**Option B of two. Do not merge both.** Sibling: #5773, which deletes
the mentions instead.
## What is broken
`browser-use/bu-30b-a3b-preview` is still a live route in the Cloud
gateway
(`backend/llm_use/gateway/pricing.py` `MODAL_MODELS`, `service.py`
`_call_modal`), but
nothing is serving it. The Modal app behind that route,
`browser-use-llm-prod` in
`browser-use/deploy-llm` (`deploy.py:18`, 2x H200 `min_containers`), was
last deployed on
2025-12-16 and has not been deployed since.
Production, service `browser-use-production-llm-use`, last 90 days: 5
Modal client
initializations and 5 `Modal LLM call failed: Error code: 503`, paired
within 14 seconds.
Every observed call failed. The gateway turns that upstream 503 into a
generic HTTP 500 for
the caller, which is why it reads as "the model does not exist".
## Why not just delete it
The weights are public and people are using them.
https://huggingface.co/browser-use/bu-30b-a3b-preview is a public repo:
31B, 2.34k downloads
in the last month, 265 likes. The model exists. Only our hosting of it
does not.
## What this PR does
Says what the model actually is - open weights you run yourself.
- `examples/models/bu_oss.py` now starts from `vllm serve` and connects
with `ChatOpenAI`
against `http://localhost:8000/v1`. No `BROWSER_USE_API_KEY`, no
dependency on the dead
Cloud route. `BU_OSS_BASE_URL` / `BU_OSS_API_KEY` override the endpoint.
- `browser_use/llm/browser_use/chat.py` - the docstring now says Cloud
does not serve it.
- `skills/open-source/references/models.md` - drops the priceless OSS
pricing row and adds a
short self-hosting section with the vLLM command from the model card and
the weights URL.
Not touched: the gateway route, and
`tests/ci/models/test_llm_browseruse.py:85`.
## Checks
`ruff check`, `ruff format --check` and `git diff --check` clean.
The rewritten example was imported and constructed in this worktree:
`ChatOpenAI(model='browser-use/bu-30b-a3b-preview',
base_url='http://localhost:8000/v1', ...)`
resolves to provider `openai` with that base URL. No agent was run, and
no GPU was started,
so the end-to-end self-hosted run is not claimed here.
<!-- This is an auto-generated description by cubic. -->
---
## Summary by cubic
Updates docs and the example for `browser-use/bu-30b-a3b-preview` so the
model is presented as open weights users host themselves instead of a
Browser Use Cloud model.
- `examples/models/bu_oss.py` now uses a vLLM server and connects via
`ChatOpenAI`, with optional `BU_OSS_BASE_URL`/`BU_OSS_API_KEY`
overrides.
- The `ChatBrowserUse` docstring states the model is self-hosted only
and points to the example.
- The skills model table drops the Cloud pricing row and adds the vLLM
command and weights URL.
<sup>Written for commit 09dbfcbca9.
Summary will update on new commits.</sup>
<a
href="https://cubic.dev/pr/browser-use/browser-use/pull/5774?utm_source=github"
target="_blank" rel="noopener noreferrer"
data-no-image-dialog="true"><picture><source
media="(prefers-color-scheme: dark)"
srcset="https://www.cubic.dev/buttons/review-in-cubic-dark.svg"><source
media="(prefers-color-scheme: light)"
srcset="https://www.cubic.dev/buttons/review-in-cubic-light.svg"><img
alt="Review in cubic"
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<!-- End of auto-generated description by cubic. -->
Navigate the web like a human does.
Find an available slot, pick a date and time, handle the CAPTCHA, and book a driving test.
Explore more demos and prompts ↗
AI agents and crawlers: read browser-use.com/llms.txt for the product map (open source, Browser Harness, Cloud browsers, Agents API, pricing) and docs.browser-use.com/llms.txt for the documentation index. Browser Use is the open-source browser agent (Python and TypeScript), a $0.02 per browser-hour cloud browser with stealth, CAPTCHA solving and residential proxies, and a hosted agent API.
Which Browser Use do I need?
- Path 1: Fully Hosted Cloud: Scale up with a fully hosted agent and browser.
- Path 2: CLI: Automate your own browser tasks.
- Path 3: Python Library: Run the open source Browser Use agent locally from your own code.
Quickstart
Path 1: Fully Hosted Cloud
Scale browser automation with our hosted agent, stealth browsers, and infrastructure for profiles, recordings, and data policies.
New Google, GitHub, or Microsoft signups get $15 cloud credit.
Path 2: CLI
Paste this prompt into Claude Code, Codex, Hermes, OpenClaw, or your favorite agent.
Install or upgrade browser-use to the latest stable version with uv using Python 3.12, run `browser-use skill install` to register the skill, and connect it to my browser. If setup or connection fails, follow https://github.com/browser-use/browser-harness/blob/main/install.md.
Path 3: Python Library
Run the Browser Use agent locally from Python, with your choice of model and a local or cloud browser:
1. Install Browser Use (Python >= 3.11):
With uv installed, run uv init --python 3.12 first if you're starting a new project.
uv add browser-use
2. Add your OpenAI API key to .env:
# .env
OPENAI_API_KEY=your-key
# BROWSER_USE_API_KEY=your-key # Optional: BU2 model or cloud browser
For either optional Browser Use service, get a Browser Use API key.
3. Save this as agent.py:
import asyncio
from browser_use import Agent, Browser, ChatBrowserUse, ChatOpenAI
from dotenv import load_dotenv
load_dotenv()
async def main():
llm = ChatOpenAI(model='gpt-5.6-luna', reasoning_effort='xhigh')
# llm = ChatBrowserUse(model='bu-2-0') # Use BU2 instead; requires BROWSER_USE_API_KEY
agent = Agent(
task="Find the number of stars of the browser-use repo",
llm=llm,
# browser=Browser(use_cloud=True), # Use a cloud browser; requires BROWSER_USE_API_KEY
)
history = await agent.run()
print(history.final_result())
if __name__ == "__main__":
asyncio.run(main())
To use BU2, replace the ChatOpenAI line with the commented ChatBrowserUse line. The cloud-browser option works with either model.
4. Run it:
uv run agent.py
The agent opens a browser, looks up the repository, and prints its answer.
Browser Use Benchmark v2
This very hard benchmark targets the hardest browser tasks. On easier tasks, even smaller models can achieve very high success rates. Results shown are from a 60-task subset of BU Bench V2.
Integrations, hosting, custom tools, MCP, and more on our Docs ↗
FAQ
Should I use the fully hosted cloud, CLI, or Python library?
- Fully Hosted Cloud: Send tasks through the API and let Browser Use run the agent, browser, and infrastructure.
- CLI: Give an existing agent (Claude Code, Codex, Hermes, OpenClaw, Pi, Cursor, etc.) browser access. You can use it interactively or in scripts.
- Python Library: Run the open source agent in your own application, with custom tools, structured output, and your choice of model.
The CLI and Python library can each connect to a local or cloud browser. A cloud browser hosts the browser; the fully hosted API runs the agent as well.
What's the best model to use?
We recommend BU2, our model optimized for browser automation: ChatBrowserUse(model='bu-2-0'). It uses BROWSER_USE_API_KEY; ChatBrowserUse() currently selects the same model.
The best choice depends on your tasks, latency, and budget. See the BU2 model card, benchmark, and supported models and pricing to compare options.
Can I use Claude / GPT / Gemini through ChatBrowserUse?
Yes. ChatBrowserUse accepts provider-prefixed model IDs through the Browser Use gateway, using BROWSER_USE_API_KEY:
from browser_use import Agent, ChatBrowserUse
llm = ChatBrowserUse(model='anthropic/claude-sonnet-4-6') # or 'google/gemini-3-pro'
agent = Agent(task='...', llm=llm)
You can also use providers directly through wrappers such as ChatOpenAI, ChatAnthropic, and ChatGoogle, with each provider's own API key. See supported models.
Do I need to provide a system prompt?
No. Agent(...) supplies the Browser Use system prompt automatically, including when you change models. Put your task in task=. Use extend_system_message to add instructions or override_system_message to replace the default prompt when you need custom behavior.
See the custom system prompt example.
Can I use custom tools with the agent?
Yes. Register a function with Tools and pass it to the agent. This example adds a tool for the current UTC time and uses BROWSER_USE_API_KEY from .env:
import asyncio
from datetime import datetime, timezone
from browser_use import ActionResult, Agent, ChatBrowserUse, Tools
from dotenv import load_dotenv
load_dotenv()
tools = Tools()
@tools.action(description='Get the current date and time in UTC.')
def get_current_time() -> ActionResult:
return ActionResult(extracted_content=datetime.now(timezone.utc).isoformat())
async def main():
agent = Agent(
task="What is the current UTC time?",
llm=ChatBrowserUse(model='bu-2-0'),
tools=tools,
)
history = await agent.run()
print(history.final_result())
if __name__ == "__main__":
asyncio.run(main())
Can I use this for free?
The Python library is free and MIT-licensed. Model inference and hosted browsers are separate: API providers, including ChatBrowserUse, and Browser Use Cloud charge for usage. You can also use a local browser and a local model through Ollama, subject to your hardware and model requirements.
Terms of Service
This open-source library is licensed under the MIT License. For Browser Use services & data policy, see our Terms of Service and Privacy Policy.
How do I handle authentication?
- Local browser: Use
Browser.from_system_chrome()to reuse a Chrome profile. See the real-browser guide and example. - Cloud browser: Follow the profile sync guide, then use
Browser(use_cloud=True, cloud_profile_id='your-profile-id').
Profile sync transfers cookies, not local storage, IndexedDB, or extensions. Some sites may require you to sign in again.
How do I solve CAPTCHAs?
Browser Use Cloud provides stealth browsers and proxies designed to reduce bot detection and CAPTCHA challenges. With the Python library, enable a cloud browser with Browser(use_cloud=True) and set BROWSER_USE_API_KEY.
Results depend on the site and challenge; no browser configuration guarantees that every CAPTCHA can be avoided or solved.
How do I go into production?
Choose how much you want to manage:
- Keep your agent code: Connect the CLI or Python library to cloud browsers for managed browser infrastructure, stealth, profiles, and recordings.
- Have us run the agent too: Use the fully hosted Cloud API to submit tasks and retrieve results.
You can also host the Python library and browsers on your own infrastructure.
Related Repositories
| Repository | What it's for |
|---|---|
| Browser Harness | Our CLI for giving AI agents control of your browser. |
| Browser Harness JS | Give your JavaScript agent control of a real browser. |
| Browser Use Pi | Run a TypeScript browser agent built on Pi. |
| Cloud SDK | Integrate Browser Use Cloud into your application. |
| Video Use | Edit videos with your coding agent. |
| macOS Harness | Give your agent control of Mac apps, browsers, and files. |
| Benchmark | Explore browser tasks and compare agent performance. |
Citation
If you use Browser Use in your research or project, please cite:
@software{browser_use2024,
author = {Müller, Magnus and Žunič, Gregor},
title = {Browser Use: Enable AI to control your browser},
year = {2024},
publisher = {GitHub},
url = {https://github.com/browser-use/browser-use}
}