Best Open Source Computer Use Agent for Windows in 2026

Most open source computer use agents were built on macOS or Linux first. Windows support often came as an afterthought, which means things like UI Automation API integration, PowerShell scripting hooks, and native Win32 accessibility tree parsing are either missing or broken in a lot of projects. If you run Windows, not every "cross-platform" agent actually works on your machine.

We tested 11 open source computer use agents on Windows 10 and Windows 11 in April 2026, running real tasks: filling out forms in desktop apps, navigating browser workflows, moving data between Excel and web apps, and automating repetitive file management. This is what works, what does not, and what you should pick depending on your use case.

Why Windows Is Different for Computer Use Agents

Windows presents unique challenges for AI agents compared to macOS or Linux:

1. UI Automation API vs. Accessibility API. Windows uses Microsoft UI Automation (UIA) as its primary accessibility framework. It exposes a different element tree structure than macOS's AXUIElement API or Linux's AT-SPI. Agents built for one platform often fail to parse the other correctly.
2. DPI scaling and multi-monitor. Windows allows per-monitor DPI scaling, which means screenshot coordinates can be wrong if the agent does not account for display scaling factors. Many vision-based agents send clicks to the wrong location on high-DPI Windows setups.
3. UAC and permission dialogs. User Account Control prompts interrupt agent workflows. The agent needs to either handle elevation prompts or run with appropriate permissions from the start.
4. Win32 vs. UWP vs. Electron. Windows apps use at least three different UI frameworks, each exposing different automation hooks. An agent that works with Electron apps (like VS Code) might fail with native Win32 apps (like legacy accounting software).
5. PowerShell integration. The best Windows agents can fall back to PowerShell for operations that are faster through scripting than GUI interaction, like bulk file renaming or registry edits.

The Complete Windows Comparison Table

We scored each agent on five dimensions specific to Windows usability. Scores are out of 10 based on our testing in April 2026.

| Agent | Windows Support | Perception Method | Local LLM | Win32 Apps | UWP/Modern Apps | License | GitHub Stars | |---|---|---|---|---|---|---|---| | UI-TARS (Alibaba) | Native | Vision (custom model) | Yes (built-in) | 8/10 | 7/10 | Apache 2.0 | 18k+ | | Open Interpreter | Native | Hybrid (vision + code) | Yes | 7/10 | 6/10 | AGPL-3.0 | 56k+ | | Browser Use | Native (browser only) | DOM + vision | Yes | N/A | N/A | MIT | 52k+ | | AgentS (Simular) | Native | Vision + accessibility | Yes | 7/10 | 7/10 | Apache 2.0 | 4k+ | | SkyPilot Agent | Partial | Vision | No | 5/10 | 4/10 | Apache 2.0 | 2k+ | | OpenAdapt | Native | Vision + accessibility | Yes | 6/10 | 5/10 | MIT | 2k+ | | Computer Use OOTB | Native | Vision (Claude API) | No | 6/10 | 5/10 | MIT | 3k+ | | PyAutoGUI + LLM | Native | Vision (screenshot) | Yes | 5/10 | 5/10 | BSD-3 | 10k+ | | OS-Copilot (OS-World) | Partial | Vision | Yes | 4/10 | 3/10 | Apache 2.0 | 1k+ | | Self-Operating Computer | Partial | Vision | Yes | 4/10 | 4/10 | MIT | 8k+ | | Navi | Experimental | Hybrid | No | 3/10 | 3/10 | MIT | 500+ |

"Native" means the agent officially supports Windows with tested releases. "Partial" means it runs on Windows but has known issues. "Experimental" means Windows support exists but is not recommended for production tasks.

Top 5 Windows Agents in Detail

1. UI-TARS (Best Overall for Windows Desktop)

UI-TARS is Alibaba's purpose-built vision model for computer use. Unlike agents that send screenshots to GPT-4o or Claude, UI-TARS uses its own 7B/72B parameter model that was trained specifically on UI screenshots and action sequences. This matters on Windows because the model understands Windows-specific UI patterns: ribbon menus, taskbar interactions, Settings app layouts, and File Explorer navigation.

What worked well on Windows:

• Excel data entry and formula creation across multiple sheets
• Navigating Windows Settings to change system preferences
• File Explorer operations including drag-and-drop between folders
• Right-click context menus, which many vision agents misidentify

What did not work:

• UAC elevation prompts caused the agent to stall
• Some Win32 apps with non-standard controls (legacy accounting software) confused the vision model
• Multi-monitor setups required manual configuration of the capture region

Best for: Users who want a fully local solution without cloud API calls. The 7B model runs on an RTX 3060 or better.

2. Open Interpreter (Best for Mixed GUI + Code Tasks)

Open Interpreter combines GUI control with code execution, making it the most versatile agent for Windows power users. It can automate a browser workflow, then drop into PowerShell to process the results, then open Excel to paste formatted data. This hybrid approach handles Windows workflows that pure vision agents cannot.

What worked well on Windows:

• Chaining browser research into Excel reports via PowerShell
• File system operations using native Windows commands
• Installing and configuring software through both GUI and CLI
• Parsing Windows Event Viewer logs and summarizing findings

What did not work:

• Pure GUI automation was slower and less accurate than UI-TARS
• Complex multi-window workflows sometimes lost track of which window was active
• High-DPI displays caused occasional click offset issues

Best for: Developers and power users who need both GUI control and scripting capabilities.

3. Browser Use (Best for Browser-Only Tasks on Windows)

If your Windows automation needs are entirely browser-based, Browser Use is the strongest option. It uses DOM-aware targeting instead of screenshots, which means it is faster and more accurate for web applications. It does not interact with desktop applications at all, but for browser tasks, nothing else comes close.

What worked well on Windows:

• Web form filling across complex multi-step processes
• Data extraction from web dashboards into structured formats
• Navigating authenticated web apps (Salesforce, Jira, etc.)
• Running multiple browser agents in parallel

What did not work:

• No desktop application support whatsoever
• Cannot interact with Electron apps outside the browser context
• File download dialogs sometimes required manual intervention

Best for: Users whose workflows are entirely web-based and who want maximum speed and reliability.

4. AgentS by Simular (Best Accessibility Tree Support on Windows)

AgentS is one of the few open source agents that takes Windows UI Automation seriously. It parses the UIA element tree to identify interactive controls, combines that data with visual context from screenshots, and generates precise actions. This dual approach makes it more reliable with native Windows apps than pure vision agents.

What worked well on Windows:

• Native Win32 application automation (Notepad, Paint, Calculator)
• Windows accessibility tree traversal for element identification
• Handling dropdown menus and combo boxes in native apps
• Consistent performance across different DPI settings

What did not work:

• Slower setup process requiring Python environment configuration
• Less community support than larger projects
• Some UWP controls were not correctly identified in the accessibility tree

Best for: Users automating native Windows desktop applications where vision-only approaches fail.

5. OpenAdapt (Best for Learning Repetitive Tasks)

OpenAdapt takes a different approach: it records you performing a task, then learns to replicate it. On Windows, it captures screenshots, mouse movements, keyboard inputs, and accessibility tree snapshots during recording. The replay engine then uses an LLM to adapt the recorded workflow to new data.

What worked well on Windows:

• Recording and replaying data entry workflows in desktop apps
• Adapting recorded workflows when form fields changed slightly
• Processing batches of similar documents with minor variations

What did not work:

• Complex branching workflows (if-then logic) confused the replay engine
• Recording multi-application workflows was unreliable
• Required significant RAM for storing recording data during long sessions

Best for: Users with highly repetitive, predictable tasks who prefer demonstration over prompting.

Setup and Installation on Windows

Every agent on this list requires Python 3.10 or later on Windows. Here is what the setup process looks like for the top three:

UI-TARS Setup

# Install with pip (requires CUDA toolkit for GPU acceleration)
pip install ui-tars
# Download the 7B model (about 14GB)
ui-tars download --model 7b
# Run the agent
ui-tars run --platform windows

The 7B model needs at least 8GB VRAM. The 72B model needs 40GB+ VRAM or can run quantized (GGUF Q4) on 24GB cards. CPU-only mode works but is too slow for real-time use.

Open Interpreter Setup

pip install open-interpreter
# Set your API key (or configure local model)
set OPENAI_API_KEY=your-key-here
# Start the interpreter
interpreter

For local LLM support, Open Interpreter works with Ollama on Windows. Install Ollama, pull a model like llama3.1:70b, and configure Open Interpreter to use it.

Browser Use Setup

pip install browser-use
playwright install chromium
# Run with your preferred LLM backend
python -c "from browser_use import Agent; Agent().run('your task here')"

Browser Use requires Playwright for browser control. The Chromium installation is automatic and does not require admin privileges.

Performance Benchmarks on Windows

We ran each agent through a standardized set of 20 tasks on Windows 11 (23H2) with an RTX 4070 GPU and 32GB RAM. Tasks included form filling, data extraction, file management, and multi-app workflows.

| Agent | Task Success Rate | Avg Time per Task | GPU Required | Setup Difficulty | |---|---|---|---|---| | UI-TARS (7B) | 72% | 45s | Yes (8GB VRAM) | Medium | | Open Interpreter | 68% | 62s | No (cloud API) | Easy | | Browser Use | 85% (browser only) | 28s | No | Easy | | AgentS | 65% | 55s | No (cloud API) | Hard | | OpenAdapt | 58% | 70s | No | Medium | | Computer Use OOTB | 60% | 50s | No (cloud API) | Easy |

Browser Use scores highest because its tasks are limited to the browser, where DOM-based targeting is inherently more reliable than vision-based desktop control. For desktop-only tasks, UI-TARS leads with 72% success rate.

Common Windows-Specific Issues and Fixes

DPI Scaling Problems

Most vision-based agents assume 100% display scaling. If your Windows display is set to 125% or 150% (common on laptops), screenshot coordinates will be offset. Fix this by either setting the agent process to DPI-unaware mode or configuring the scaling factor in the agent's config.

# For PyAutoGUI-based agents on Windows
import ctypes
ctypes.windll.shcore.SetProcessDpiAwareness(2)  # Per-monitor DPI aware

UAC Dialog Handling

No open source agent can interact with UAC prompts by default because the secure desktop is a separate session. Two workarounds:

• Run the agent (and the task) as administrator from the start
• Disable UAC for specific applications using compatibility settings (not recommended for security reasons)

Antivirus Interference

Windows Defender sometimes flags agent mouse/keyboard simulation as suspicious behavior. Add your Python environment and the agent's executable to Windows Defender exclusions if you notice tasks failing silently.

Windows Terminal vs. CMD vs. PowerShell

Agents that execute shell commands may target the wrong terminal. Most agents default to cmd.exe, but PowerShell is usually more capable. Configure the shell explicitly:

# Open Interpreter example
interpreter.computer.terminal.shell = "powershell"

What About macOS? Fazm for Desktop Agent Users on Mac

If you landed here looking for a computer use agent but you run macOS, the landscape is different. macOS has a richer accessibility API (AXUIElement) that gives agents structured data about every UI element on screen, making desktop automation more reliable than vision-only approaches.

Fazm is a desktop agent built specifically for macOS that uses the accessibility API for perception instead of screenshots. It runs locally, supports local LLMs, and handles cross-app workflows between native Mac apps like Finder, Mail, Calendar, and Numbers. Where Windows agents struggle with the fragmentation between Win32, UWP, and Electron, macOS has a unified accessibility layer that Fazm leverages for consistent behavior across all apps.

Key differences from Windows agents:

• No DPI scaling issues because macOS handles Retina scaling at the framework level
• No UAC interruptions since macOS permission prompts are in-process
• Accessibility tree is always available without requiring apps to opt in (unlike some Win32 apps on Windows)

If you are on macOS, check out Fazm instead of trying to adapt a Windows-focused agent.

How to Evaluate a Windows Computer Use Agent for Your Workflow

Before committing to any agent, run this checklist against your actual tasks:

1. List the applications involved. If they are all browser-based, Browser Use is your answer. If they include native desktop apps, you need UI-TARS, AgentS, or Open Interpreter.
2. Check your GPU. If you want fully local execution without cloud API calls, you need a GPU with at least 8GB VRAM for UI-TARS 7B. If you are fine with cloud APIs, any agent works on CPU-only machines.
3. Test with your DPI settings. Run the agent at your normal display scaling before investing time in complex workflows. Many agents break above 100% scaling.
4. Try a simple task first. Open Notepad, type a sentence, save the file. If the agent cannot do this reliably, it will not handle your real workflows.
5. Check error recovery. Deliberately cause a failure (close the target window mid-task) and see if the agent recovers or loops forever.

Frequently Asked Questions

Which open source computer use agent has the best Windows support in 2026?

UI-TARS from Alibaba has the most reliable Windows desktop support as of April 2026. Its custom vision model was trained on Windows UI screenshots, so it recognizes Windows-specific elements like ribbon menus, taskbar buttons, and Settings app layouts better than general-purpose models. For browser-only tasks, Browser Use is more accurate and faster.

Can I run a computer use agent on Windows without a GPU?

Yes, but with trade-offs. Agents that use cloud APIs (Open Interpreter with GPT-4o, Computer Use OOTB with Claude) work on any Windows machine without a GPU. For fully local execution, UI-TARS offers a quantized 7B model that runs on CPU, but expect 3 to 5 seconds per action instead of under 1 second with GPU acceleration.

Are these agents safe to use on a production Windows machine?

Use them on a test machine or VM first. Computer use agents simulate real mouse clicks and keystrokes, which means they can accidentally delete files, send emails, or modify system settings. All agents on this list are open source so you can audit the code, but none of them have safety guarantees that prevent unintended actions. Run them in a sandboxed environment until you trust the agent with specific workflows.

How do Windows computer use agents compare to macOS agents like Fazm?

Windows agents rely more heavily on screenshot analysis (vision-based perception) because Windows UI Automation support varies by app framework. macOS agents like Fazm can use the accessibility API for structured element data, which is faster and more accurate. On Windows, you typically need a GPU for local vision models; on macOS, the accessibility API works on CPU. The trade-off is that Windows has more agent options, while macOS agents tend to be more reliable per-task because of the consistent accessibility layer.

The Bottom Line

For Windows desktop automation, UI-TARS is the strongest open source option in April 2026, especially if you have a GPU for local inference. Open Interpreter wins for mixed GUI-and-scripting workflows. Browser Use dominates browser-only tasks. The rest of the field is catching up but still has rough edges on Windows.

If you are evaluating agents for a team, start with Browser Use for web workflows and UI-TARS for desktop tasks. Run them in a VM first, test with your actual applications at your display scaling, and expect to spend a few hours on initial configuration. The technology works, but Windows support across the open source ecosystem is still maturing compared to macOS and Linux.