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Overview

Browser Use supports various LangChain chat models. Here’s how to configure and use the most popular ones. The full list is available in the LangChain documentation.

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Model Recommendations

We have yet to test performance across all models. Currently, we achieve the best results using GPT-4o with an 89% accuracy on the WebVoyager Dataset. DeepSeek-V3 is 30 times cheaper than GPT-4o. Gemini-2.0-exp is also gaining popularity in the community because it is currently free. We also support local models, like Qwen 2.5, but be aware that small models often return the wrong output structure-which lead to parsing errors. We believe that local models will improve significantly this year.

All models require their respective API keys. Make sure to set them in your environment variables before running the agent.

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Supported Models

All LangChain chat models, which support tool-calling are available. We will document the most popular ones here.

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OpenAI

OpenAI’s GPT-4o models are recommended for best performance.

from langchain_openai import ChatOpenAI
from browser_use import Agent

# Initialize the model
llm = ChatOpenAI(
    model="gpt-4o",
    temperature=0.0,
)

# Create agent with the model
agent = Agent(
    task="Your task here",
    llm=llm
)

Required environment variables:

.env
OPENAI_API_KEY=

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Anthropic

from langchain_anthropic import ChatAnthropic
from browser_use import Agent

# Initialize the model
llm = ChatAnthropic(
    model_name="claude-3-5-sonnet-20240620",
    temperature=0.0,
    timeout=100, # Increase for complex tasks
)

# Create agent with the model
agent = Agent(
    task="Your task here",
    llm=llm
)

And add the variable:

.env
ANTHROPIC_API_KEY=

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Azure OpenAI

from langchain_openai import AzureChatOpenAI
from browser_use import Agent
from pydantic import SecretStr
import os

# Initialize the model
llm = AzureChatOpenAI(
    model="gpt-4o",
    api_version='2024-10-21',
    azure_endpoint=os.getenv('AZURE_OPENAI_ENDPOINT', ''),
    api_key=SecretStr(os.getenv('AZURE_OPENAI_KEY', '')),
)

# Create agent with the model
agent = Agent(
    task="Your task here",
    llm=llm
)

Required environment variables:

.env
AZURE_OPENAI_ENDPOINT=https://your-endpoint.openai.azure.com/
AZURE_OPENAI_KEY=

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Gemini

from langchain_google_genai import ChatGoogleGenerativeAI
from browser_use import Agent
from pydantic import SecretStr
import os

# Initialize the model
llm = ChatGoogleGenerativeAI(model='gemini-2.0-flash-exp', api_key=SecretStr(api_key))

# Create agent with the model
agent = Agent(
    task="Your task here",
    llm=llm
)

Required environment variables:

.env
GEMINI_API_KEY=

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DeepSeek-V3

The community likes DeepSeek-V3 for its low price, no rate limits, open-source nature, and good performance. The example is available here.

from langchain_openai import ChatOpenAI
from browser_use import Agent
from pydantic import SecretStr


# Initialize the model
llm=ChatOpenAI(base_url='https://api.deepseek.com/v1', model='deepseek-chat', api_key=SecretStr(api_key))

# Create agent with the model
agent = Agent(
    task="Your task here",
    llm=llm,
    use_vision=False
)

Required environment variables:

.env
DEEPSEEK_API_KEY=

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DeepSeek-R1

We support DeepSeek-R1. Its not fully tested yet, more and more functionality will be added, like e.g. the output of it’sreasoning content. The example is available here. It does not support vision. The model is open-source so you could also use it with Ollama, but we have not tested it.

from langchain_openai import ChatOpenAI
from browser_use import Agent
from pydantic import SecretStr


# Initialize the model
llm=ChatOpenAI(base_url='https://api.deepseek.com/v1', model='deepseek-reasoner', api_key=SecretStr(api_key))

# Create agent with the model
agent = Agent(
    task="Your task here",
    llm=llm,
    use_vision=False
)

Required environment variables:

.env
DEEPSEEK_API_KEY=

​
Ollama

Many users asked for local models. Here they are.

  1. Download Ollama from here
  2. Run ollama pull model_name. Pick a model which supports tool-calling from here
  3. Run ollama start
from langchain_ollama import ChatOllama
from browser_use import Agent
from pydantic import SecretStr


# Initialize the model
llm=ChatOllama(model="qwen2.5", num_ctx=32000)

# Create agent with the model
agent = Agent(
    task="Your task here",
    llm=llm
)

Required environment variables: None!

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Coming soon

(We are working on it)

  • Groq
  • Github
  • Fine-tuned models

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