agent-guard 0.1.2

An operational monitoring library for Crew AI applications.

Agent Guard

Agent Guard is an AI AgentsOps monitoring library for Crew AI applications. It captures essential metrics such as token counts, costs, execution time, resource utilization, carbon emissions, and detailed logs. Additionally, it offers both textual and visual representations of the collected data via a Command-Line Interface (CLI) or a Streamlit dashboard.

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 Guard via PyPI using pip:

pip install agent-guard

Usage

Basic Example

Here's a simple example of how to use Agent Guard to monitor a Crew AI workflow:

from agent_guard import AgentGuardExtended

# Initialize Agent Guard
rag_guard = AgentGuardExtended(model="gpt-4o")

# Start monitoring
rag_guard.start()

# Your Crew AI operations go here
# For example:
# output = your_crew_ai_function()

# Set token counts (replace with actual values)
input_text = "Your input text here."
output_text = "Your output text here."
rag_guard.set_token_counts(input_text=input_text, output_text=output_text)

# End monitoring
rag_guard.end()

# Visualize the results
rag_guard.visualize(method='cli')  # For CLI summary

Integration with Crew AI

Integrate Agent Guard with a Crew AI application to monitor operational metrics effectively.

app.py

# app.py

from agent_guard import AgentGuardExtended
from crewai_tools import ScrapeWebsiteTool, FileWriterTool, TXTSearchTool
from crewai import Agent, Task, Crew
import os
from dotenv import load_dotenv

# Load environment variables from the .env file
load_dotenv()
os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY')

# Initialize Agent Guard
rag_guard = AgentGuardExtended(model="gpt-4o")  # Specify the model being used

# Start monitoring
rag_guard.start()

# Initialize the ScrapeWebsiteTool
scrape_tool = ScrapeWebsiteTool(website_url='https://en.wikipedia.org/wiki/Artificial_intelligence')  

# Extract the text
text = scrape_tool.run()
print("Scraped Text:", text[:500], "...")  # Print first 500 characters for brevity

# Initialize the FileWriterTool
file_writer_tool = FileWriterTool()
text_cleaned = text.encode("ascii", "ignore").decode()
# Write content to a file in a specified directory
write_result = file_writer_tool._run(filename='ai.txt', content=text_cleaned, overwrite="True")
print("File Write Result:", write_result)

# Initialize the TXTSearchTool
txt_search_tool = TXTSearchTool(txt='ai.txt')
context = txt_search_tool.run('What is natural language processing?')
print("Context for NLP:", context)

# Create the Agent
data_analyst = Agent(
    role='Educator',
    goal=f'Based on the context provided, answer the question - What is Natural Language Processing? Context - {context}',
    backstory='You are a data expert',
    verbose=True,
    allow_delegation=False,
    tools=[txt_search_tool]
)

# Create the Task
test_task = Task(
    description="Understand the topic and give the correct response",
    tools=[txt_search_tool],
    agent=data_analyst,
    expected_output='Provide a correct response about Natural Language Processing.'
)

# Create the Crew
crew = Crew(
    agents=[data_analyst],
    tasks=[test_task]
)

# Kickoff the Crew
output = crew.kickoff()
print("Crew Output:", output)

# Set token counts using CrewOutput
rag_guard.set_token_usage_from_crew_output(output)

# End monitoring
rag_guard.end()

# Optionally, visualize the results
rag_guard.visualize(method='cli')  # For CLI summary

API Reference

AgentGuard

The AgentGuard class is the core component responsible for monitoring and logging operational metrics.

Initialization

AgentGuard(model: str, enable_monitoring: bool = True)

• Parameters:
  • model (str): The model name (e.g., "gpt-4o").
  • enable_monitoring (bool, optional): Flag to enable or disable resource monitoring. Defaults to True.

Methods

• start(): Starts monitoring resources and logging.
• end(): Stops monitoring, calculates metrics, and logs the results.
• set_token_counts(input_text: str, output_text: str): Sets the token counts based on input and output texts.
• set_token_usage_from_crew_output(crew_output): Extracts token usage from a CrewOutput object.
• set_carbon_emissions_resource_based(avg_cpu_usage: float, avg_memory_usage: float, emission_factor: float = 0.453): Calculates CO2 emissions based on resource usage.
• count_tokens(text: str) -> int: Counts the number of tokens in a given text.

AgentGuardExtended

The AgentGuardExtended class extends AgentGuard by adding visualization capabilities.

Initialization

AgentGuardExtended(model: str, enable_monitoring: bool = True)

• Parameters:
  • model (str): The model name (e.g., "gpt-4o").
  • enable_monitoring (bool, optional): Flag to enable or disable resource monitoring. Defaults to True.

Methods

• Inherits all methods from AgentGuard.
• visualize(method='cli'): Visualizes the collected metrics.
  • Parameters:
    • method (str): The visualization method, either 'cli' or 'streamlit'.

Logging

Agent Guard uses a logging mechanism to record all operations and metrics.

• Log File: agent_guard.log
• Log Contents:
  • Monitoring start and end times
  • Token counts
  • Costs
  • CPU and memory usage
  • Carbon emissions

Logger Class: Responsible for writing logs to both the console and the log file.

Visualization

Agent Guard provides visualization tools to display the collected metrics.

CLI Summary

Prints a summary of all metrics directly to the console.

rag_guard.visualize(method='cli')

Streamlit Dashboard

Launches a web-based dashboard displaying detailed metrics and graphs.

Creating a Streamlit Script

Create a separate script, e.g., visualize.py:

# visualize.py

from agent_guard import AgentGuardExtended

# Initialize Agent Guard with the same model
rag_guard = AgentGuardExtended(model="gpt-4o")

# Set token counts and other metrics manually or load from logs
# For demonstration, we'll use dummy data
input_text = "Example input text."
output_text = "Example output text generated by the AI."

rag_guard.set_token_counts(input_text=input_text, output_text=output_text)
rag_guard.end()

# Launch Streamlit dashboard
rag_guard.visualize(method='streamlit')

Running the Streamlit App

Execute the following command to launch the dashboard:

streamlit run visualize.py

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the Repository

2. Create a Feature Branch

   git checkout -b feature/YourFeature
   

3. Commit Your Changes

   git commit -m "Add Your Feature"
   

4. Push to the Branch

   git push origin feature/YourFeature
   

5. Open a Pull Request

License

This project is licensed under the MIT License.

Contact

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