Supported Models

OpenBrowser supports a wide variety of LLM providers. Choose the one that best fits your needs.

Google Gemini example

GEMINI_API_KEY is deprecated and should be named GOOGLE_API_KEY as of 2025-05.

from openbrowser import Agent, ChatGoogle
from dotenv import load_dotenv

# Read GOOGLE_API_KEY into env
load_dotenv()

# Initialize the model
llm = ChatGoogle(model='gemini-flash-latest')

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

Required environment variables:

.env

GOOGLE_API_KEY=

OpenAI example

O3 model is recommended for best accuracy.

from openbrowser import Agent, ChatOpenAI

# Initialize the model
llm = ChatOpenAI(
    model="o3",
)

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

Required environment variables:

.env

OPENAI_API_KEY=

You can use any OpenAI compatible model by passing the model name to the ChatOpenAI class using a custom URL (or any other parameter that would go into the normal OpenAI API call).

Anthropic example

from openbrowser import Agent, ChatAnthropic

# Initialize the model
llm = ChatAnthropic(
    model="claude-sonnet-4-0",
)

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

And add the variable:

.env

ANTHROPIC_API_KEY=

Azure OpenAI example

from openbrowser import Agent, ChatAzureOpenAI
from pydantic import SecretStr
import os

# Initialize the model
llm = ChatAzureOpenAI(
    model="o4-mini",
)

# 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_API_KEY=

AWS Bedrock example

AWS Bedrock provides access to multiple model providers through a single API. We support both a general AWS Bedrock client and provider-specific convenience classes.

General AWS Bedrock (supports all providers)

from openbrowser import Agent, ChatAWSBedrock

# Works with any Bedrock model (Anthropic, Meta, AI21, etc.)
llm = ChatAWSBedrock(
    model="anthropic.claude-3-5-sonnet-20240620-v1:0",  # or any Bedrock model
    aws_region="us-east-1",
)

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

Anthropic Claude via AWS Bedrock (convenience class)

from openbrowser import Agent, ChatAnthropicBedrock

# Anthropic-specific class with Claude defaults
llm = ChatAnthropicBedrock(
    model="anthropic.claude-3-5-sonnet-20240620-v1:0",
    aws_region="us-east-1",
)

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

AWS Authentication

Required environment variables:

.env

AWS_ACCESS_KEY_ID=
AWS_SECRET_ACCESS_KEY=
AWS_DEFAULT_REGION=us-east-1

You can also use AWS profiles or IAM roles instead of environment variables. The implementation supports:

Environment variables (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_DEFAULT_REGION)
AWS profiles and credential files
IAM roles (when running on EC2)
Session tokens for temporary credentials
AWS SSO authentication (aws_sso_auth=True)

Groq example

from openbrowser import Agent, ChatGroq

llm = ChatGroq(model="meta-llama/llama-4-maverick-17b-128e-instruct")

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

Required environment variables:

.env

GROQ_API_KEY=

Oracle Cloud Infrastructure (OCI) example

OCI provides access to various generative AI models including Meta Llama, Cohere, and other providers through their Generative AI service.

from openbrowser import Agent, ChatOCIRaw

# Initialize the OCI model
llm = ChatOCIRaw(
    model_id="ocid1.generativeaimodel.oc1.us-chicago-1.amaaaaaask7dceya...",
    service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
    compartment_id="ocid1.tenancy.oc1..aaaaaaaayeiis5uk2nuubznrekd...",
    provider="meta",  # or "cohere"
    temperature=0.7,
    max_tokens=800,
    top_p=0.9,
    auth_type="API_KEY",
    auth_profile="DEFAULT"
)

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

Required setup:

Set up OCI configuration file at ~/.oci/config
Have access to OCI Generative AI models in your tenancy
Install the OCI Python SDK: uv add oci or pip install oci

Authentication methods supported:

API_KEY: Uses API key authentication (default)
INSTANCE_PRINCIPAL: Uses instance principal authentication
RESOURCE_PRINCIPAL: Uses resource principal authentication

Ollama

Install Ollama: https://github.com/ollama/ollama
Run ollama serve to start the server
In a new terminal, install the model you want to use: ollama pull llama3.1:8b (this has 4.9GB)

from openbrowser import Agent, ChatOllama

llm = ChatOllama(model="llama3.1:8b")

Langchain

Example on how to use Langchain with OpenBrowser.

Currently, only qwen-vl-max is recommended for OpenBrowser. Other Qwen models, including qwen-max, have issues with the action schema format. Smaller Qwen models may return incorrect action schema formats (e.g., actions: [{"navigate": "google.com"}] instead of [{"navigate": {"url": "google.com"}}]). If you want to use other models, add concrete examples of the correct action format to your prompt.

from openbrowser import Agent, ChatOpenAI
from dotenv import load_dotenv
import os

load_dotenv()

# Get API key from https://modelstudio.console.alibabacloud.com/?tab=playground#/api-key
api_key = os.getenv('ALIBABA_CLOUD')
base_url = 'https://dashscope-intl.aliyuncs.com/compatible-mode/v1'

llm = ChatOpenAI(model='qwen-vl-max', api_key=api_key, base_url=base_url)

agent = Agent(
    task="Your task here",
    llm=llm,
    use_vision=True
)

Required environment variables:

.env

ALIBABA_CLOUD=

ModelScope example

from openbrowser import Agent, ChatOpenAI
from dotenv import load_dotenv
import os

load_dotenv()

# Get API key from https://www.modelscope.cn/docs/model-service/API-Inference/intro
api_key = os.getenv('MODELSCOPE_API_KEY')
base_url = 'https://api-inference.modelscope.cn/v1/'

llm = ChatOpenAI(model='Qwen/Qwen2.5-VL-72B-Instruct', api_key=api_key, base_url=base_url)

agent = Agent(
    task="Your task here",
    llm=llm,
    use_vision=True
)

Required environment variables:

.env

MODELSCOPE_API_KEY=

Other models (DeepSeek, Novita, OpenRouter…)

We support all other models that can be called via OpenAI compatible API. We are open to PRs for more providers.

DeepSeek example

from openbrowser import Agent
from openbrowser.llm import ChatDeepSeek
import os

deepseek_api_key = os.getenv('DEEPSEEK_API_KEY')

llm = ChatDeepSeek(
    base_url='https://api.deepseek.com/v1',
    model='deepseek-chat',
    api_key=deepseek_api_key,
)

agent = Agent(
    task='Your task here',
    llm=llm,
    use_vision=False,
)

Required environment variables:

.env

DEEPSEEK_API_KEY=

Novita example

from openbrowser import Agent, ChatOpenAI
import os

api_key = os.getenv('NOVITA_API_KEY')

agent = Agent(
    task='Your task here',
    llm=ChatOpenAI(
        base_url='https://api.novita.ai/v3/openai',
        model='deepseek/deepseek-v3-0324',
        api_key=api_key,
    ),
    use_vision=False,
)

Required environment variables:

.env

NOVITA_API_KEY=

OpenRouter example

from openbrowser import Agent, ChatOpenAI
import os

llm = ChatOpenAI(
    model='x-ai/grok-4',
    base_url='https://openrouter.ai/api/v1',
    api_key=os.getenv('OPENROUTER_API_KEY'),
)

agent = Agent(
    task='Your task here',
    llm=llm,
)

Required environment variables:

.env

OPENROUTER_API_KEY=

Browser-Use LLM example

ChatBrowserUse() is an external LLM service from browser-use.com optimized for browser automation tasks.

from openbrowser import Agent, ChatBrowserUse

# Initialize the model
llm = ChatBrowserUse()

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

Required environment variables:

.env

BROWSER_USE_API_KEY=

Get your API key from browser-use.com.

Get Started

Customize

Examples

Development

Google Gemini example

OpenAI example

Anthropic example

Azure OpenAI example

AWS Bedrock example

General AWS Bedrock (supports all providers)

Anthropic Claude via AWS Bedrock (convenience class)

AWS Authentication

Groq example

Oracle Cloud Infrastructure (OCI) example

Ollama

Langchain

Qwen example

ModelScope example

Other models (DeepSeek, Novita, OpenRouter…)

DeepSeek example

Novita example

OpenRouter example

Browser-Use LLM example

​Google Gemini example

​OpenAI example

​Anthropic example

​Azure OpenAI example

​AWS Bedrock example

​General AWS Bedrock (supports all providers)

​Anthropic Claude via AWS Bedrock (convenience class)

​AWS Authentication

​Groq example

​Oracle Cloud Infrastructure (OCI) example

​Ollama

​Langchain

​Qwen example

​ModelScope example

​Other models (DeepSeek, Novita, OpenRouter…)

​DeepSeek example

​Novita example

​OpenRouter example

​Browser-Use LLM example

Google Gemini example

OpenAI example

Anthropic example

Azure OpenAI example

AWS Bedrock example

General AWS Bedrock (supports all providers)

Anthropic Claude via AWS Bedrock (convenience class)

AWS Authentication

Groq example

Oracle Cloud Infrastructure (OCI) example

Ollama

Langchain

Qwen example

ModelScope example

Other models (DeepSeek, Novita, OpenRouter…)

DeepSeek example

Novita example

OpenRouter example

Browser-Use LLM example