> ## Documentation Index
> Fetch the complete documentation index at: https://docs.agentops.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Quickstart
> Start using AgentOps with just 2 lines of code
The AgentOps app is open source—explore the code in our GitHub app directory.
```bash pip theme={null}
pip install agentops
```
```bash poetry theme={null}
poetry add agentops
```
Make sure to call `agentops.init` before calling any `openai`, `cohere`, `crew`, etc models.
Get an AgentOps API key [here](https://app.agentops.ai/settings/projects)
```python python theme={null}
import agentops
agentops.init()
```
Set your API key as an `.env` variable for easy access.
Execute your program and visit [app.agentops.ai/drilldown](https://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
[Give us a star](https://github.com/AgentOps-AI/agentops) if you liked AgentOps! (you may be our 3,000th 😊)
## More basic functionality
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 python theme={null}
# 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
```
If you use specific named agents within your system, you can create agent spans that contain all downstream operations using the `@agent` decorator.
```python python theme={null}
# 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}"
```
Create a session to group all your agent operations by using the `@session` decorator. Sessions serve as the root span for all operations.
```python python theme={null}
# 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()
```
## Example Code
Here is the complete code from the sections above
```python python theme={null}
import agentops
from agentops.sdk.decorators import session, agent, operation
# Initialize AgentOps
agentops.init()
# 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()
```
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!
## Explore our more advanced functionality!
Record all of your operations the way AgentOps intends.
Associate operations with specific named agents.