agent-spy-nidhish 0.1.0
An operational monitoring library for AI agent applications
License: MIT
Requires Python: >=3.8
Keywords: ai, agent, monitoring, tokens, cost, performance

# 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](https://pypi.org/project/agent-spy-monitor/) using `pip`:

```bash
pip install agent-spy-monitor
```

## Quick Start

```python
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

```python
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

```python
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

```python
# 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](https://github.com/Nidhish-714)

## Changelog

### v0.1.0
- Initial release
- Token counting and cost calculation
- Performance monitoring
- Carbon emissions estimation
- CLI and Streamlit visualization