Best Open Source Computer Use Agents in 2026 for Local Desktop Control

If you want an AI agent that controls your desktop, you have two choices: trust a cloud service with screenshots of everything on your screen, or run something open source locally. The second option barely existed 18 months ago. Now there are half a dozen credible projects, each taking a different approach to the same problem.

We tested the leading open source computer use agents that can run on your own machine. No cloud screenshots, no vendor lock-in, no wondering what happens to your data. Here is what actually works in April 2026.

What "Computer Use" Means in Practice

Computer use is when an AI model operates your computer the way you do: seeing what is on screen, moving the mouse, clicking buttons, typing text, switching between apps. The term comes from Anthropic's Computer Use launch in late 2024, but the category has expanded well beyond one vendor.

There are three technical approaches these agents use:

1. Screenshot analysis (vision-based): capture the screen, send it to a multimodal model, get back coordinates to click
2. Accessibility API: read the structured UI tree that macOS, Windows, and Linux expose for assistive technology
3. Hybrid: combine screenshots for visual context with accessibility data for precise targeting

The open source agents below use different combinations of these methods. The approach matters because it determines speed, accuracy, and how much data leaves your machine.

The Agents We Tested

| Agent | Platform | Approach | License | Local LLM Support | Primary Language | |---|---|---|---|---|---| | Fazm | macOS | Accessibility API + vision | MIT | Yes (Ollama) | Swift | | OpenAdapt | Windows, Linux, macOS | Screenshot + RPA recording | MIT | Partial | Python | | OS-Copilot | Linux, macOS | Screenshot + shell | Apache 2.0 | Yes | Python | | Open Interpreter | Cross-platform | Code execution + vision | AGPL-3.0 | Yes | Python | | Agent.exe | macOS, Windows | Screenshot (Claude API) | MIT | No | TypeScript | | Computer Use OOTB | Cross-platform | Screenshot (Anthropic API) | Apache 2.0 | No | Python |

How We Evaluated

We ran each agent through five tasks on a clean macOS install:

1. Open Safari, search for a term, copy the first result URL
2. Create a new folder on the Desktop, rename it, move a file into it
3. Open System Settings, change the display brightness
4. Fill out a multi-field web form in Chrome
5. Open a spreadsheet app, enter data into three cells

We measured success rate (did the task complete?), speed (wall-clock seconds), and whether the agent needed cloud API calls to function.

Agent-by-Agent Breakdown

Fazm

Fazm takes a different path from most computer use agents. Instead of screenshotting the entire display and sending it to a vision model, it reads the macOS accessibility tree directly. This gives it structured data about every button, text field, menu item, and label on screen, with exact coordinates and roles.

The result is faster and more private. There is no screenshot leaving your machine unless you explicitly enable vision mode for ambiguous UI elements. The accessibility approach means Fazm can identify a "Save" button by its label rather than guessing from pixels, which makes actions more reliable for standard macOS apps.

Fazm supports local models through Ollama for the reasoning layer. You can run Llama 3 or Mistral locally and keep the entire pipeline on-device. For harder tasks, you can switch to Claude or GPT via API.

Strengths: native macOS performance, accessibility-first approach, fully local option, voice control Limitations: macOS only, accessibility tree coverage varies by app (Electron apps are often sparse)

OpenAdapt

OpenAdapt started as an RPA recording tool and evolved into a computer use agent. You demonstrate a task once, and OpenAdapt tries to generalize and replay it. The recording captures screenshots, mouse movements, keystrokes, and window state.

This "learning by demonstration" approach is different from the other agents here, which all start from a text instruction. It works well for repetitive tasks with consistent UI, but struggles when the UI changes between runs (different window positions, different content in lists).

OpenAdapt supports local models for the reasoning step, though the screenshot-based perception still benefits from larger vision models. It runs on all three major platforms.

Strengths: cross-platform, demonstration-based learning, good for repetitive workflows Limitations: brittle when UI changes, recording step adds friction, large dependency footprint

OS-Copilot

OS-Copilot takes a modular approach. It has separate components for perception (screenshots), planning (LLM reasoning), and action (shell commands, GUI clicks). The architecture makes it easy to swap out models or add new action types.

It works well on Linux, where shell commands can do most of the heavy lifting. On macOS, it falls back to screenshots and coordinate-based clicking, which is slower and less reliable than accessibility-based approaches.

OS-Copilot has solid local LLM support. You can point it at any OpenAI-compatible API endpoint, which means Ollama, LM Studio, or vLLM all work out of the box.

Strengths: modular architecture, strong Linux support, easy model swapping Limitations: macOS support is weaker, coordinate clicking can miss targets, no accessibility API integration

Open Interpreter

Open Interpreter is the most well-known project on this list. It started as a "ChatGPT Code Interpreter that runs locally" and has grown into a general-purpose agent that can execute code, control browsers, and interact with desktop applications.

The 01 hardware project aside, the core Open Interpreter software is a solid code-execution agent. Its computer use capabilities come through a vision mode that screenshots the display and uses models like GPT-4o or Claude to decide actions. It can also fall back to pure code execution, writing and running Python or shell scripts to accomplish tasks.

Strengths: mature project, large community, strong code execution, flexible model support Limitations: AGPL license may be a dealbreaker for commercial use, GUI control is secondary to code execution, screenshot-based vision is slower than accessibility approaches

Agent.exe

Agent.exe is a lightweight Electron app that wraps Claude's computer use capability in a desktop GUI. You type a task, it takes screenshots, sends them to the Claude API, and executes the returned actions.

It is the simplest agent on this list to set up. Download, add your API key, done. But it has a hard dependency on Claude's API, which means every action sends a screenshot to Anthropic's servers. There is no local model option.

Strengths: dead simple setup, clean UI, reliable because it uses Claude directly Limitations: requires Claude API (not local), every screenshot goes to Anthropic, limited customization

Computer Use OOTB

Computer Use OOTB (Out Of The Box) is a reference implementation that packages Anthropic's computer use demo into something you can actually run. It wraps the screenshot-and-click loop with better error handling, retry logic, and multi-monitor support.

Like Agent.exe, it depends on the Anthropic API. It is more of a developer toolkit than an end-user agent. Useful if you want to build on top of Claude's computer use but do not want to write the plumbing yourself.

Strengths: good developer foundation, handles edge cases the demo ignores, well-documented Limitations: API-dependent, not designed for end users, no local model support

Architecture: How Local Desktop Control Works

The core loop is the same across every agent: observe the screen state, send it to a language model for reasoning, execute the planned action, then observe again. The key difference between agents is the observe step. Accessibility API gives you structured, labeled elements. Screenshots give you raw pixels. The former is faster and more precise; the latter works with any application, even games or custom-rendered UIs.

Accessibility API vs. Screenshot: The Core Tradeoff

This is the most important architectural decision in any computer use agent. It determines everything from latency to privacy to reliability.

| Factor | Accessibility API | Screenshot (Vision) | |---|---|---| | Speed | ~50ms per read | ~2-5s per screenshot + inference | | Privacy | Text-only, structured data | Full pixel capture of screen | | Accuracy | Exact element coordinates | Coordinate estimation from pixels | | Coverage | Standard UI elements only | Anything visible on screen | | Custom UIs | Often fails (games, canvas) | Works everywhere | | Model requirement | Smaller models work fine | Needs vision-capable model | | Data size | ~5-20KB per tree | ~500KB-2MB per screenshot |

Setting Up a Local Agent: What You Actually Need

The hardware requirements vary dramatically depending on which agent you choose and whether you want to run the LLM locally or use an API.

For accessibility-based agents (like Fazm):

• Any Mac from the last 5 years
• 8GB RAM minimum
• No GPU needed if using API models
• For local LLM: 16GB RAM for 7B models, 32GB+ for 13B+ models

For screenshot-based agents:

• Same hardware, but inference is slower
• Vision model calls take 2-5 seconds each (API) or 10-30 seconds (local, depending on model and hardware)
• GPU helps significantly for local vision models

Quick Start with Fazm

# Clone and build
git clone https://github.com/m13v/fazm.git
cd fazm
swift build

# Grant accessibility permissions when prompted
# System Settings > Privacy & Security > Accessibility

# Run with Ollama (local)
ollama pull llama3.1
fazm --model ollama:llama3.1

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

Quick Start with Open Interpreter

pip install open-interpreter

# With local model
interpreter --local --model ollama/llama3.1

# With vision mode for computer use
interpreter --os --model gpt-4o

Quick Start with OS-Copilot

git clone https://github.com/OS-Copilot/OS-Copilot.git
cd OS-Copilot
pip install -r requirements.txt

# Point at local Ollama
export OPENAI_API_BASE=http://localhost:11434/v1
python main.py --task "Open the calculator app"

Common Pitfalls

• Accessibility permissions are easy to forget. On macOS, every agent that reads the UI tree or sends synthetic clicks needs explicit permission in System Settings > Privacy & Security > Accessibility. If the agent silently does nothing, check this first.

• Local models are not yet great at coordinate prediction. If you run a 7B model locally and ask it to click a specific button from a screenshot, expect ~60% accuracy. The model needs to map pixel coordinates from an image, which smaller models handle poorly. Accessibility-based agents sidestep this entirely because coordinates come from the OS.

• Electron apps have terrible accessibility trees. Apps like Slack, Discord, VS Code, and Notion expose minimal accessibility data. A screenshot-based agent will outperform an accessibility-based one on these apps. Native apps (Finder, Safari, Mail, Preview) expose excellent accessibility data.

• The agent loop can spin endlessly. If the agent fails an action, it observes the (unchanged) screen, tries again, fails again. Set a max retry count. Most agents have this built in, but check the config.

• Screen resolution affects vision agents. A 4K display generates much larger screenshots and costs more tokens. Some agents auto-resize before sending; others do not. Check whether your agent downscales.

Privacy: What Actually Stays Local

The whole point of running open source locally is privacy. But "open source" does not automatically mean "local." Several of these agents still send data to cloud APIs by default.

If privacy is your primary concern, filter the list to agents that support Ollama or another local inference backend. Then verify by checking network traffic: sudo tcpdump -i any -n port 443 while the agent runs. If you see outbound connections to api.anthropic.com or api.openai.com, something is calling home.

Which Agent Should You Pick?

The answer depends on three things: your operating system, whether you need fully local operation, and what kind of tasks you are automating.

If you are on macOS and want fully local operation: Fazm. The accessibility API approach is faster and more private than screenshotting, and it runs with Ollama for on-device reasoning.

If you are on Linux and want a general-purpose agent: OS-Copilot. Its modular architecture and shell command integration work best on Linux where the command line can handle most tasks.

If you want the simplest setup and do not mind API calls: Agent.exe. Download it, paste your Claude API key, and it works.

If you want to automate repetitive workflows by demonstration: OpenAdapt. Record a task once and let it replay.

If you primarily need code execution with some GUI control: Open Interpreter. The strongest code execution agent, with vision as a secondary capability.

What is Coming Next

The computer use space is moving fast. A few trends to watch:

1. Apple Intelligence integration. Apple's on-device models are getting more capable. macOS Tahoe (expected fall 2026) may expose new APIs that make local computer use agents significantly more powerful.

2. Smaller, faster vision models. Models like Moondream and PaLI-Gemma are approaching usable accuracy for coordinate prediction at 2-3B parameters. This will make fully local screenshot-based agents practical on consumer hardware.

3. Standardized tool protocols. MCP (Model Context Protocol) is becoming the de facto standard for connecting agents to tools. Agents that adopt MCP get access to a growing ecosystem of server integrations without custom code.

4. Multi-agent coordination. Running multiple specialized agents on the same desktop (one for browser tasks, one for file management, one for communication) instead of one generalist agent. Early but promising.

Wrapping Up

Open source computer use agents have gone from "interesting demo" to "daily driver" in about a year. The accessibility API approach gives you the best combination of speed, privacy, and reliability for standard desktop applications. Screenshot-based agents cover the edge cases where accessibility data is missing. Running everything locally with Ollama means your screen content never leaves your machine.

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