Agent Settings
Learn how to configure the agent
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.
Basic Settings
Required Parameters
task
: The instruction for the agent to executellm
: A LangChain chat model instance. See LangChain Models for supported models.
Agent Behavior
Control how the agent operates:
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 toTrue
.- 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.system_prompt_class
: Custom system prompt class. See System Prompt for customization options.
Vision capabilities are recommended for better web interaction understanding, but can be disabled to reduce costs or when using models without vision support.
(Reuse) Browser Configuration
You can configure how the agent interacts with the browser. To see more Browser
options refer to the Browser Settings documentation.
Reuse Existing Browser
browser
: A Browser Use Browser instance. When provided, the agent will reuse this browser instance and automatically create new contexts for each run()
.
Remember: in this scenario the Browser
will not be closed automatically.
Reuse Existing Browser Context
browser_context
: A Playwright browser context. Useful for maintaining persistent sessions. See Persistent Browser for more details.
For more information about how browser context works, refer to the Playwright documentation.
You can reuse the same context for multiple agents. If you do nothing, the
browser will be automatically created and closed on run()
completion.
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.
Agent History
The method returns an AgentHistoryList
object containing the complete execution history. This history is invaluable for debugging, analysis, and creating reproducible scripts.
The AgentHistoryList
provides many helper methods to analyze the execution:
final_result()
: Get the final extracted contentis_done()
: Check if the agent completed successfullyhas_errors()
: Check if any errors occurredmodel_thoughts()
: Get the agent’s reasoning processaction_results()
: Get results of all actions
For a complete list of helper methods and detailed history analysis capabilities, refer to the AgentHistoryList source code.
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.
Run with planner model
You can configure the agent to use a separate planner model for high-level task planning:
Planner Parameters
planner_llm
: A LangChain 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 toTrue
.planner_interval
: Number of steps between planning phases. Defaults to4
.
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
The planner model is optional. If not specified, the agent will not use the planner model.
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