agent-spy-nidhish

Agent Spy Monitor

An operational monitoring library for AI agent applications that tracks token usage, costs, performance metrics, and environmental impact.

Features

1. Token Counting

   • Total tokens
   • Input tokens
   • Output tokens

2. Cost Calculation

   • Based on the token usage and model pricing

3. Performance Metrics

   • Time taken for each call
   • CPU and memory consumption

4. Environmental Impact

   • Estimated CO₂ emissions

5. Logging

   • Comprehensive logs of all operations

6. Visualization

   • CLI summaries
   • Streamlit dashboard for detailed insights

Installation

You can install Agent Spy Monitor via PyPI using pip:

pip install agent-spy-monitor

Quick Start

from agent_spy import AgentSpy

# Initialize the monitor
spy = AgentSpy(model="gpt-4o")

# Start monitoring
spy.start()

# Your AI agent code here
input_text = "Hello, how are you?"
output_text = "I'm doing well, thank you!"

# Set token counts
spy.set_token_counts(input_text, output_text)

# End monitoring
spy.end()

# View results
print(f"Total cost: ${spy.cost:.6f}")
print(f"Total tokens: {spy.total_tokens}")
print(f"Carbon emissions: {spy.carbon_emissions:.6f} kg CO2")

Advanced Usage

Using with CrewAI

from agent_spy import AgentSpy

spy = AgentSpy(model="gpt-4o")
spy.start()

# Run your CrewAI workflow
crew_output = your_crew.kickoff()

# Extract token usage from CrewAI output
spy.set_token_usage_from_crew_output(crew_output)
spy.end()

Extended Monitoring with Visualization

from agent_spy import AgentSpyExtended

spy = AgentSpyExtended(model="gpt-4o")
spy.start()

# Your AI operations here

spy.end()

# Show CLI summary
spy.visualize(method='cli')

# Or launch Streamlit dashboard
spy.visualize(method='streamlit')

Supported Models

The library supports cost calculation for various AI models:

• OpenAI: GPT-4o, GPT-4o-mini, GPT-4, GPT-3.5-turbo, and more
• Anthropic: Claude-3 variants, Claude-2, Claude Instant
• Google: Gemini Pro, Gemini Flash

Configuration Options

# Disable resource monitoring for better performance
spy = AgentSpy(model="gpt-4o", enable_monitoring=False)

# Custom encoding for token counting
spy = AgentSpy(model="custom-model", encoding_name="cl100k_base")

API Reference

AgentSpy Class

Methods:

• start(): Begin monitoring
• end(): Stop monitoring and calculate metrics
• set_token_counts(input_text, output_text): Manually set token counts
• set_token_usage_from_crew_output(crew_output): Extract from CrewAI output
• count_tokens(text): Count tokens in text

Properties:

• cost: Total cost in USD
• total_tokens: Total token count
• input_tokens: Input token count
• output_tokens: Output token count
• total_time: Execution time in seconds
• carbon_emissions: Estimated CO₂ emissions in kg

AgentSpyExtended Class

Extends AgentSpy with visualization capabilities:

Additional Methods:

• visualize(method='cli'): Show CLI summary
• visualize(method='streamlit'): Launch Streamlit dashboard

License

MIT License

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Author

Nidhish Wakodikar

• Email: nidhishwakodikar@gmail.com
• GitHub: @nidhishwakodikar

Changelog

v0.1.0

• Initial release
• Token counting and cost calculation
• Performance monitoring
• Carbon emissions estimation
• CLI and Streamlit visualization