> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-opensw-1782332329-96d87c7.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# ChatXAI integration
> Integrate with the ChatXAI chat model using LangChain Python.
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).
[xAI](https://console.x.ai/) offers an API to interact with Grok models.
**API Reference**
For detailed documentation of all features and configuration options, head to the [`ChatXAI`](https://reference.langchain.com/python/langchain-xai/chat_models/ChatXAI) API reference.
## Overview
### Integration details
| Class | Package | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/xai) | Downloads | Version |
| :------------------------------------------------------------------------------------ | :----------------------------------------------------------------------- | :----------: | :---------------------------------------------------------------: | :--------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------: |
| [`ChatXAI`](https://reference.langchain.com/python/langchain-xai/chat_models/ChatXAI) | [`langchain-xai`](https://reference.langchain.com/python/langchain-xai/) | beta | ✅ |  |  |
### Model features
| [Tool calling](/oss/python/langchain/tools) | [Structured output](/oss/python/langchain/structured-output) | [Image input](/oss/python/langchain/messages#multimodal) | Audio input | Video input | [Token-level streaming](/oss/python/langchain/streaming#llm-tokens) | Native async | [Token usage](/oss/python/langchain/models#token-usage) | [Logprobs](/oss/python/langchain/models#log-probabilities) |
| :-----------------------------------------: | :----------------------------------------------------------: | :------------------------------------------------------: | :---------: | :---------: | :-----------------------------------------------------------------: | :----------: | :-----------------------------------------------------: | :--------------------------------------------------------: |
| ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ |
## Setup
To access xAI models, you'll need to create an xAI account, get an API key, and install the `langchain-xai` integration package.
### Credentials
Head to [this page](https://console.x.ai/) to sign up for xAI and generate an API key. Once you've done this, set the `XAI_API_KEY` environment variable:
```python theme={null}
import getpass
import os
if "XAI_API_KEY" not in os.environ:
os.environ["XAI_API_KEY"] = getpass.getpass("Enter your xAI API key: ")
```
To enable automated tracing of your model calls, set your [LangSmith](/langsmith/observability) API key:
```python theme={null}
os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
os.environ["LANGSMITH_TRACING"] = "true"
```
### Installation
The LangChain xAI integration lives in the `langchain-xai` package:
```python theme={null}
pip install -qU langchain-xai
```
## Instantiation
Now we can instantiate our model object and generate chat completions:
```python theme={null}
from langchain_xai import ChatXAI
llm = ChatXAI(
model="grok-beta",
temperature=0,
max_tokens=None,
timeout=None,
max_retries=2,
# other params...
)
```
## Invocation
```python theme={null}
messages = [
(
"system",
"You are a helpful assistant that translates English to French. Translate the user sentence.",
),
("human", "I love programming."),
]
ai_msg = llm.invoke(messages)
ai_msg
```
```text theme={null}
AIMessage(content="J'adore programmer.", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 30, 'total_tokens': 36, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'grok-beta', 'system_fingerprint': 'fp_14b89b2dfc', 'finish_reason': 'stop', 'logprobs': None}, id='run-adffb7a3-e48a-4f52-b694-340d85abe5c3-0', usage_metadata={'input_tokens': 30, 'output_tokens': 6, 'total_tokens': 36, 'input_token_details': {}, 'output_token_details': {}})
```
```python theme={null}
print(ai_msg.content)
```
```text theme={null}
J'adore programmer.
```
## Tool calling
ChatXAI has a [tool calling](https://docs.x.ai/docs#capabilities) (we use "tool calling" and "function calling" interchangeably here) API that lets you describe tools and their arguments, and have the model return a JSON object with a tool to invoke and the inputs to that tool. Tool-calling is extremely useful for building tool-using chains and agents, and for getting structured outputs from models more generally.
### ChatXAI.bind\_tools()
With `ChatXAI.bind_tools`, we can easily pass in Pydantic classes, dict schemas, LangChain tools, or even functions as tools to the model. Under the hood, these are converted to an OpenAI tool schema, which looks like:
```
{
"name": "...",
"description": "...",
"parameters": {...} # JSONSchema
}
```
and passed in every model invocation.
```python theme={null}
from pydantic import BaseModel, Field
class GetWeather(BaseModel):
"""Get the current weather in a given location"""
location: str = Field(description="The city and state, e.g. San Francisco, CA")
llm_with_tools = llm.bind_tools([GetWeather])
```
```python theme={null}
ai_msg = llm_with_tools.invoke(
"what is the weather like in San Francisco",
)
ai_msg
```
```text theme={null}
AIMessage(content='I am retrieving the current weather for San Francisco.', additional_kwargs={'tool_calls': [{'id': '0', 'function': {'arguments': '{"location":"San Francisco, CA"}', 'name': 'GetWeather'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 11, 'prompt_tokens': 151, 'total_tokens': 162, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'grok-beta', 'system_fingerprint': 'fp_14b89b2dfc', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-73707da7-afec-4a52-bee1-a176b0ab8585-0', tool_calls=[{'name': 'GetWeather', 'args': {'location': 'San Francisco, CA'}, 'id': '0', 'type': 'tool_call'}], usage_metadata={'input_tokens': 151, 'output_tokens': 11, 'total_tokens': 162, 'input_token_details': {}, 'output_token_details': {}})
```
## Live search
xAI supports a [Live Search](https://docs.x.ai/docs/guides/live-search) feature that enables Grok to ground its answers using results from web searches:
```python theme={null}
from langchain_xai import ChatXAI
llm = ChatXAI(
model="grok-3-latest",
search_parameters={
"mode": "auto",
# Example optional parameters below:
"max_search_results": 3,
"from_date": "2025-05-26",
"to_date": "2025-05-27",
},
)
llm.invoke("Provide me a digest of world news in the last 24 hours.")
```
See [xAI docs](https://docs.x.ai/docs/guides/live-search) for the full set of web search options.
***
## API reference
For detailed documentation of all `ChatXAI` features and configurations, head to the [API reference](https://reference.langchain.com/python/integrations/langchain_xai/).
***
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[Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/oss/python/integrations/chat/xai.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).