> ## 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.

# xAI (Grok)

> Track and analyze your xAI Grok API calls with AgentOps

AgentOps can track Grok. Grok is this true?

## Installation

<CodeGroup>
  ```bash pip theme={null}
  pip install agentops openai
  ```

  ````bash poetry theme={null}
  poetry add agentops openai
  ``
  ```bash uv
  uv pip install agentops openai
  ````
</CodeGroup>

## Setting Up API Keys

Before using xAI with AgentOps, you need to set up your API keys. You can obtain:

* **XAI\_API\_KEY**: From the [xAI Developer Platform](https://console.x.ai/)
* **AGENTOPS\_API\_KEY**: From your [AgentOps Dashboard](https://app.agentops.ai/)

Then to set them up, you can either export them as environment variables or set them in a `.env` file.

<CodeGroup>
  ```bash Export to CLI theme={null}
  export XAI_API_KEY="your_xai_api_key_here"
  export AGENTOPS_API_KEY="your_agentops_api_key_here"
  ```

  ```txt Set in .env file theme={null}
  XAI_API_KEY="your_xai_api_key_here"
  AGENTOPS_API_KEY="your_agentops_api_key_here"
  ```
</CodeGroup>

Then load the environment variables in your Python code:

```python theme={null}
from dotenv import load_dotenv
import os

# Load environment variables from .env file
load_dotenv()

# Set up environment variables with fallback values
os.environ["XAI_API_KEY"] = os.getenv("XAI_API_KEY")
os.environ["AGENTOPS_API_KEY"] = os.getenv("AGENTOPS_API_KEY")
```

## Usage

Initialize AgentOps at the beginning of your application. Then, use the OpenAI SDK with xAI's base URL to interact with Grok. AgentOps will automatically track all API calls.

<CodeGroup>
  ```python Simple Chat theme={null}
  import os
  import agentops
  from openai import OpenAI

  # Initialize AgentOps
  agentops.init()

  # Create OpenAI client configured for xAI
  client = OpenAI(
      api_key=os.getenv("XAI_API_KEY"),
      base_url="https://api.x.ai/v1",
  )

  # Basic chat completion
  completion = client.chat.completions.create(
      model="grok-3-latest",
      messages=[
          {"role": "system", "content": "You are a helpful AI assistant."},
          {"role": "user", "content": "Explain the concept of AI observability in simple terms."},
      ],
  )

  print(completion.choices[0].message.content)
  ```

  ```python Streaming Chat theme={null}
  import os
  import agentops
  from openai import OpenAI

  # Initialize AgentOps
  agentops.init()

  # Create OpenAI client configured for xAI
  client = OpenAI(
      api_key=os.getenv("XAI_API_KEY"),
      base_url="https://api.x.ai/v1",
  )

  # Streaming chat completion
  stream = client.chat.completions.create(
      model="grok-3-latest",
      messages=[
          {"role": "system", "content": "You are a helpful AI assistant."},
          {"role": "user", "content": "Tell me about the latest developments in AI."},
      ],
      stream=True,
  )

  for chunk in stream:
      if chunk.choices[0].delta.content is not None:
          print(chunk.choices[0].delta.content, end="")
  ```
</CodeGroup>

## Examples

<CardGroup cols={2}>
  <Card title="Grok Simple Example" icon="notebook" href="/v2/examples/xai">
    Basic usage patterns for Grok LLM
  </Card>

  <Card title="Grok Vision Example" icon="notebook" href="https://github.com/AgentOps-AI/agentops/blob/main/examples/xai/grok_vision_examples.ipynb" newTab={true}>
    Demonstrates using Grok with vision capabilities.
  </Card>
</CardGroup>

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