AgentIndex icon
AgentIndex
cua logo
cua
★ 31.1k
vs
AReaL logo
AReaL
★ 5.9k

cua vs AReaL

cua: Cua is an open-source platform designed for building, benchmarking, and deploying AI agents capable of interacting with any computer. It provides isolated, self-hostable sandboxes using technologies like Docker, QEMU, and Apple Vz for agentic UI automation and secure code execution.; AReaL: AReaL is an open-source, fully asynchronous reinforcement learning training system designed for large reasoning and agentic models. It offers exceptional flexibility, industry-leading speed, and scalability from a single node to over 1,000 GPUs, achieving state-of-the-art performance.

01

TL;DR

cua logo
cuaLLM Infra · ★ 31.1k
When to choose:
  • You are building Computer-Use agents and need robust, self-hostable isolated desktop/VM sandboxes (Docker, QEMU, Apple Vz).
  • You need native app operating drivers, standardized benchmarking harnesses, and secure isolation for autonomous agent task execution.
When to skip:
  • You only need simple UI macros on your personal physical machine (Windows-MCP or local desktop helpers are easier).
  • Your agents interact solely via text APIs, shell commands, or databases and have no need for a virtual graphical desktop.
Read cua full review →
AReaL logo
AReaLLLM Infra · ★ 5.9k

Training Reasoning Agents: Developing AI agents capable of complex mathematical, coding, and general reasoning tasks.

Read AReaL full review →
02

Side-by-Side Comparison

Field
cua logocua
AReaL logoAReaL
Category
LLM Infra
LLM Infra
Stars
★ 31.1k
★ 5.9k
License
MIT
Apache-2.0
Install Command
curl -fsSL https://cua.ai/install.sh | sh
pip install -e ".[dev,docs]"
Updated
1d ago
1d ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
AI Agents, Virtualization, UI Automation
Reinforcement Learning, Large Language Models, Asynchronous Systems
03

Features

cua logocua
01Build AI agents for desktop UI automation and interaction.
02Provide isolated code execution environments (sandboxes).
03Benchmark computer-use models with standardized tasks.
04Train agents using reinforcement learning environments.
05Manage high-performance macOS/Linux VMs on Apple Silicon.
AReaL logoAReaL
01Fully Asynchronous RL Training: Enables stable, industry-leading speed for reinforcement learning.
02Scalability: Seamlessly adapts from single-node setups to over 1,000 GPUs.
03Flexible Agentic Rollout: Easy customization for multi-turn agentic workflows and integration with external frameworks.
04Cutting-Edge Performance: Achieves state-of-the-art results for math, coding, and search agents.
05Open-Source & Reproducible: Provides full training details, data, and infrastructure to reproduce results.
04

Use Cases

cua logocua
↳Developing and deploying AI agents for autonomous desktop interaction and task completion.
↳Creating secure, isolated code execution environments for AI coding assistants and development workflows.
↳Benchmarking and training computer-use agents using standardized tasks and reinforcement learning.
AReaL logoAReaL
↳Training Reasoning Agents: Developing AI agents capable of complex mathematical, coding, and general reasoning tasks.
↳Large Language Model Alignment (RLHF): Fine-tuning LLMs using Reinforcement Learning from Human Feedback.
↳Multi-Turn Agentic Workflows: Implementing and customizing iterative agent behaviors with self-correction and tool integration.
05

Best For

cua logocua
Most PopularEssentialTrending
AReaL logoAReaL
Trending
FAQ

FAQ

What is the difference between cua and AReaL?
Both cua and AReaL are in the LLM Infra category. cua has 31.1k stars, while AReaL has 5.9k stars.
Which is better, cua or AReaL?
The best choice depends on your use case. Choose cua if Developing and deploying AI agents for autonomous desktop interaction and task completion., and AReaL if Training Reasoning Agents: Developing AI agents capable of complex mathematical, coding, and general reasoning tasks..
Is cua free or open source?
Yes, cua is open source on GitHub (MIT).
Is AReaL free or open source?
Yes, AReaL is open source on GitHub (Apache-2.0).
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