ray vs awesome-llm-apps
A neutral, constraint-first comparison - live GitHub stats and typed relationships, not marketing.
| ray | awesome-llm-apps | |
|---|---|---|
| Tagline | Unified framework for scaling AI and Python applications | 100+ AI Agent & RAG apps you can actually run — clone, customize, ship. |
| Stars | 43k | 117k |
| Forks | 7.8k | 17k |
| Open issues | 3.5k | 6 |
| Language | Python | Python |
| License | Apache-2.0 | Apache-2.0 |
| Last pushed | Jul 7, 2026 | Jun 15, 2026 |
| Categories | Developer Tools, Inference & Serving, Data & Retrieval, Model Training, LLM Frameworks | AI Agents, LLM Frameworks |
ray
Ray is a compute engine that includes a distributed runtime core and libraries tailored for AI tasks like ML training, hyperparameter tuning, reinforcement learning, and serving. It supports data scalability through Datasets, facilitating efficient distribution of datasets across clusters.
Python
awesome-llm-apps
A repository containing a collection of AI agent and Retrieval-Augmented Generation (RAG) applications that are ready to be cloned, customized, and deployed. The projects cover various aspects such as AI agents, always-on agents, multi-agent teams, RAG techniques, voice agents, fine-tuning for specific use cases, and more.
Python