There is no best computer use agent, and any roundup that opens with a ranked list of benchmark scores is telling you more about its affiliate deals than about the software. What there is: a field of roughly twenty serious options that differ enormously in what they control, who runs the machine, and who they were built for. Here they are sorted by the job.
Every entry links to its profile in the directory, where the licence, access model and primary source are recorded.
Best for full desktop control
If the task needs to reach a native application — not a web app in a browser, an actual desktop program — this is the reference implementation of that idea. Anthropic exposes it as a tool inside the Messages API: the model sees screenshots and returns actions, and you run the loop against a machine you provide.
The catch is that last clause. You supply, sandbox and secure the desktop, which is real infrastructure work and is where most of the risk in this category lives. If that sentence sounds like someone else's problem, it is not, and you want a hosted product instead.
Best for getting something working this week
The most widely adopted open-source route into the category, and the one we suggest to most people starting out. It is a Python library that hands a browser to whatever model you point at it, MIT licensed, and small enough to understand in a sitting.
Being model-agnostic is the strength and the caveat: results vary sharply with the model behind it, and the library is not what determines quality. That is a useful thing to learn early, because it reframes the whole evaluation problem.
Best if you already have Playwright tests
Browserbase's framework layers natural-language actions over Playwright, so deterministic selectors and model calls live in the same script. You use the model only where the page is genuinely unpredictable and keep fast, cheap, reliable code everywhere else.
For a team with an existing browser automation suite this is by some distance the lowest-friction option — you are extending something you already run rather than adopting a new paradigm. TypeScript-first, so Python shops will find the ecosystem thinner.
Best for running entirely on your own hardware
Almost every "open source" option in this field is an open harness that calls a closed hosted model. ByteDance's UI-TARS is the significant exception: the model weights themselves are available under Apache-2.0, with a companion desktop application.
That makes it the answer for air-gapped environments, regulated data, or anywhere screenshots of the work cannot leave the building — which is a more common constraint than the hosted-first framing of this category admits. The trade is hardware: running it well needs real GPU capacity, so you swap an API bill for a capex line.
Best for a non-developer with a task
The only entry here a person can simply use. It works in a hosted virtual browser with an attached terminal, so the sandboxing problem — the hardest part of every other option on this page — belongs to OpenAI rather than to you.
The corresponding limit is that you cannot embed it in anything. It is a product, not a platform. If your goal is a feature in your own software, look at the API options in Claude vs OpenAI vs Gemini: three approaches to computer use.
Best for enterprise legacy applications
The most common real-world reason to want this technology is software that has no API and never will — and that software disproportionately lives inside large organisations already running Microsoft. Copilot Studio's computer use sits alongside the identity, governance and audit tooling those teams have, which matters more for procurement than any capability difference.
Outside that estate the calculus changes completely, and this stops being an obvious pick.
Best for a persistent assistant on your own hardware
An open-source personal assistant from a non-profit foundation, MIT licensed, running on your own devices and reachable through the messaging apps you already have open. It can take desktop control, and unlike most of this list it is designed to sit there permanently rather than be invoked for a task.
Its documentation states the consequence plainly, and so will we: tools run on the host for the main session unless you configure sandboxing. That is the correct warning and it is worth acting on before you connect anything sensitive.
Best for local files across native applications
Perplexity moved its cloud agent onto the machine in front of you, where it can reach local files and native applications — comparing documents held in different programs, pulling notes out of one app to draft in another. For personal workflows that span several pieces of desktop software with no integration between them, this is the most direct answer currently shipping.
It works on your real files rather than a disposable sandbox, and it is subscription-gated. Its cloud sibling Perplexity Computer is the option if you would rather the work happened somewhere other than your laptop.
Best for the sandbox problem
Not an agent. Infrastructure — containerised macOS and Linux desktops for agents to work inside. It is on this list because "who runs the machine, and what happens when the agent does something unintended" is the question that stalls most serious deployments, and this is the project that treats it as the product rather than as an exercise for the reader.
You still bring the model and the loop.
Best for workflows that break conventional scripts
Vision-driven browser automation aimed squarely at the case where you are running the same workflow across many sites, or against sites that change their markup often enough to keep breaking selector-based scripts. Working from what the page looks like rather than how it is marked up is a genuine advantage there.
Note the licence: AGPL-3.0, which unlike the MIT and Apache options elsewhere on this page has real implications if you are building a hosted service on top. See Open source computer use agents: what's actually open.
Best for learning how any of this works
A deliberately minimal framework for pointing a multimodal model at your own screen. Its value is legibility — small enough to read end to end and understand exactly what the loop does, which is worth an afternoon before you commit to anything larger. Treat it as something to learn from rather than to run in production.
Best for processes easier to demonstrate than describe
Learns from recorded human demonstrations rather than written instructions, which sidesteps the genuinely hard problem of describing a GUI workflow precisely enough in prose. For repetitive internal processes that is often the shortest path.
One caution worth stating plainly: recordings of real work frequently contain credentials, and a demonstration-based tool is only as safe as your recording hygiene.
Why there are no scores on this page
Because the numbers in circulation do not compare. Published results for the same agents on the same benchmark vary by wide margins depending on step budget, harness, benchmark variant and date, and most secondary sources citing them are citing each other rather than a leaderboard. Ranking this list on a figure would give it a precision it has not earned.
The full argument, with what to demand before believing any published score, is in Why computer use benchmark scores don't compare. If you are still working out whether this category fits your problem at all, start with What is a computer use agent?, then the use cases for nine concrete tasks and where each one falls over.