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

# ChatMistral

> Mistral AI LLM provider for Browser Use

## Overview

ChatMistral provides integration with Mistral AI's language models, including Mistral Large, Mistral Medium, and Mistral Small with optimized schema sanitization for reliable structured outputs.

## Basic Usage

```python theme={null}
from browser_use import Agent, ChatMistral
import asyncio

async def main():
    llm = ChatMistral(
        model='mistral-medium-latest',
        api_key='your_mistral_api_key'
    )
    agent = Agent(
        task="Find the number 1 post on Show HN",
        llm=llm,
    )
    await agent.run()

if __name__ == "__main__":
    asyncio.run(main())
```

## Configuration

### Required Parameters

<ParamField path="model" type="str" default="mistral-medium-latest">
  Mistral model to use. Common options:

  * `mistral-medium-latest`: Balanced performance (default)
  * `mistral-large`: Most powerful Mistral model
  * `mistral-small`: Fast and cost-effective
  * Or any other Mistral model identifier
</ParamField>

### Model Parameters

<ParamField path="temperature" type="float" default="0.2">
  Sampling temperature (0.0 to 1.0). Controls randomness in responses.
</ParamField>

<ParamField path="top_p" type="float" default="None">
  Nucleus sampling parameter (0.0 to 1.0).
</ParamField>

<ParamField path="max_tokens" type="int" default="4096">
  Maximum tokens to generate. Mistral uses `max_tokens` (not `max_completion_tokens`).
</ParamField>

<ParamField path="seed" type="int" default="None">
  Random seed for deterministic output.
</ParamField>

<ParamField path="safe_prompt" type="bool" default="False">
  Enable Mistral's safe prompt mode for content filtering.
</ParamField>

### Client Parameters

<ParamField path="api_key" type="str" default="None">
  Mistral API key. Falls back to `MISTRAL_API_KEY` environment variable.

  <Note>
    Get your API key at [console.mistral.ai](https://console.mistral.ai)
  </Note>
</ParamField>

<ParamField path="base_url" type="str" default="https://api.mistral.ai/v1">
  Base URL for Mistral API. Can be overridden with `MISTRAL_BASE_URL` environment variable.
</ParamField>

<ParamField path="timeout" type="float" default="None">
  Request timeout in seconds or httpx.Timeout object.
</ParamField>

<ParamField path="max_retries" type="int" default="5">
  Maximum number of retries for failed requests.
</ParamField>

<ParamField path="default_headers" type="dict" default="None">
  Additional headers to include in all requests.
</ParamField>

<ParamField path="default_query" type="dict" default="None">
  Additional query parameters for all requests.
</ParamField>

<ParamField path="http_client" type="httpx.AsyncClient" default="None">
  Custom async HTTP client instance.
</ParamField>

## Advanced Usage

### Structured Output with JSON Schema

ChatMistral uses Mistral's native JSON schema support with automatic schema optimization:

```python theme={null}
from browser_use import Agent, ChatMistral
from pydantic import BaseModel

class Product(BaseModel):
    name: str
    price: float
    description: str
    in_stock: bool

llm = ChatMistral(
    model='mistral-large',
    api_key='your_mistral_api_key',
)

agent = Agent(
    task="Extract product information",
    llm=llm,
    output_model_schema=Product,
)

result = await agent.run()
print(result.structured_output)  # Product instance
```

<Note>
  ChatMistral includes automatic schema sanitization to ensure compatibility with Mistral's JSON schema requirements.
</Note>

### Custom Base URL

```python theme={null}
from browser_use import Agent, ChatMistral

llm = ChatMistral(
    model='mistral-medium-latest',
    base_url='https://custom-mistral-endpoint.com/v1',
    api_key='your_api_key',
)

agent = Agent(task="Your task", llm=llm)
```

### Safe Prompt Mode

```python theme={null}
from browser_use import Agent, ChatMistral

llm = ChatMistral(
    model='mistral-medium-latest',
    api_key='your_mistral_api_key',
    safe_prompt=True,  # Enable content filtering
)

agent = Agent(task="Your task", llm=llm)
```

### Custom Headers and Query Parameters

```python theme={null}
from browser_use import Agent, ChatMistral

llm = ChatMistral(
    model='mistral-medium-latest',
    api_key='your_mistral_api_key',
    default_headers={'X-Custom-Header': 'value'},
    default_query={'custom_param': 'value'},
)

agent = Agent(task="Your task", llm=llm)
```

## Environment Setup

```bash .env theme={null}
MISTRAL_API_KEY=your_api_key_here
# Optional: Custom base URL
MISTRAL_BASE_URL=https://api.mistral.ai/v1
```

## Error Handling

```python theme={null}
from browser_use import Agent, ChatMistral
from browser_use.llm.exceptions import (
    ModelProviderError,
    ModelRateLimitError
)

try:
    llm = ChatMistral(
        model='mistral-medium-latest',
        api_key='your_mistral_api_key',
    )
    agent = Agent(task="Your task", llm=llm)
    result = await agent.run()
except ModelRateLimitError as e:
    print(f"Rate limit exceeded: {e.message}")
    print(f"Status code: {e.status_code}")
except ModelProviderError as e:
    print(f"API error: {e.message}")
    print(f"Status code: {e.status_code}")
```

## Properties

### provider

Returns the provider name: `"mistral"`

```python theme={null}
llm = ChatMistral(
    model='mistral-medium-latest',
    api_key='your_mistral_api_key',
)
print(llm.provider)  # "mistral"
```

### name

Returns the model name.

```python theme={null}
llm = ChatMistral(
    model='mistral-medium-latest',
    api_key='your_mistral_api_key',
)
print(llm.name)  # "mistral-medium-latest"
```

## Methods

### ainvoke()

Asynchronously invoke the model with messages.

```python theme={null}
from browser_use.llm.messages import SystemMessage, UserMessage

llm = ChatMistral(
    model='mistral-medium-latest',
    api_key='your_mistral_api_key',
)

messages = [
    SystemMessage(content="You are a helpful assistant"),
    UserMessage(content="What is Browser Use?")
]

response = await llm.ainvoke(messages)
print(response.completion)     # String response
print(response.usage)          # Token usage
```

#### Parameters

* **messages** (`list[BaseMessage]`): List of messages
* **output\_format** (`type[T] | None`): Optional Pydantic model for structured output

#### Returns

`ChatInvokeCompletion[T] | ChatInvokeCompletion[str]` with:

* `completion`: Response content (string or structured output)
* `usage`: Token usage including:
  * `prompt_tokens`: Input tokens
  * `completion_tokens`: Output tokens
  * `total_tokens`: Total tokens used
* `stop_reason`: Not exposed by Mistral implementation

## Implementation Details

### Direct HTTP API

ChatMistral uses direct HTTP requests instead of the official SDK for better control:

* Custom retry logic with httpx transport
* Automatic schema sanitization for Mistral compatibility
* Flexible message content handling (string and list formats)
* Custom error parsing for better error messages

### Schema Optimization

The implementation includes `MistralSchemaOptimizer` that:

* Ensures strict JSON schema compatibility
* Removes unsupported schema features
* Optimizes nested object structures
* Validates schema before sending to API

## Model Capabilities

### Mistral Large

* Most powerful Mistral model
* Best for complex reasoning tasks
* Strong multilingual support
* Excellent code generation

### Mistral Medium

* Balanced performance and cost
* Good for general tasks
* Fast inference speed
* Recommended default

### Mistral Small

* Fastest and most cost-effective
* Great for simple tasks
* High throughput
* Low latency

## Related

* [ChatBrowserUse](/api/llm/chat-browser-use) - Recommended provider
* [ChatOpenAI](/api/llm/chat-openai)
* [ChatAnthropic](/api/llm/chat-anthropic)