These tools provide powerful features for searching logs and creating visual dashboards.

Example: Sending Logs to Elasticsearch

pythonCopyEditfrom elasticsearch import Elasticsearch
import logging

# Set up Elasticsearch client
es = Elasticsearch([{'host': 'localhost', 'port': 9200}])

# Custom logging handler for Elasticsearch
class ElasticsearchHandler(logging.Handler):
    def emit(self, record):
        log_entry = self.format(record)
        es.index(index='cleo_logs', body={'message': log_entry})

# Set up the logging configuration to send logs to Elasticsearch
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s',
    handlers=[ElasticsearchHandler()]
)

In this example, logs are sent to an Elasticsearch cluster for centralized storage and analysis. You can further enhance this setup by integrating it with Kibana for visualization.

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Real-Time Monitoring

Real-time monitoring helps you track the status and performance of agents as they execute tasks. Cleo provides built-in functionality to monitor agent activities, task progress, and system health in real-time.

1. Agent Health and Status

Each agent in Cleo can be monitored for its health, task progress, and any error conditions. Agents can report their status periodically, which can be captured in logs or visualized using a dashboard.

Example: Reporting Agent Health

pythonCopyEditimport time

def report_agent_health(agent):
    # Simulate checking the health of the agent
    while True:
        # Report status every 60 seconds
        logging.info(f"Agent {agent.id} is healthy.")
        time.sleep(60)

# Example usage: Run health monitoring for an agent
report_agent_health(agent)

In this example, the agent’s health is reported every 60 seconds, allowing you to track its status over time.

2. Task Progress Monitoring

You can track the progress of long-running tasks and display real-time updates on their status. For instance, you can use progress bars or percentage indicators to show how far along a task is.

Example: Task Progress Monitoring

pythonCopyEditfrom tqdm import tqdm

def execute_long_task(agent):
    for i in tqdm(range(100), desc="Executing Task"):
        # Simulate task progress
        time.sleep(0.1)
    return "Task completed"

# Example usage: Monitor the progress of a long-running task
execute_long_task(agent)

In this example, the tqdm library is used to display a progress bar while the task is running, providing real-time feedback to the user.

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Visualizing Logs and Metrics

For more advanced monitoring, you can use visualization tools such as Grafana or Kibana to create custom dashboards for visualizing task execution times, agent health metrics, and other system performance data.

1. Task Execution Metrics

You can track important metrics like task duration, success/failure rates, and agent resource usage. These metrics can be aggregated and displayed in a dashboard for easy monitoring.

Example: Sending Task Metrics to Grafana

pythonCopyEditimport requests

def send_task_metrics_to_grafana(task_name, duration, status):
    payload = {
        'task_name': task_name,
        'duration': duration,
        'status': status
    }
    requests.post("http://grafana_server/api/metrics", json=payload)

# Example usage: Send task execution metrics to Grafana
send_task_metrics_to_grafana("example_task", 12.5, "success")

In this example, task metrics are sent to a Grafana server for visualization. You can set up custom metrics dashboards to track performance and system health.