> ## 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.

# MistralAI integration

> Integrate with the MistralAI LLM using LangChain JavaScript.

<Warning>
  You are currently on a page documenting the use of Mistral models as text completion models. Many popular models available on Mistral are [chat completion models](/oss/javascript/langchain/models).

  You may be looking for [this page instead](/oss/javascript/integrations/chat/mistral/).
</Warning>

<Tip>
  **Want to run Mistral's models locally? Check out our [Ollama integration](/oss/javascript/integrations/chat/ollama).**
</Tip>

[Mistral AI](https://mistral.ai/) is a platform that offers hosting for their powerful [open source models](https://docs.mistral.ai/getting-started/models/).

This will help you get started with MistralAI completion models (LLMs) using LangChain. For detailed documentation on `MistralAI` features and configuration options, please refer to the [API reference](https://reference.langchain.com/javascript/langchain-mistralai/MistralAI).

## Overview

### Integration details

| Class                                                                                   | Package                                                                      | Local | Serializable | PY support |                                               Downloads                                              |                                              Version                                              |
| :-------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------- | :---: | :----------: | :--------: | :--------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------: |
| [`MistralAI`](https://reference.langchain.com/javascript/langchain-mistralai/MistralAI) | [`@langchain/mistralai`](https://www.npmjs.com/package/@langchain/mistralai) |   ❌   |       ✅      |      ❌     | ![NPM - Downloads](https://img.shields.io/npm/dm/@langchain/mistralai?style=flat-square\&label=%20&) | ![NPM - Version](https://img.shields.io/npm/v/@langchain/mistralai?style=flat-square\&label=%20&) |

## Setup

To access MistralAI models you'll need to create a MistralAI account, get an API key, and install the `@langchain/mistralai` integration package.

### Credentials

Head to [console.mistral.ai](https://console.mistral.ai/) to sign up to MistralAI and generate an API key. Once you've done this set the `MISTRAL_API_KEY` environment variable:

```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
export MISTRAL_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 MistralAI integration lives in the `@langchain/mistralai` package:

<CodeGroup>
  ```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  npm install @langchain/mistralai @langchain/core
  ```

  ```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  yarn add @langchain/mistralai @langchain/core
  ```

  ```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  pnpm add @langchain/mistralai @langchain/core
  ```
</CodeGroup>

## Instantiation

Now you can instantiate the model and generate text completions:

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { MistralAI } from "@langchain/mistralai"

const llm = new MistralAI({
  model: "codestral-latest",
  temperature: 0,
  maxTokens: undefined,
  maxRetries: 2,
  // other params...
})
```

## Invocation

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
const inputText = "MistralAI is an AI company that "

const completion = await llm.invoke(inputText)
completion
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
 has developed Mistral 7B, a large language model (LLM) that is open-source and available for commercial use. Mistral 7B is a 7 billion parameter model that is trained on a diverse and high-quality dataset, and it has been fine-tuned to perform well on a variety of tasks, including text generation, question answering, and code interpretation.

MistralAI has made Mistral 7B available under a permissive license, allowing anyone to use the model for commercial purposes without having to pay any fees. This has made Mistral 7B a popular choice for businesses and organizations that want to leverage the power of large language models without incurring high costs.

Mistral 7B has been trained on a diverse and high-quality dataset, which has enabled it to perform well on a variety of tasks. It has been fine-tuned to generate coherent and contextually relevant text, and it has been shown to be capable of answering complex questions and interpreting code.

Mistral 7B is also a highly efficient model, capable of processing text at a fast pace. This makes it well-suited for applications that require real-time responses, such as chatbots and virtual assistants.

Overall, Mistral 7B is a powerful and versatile large language model that is open-source and available for commercial use. Its ability to perform well on a variety of tasks, its efficiency, and its permissive license make it a popular choice for businesses and organizations that want to leverage the power of large language models.
```

## Hooks

Mistral AI supports custom hooks for three events: beforeRequest, requestError, and response. Examples of the function signature for each hook type can be seen below:

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
const beforeRequestHook = (req: Request): Request | void | Promise<Request | void> => {
    // Code to run before a request is processed by Mistral
};

const requestErrorHook = (err: unknown, req: Request): void | Promise<void> => {
    // Code to run when an error occurs as Mistral is processing a request
};

const responseHook = (res: Response, req: Request): void | Promise<void> => {
    // Code to run before Mistral sends a successful response
};
```

To add these hooks to the model, either pass them as arguments and they are automatically added:

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { MistralAI } from "@langchain/mistralai"

const modelWithHooks = new MistralAI({
    model: "codestral-latest",
    temperature: 0,
    maxRetries: 2,
    beforeRequestHooks: [ beforeRequestHook ],
    requestErrorHooks: [ requestErrorHook ],
    responseHooks: [ responseHook ],
    // other params...
});
```

Or assign and add them manually after instantiation:

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { MistralAI } from "@langchain/mistralai"

const model = new MistralAI({
    model: "codestral-latest",
    temperature: 0,
    maxRetries: 2,
    // other params...
});

model.beforeRequestHooks = [ ...model.beforeRequestHooks, beforeRequestHook ];
model.requestErrorHooks = [ ...model.requestErrorHooks, requestErrorHook ];
model.responseHooks = [ ...model.responseHooks, responseHook ];

model.addAllHooksToHttpClient();
```

The method addAllHooksToHttpClient clears all currently added hooks before assigning the entire updated hook lists to avoid hook duplication.

Hooks can be removed one at a time, or all hooks can be cleared from the model at once.

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
model.removeHookFromHttpClient(beforeRequestHook);

model.removeAllHooksFromHttpClient();
```

***

## API reference

For detailed documentation of all `MistralAI` features and configurations head to the [API reference](https://reference.langchain.com/javascript/langchain-mistralai/MistralAI).

***

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