> ## 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.
# ChatBrowserUse
> Browser Use optimized LLM provider with fastest performance and lowest cost
## Overview
ChatBrowserUse is the recommended LLM provider for Browser Use, offering the fastest and most cost-effective models specifically optimized for browser automation tasks. It achieves 3-5x faster task completion compared to standard models.
Get started with \$10 of free LLM credits at [cloud.browser-use.com/new-api-key](https://cloud.browser-use.com/new-api-key)
## Basic Usage
```python theme={null}
from browser_use import Agent, ChatBrowserUse
import asyncio
async def main():
llm = ChatBrowserUse()
agent = Agent(
task="Find the number 1 post on Show HN",
llm=llm,
)
await agent.run()
if __name__ == "__main__":
asyncio.run(main())
```
## Configuration
### Parameters
Model name to use. Available options:
* `bu-latest` or `bu-1-0`: Default model
* `bu-2-0`: Latest premium model
* `browser-use/bu-30b-a3b-preview`: Browser Use Open Source Model
API key for browser-use cloud. Defaults to `BROWSER_USE_API_KEY` environment variable.
Get your API key at [cloud.browser-use.com/new-api-key](https://cloud.browser-use.com/new-api-key)
Base URL for the API. Defaults to `BROWSER_USE_LLM_URL` environment variable or `https://llm.api.browser-use.com`.
Request timeout in seconds.
Maximum number of retries for transient errors (429, 500, 502, 503, 504).
Base delay in seconds for exponential backoff.
Maximum delay in seconds between retries.
## Advanced Usage
### Custom Model Selection
```python theme={null}
from browser_use import Agent, ChatBrowserUse
# Use premium model
llm = ChatBrowserUse(model='bu-2-0')
# Use open source model
llm = ChatBrowserUse(model='browser-use/bu-30b-a3b-preview')
agent = Agent(
task="Complex browser automation task",
llm=llm,
)
```
### With Session ID for Sticky Routing
```python theme={null}
from browser_use import Agent, ChatBrowserUse
llm = ChatBrowserUse()
agent = Agent(
task="Task requiring session persistence",
llm=llm,
)
# The session_id is automatically managed by the agent
# Same session routes to the same container for consistency
```
### Custom Base URL
```python theme={null}
import os
from browser_use import Agent, ChatBrowserUse
# Set via environment variable
os.environ['BROWSER_USE_LLM_URL'] = 'https://custom-endpoint.com'
# Or set directly
llm = ChatBrowserUse(
base_url='https://custom-endpoint.com',
api_key='your-api-key'
)
agent = Agent(task="Your task", llm=llm)
```
## Environment Setup
```bash .env theme={null}
BROWSER_USE_API_KEY=your_api_key_here
# Optional: Custom endpoint
BROWSER_USE_LLM_URL=https://llm.api.browser-use.com
```
## Error Handling
ChatBrowserUse automatically handles common errors with exponential backoff retry logic:
* **Rate Limits (429)**: Automatic retry with backoff
* **Server Errors (500, 502, 503, 504)**: Automatic retry with backoff
* **Network Errors**: Automatic retry with backoff
* **Authentication (401)**: Raised immediately as `ModelProviderError`
* **Insufficient Credits (402)**: Raised immediately as `ModelProviderError`
```python theme={null}
from browser_use import Agent, ChatBrowserUse
from browser_use.llm.exceptions import ModelProviderError, ModelRateLimitError
try:
llm = ChatBrowserUse(max_retries=3)
agent = Agent(task="Your task", llm=llm)
result = await agent.run()
except ModelRateLimitError as e:
print(f"Rate limit exceeded: {e.message}")
except ModelProviderError as e:
print(f"Provider error: {e.message} (Status: {e.status_code})")
```
## Properties
### provider
Returns the provider name: `"browser-use"`
```python theme={null}
llm = ChatBrowserUse()
print(llm.provider) # "browser-use"
```
### name
Returns the model name.
```python theme={null}
llm = ChatBrowserUse(model='bu-2-0')
print(llm.name) # "bu-2-0"
```
## Methods
### ainvoke()
Asynchronously invoke the model with messages.
```python theme={null}
from browser_use.llm.messages import SystemMessage, UserMessage
llm = ChatBrowserUse()
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 information
```
#### Parameters
* **messages** (`list[BaseMessage]`): List of messages to send
* **output\_format** (`type[T] | None`): Optional Pydantic model for structured output
* **request\_type** (`str`): Type of request - `"browser_agent"` or `"judge"`
* **session\_id** (`str | None`): Session ID for sticky routing (same session → same container)
#### Returns
`ChatInvokeCompletion[T] | ChatInvokeCompletion[str]` with:
* `completion`: Response content (string or structured output)
* `usage`: Token usage information (prompt\_tokens, completion\_tokens, total\_tokens)
## Why ChatBrowserUse?
1. **Optimized for Browser Automation**: Models fine-tuned specifically for browser tasks
2. **3-5x Faster**: Completes tasks significantly faster than generic models
3. **Lowest Cost**: Most cost-effective solution for browser automation
4. **Built-in Retry Logic**: Automatic handling of rate limits and transient errors
5. **Easy Setup**: Simple API key configuration with \$10 free credits
## Related
* [Getting Started](/quickstart)
* [Supported Models](/api/llm/chat-openai)
* [Agent Configuration](/api/agent)