agentops/docs/v1/quickstart.mdx

---
title: "Quickstart"
description: "Start using AgentOps with just 2 lines of code"
---
import CodeTooltip from '/snippets/add-code-tooltip.mdx'
import EnvTooltip from '/snippets/add-env-tooltip.mdx'

<Note title="Open Source">
The AgentOps app is open source—explore the code in our <a href="https://github.com/AgentOps-AI/agentops/tree/main/app">GitHub app directory</a>.
</Note>

<Steps>
  <Step title="Install the AgentOps SDK">
    <CodeGroup>
      ```bash pip 
      pip install agentops
      ```
      ```bash poetry
      poetry add agentops
      ```
    </CodeGroup>
  </Step>
  <Step title="Add 2 lines of code">
    <CodeTooltip />
    Get an AgentOps API key [here](https://app.agentops.ai/settings/projects)
      <CodeGroup>
        ```python python
        import agentops
        agentops.init(<INSERT YOUR API KEY HERE>)
        ```
      </CodeGroup>
    <EnvTooltip />
  </Step>
  <Step title="Run your agent">
    Execute your program and visit [app.agentops.ai/drilldown](https://app.agentops.ai/drilldown) to observe your Agent! 🕵️

    <Tip>After your run, AgentOps prints a clickable URL to console linking directly to your session in the Dashboard</Tip> 
     <div/>{/* Intentionally blank div for newline */}

     <Frame type="glass" caption="Clickable link to session">
        <img height="200" src="https://github.com/AgentOps-AI/agentops/blob/main/docs/images/link-to-session.gif?raw=true" />
    </Frame>
  </Step>
</Steps>
<Check>[Give us a star](https://github.com/AgentOps-AI/agentops) if you liked AgentOps! (you may be our <span id="stars-text">3,000th</span> 😊)</Check>

## More basic functionality

<CardGroup cols={1}>

<Card icon="code" title="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 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
```

</Card>
<Card icon="robot" title="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 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}"
```

</Card>
<Card icon="stop" title="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 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()
  ```
</Card>
</CardGroup>


## Example Code

Here is the complete code from the sections above

```python 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()
```

<Card
  title="Simple Code Example"
  icon="square-code"
  href="https://github.com/AgentOps-AI/agentops-py/blob/main/examples/openai-gpt.ipynb"
>
  Jupyter Notebook with sample code that you can run!
</Card>

<Check>
  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!
</Check>

## Explore our more advanced functionality!

<CardGroup cols={2}>
  <Card
    title="Examples and Video Guides"
    icon="square-code"
    href="/v1/examples"
  >
    Record all of your operations the way AgentOps intends.
  </Card>
  <Card title="Tracking Agents" icon="robot" href="/v1/usage/tracking-agents">
    Associate operations with specific named agents.
  </Card>
</CardGroup>

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