Agent Settings

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Overview

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

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Basic Settings

from openoperator import Agent, LLM

# Define LLM
llm=LLM(model="openai/gpt-4o")

# Create Agent
agent = Agent(llm=llm)

# Add a task
agent.add_task(Search for latest news about AI")

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Agent Behavior

Control how the agent operates:

# Define LLM
llm=LLM(model="openai/gpt-4o")

agent = Agent(
    llm=llm,
    controller=custom_controller,  # For custom tool calling
    use_vision=True,              # Enable vision capabilities
    save_conversation_path="logs/conversation.json"  # 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.

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(Reuse) Browser Configuration

You can configure how the agent interacts with the browser. To see more Browser options refer to the Browser Settings documentation.

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Reuse Existing Browser

browser: A OpenOperator Browser instance. When provided, the agent will reuse this browser instance and automatically create new contexts for each run().

from openoperator import Agent, Browser
from browser_use.browser.context import BrowserContext

# Define LLM
llm=LLM(model="openai/gpt-4o")

# Reuse existing browser
browser = Browser()
agent = Agent(
    llm=llm,
    browser=browser  # Browser instance will be reused
)

# Add a task
agent.add_task("What's the mass of pluto?")

# Run agent
await agent.run()

# Manually close the browser
await browser.close()

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Reuse Existing Browser Context

browser_context: A Playwright browser context. Useful for maintaining persistent sessions. See Persistent Browser for more details.

from openoperator import Agent, Browser
from playwright.async_api import BrowserContext

# Define LLM
llm=LLM(model="openai/gpt-4o")

# Use specific browser context (preferred method)
async with await browser.new_context() as context:
    agent = Agent(
        llm=llm,
        browser_context=context  # Use persistent context
    )

    # Add a task
    agent.add_task("Summarize today's top 3 news articles.")

    # Run the agent
    await agent.run()

    # Pass the context to the next agent
    next_agent = Agent(
        task=task2,
        llm=llm,
        browser_context=context
    )

    ...

await browser.close()

For more information about how browser context works, refer to the Playwright documentation.

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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

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 = [
	{'open_tab': {'url': 'https://www.google.com'}},
	{'open_tab': {'url': 'https://en.wikipedia.org/wiki/Randomness'}},
	{'scroll_down': {'amount': 1000}},
	{'extract_content': {'include_links': False}},
]
agent = Agent(
	initial_actions=initial_actions,
	llm=llm
)