Cua AI vs Agent-S3: Computer Use Agent Frameworks

The field of Computer Use Agents (CUAs)—AI that can see a screen, move a mouse, and type on a keyboard just like a person—is advancing at an incredible pace [4]. These agents promise to automate complex digital tasks, transforming workflows across every industry [5]. For developers looking to build on this technology, choosing the right framework is a critical first step.

Two of the most prominent open-source frameworks today are Cua and Agent-S3. While both aim to create capable computer-use agents, they do so with fundamentally different philosophies and architectures. This article compares Cua AI and Agent-S3 to help you decide which framework is best suited for your project, focusing on architecture, performance, security, and ease of use.

Core Philosophy and Architecture

A framework's design philosophy dictates its strengths and weaknesses. Cua is built for robust, secure, and scalable production environments, while Agent-S3 is geared more toward academic benchmarking and research.

Cua: Security and Flexibility Through Containerization

At Cua, our philosophy is centered on security, modularity, and developer experience. We believe that for agents to be truly useful, they must operate in a safe, controlled environment. That's why we built Cua Containers—lightweight, sandboxed virtual environments that let an agent control a full operating system without any risk to your local machine. This approach provides:

Security by Default: Agents execute in isolated containers, preventing them from accessing or modifying your personal files and system settings. This is a critical feature for deploying agents in production.
High Performance: Our containers are optimized for Apple Silicon using the native Virtualization.Framework, achieving up to 97% of native CPU speed [2]. We also provide optimized Docker images for Linux and Windows.
Composite Agents: We recognize that one model can't do it all. Our recent Cua Agent framework 0.4 release introduced Composite Agents, allowing you to combine a powerful "planning" model (like GPT-5 or Claude Sonnet 4.5) with a specialized "grounding" model for visual understanding. This modularity lets you build the best possible agent for your specific task [3].

Agent-S3: A Monolithic, Worker-Focused Approach

Agent-S3 is an open agentic framework designed to mimic human computer interaction [8]. Its architecture uses a "flat worker-only policy," where a single model is responsible for observation, decision-making, and action [7]. While this design has proven effective in benchmarks, it directly executes Python code on the host machine to control the mouse and keyboard. This poses significant security risks and makes it less suitable for deployment outside of controlled research environments.

Performance and Benchmarks

Benchmarks are a useful, if imperfect, measure of an agent's capability.

Agent-S3's Benchmark Achievements

Agent-S3 has put up impressive numbers, achieving a score of 69.9% on the OSWorld benchmark, which is nearing the human performance level of 72% [6]. This score demonstrates strong performance on a standardized set of tasks in a specific environment.

Cua: Performance in the Real World

While we respect standardized benchmarks, our focus at Cua is on building a framework that is performant, reliable, and adaptable for real-world applications. Our Composite Agents architecture allows you to pair best-in-class models to achieve state-of-the-art performance on your specific tasks.

Furthermore, we provide a robust, open-source infrastructure for benchmarking and evaluation [1]. With Cua, you can:

Run standardized benchmarks to measure and compare the performance of different agent configurations.
Use reinforcement learning to automatically optimize agent behavior.
Track success rates, execution time, and other key metrics to drive data-driven improvements.

Getting Started: An Actionable Comparison

The difference in philosophy is most apparent when you start building. Cua offers a streamlined, secure path, while Agent-S3 requires a more manual, DIY approach.

The Cua Path: Simple, Secure, and Ready to Scale

Getting started with Cua is designed to be as simple as possible.

Install the SDK:

bash
pip install cua-agent

Write Your Agent Code: Initialize a secure Computer instance and a ComputerAgent. Our SDK abstracts away all the complexity of model providers and environment setup.

python
from agent import ComputerAgent
from computer import Computer

# Initialize a secure, sandboxed Computer instance
computer = Computer(
    os_type="linux",
    provider_type="docker",
    name="trycua/cua-ubuntu:latest"
)

# Initialize the agent with a composite model
# Here, we combine a grounding model with a powerful planning model
agent = ComputerAgent(
    model="huggingface-local/HelloKKMe/GTA1-7B+openai/gpt-5",
    tools=[computer]
)

# Run your task
messages = [{"role": "user", "content": "Open the web browser and navigate to cua.ai"}]

async for result in agent.run(messages):
    print(result)

With Cua, you're building inside a secure container from the first line of code.

The Agent-S3 Path: Manual Configuration and Local Execution

Setting up Agent-S3 involves several manual steps and exposes your local machine to security risks. The process typically involves:

Installing the library (pip install gui-agents).
Installing system dependencies like Tesseract (brew install tesseract).
Configuring API keys for your main model, grounding model, and other services via environment variables.
Running a lengthy CLI command with multiple required parameters for the model provider, grounding URL, model names, and screen resolution.

Crucially, Agent-S3 executes code directly on your computer. Its documentation even includes the warning: "The agent runs Python code to control your computer - use with care." This makes it a risky choice for anything beyond personal experimentation.

A Comparison at a Glance

Feature	Cua	Agent-S3
Core Architecture	Modular Composite Agents (planning + grounding)	Monolithic "worker-only" policy
Security	Sandboxed Cua Containers (Docker, macOS VM)	Runs directly on host machine (high risk)
Model Support	Unified API for 100+ models; easily mix-and-match	Requires model-specific configuration and parameters
Ease of Use	Simple SDK, abstracts away complexity	Complex CLI with many required flags
Benchmarking	Built-in tools for custom benchmarking and RL	Focused on replicating specific public benchmarks
Best For	Production applications, scalable deployments, security-conscious developers	Academic research, benchmark replication

Which Framework is Right for You?

Both Cua and Agent-S3 are valuable contributions to the open-source AI community. The right choice depends entirely on your goals.

You should choose Agent-S3 if:

Your primary goal is academic research or replicating results from the OSWorld benchmark.
You are comfortable with a complex setup process and understand the security risks of running an agent directly on your local machine.

You should choose Cua if:

You are building a production-ready application and prioritize security, stability, and scalability.
You want the flexibility to combine the best models for planning and vision using Composite Agents.
You value a streamlined developer experience with a simple, powerful Agent library.

Ready to build powerful, secure, and scalable Computer-use agents? Get started with our Quickstart guide and see what you can build with Cua today.

Citations

[1] https://github.com/trycua/cua

[2] https://ycombinator.com/companies/cua

[3] https://trycua.com/blog/composite-agents

[4] https://openai.com/index/computer-using-agent

[5] https://rapidinnovation.io/post/computer-using-agent-cua-models

[6] https://simular.ai/articles/agent-s3

[7] https://medium.com/@zhanghaolin66/from-gui-to-code-how-agent-s3-bridges-the-gap-for-smarter-ai-agents-d96ba1c43c33

[8] https://github.com/simular-ai/Agent-S