[Add] browser-use and main.py
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browser-use/docs/customize/supported-models.mdx
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browser-use/docs/customize/supported-models.mdx
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---
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title: "Supported Models"
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description: "Guide to using different LangChain chat models with Browser Use"
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icon: "robot"
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---
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## Overview
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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](https://python.langchain.com/docs/integrations/chat/).
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## Model Recommendations
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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](https://browser-use.com/posts/sota-technical-report). 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.
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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.
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<Note>
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All models require their respective API keys. Make sure to set them in your
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environment variables before running the agent.
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</Note>
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## Supported Models
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All LangChain chat models, which support tool-calling are available. We will document the most popular ones here.
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### OpenAI
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OpenAI's GPT-4o models are recommended for best performance.
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```python
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from langchain_openai import ChatOpenAI
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from browser_use import Agent
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# Initialize the model
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llm = ChatOpenAI(
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model="gpt-4o",
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temperature=0.0,
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)
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# Create agent with the model
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agent = Agent(
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task="Your task here",
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llm=llm
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)
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```
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Required environment variables:
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```bash .env
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OPENAI_API_KEY=
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```
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### Anthropic
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```python
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from langchain_anthropic import ChatAnthropic
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from browser_use import Agent
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# Initialize the model
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llm = ChatAnthropic(
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model_name="claude-3-5-sonnet-20240620",
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temperature=0.0,
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timeout=100, # Increase for complex tasks
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)
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# Create agent with the model
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agent = Agent(
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task="Your task here",
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llm=llm
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)
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```
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And add the variable:
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```bash .env
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ANTHROPIC_API_KEY=
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```
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### Azure OpenAI
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```python
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from langchain_openai import AzureChatOpenAI
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from browser_use import Agent
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from pydantic import SecretStr
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import os
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# Initialize the model
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llm = AzureChatOpenAI(
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model="gpt-4o",
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api_version='2024-10-21',
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azure_endpoint=os.getenv('AZURE_OPENAI_ENDPOINT', ''),
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api_key=SecretStr(os.getenv('AZURE_OPENAI_KEY', '')),
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)
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# Create agent with the model
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agent = Agent(
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task="Your task here",
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llm=llm
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)
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```
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Required environment variables:
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```bash .env
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AZURE_OPENAI_ENDPOINT=https://your-endpoint.openai.azure.com/
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AZURE_OPENAI_KEY=
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```
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### Gemini
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> [!IMPORTANT]
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> `GEMINI_API_KEY` was the old environment var name, it should be called `GOOGLE_API_KEY` as of 2025-05.
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```python
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from langchain_google_genai import ChatGoogleGenerativeAI
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from browser_use import Agent
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from dotenv import load_dotenv
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# Read GOOGLE_API_KEY into env
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load_dotenv()
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# Initialize the model
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llm = ChatGoogleGenerativeAI(model='gemini-2.0-flash-exp')
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# Create agent with the model
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agent = Agent(
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task="Your task here",
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llm=llm
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)
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```
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Required environment variables:
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```bash .env
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GOOGLE_API_KEY=
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```
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### DeepSeek-V3
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The community likes DeepSeek-V3 for its low price, no rate limits, open-source nature, and good performance.
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The example is available [here](https://github.com/browser-use/browser-use/blob/main/examples/models/deepseek.py).
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```python
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from langchain_openai import ChatOpenAI
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from browser_use import Agent
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from pydantic import SecretStr
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from dotenv import load_dotenv
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load_dotenv()
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api_key = os.getenv("DEEPSEEK_API_KEY")
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# Initialize the model
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llm=ChatOpenAI(base_url='https://api.deepseek.com/v1', model='deepseek-chat', api_key=SecretStr(api_key))
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# Create agent with the model
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agent = Agent(
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task="Your task here",
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llm=llm,
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use_vision=False
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)
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```
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Required environment variables:
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```bash .env
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DEEPSEEK_API_KEY=
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```
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### DeepSeek-R1
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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.
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The example is available [here](https://github.com/browser-use/browser-use/blob/main/examples/models/deepseek-r1.py).
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It does not support vision. The model is open-source so you could also use it with Ollama, but we have not tested it.
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```python
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from langchain_openai import ChatOpenAI
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from browser_use import Agent
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from pydantic import SecretStr
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from dotenv import load_dotenv
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load_dotenv()
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api_key = os.getenv("DEEPSEEK_API_KEY")
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# Initialize the model
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llm=ChatOpenAI(base_url='https://api.deepseek.com/v1', model='deepseek-reasoner', api_key=SecretStr(api_key))
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# Create agent with the model
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agent = Agent(
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task="Your task here",
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llm=llm,
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use_vision=False
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)
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```
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Required environment variables:
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```bash .env
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DEEPSEEK_API_KEY=
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```
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### Ollama
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Many users asked for local models. Here they are.
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1. Download Ollama from [here](https://ollama.ai/download)
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2. Run `ollama pull model_name`. Pick a model which supports tool-calling from [here](https://ollama.com/search?c=tools)
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3. Run `ollama start`
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```python
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from langchain_ollama import ChatOllama
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from browser_use import Agent
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from pydantic import SecretStr
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# Initialize the model
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llm=ChatOllama(model="qwen2.5", num_ctx=32000)
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# Create agent with the model
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agent = Agent(
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task="Your task here",
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llm=llm
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)
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```
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Required environment variables: None!
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### Novita AI
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[Novita AI](https://novita.ai) is an LLM API provider that offers a wide range of models. Note: choose a model that supports function calling.
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```python
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from langchain_openai import ChatOpenAI
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from browser_use import Agent
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from pydantic import SecretStr
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from dotenv import load_dotenv
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import os
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load_dotenv()
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api_key = os.getenv("NOVITA_API_KEY")
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# Initialize the model
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llm = ChatOpenAI(base_url='https://api.novita.ai/v3/openai', model='deepseek/deepseek-v3-0324', api_key=SecretStr(api_key))
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# Create agent with the model
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agent = Agent(
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task="Your task here",
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llm=llm,
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use_vision=False
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)
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```
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Required environment variables:
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```bash .env
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NOVITA_API_KEY=
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```
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### X AI
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[X AI](https://x.ai) is an LLM API provider that offers a wide range of models. Note: choose a model that supports function calling.
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```python
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from langchain_openai import ChatOpenAI
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from browser_use import Agent
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from pydantic import SecretStr
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from dotenv import load_dotenv
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import os
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load_dotenv()
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api_key = os.getenv("GROK_API_KEY")
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# Initialize the model
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llm = ChatOpenAI(
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base_url='https://api.x.ai/v1',
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model='grok-3-beta',
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api_key=SecretStr(api_key)
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)
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# Create agent with the model
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agent = Agent(
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task="Your task here",
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llm=llm,
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use_vision=False
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)
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```
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Required environment variables:
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```bash .env
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GROK_API_KEY=
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```
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## Coming soon
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(We are working on it)
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- Groq
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- Github
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- Fine-tuned models
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