agent-mont 0.1.3

A comprehensive monitoring solution for AI agent frameworks including Langchain, CrewAI, and Agno. AgentMont provides enhanced metrics and deep observability for production AI agent applications.

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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