langchain-models.mdx

title	LangChain Chat Models
description	Guide to using different LangChain chat models with Browser Use
icon	robot

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.

Model Recommendations

We have yet to test performance across all models. Currently, we recommend using GPT-4o. It achieves 89% accuracy on WebVoyager Dataset.

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

Supported Models

All LangChain chat models are supported. We will document the most popular ones here.

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:

OPENAI_API_KEY=

Anthropic

Claude models provide excellent performance and can handle complex tasks well.

from langchain_anthropic import ChatAnthropic
from browser_use import Agent

# Initialize the model
llm = ChatAnthropic(
    model_name="claude-3-sonnet-20240229",
    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:

ANTHROPIC_API_KEY=

Azure OpenAI

If you're using Azure OpenAI services, you can configure the model like this:

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:

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

Gemini

Get your API key from https://aistudio.google.com/apikey Make sure to use browser-use>=0.1.18

And add the variable:

GEMINI_API_KEY=

Subsequently, you can use the model as following:

import asyncio
import os

from dotenv import load_dotenv
from langchain_google_genai import ChatGoogleGenerativeAI
from pydantic import SecretStr

from browser_use import Agent

load_dotenv()
api_key = os.getenv('GEMINI_API_KEY')
if not api_key:
	raise ValueError('GEMINI_API_KEY is not set')

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


async def run_search():
	agent = Agent(
		task=(
			'Go to url r/LocalLLaMA subreddit and search for "browser use" in the search bar and click on the first post and find the funniest comment'
		),
		llm=llm,
		max_actions_per_step=4,
		tool_call_in_content=False,
	)

	await agent.run(max_steps=25)


if __name__ == '__main__':
	asyncio.run(run_search())

Qwen

Check Qwen example

import asyncio
import os

from langchain_ollama import ChatOllama

from browser_use import Agent


async def run_search():
	agent = Agent(
		task=(
			'1. Go to https://www.reddit.com/r/LocalLLaMA'
			"2. Search for 'browser use' in the search bar"
			'3. Click search'
			'4. Call done'
		),
		llm=ChatOllama(
			# model='qwen2.5:32b-instruct-q4_K_M',
			# model='qwen2.5:14b',
			model='qwen2.5:latest',
			num_ctx=128000,
		),
		max_actions_per_step=1,
		tool_call_in_content=False,
	)

	await agent.run()


if __name__ == '__main__':
	asyncio.run(run_search())

Ollama

Check llama example

import os

# Optional: Disable telemetry
# os.environ["ANONYMIZED_TELEMETRY"] = "false"

# Optional: Set the OLLAMA host to a remote server
# os.environ["OLLAMA_HOST"] = "http://x.x.x.x:11434"

import asyncio
from browser_use import Agent
from langchain_ollama import ChatOllama


async def run_search() -> str:
    agent = Agent(
        task="Search for a 'browser use' post on the r/LocalLLaMA subreddit and open it.",
        llm=ChatOllama(
            model="qwen2.5:32b-instruct-q4_K_M",
            num_ctx=32000,
        ),
    )

    result = await agent.run()
    return result


async def main():
    result = await run_search()
    print("\n\n", result)


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

granite3.1

Coming soon

(Sorry, we are working on it)

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• Github