> ## 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.
# ChatCerebras integration
> Integrate with the ChatCerebras chat model using LangChain JavaScript.
[Cerebras](https://cerebras.ai/) is a model provider that serves open source models with an emphasis on speed. The Cerebras CS-3 system, powered by the Wafer-Scale Engine-3 (WSE-3), represents a new class of AI supercomputer that sets the standard for generative AI training and inference with unparalleled performance and scalability.
With Cerebras as your inference provider, you can:
* Achieve unprecedented speed for AI inference workloads
* Build commercially with high throughput
* Effortlessly scale your AI workloads with our seamless clustering technology
Our CS-3 systems can be quickly and easily clustered to create the largest AI supercomputers in the world, making it simple to place and run the largest models. Leading corporations, research institutions, and governments are already using Cerebras solutions to develop proprietary models and train popular open-source models.
This will help you getting started with `ChatCerebras` [chat models](/oss/javascript/langchain/models). For detailed documentation of all `ChatCerebras` features and configurations head to the [API reference](https://reference.langchain.com/javascript/langchain-cerebras/ChatCerebras).
## Overview
### Integration details
| Class | Package | Serializable | [PY support](https://python.langchain.com/docs/integrations/chat/cerebras) | Downloads | Version |
| :------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------- | :----------: | :------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------: |
| [`ChatCerebras`](https://reference.langchain.com/javascript/langchain-cerebras/ChatCerebras) | [`@langchain/cerebras`](https://www.npmjs.com/package/@langchain/cerebras) | ❌ | ✅ |  |  |
### Model features
See the links in the table headers below for guides on how to use specific features.
| [Tool calling](/oss/javascript/langchain/tools) | [Structured output](/oss/javascript/langchain/structured-output) | [Image input](/oss/javascript/langchain/messages#multimodal) | Audio input | Video input | [Token-level streaming](/oss/javascript/langchain/streaming/) | [Token usage](/oss/javascript/langchain/models#token-usage) | [Logprobs](/oss/javascript/langchain/models#log-probabilities) |
| :---------------------------------------------: | :--------------------------------------------------------------: | :----------------------------------------------------------: | :---------: | :---------: | :-----------------------------------------------------------: | :---------------------------------------------------------: | :------------------------------------------------------------: |
| ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ |
## Setup
To access ChatCerebras models you'll need to create a Cerebras account, get an API key, and install the `@langchain/cerebras` integration package.
### Credentials
Get an API Key from [cloud.cerebras.ai](https://cloud.cerebras.ai) and add it to your environment variables:
```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
export CEREBRAS_API_KEY="your-api-key"
```
If you want to get automated tracing of your model calls you can also set your [LangSmith](/langsmith/observability) API key by uncommenting below:
```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# export LANGSMITH_TRACING="true"
# export LANGSMITH_API_KEY="your-api-key"
```
### Installation
The LangChain ChatCerebras integration lives in the `@langchain/cerebras` package:
```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
npm install @langchain/cerebras @langchain/core
```
```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
yarn add @langchain/cerebras @langchain/core
```
```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pnpm add @langchain/cerebras @langchain/core
```
## Instantiation
Now we can instantiate our model object and generate chat completions:
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { ChatCerebras } from "@langchain/cerebras"
const llm = new ChatCerebras({
model: "llama-3.3-70b",
temperature: 0,
maxTokens: undefined,
maxRetries: 2,
// other params...
})
```
## Invocation
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
const aiMsg = await llm.invoke([
{
role: "system",
content: "You are a helpful assistant that translates English to French. Translate the user sentence.",
},
{ role: "user", content: "I love programming." },
])
aiMsg
```
```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
AIMessage {
"id": "run-17c7d62d-67ac-4677-b33a-18298fc85e35",
"content": "J'adore la programmation.",
"additional_kwargs": {},
"response_metadata": {
"id": "chatcmpl-2d1e2de5-4239-46fb-af2a-6200d89d7dde",
"created": 1735785598,
"model": "llama-3.3-70b",
"system_fingerprint": "fp_2e2a2a083c",
"object": "chat.completion",
"time_info": {
"queue_time": 0.00009063,
"prompt_time": 0.002163031,
"completion_time": 0.012339628,
"total_time": 0.01640915870666504,
"created": 1735785598
}
},
"tool_calls": [],
"invalid_tool_calls": [],
"usage_metadata": {
"input_tokens": 55,
"output_tokens": 9,
"total_tokens": 64
}
}
```
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
console.log(aiMsg.content)
```
```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
J'adore la programmation.
```
## Json invocation
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
const messages = [
{
role: "system",
content: "You are a math tutor that handles math exercises and makes output in json in format { result: number }.",
},
{ role: "user", content: "2 + 2" },
];
const aiInvokeMsg = await llm.invoke(messages, { response_format: { type: "json_object" } });
// if you want not to pass response_format in every invoke, you can bind it to the instance
const llmWithResponseFormat = llm.bind({ response_format: { type: "json_object" } });
const aiBindMsg = await llmWithResponseFormat.invoke(messages);
// they are the same
console.log({ aiInvokeMsgContent: aiInvokeMsg.content, aiBindMsg: aiBindMsg.content });
```
```json theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
{ aiInvokeMsgContent: '{"result":4}', aiBindMsg: '{"result":4}' }
```
***
## API reference
For detailed documentation of all `ChatCerebras` features and configurations head to the [API reference](https://reference.langchain.com/javascript/langchain-cerebras/ChatCerebras).
***
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[Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/oss/javascript/integrations/chat/cerebras.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).