Agent Settings

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Overview

The Agent class is the core component of Browser Use that handles browser automation. Here are the main configuration options you can use when initializing an agent.

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Basic Settings

from browser_use import Agent
from browser_use.llm import ChatOpenAI

agent = Agent(
    task="Search for latest news about AI",
    llm=ChatOpenAI(model="gpt-4o"),
)

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Required Parameters

• task: The instruction for the agent to execute
• llm: A chat model instance. See Supported Models for supported models.

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Agent Behavior

Control how the agent operates:

agent = Agent(
    task="your task",
    llm=llm,
    controller=custom_controller,  # For custom tool calling
    use_vision=True,              # Enable vision capabilities
    save_conversation_path="logs/conversation"  # Save chat logs
)

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Behavior Parameters

• controller: Registry of functions the agent can call. Defaults to base Controller. See Custom Functions for details.
• use_vision: Enable/disable vision capabilities. Defaults to True.
  • When enabled, the model processes visual information from web pages
  • Disable to reduce costs or use models without vision support
  • For GPT-4o, image processing costs approximately 800-1000 tokens (~$0.002 USD) per image (but this depends on the defined screen size)
• save_conversation_path: Path to save the complete conversation history. Useful for debugging.
• override_system_message: Completely replace the default system prompt with a custom one.
• extend_system_message: Add additional instructions to the default system prompt.

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Reuse Existing Browser Context

By default browser-use launches its own builtin browser using playwright chromium. You can also connect to a remote browser or pass any of the following existing playwright objects to the Agent: page, browser_context, browser, browser_session, or browser_profile.

These all get passed down to create a BrowserSession for the Agent:

agent = Agent(
    task='book a flight to fiji',
    llm=llm,
    browser_profile=browser_profile,  # use this profile to create a BrowserSession
    browser_session=BrowserSession(   # use an existing BrowserSession
      cdp_url=...,                      # remote CDP browser to connect to
      # or
      wss_url=...,                      # remote wss playwright server provider
      # or
      browser_pid=...                   # pid of a locally running browser process to attach to
      # or
      executable_path=...               # provide a custom chrome binary path
      # or
      channel=...                       # specify chrome, chromium, ms-edge, etc.
      # or
      page=page,                        # use an existing playwright Page object
      # or
      browser_context=browser_context,  # use an existing playwright BrowserContext object
      # or
      browser=browser,                  # use an existing playwright Browser object
    ),
)

For example, to connect to an existing browser over CDP you could do:

agent = Agent(
    ...
    browser_session=BrowserSession(cdp_url='http://localhost:9222'),
)

For example, to connect to a local running chrome instance you can do:

agent = Agent(
    ...
    browser_session=BrowserSession(browser_pid=1234),
)

See Connect to your Browser for more info.

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Running the Agent

The agent is executed using the async run() method:

• max_steps (default: 100) Maximum number of steps the agent can take during execution. This prevents infinite loops and helps control execution time.

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Agent History

The method returns an AgentHistoryList object containing the complete execution history. This history is invaluable for debugging, analysis, and creating reproducible scripts.

# Example of accessing history
history = await agent.run()

# Access (some) useful information
history.urls()              # List of visited URLs
history.screenshots()       # List of screenshot paths
history.action_names()      # Names of executed actions
history.extracted_content() # Content extracted during execution
history.errors()           # Any errors that occurred
history.model_actions()     # All actions with their parameters

The AgentHistoryList provides many helper methods to analyze the execution:

• final_result(): Get the final extracted content
• is_done(): Check if the agent completed successfully
• has_errors(): Check if any errors occurred
• model_thoughts(): Get the agent’s reasoning process
• action_results(): Get results of all actions

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Run initial actions without LLM

With this example you can run initial actions without the LLM. Specify the action as a dictionary where the key is the action name and the value is the action parameters. You can find all our actions in the Controller source code.

initial_actions = [
	{'go_to_url': {'url': 'https://www.google.com', 'new_tab': True}},
	{'go_to_url': {'url': 'https://en.wikipedia.org/wiki/Randomness', 'new_tab': True}},
	{'scroll_down': {'amount': 1000}},
]
agent = Agent(
	task='What theories are displayed on the page?',
	initial_actions=initial_actions,
	llm=llm,
)

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Run with message context

You can configure the agent and provide a separate message to help the LLM understand the task better.

from browser_use.llm import ChatOpenAI

agent = Agent(
    task="your task",
    message_context="Additional information about the task",
    llm = ChatOpenAI(model='gpt-4o')
)

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Run with planner model

You can configure the agent to use a separate planner model for high-level task planning:

from browser_use.llm import ChatOpenAI

# Initialize models
llm = ChatOpenAI(model='gpt-4o')
planner_llm = ChatOpenAI(model='o3-mini')

agent = Agent(
    task="your task",
    llm=llm,
    planner_llm=planner_llm,           # Separate model for planning
    use_vision_for_planner=False,      # Disable vision for planner
    planner_interval=4                 # Plan every 4 steps
)

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Planner Parameters

• planner_llm: A chat model instance used for high-level task planning. Can be a smaller/cheaper model than the main LLM.
• use_vision_for_planner: Enable/disable vision capabilities for the planner model. Defaults to True.
• planner_interval: Number of steps between planning phases. Defaults to 1.

Using a separate planner model can help:

• Reduce costs by using a smaller model for high-level planning
• Improve task decomposition and strategic thinking
• Better handle complex, multi-step tasks

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Optional Parameters

• message_context: Additional information about the task to help the LLM understand the task better.
• initial_actions: List of initial actions to run before the main task.
• max_actions_per_step: Maximum number of actions to run in a step. Defaults to 10.
• max_failures: Maximum number of failures before giving up. Defaults to 3.
• retry_delay: Time to wait between retries in seconds when rate limited. Defaults to 10.
• generate_gif: Enable/disable GIF generation. Defaults to False. Set to True or a string path to save the GIF.

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Memory

Memory management in browser-use has been significantly improved since version 0.3.2. The agent’s context handling and state management are now robust enough that the previous memory system (mem0) is no longer needed or supported.

The agent maintains its context and task progress through:

• Detailed history tracking of actions and results
• Structured state management
• Clear goal setting and evaluation at each step

The enable_memory parameter has been removed as the new system provides better context management by default.