AgentOps makes it easy to track operations and interactions in your AI applications with minimal setup.

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Basic Setup

The simplest way to get started with AgentOps is to initialize it at the beginning of your application:

import agentops

# Initialize AgentOps with your API key
agentops.init("your-api-key")

That’s it! This single line of code will:

  • Automatically create a session for tracking your application run
  • Intercept and track all LLM calls to supported providers (OpenAI, Anthropic, etc.)
  • Record relevant metrics such as token counts, costs, and response times

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Automatic Instrumentation

AgentOps automatically instruments calls to popular LLM providers without requiring any additional code:

import agentops
from openai import OpenAI

# Initialize AgentOps
agentops.init("your-api-key")

# Make LLM calls as usual - AgentOps will track them automatically
client = OpenAI()
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello, world!"}]
)

This works with many popular LLM providers including:

  • OpenAI
  • Anthropic
  • Google (Gemini)
  • Cohere
  • And more

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Advanced: Using Decorators for Detailed Instrumentation

For more detailed tracking, AgentOps provides decorators that allow you to explicitly instrument your code. This is optional but can provide more context in the dashboard.

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@operation Decorator

The @operation decorator helps track specific operations in your application:

from agentops.sdk.decorators import operation

@operation
def process_data(data):
    # Process the data
    return result

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@agent Decorator

If you use agent classes, you can track them with the @agent decorator:

from agentops.sdk.decorators import agent, operation

@agent
class ResearchAgent:
    @operation
    def search(self, query):
        # Implementation of search
        return f"Results for: {query}"

def research_workflow(topic):
    agent = ResearchAgent()
    results = agent.search(topic)
    return results
    
results = research_workflow("quantum computing")

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Advanced Session Management

If you need more control over session lifecycle, you can disable automatic session creation:

import agentops
from agentops.sdk.decorators import session

# Disable automatic session creation
agentops.init("your-api-key", auto_start_session=False)

@session
def my_workflow():
    # Your code here
    pass
    
# Run the workflow to create a session
my_workflow()

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Best Practices

  1. Keep it Simple: For most applications, just initializing AgentOps with agentops.init() is sufficient.

  2. Use Decorators Sparingly: Only add decorators when you need more detailed tracking of specific operations.

  3. Meaningful Operation Names: When using decorators, choose descriptive names to make them easier to identify in the dashboard.