The AgentOps app is open source—explore the code in our GitHub app directory.
1

Install the AgentOps SDK

pip install agentops
2

Add 2 lines of code

Make sure to call agentops.init before calling any openai, cohere, crew, etc models.
Get an AgentOps API key here
import agentops
agentops.init(<INSERT YOUR API KEY HERE>)
Set your API key as an .env variable for easy access.
3

Run your agent

Execute your program and visit app.agentops.ai/drilldown to observe your Agent! 🕵️
After your run, AgentOps prints a clickable URL to console linking directly to your session in the Dashboard

Clickable link to session

Give us a star if you liked AgentOps! (you may be our 3,000th 😊)

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More basic functionality

Decorate Operations

You can instrument functions inside your code with the @operation decorator, which will create spans that track function execution, parameters, and return values. These operations will be displayed in your session visualization alongside LLM calls.
python
# Instrument a function as an operation
from agentops.sdk.decorators import operation

@operation
def process_data(data):
    # Your function logic here
    result = data.upper()
    return result

Track Agents

If you use specific named agents within your system, you can create agent spans that contain all downstream operations using the @agent decorator.
python
# Create an agent class
from agentops.sdk.decorators import agent, operation

@agent
class MyAgent:
    def __init__(self, name):
        self.name = name
        
    @operation
    def perform_task(self, task):
        # Agent task logic here
        return f"Completed {task}"

Creating Sessions

Create a session to group all your agent operations by using the @session decorator. Sessions serve as the root span for all operations.
python
# Create a session
from agentops.sdk.decorators import session

@session
def my_workflow():
    # Your session code here
    agent = MyAgent("research-agent")
    result = agent.perform_task("data analysis")
    return result
    
# Run the session
my_workflow()

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Example Code

Here is the complete code from the sections above
python
import agentops
from agentops.sdk.decorators import session, agent, operation

# Initialize AgentOps
agentops.init(<INSERT YOUR API KEY HERE>)

# Create an agent class
@agent
class MyAgent:
    def __init__(self, name):
        self.name = name
        
    @operation
    def perform_task(self, task):
        # Agent task logic here
        return f"Completed {task}"

# Create a session
@session
def my_workflow():
    # Your session code here
    agent = MyAgent("research-agent")
    result = agent.perform_task("data analysis")
    return result
    
# Run the session
my_workflow()

Simple Code Example

Jupyter Notebook with sample code that you can run!
That’s all you need to get started! Check out the documentation below to see how you can record other operations. AgentOps is a lot more powerful this way!

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Explore our more advanced functionality!

Examples and Video Guides

Record all of your operations the way AgentOps intends.

Tracking Agents

Associate operations with specific named agents.