managed-browser 0.1.3

Allows seamless interplay between agentic browser-use and Playwright internals

managed-browser

A simple wrapper to integrate language-model agents into your existing Playwright (Python) scripts via the popular browser-use library, with built-in Playwright tracing support.

Supports

• Python 3.11+

Objective

Agents are still unreliable and far from production-ready—especially web agents, given the enormous diversity and challenges of navigating real-world sites. This package lets you keep your existing Playwright flows and seamlessly “drop in” agent tasks where you need them, without wrestling with a clunky API. It also exposes Playwright’s powerful tracing features around each agent run so you can debug and audit exactly what happened in the browser.

Installation

Requires Python 3.11+ and a PEP 517-compatible build backend.

# UV
uv add managed-browser
# Pip
pip install managed-browser

Usage

import asyncio
from browser_use import BrowserConfig
from langchain_openai import ChatOpenAI
from managed_browser import BrowserManager

async def main():
    # Initialize the manager with your Playwright config
    bm = BrowserManager(
        browser_config=BrowserConfig(headless=False)
    )

    # Your LLM of choice
    llm = ChatOpenAI(model='gpt-4o')

    # Create a managed context (with tracing enabled under the hood)
    async with bm.managed_context() as session:
        page = await session.browser_context.new_page()

        # -- Your deterministic Playwright steps --
        await page.goto("https://github.com", wait_until="domcontentloaded")
        await page.wait_for_timeout(1_000)
        await page.evaluate("window.scrollTo(0, document.body.scrollHeight)")
        await page.wait_for_timeout(1_000)

        # -- Drop in an agent task --
        agent = session.make_agent(
            llm=llm,
            task=(
                "Extract the visible link text in the website's footer. "
                "Return your answer wrapped in <result>...</result>."
            )
        )
        result = await agent.run()
        print(result)

    # After exit, Playwright trace(s) for the agent run will be saved in ./playwright_traces/

if __name__ == '__main__':
    asyncio.run(main())

API Reference

BrowserManager

BrowserManager(
    browser_config: BrowserConfig,
    *,
    trace_dir: Optional[str] = "./playwright_traces"
)

• browser_config: your browser-use/Playwright settings
• trace_dir: where to dump Playwright trace ZIPs

.managed_context()

An async context manager yielding a ManagedSession:

• session.browser_context → Playwright BrowserContext
• session.make_agent(llm: BaseChatModel, task: str, **kwargs) → ready-to-run agent

ManagedSession.make_agent(...)

Wraps browser-use arguments into an LLM agent. Returns an object with a .run() coroutine.

Tracing

By default, each agent.run() invocation is wrapped in a Playwright trace. Trace files (ZIP) will appear under trace_dir with timestamps for easy playback:

# Playwright CLI
npx playwright show-trace ./playwright_traces/<timestamp>.zip

Contributing

Contributions, issues, and feature requests are welcome!
Please fork the repo and submit a pull request, or open an issue for discussion.

License

This project is licensed under the MIT License. See LICENSE for details.