dify
MCPNOASSERTION
Open-source LLM app development platform for building AI workflows, RAG pipelines, and agents.
153k·LLM Ops·Open source
cua
MCPOpen sourceOpen-source drivers, cloud fleets, and benchmarks for building and evaluating computer-use AI agents.
28kMIT
Scale computer-use 2.0 with open-source drivers, cross-OS fleets, and benchmarks for training, evaluation, and data generation.
Pros
- +Cross-OS desktop automation drivers for macOS, Windows, and Linux
- +Isolated cloud desktop fleets plus local macOS/Linux VMs via Lume
- +Built-in benchmarks and specialized CUA-S1 decision models for evaluation
Cons
- −Cloud fleet pools can retain paid capacity after a claim ends, requiring careful cleanup
- −Background delivery and full automation depend on platform-specific support boundaries
- −Broad surface area (drivers, VMs, SDKs, benchmarks) implies a steep learning curve
Target audience: AI engineers and researchers building, training, or evaluating computer-use agents that operate real desktop environments.
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