AgentIndex icon
AgentIndex
AReaL logo
AReaL
★ 5.9k
vs
cua logo
cua
★ 30.7k

AReaL vs cua

AReaL: AReaL is a fully asynchronous reinforcement learning system from Tsinghua IIIS and Ant Group for training large reasoning and agentic models. Version 2.0 splits it into training, inference, agent and weight-update microservices, and black-box agent apps can be trained online by pointing their base_url at 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.

01

TL;DR

AReaL logo
AReaLLLM Infra · ★ 5.9k

RL fine-tuning of reasoning models on math and coding tasks

Read AReaL full review →
cua logo
cuaLLM Infra · ★ 30.7k
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 →
02

Side-by-Side Comparison

Field
AReaL logoAReaL
cua logocua
Category
LLM Infra
LLM Infra
Stars
★ 5.9k
★ 30.7k
License
Apache-2.0
MIT
Install Command
git clone https://github.com/areal-project/AReaL && cd AReaL && pip install uv && uv sync --extra cuda
curl -fsSL https://cua.ai/install.sh | sh
Updated
1d ago
1d ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
agent, agent-framework, llm
AI Agents, Virtualization, UI Automation
03

Features

AReaL logoAReaL
01Fully asynchronous RL training, reported 2.77x faster than synchronous systems in v0.3
02Online RL for black-box agents by replacing base_url and api_key
03Microservice architecture in 2.0 with separate training, inference, agent and weight-update services
04SGLang or vLLM inference backends, Ray clusters and Ascend NPU support
05Examples for math, SWE, search agents, tau2-bench and OpenClaw agents
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.
04

Use Cases

AReaL logoAReaL
↳RL fine-tuning of reasoning models on math and coding tasks
↳Training multi-turn agents such as search or customer-service agents
↳Continuously improving an existing agent app via online RL
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.
05

Best For

AReaL logoAReaL
EssentialTrending
cua logocua
Most PopularEssentialTrending
FAQ

FAQ

What is the difference between AReaL and cua?
Both AReaL and cua are in the LLM Infra category. AReaL has 5.9k stars, while cua has 30.7k stars.
Which is better, AReaL or cua?
The best choice depends on your use case. Choose AReaL if RL fine-tuning of reasoning models on math and coding tasks, and cua if Developing and deploying AI agents for autonomous desktop interaction and task completion..
Is AReaL free or open source?
Yes, AReaL is open source on GitHub (Apache-2.0).
Is cua free or open source?
Yes, cua is open source on GitHub (MIT).
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