> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
> Use this file to discover all available pages before exploring further.

# xAI integrations

> Integrate with xAI using LangChain Python.

<Warning>
  This page makes reference to Grok models provided by [xAI](https://docs.x.ai/docs/overview) - not to be confused with [Groq](https://console.groq.com/docs/overview), a separate AI hardware and software company. See the [Groq provider page](/oss/python/integrations/providers/groq).
</Warning>

[xAI](https://console.x.ai) offers an API to interact with Grok models. This example goes over how to use LangChain to interact with xAI models.

## Installation

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pip install -U langchain-xai
```

## Environment

To use xAI, you'll need to [create an API key](https://console.x.ai/). The API key can be passed in as an init param `xai_api_key` or set as environment variable `XAI_API_KEY`.

## Example

See [ChatXAI docs](/oss/python/integrations/chat/xai) for detail and supported features.

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# Querying chat models with xAI

from langchain_xai import ChatXAI

chat = ChatXAI(
    # xai_api_key="YOUR_API_KEY",
    model="grok-4",
)

# stream the response back from the model
stream = chat.stream_events("Tell me fun things to do in NYC", version="v3")
for token in stream.text:
    print(token, end="", flush=True)

# if you don't want to do streaming, you can use the invoke method
# chat.invoke("Tell me fun things to do in NYC")
```

***

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