cua
★ 31.1k
vs
xLAM
★ 641
cua vs xLAM
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.; xLAM: xLAM is a research repository for Large Action Models (LAMs), which aggregates and unifies agent trajectories from diverse environments into a consistent format. It streamlines the creation of a generic data loader optimized for agent training, enabling robust model development across various scenarios.
01
TL;DR
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.
xLAMLLM Infra · ★ 641
Function calling in LLMs
02
Side-by-Side Comparison
Field
cuaxLAMCategory
LLM Infra
LLM Infra
Stars
★ 31.1k
★ 641
License
MIT
Apache-2.0
Install Command
curl -fsSL https://cua.ai/install.sh | sh
pip install -e .
Updated
1d ago
4mo ago
Open Source
Yes
Yes
Tags
AI Agents, Virtualization, UI Automation
Large Action Models, Function Calling, Agent Training
03
Features
cua01Build 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.
xLAM01Aggregates agent trajectories from distinct environments
02Standardizes and unifies trajectories into a consistent format
03Optimized generic data loader for agent training
04Maintains equilibrium across different data sources during training
05Supports efficient inference with Transformers and vLLM
04
Use Cases
cua↳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.
xLAM↳Function calling in LLMs
↳Training autonomous agents
↳Multi-turn conversation processing
05
Best For
cuaMost PopularEssentialTrending
xLAM—
FAQ
FAQ
What is the difference between cua and xLAM?
Both cua and xLAM are in the LLM Infra category. cua has 31.1k stars, while xLAM has 641 stars.
Which is better, cua or xLAM?
The best choice depends on your use case. Choose cua if Developing and deploying AI agents for autonomous desktop interaction and task completion., and xLAM if Function calling in LLMs.
Is cua free or open source?
Yes, cua is open source on GitHub (MIT).
Is xLAM free or open source?
Yes, xLAM is open source on GitHub (Apache-2.0).
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