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

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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=

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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).

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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=

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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=

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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.

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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
)

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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)

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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=

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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:
  1. Set up OCI configuration file at ~/.oci/config
  2. Have access to OCI Generative AI models in your tenancy
  3. 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

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Ollama

  1. Install Ollama: https://github.com/ollama/ollama
  2. Run ollama serve to start the server
  3. 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")

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Langchain

Example on how to use Langchain with OpenBrowser.

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Qwen example

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=

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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=

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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.

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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=

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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=

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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=

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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.