- Category
- AI Agents
- Rank
- No. 883Tools index
- Pricing
- Open Source
- Type
- AGENT
- Builder
- browser-use
- GitHub
- 2.0k stars
- Date
About
Browser Use's library for making Mac apps accessible to AI agents — drives the macOS Accessibility API to let LLMs operate native software.
What it does
It turns a natural-language task into a repeated observe, decide, act loop. The model receives an indexed description of the active app’s controls and context, returns structured action sequences, then checks resulting interface state. Actions include opening apps, clicking, entering text, scrolling, focusing windows, and running AppleScript.
Why it's ranked here
Worth a supervised trial for engineers exploring agent-driven desktop workflows, but not dependable automation infrastructure yet. The action model, state history, retries, and result verification show useful engineering depth. However, the project explicitly warns against unsupervised operation, describes varying success rates, and still lists reliability, self-correction, and testing as unfinished work.
What's good
The agent reasons over controls with roles, titles, values, enabled status, and supported actions instead of relying only on screen coordinates. It distinguishes actionable controls from contextual text, supports several actions per model step on stable interfaces, and advises single-action steps on changing interfaces. Structured outputs, saved histories, retries, and explicit completion results make runs easier to inspect.
Tradeoffs
The automation can access stored passwords, authentication flows, private credentials, and every available interface component. The authors say it will not stop at captchas and should not run unattended. Reliability varies with prompts, Gemini is described as less reliable, and local inference remains a roadmap goal. The supplied implementation also contains an unfinished state method, reinforcing its early-stage status.
How to use it well
Use it for supervised experiments, demos, and carefully bounded personal Mac tasks where a human can watch every action. Start with stable apps, explicit prompts, small action batches, and no sensitive accounts. Engineers can embed the Python library or use its Gradio interface. It does not yet cover private local inference, dependable unattended automation, or iPhone and iPad control.
Technical notes+
pyproject.toml defines the mlx-use Python package for Python 3.11 or newer, built with Hatchling, with PyObjC, PyCocoa, LangChain provider adapters, Playwright, Pydantic, and Gradio dependencies. mlx_use/__init__.py exports Agent, Controller, prompt, action, result, and history types as the library surface. mlx_use/agent/service.py builds dynamic Pydantic action schemas, requests structured LLM output, limits actions per step, executes them through the controller, records history, retries failures, and emits telemetry. mlx_use/mac/tree.py walks Accessibility API elements, indexes interactive nodes, retains contextual text, caps traversal at depth 30 and 250 children, and caches native element references. mlx_use/mac/actions.py performs accessibility actions and value assignment. gradio_app/app.py supplies agent, automation, and configuration tabs with single-task concurrency. mlx_use/mac/context.py defines get_state twice, with the later definition containing only pass, and references several unimported Accessibility and workspace symbols, so that context class appears incomplete in the supplied source.
Observed
- License
- MIT License classifier in project metadata
- Primary language
- Python
- Package
- Published install name is mlx-use; Hatchling build backend; Python 3.11 or newer
- Interfaces
- Importable Python library and Gradio web interface
- Platform
- Targets MacBook application control through macOS Accessibility and Objective-C frameworks
- Model providers
- README documents OpenAI, Anthropic, and Gemini API keys; package dependencies include additional LangChain provider adapters
- Development support
- Optional development dependencies include pytest and pytest-asyncio
Read from README.md, pyproject.toml, conftest.py, mlx_use/utils.py, gradio_app/app.py, mlx_use/__init__.py, mlx_use/logging_config.py, mlx_use/mac/tree.py, mlx_use/mac/views.py, mlx_use/agent/views.py, mlx_use/mac/actions.py, mlx_use/mac/context.py, mlx_use/mac/element.py, mlx_use/agent/prompts.py, mlx_use/agent/service.py.
What it can do
Control native macOS applications via AI agents
AI agent commands and target macOS application → Automated application interactions and responses
Read UI elements from macOS applications
Target macOS application interface → Accessible UI element data and structure
Click buttons and UI controls in macOS apps
UI element coordinates or identifiers → Executed click actions and state changes
Type text into macOS application fields
Text content and target input field → Text entered into specified application field
Navigate application menus and windows
Menu path or window navigation commands → Application state changes and menu interactions
Extract text content from macOS applications
Target application window or element → Readable text content from UI elements
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