agent-mont 0.1.0

A next-level AgentOps monitoring library for Crew AI applications with enhanced metrics.

Agent Mont

Advanced Monitoring for AI Agents

Comprehensive metrics, insights, and visualization for Phidata and Crew AI applications

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📊 Overview

Agent Mont is an advanced AgentOps monitoring library designed specifically for Phidata and Crew AI applications. It provides real-time insights into your AI agents' performance, resource utilization, and environmental impact.

✨ Key Features

• 📝 Token Tracking - Precise accounting of input, output, and total tokens
• 💰 Cost Analysis - Real-time cost calculation based on model-specific pricing
• ⚡ Performance Metrics - Detailed execution time and resource utilization
• 🔄 Throughput Monitoring - Tokens processed per second and operation latency
• 🌱 Environmental Impact - Carbon emission estimates for responsible AI deployment
• 📋 Comprehensive Logging - Detailed operational logs for debugging and analysis
• 📊 Interactive Visualization - CLI summaries and Streamlit dashboard for visual insights

🚀 Installation

pip install agent-mont

🔍 Usage

Basic Example

from agent_mont import AgentMontExtended

# Initialize Agent Mont monitoring
mont = AgentMontExtended(model="gpt-4o")

# Start monitoring
mont.start()

# Your Crew AI operations go here
# result = your_ai_function()

# Set token counts
input_text = "Your input text here."
output_text = "Your output text here."
mont.set_token_counts(input_text=input_text, output_text=output_text)

# Measure operation latency
# result = mont.measure_operation(your_function, arg1, arg2)

# End monitoring
mont.end()

# Visualize the results
mont.visualize(method='cli')

Integration with Crew AI

from agent_mont import AgentMontExtended
from crewai import Crew, Agent, Task

# Initialize monitoring
mont = AgentMontExtended(model="gpt-4o")
mont.start()

# Define your Crew agents and tasks
agent = Agent(...)
task = Task(...)
crew = Crew(agents=[agent], tasks=[task])

# Run crew process
result = crew.kickoff()

# Extract token usage from crew output
mont.set_token_usage_from_crew_output(result)

# End monitoring and display metrics
mont.end()
mont.visualize(method='cli')

📖 API Reference

AgentMont Initialization

AgentMont(
    model: str,                      # LLM model identifier (e.g., "gpt-4o")
    enable_monitoring: bool = True,  # Toggle monitoring on/off
    encoding_name: str = "cl100k_base"  # Token encoding scheme
)

Core Methods

Method	Description
start()	Begin resource and metric monitoring
end()	Stop monitoring and compute all metrics
set_token_counts(input_text, output_text)	Set token usage based on text
measure_operation(func, *args, **kwargs)	Wrap a function call to record its latency
set_token_usage_from_crew_output(crew_output)	Extract token usage from a CrewOutput
visualize(method)	Visualize metrics using CLI or Streamlit

📈 Visualization

CLI Summary

mont.visualize(method='cli')

Example output:

========== AGENT MONT METRICS ==========
Model: gpt-4o
Execution Time: 2.34s
Total Tokens: 1,245
Cost: $0.0237
Throughput: 532.1 tokens/sec
CPU Usage: 23.7%
Memory Usage: 145.2 MB
Carbon Emissions: 0.00021 gCO2eq
=======================================

Streamlit Dashboard

Create a visualization script:

# visualize.py
from agent_mont import AgentMontExtended

mont = AgentMontExtended(model="gpt-4o")
# Load metrics from logs or set directly
mont.set_token_counts("Example input", "Example output")
mont.end()
mont.visualize(method='streamlit')

Run the dashboard:

streamlit run visualize.py

📝 Logging

All events and metrics are logged to agent_mont.log with timestamps and event categories:

2025-03-15 14:32:17 [INFO] Agent Mont initialized with model: gpt-4o
2025-03-15 14:32:18 [INFO] Monitoring started
2025-03-15 14:32:20 [INFO] Token counts set: 145 input, 267 output
2025-03-15 14:32:21 [INFO] Monitoring ended
2025-03-15 14:32:21 [INFO] Metrics calculated: 412 tokens, $0.0082 cost, 1.73s execution time

🤝 Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

📧 Contact

Faisal Azeez - [e-mail]

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