Best Open Source Computer Use Agent in 2026

The number of open source computer use agents has tripled since Anthropic shipped their first computer use demo in October 2024. Eighteen months later, the category has split into distinct niches: desktop agents, browser agents, mobile agents, and hybrid tools that try to cover everything. Picking the right one means knowing what you actually need it to do.

We tested 12 open source projects across real tasks in March and April 2026. This is what we found.

What Makes a Computer Use Agent "Good" in 2026

Before comparing individual projects, it helps to know what separates the ones that work from the ones that look impressive in a demo but fail on real tasks.

Four things matter most:

1. Perception method. Does the agent read the screen via screenshots (vision), the OS accessibility API, or both? This determines speed, accuracy, and privacy.
2. Action reliability. Can it actually click the right button, type in the right field, and scroll to the right place? Many agents nail demos but fail when a dialog box appears unexpectedly.
3. Local execution. Can you run the entire stack (perception + reasoning + action) on your own hardware, or does every step require a cloud API call that sends your screen data to a third party?
4. Recovery from failure. When the agent clicks the wrong element or the UI changes mid-task, does it detect the error and retry, or does it spin in a loop repeating the same failed action?

The Complete Comparison Table

| Agent | Category | Perception | Platforms | License | Local LLM | Stars (Apr 2026) | |---|---|---|---|---|---|---| | Fazm | Desktop | Accessibility API + vision | macOS | MIT | Yes (Ollama) | 3.2k | | Browser Use | Browser | DOM + vision | Cross-platform | MIT | Yes | 52k | | Open Interpreter | Hybrid | Code + vision | Cross-platform | AGPL-3.0 | Yes | 57k | | OS-Copilot | Desktop | Screenshot + shell | Linux, macOS | Apache 2.0 | Yes | 2.8k | | OpenAdapt | Desktop | Screenshot + recording | Cross-platform | MIT | Partial | 1.9k | | Agent.exe | Desktop | Screenshot | macOS, Windows | MIT | No | 3.1k | | Computer Use OOTB | Desktop | Screenshot | Cross-platform | Apache 2.0 | No | 4.5k | | Skyvern | Browser | DOM + vision | Cross-platform | AGPL-3.0 | No | 10k | | LaVague | Browser | DOM + vision | Cross-platform | Apache 2.0 | Yes | 5.3k | | Anthropic CUA | Desktop | Screenshot | Cross-platform | MIT | No | 7.2k | | UI-TARS | Desktop | Screenshot (custom model) | Cross-platform | Apache 2.0 | Yes (native) | 3.8k | | SeeAct | Browser | Screenshot | Cross-platform | MIT | Partial | 1.5k |

Decision Flowchart

Desktop Agents: Deep Dive

Desktop agents control your entire operating system: native apps, system settings, file management, multi-app workflows. The perception method matters more here than in any other category because native apps expose wildly different levels of information depending on the OS and toolkit.

Fazm (Our Pick for macOS)

Fazm reads the macOS accessibility tree, which gives it structured data about every button, label, text field, and menu item. Instead of guessing where to click from a screenshot, it knows exactly what element it is targeting and where that element sits on screen.

The practical difference is speed. An accessibility tree read takes about 50ms. A screenshot round-trip through a vision model takes 2 to 5 seconds with a cloud API, or 10 to 30 seconds with a local model. For a 10-step task, that is the difference between finishing in under a second versus waiting over a minute.

# Install and run Fazm
git clone https://github.com/m13v/fazm.git
cd fazm && swift build

# Fully local with Ollama
ollama pull llama3.1
fazm --model ollama:llama3.1

# Or use Claude API
export ANTHROPIC_API_KEY=sk-ant-...
fazm --model claude-sonnet-4-20250514

Fazm also supports voice commands, so you can tell it what to do without typing. The voice pipeline runs through Apple's on-device speech recognition, keeping audio local.

OS-Copilot (Best for Linux)

OS-Copilot splits its architecture into perception, planning, and action modules. On Linux, the action layer leans heavily on shell commands, which means it can manipulate files, manage processes, and configure systems without touching the GUI at all. When GUI interaction is needed, it falls back to screenshots.

This modular approach makes it easy to swap models. Point it at any OpenAI-compatible API endpoint (Ollama, LM Studio, vLLM) and it works.

UI-TARS (Best Custom Vision Model)

UI-TARS from Alibaba is different from every other agent on this list. Instead of using a general-purpose LLM to interpret screenshots, it ships its own fine-tuned vision model trained specifically for UI understanding. The model outputs structured action predictions (element type, coordinates, action) without needing prompt engineering.

The result is more consistent than sending screenshots to GPT-4o or Claude and hoping the coordinate prediction is accurate. The tradeoff is that UI-TARS' model is large (7B parameters) and requires a GPU with at least 16GB VRAM for reasonable inference speed.

Agent.exe and Computer Use OOTB

Both are thin wrappers around Anthropic's computer use API. Agent.exe packages it in a clean Electron GUI. Computer Use OOTB is a developer toolkit with better error handling and multi-monitor support. Neither supports local models. Every action sends a screenshot to Anthropic's servers.

Good for prototyping and testing. Not ideal if privacy matters or if you need to control costs at scale.

Browser Agents: Deep Dive

Browser agents automate web applications: filling forms, navigating sites, extracting data, running multi-step workflows in Chrome or Firefox. They have a structural advantage over desktop agents because the DOM provides perfect element identification. No coordinate guessing, no accessibility API quirks.

Browser Use (Top Pick for Browser Tasks)

Browser Use wraps Playwright with an AI reasoning layer. It reads the DOM to identify interactive elements, uses a vision model for context when needed, and executes actions through Playwright's API. Because Playwright provides exact element selectors, click accuracy is nearly 100% on standard web pages.

from browser_use import Agent
from langchain_openai import ChatOpenAI

agent = Agent(
    task="Go to amazon.com and find the cheapest USB-C hub with at least 4 ports",
    llm=ChatOpenAI(model="gpt-4o"),
)
result = await agent.run()

Browser Use supports running with local models by swapping the LLM backend to Ollama. Performance drops compared to GPT-4o or Claude, but it works for simpler tasks.

Skyvern (Best for Workflow Automation)

Skyvern targets business workflow automation. It can handle multi-page forms, CAPTCHA-like challenges, and sites that require authentication. It combines DOM parsing with vision models and has built-in support for chaining multiple browser actions into repeatable workflows.

The AGPL license means commercial use has strings attached. For personal use or internal tools, that is fine.

LaVague (Best for Testing Pipelines)

LaVague is built on Selenium and integrates well with existing test infrastructure. If you already have a Selenium grid or a CI pipeline that runs browser tests, LaVague can plug in and add AI-driven exploration on top of your existing setup.

Hybrid Agents

Open Interpreter (Best All-Rounder)

Open Interpreter is the Swiss army knife. It started as a local Code Interpreter alternative and has grown into a general-purpose agent that can execute code, browse the web, and control desktop applications through vision mode.

Its strongest capability is still code execution. When a task can be solved by writing and running a Python script, Open Interpreter will outperform every other agent on this list. GUI control is a secondary capability that works but is slower than purpose-built desktop or browser agents.

# Local model, no cloud calls
interpreter --local --model ollama/llama3.1

# Vision mode for GUI tasks
interpreter --os --model gpt-4o

Perception Methods Compared

The single most important architectural decision in a computer use agent is how it "sees" the screen. Here is how the three main approaches stack up in 2026:

| Factor | Accessibility API | Screenshot + Vision LLM | DOM Parsing (Browser) | |---|---|---|---| | Speed per read | ~50ms | 2-5s (API) / 10-30s (local) | ~100ms | | Data size | 5-20 KB | 500 KB - 2 MB | 10-50 KB | | Click accuracy | 99%+ (exact coordinates) | ~80-90% (coordinate estimation) | 99%+ (exact selectors) | | Works with custom UIs | No (standard widgets only) | Yes (any visible content) | No (web only) | | Privacy | Text only, structured | Full screen pixels | Page HTML | | Model requirements | Small models work | Vision-capable, larger models | Small models work |

Privacy: What Data Leaves Your Machine

If you are automating tasks that involve sensitive information (email, banking, medical records, passwords), you need to know exactly what data each agent sends to external servers.

To verify what your agent sends, monitor network traffic while it runs:

# macOS/Linux: watch for outbound HTTPS connections
sudo tcpdump -i any -n port 443 2>/dev/null | grep -E "api\.(anthropic|openai)\.com"

If you see connections to api.anthropic.com or api.openai.com, your screen content is leaving the machine.

Common Pitfalls

• Skipping accessibility permissions on macOS. Every agent that reads the UI tree or sends synthetic click events needs explicit permission in System Settings > Privacy & Security > Accessibility. The agent will silently fail without it. Grant the permission for the specific binary, not the terminal app.

• Using a small local model for screenshot interpretation. A 7B parameter model trying to predict click coordinates from a screenshot hits about 60% accuracy. You need at least a 13B model, or ideally a fine-tuned vision model like UI-TARS, for reliable coordinate prediction. Accessibility-based agents sidestep this entirely.

• Forgetting that Electron apps expose bad accessibility data. Slack, Discord, VS Code, Notion, and most Electron-based apps have minimal accessibility trees. If your workflow involves these apps, you need a screenshot-based agent for those steps, even if you use accessibility for everything else.

• Not setting a retry limit. When an agent fails an action, it observes the unchanged screen, tries again, fails again, and loops forever. Every agent should have a max retry count per action. Most have this in their config, but the default is sometimes infinite.

• Running browser agents headless when you need visual verification. Headless mode is faster but you cannot see what the agent is doing. For debugging and initial setup, always run headed. Switch to headless only after confirming the workflow works.

Quick Start: Running Your First Computer Use Agent

The fastest path from zero to a working agent depends on what you are automating.

For macOS desktop tasks:

git clone https://github.com/m13v/fazm.git
cd fazm && swift build
fazm --model ollama:llama3.1

For browser tasks:

pip install browser-use
python -c "
from browser_use import Agent
from langchain_openai import ChatOpenAI
import asyncio

agent = Agent(
    task='Search Google for best open source computer use agent',
    llm=ChatOpenAI(model='gpt-4o'),
)
asyncio.run(agent.run())
"

For code execution + occasional GUI:

pip install open-interpreter
interpreter --local --model ollama/llama3.1

What Changed in 2026

The computer use landscape looks very different from early 2025. Here are the biggest shifts:

1. Custom vision models replaced general-purpose LLMs for perception. UI-TARS showed that a fine-tuned 7B model can outperform GPT-4o at coordinate prediction while running locally. Expect more agents to ship their own perception models.

2. Accessibility API adoption grew. Fazm proved that reading the OS-level UI tree is faster and more private than screenshotting. Other projects are starting to integrate accessibility APIs alongside vision.

3. Browser agents matured. Browser Use went from a research project to a production tool with 52k GitHub stars. DOM-based perception solved the accuracy problem that plagued screenshot-based browser agents.

4. MCP became the standard connector. Model Context Protocol lets agents talk to external tools and services without custom integration code. Most agents on this list now support MCP servers for extending their capabilities.

5. License diversity increased. The early agents were mostly AGPL or research-only. In 2026, MIT and Apache 2.0 licensed options cover every category, making commercial adoption straightforward.

Wrapping Up

The best open source computer use agent in 2026 depends on what you are automating. For macOS desktop control, Fazm's accessibility API approach gives you the best speed and privacy. For browser automation, Browser Use's DOM parsing delivers near-perfect accuracy. For general-purpose tasks that mix code execution with GUI control, Open Interpreter remains the most flexible option. And if you want a completely self-contained system with no cloud dependency at all, UI-TARS ships its own vision model.

The category is moving fast. Check back on the repositories linked above; what is true in April 2026 may shift by summer.

Fazm is an open source macOS AI agent that controls your desktop through the accessibility API. Open source on GitHub.