--- title: "OpenRLHF vs ray" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/openrlhf-openrlhf-vs-ray-project-ray" tools: ["openrlhf-openrlhf", "ray-project-ray"] --- # OpenRLHF vs ray *GraphCanon updated Sep 20, 2026* ## Verdict Pick OpenRLHF if openRLHF is a scalable and high-performance reinforcement learning framework from human feedback, built on Ray for distributed scheduling and vLLM for efficient inference, supporting models up to 70B+ parameters; pick ray if ray is a unified framework for scaling AI and Python applications, offering a core distributed runtime and AI libraries for accelerating ML workloads. [OpenRLHF](https://openrlhf.readthedocs.io/) reports 10k GitHub stars, 1.0k forks, and 380 open issues, last pushed Sep 17, 2026. [ray](https://ray.io) has 44k stars, 8.1k forks, and 3.6k open issues, last pushed Sep 18, 2026. Figures are from public GitHub metadata via [OpenRLHF's repository](https://github.com/OpenRLHF/OpenRLHF) and [ray's repository](https://github.com/ray-project/ray). | | [OpenRLHF](/tools/openrlhf-openrlhf.md) | [ray](/tools/ray-project-ray.md) | | --- | --- | --- | | Tagline | Scalable and High-performance Agentic RL Framework based on Ray | Unified framework for scaling AI and Python applications | | Stars | 10,014 | 43,863 | | Forks | 1,019 | 8,052 | | Open issues | 380 | 3,590 | | Language | Python | Python | | Adopt for | OpenRLHF is a scalable and high-performance reinforcement learning framework from human feedback, built on Ray for distributed scheduling and vLLM for efficient inference, supporting models up to 70B+ parameters. | Ray is a unified framework for scaling AI and Python applications, offering a core distributed runtime and AI libraries for accelerating ML workloads. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Inference & Serving, Model Training | Developer Tools, Evaluation & Observability, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [OpenRLHF](/tools/openrlhf-openrlhf.md) | [ray](/tools/ray-project-ray.md) | | --- | --- | --- | | Days since push | 1d | 0d | | Open issues (now) | 380 | 3.6k | | Stars delta | +123 (30d) | +337 (30d) | | Open issues delta | +13 (30d) | +107 (30d) | | Full report | [trust report](/tools/openrlhf-openrlhf/trust.md) | [trust report](/tools/ray-project-ray/trust.md) | **Typed relationship:** OpenRLHF _(depends on)_ ray OpenRLHF leverages Ray for efficient distributed scheduling, separating Actor, Reward, Reference, and Critic models across different GPUs. ## Shared compatibility - **Python**: [OpenRLHF](/tools/openrlhf-openrlhf.md) - Python runtime; [ray](/tools/ray-project-ray.md) - Python runtime ## Decision facts: OpenRLHF - **Adopt for:** OpenRLHF is a scalable and high-performance reinforcement learning framework from human feedback, built on Ray for distributed scheduling and vLLM for efficient inference, supporting models up to 70B+ parameters. ## Decision facts: ray - **Adopt for:** Ray is a unified framework for scaling AI and Python applications, offering a core distributed runtime and AI libraries for accelerating ML workloads. ## Choose when ### Choose OpenRLHF if… - OpenRLHF leverages Ray for efficient distributed scheduling, separating Actor, Reward, Reference, and Critic models across different GPUs. - Tags unique to OpenRLHF: proximal-policy-optimization, raylib, reinforcement learning, reinforcement-learning-from-human-feedba. - When you need to train large models up to 70B+ parameters with efficient distributed scheduling and high-performance inference. ### Choose ray if… - OpenRLHF leverages Ray for efficient distributed scheduling, separating Actor, Reward, Reference, and Critic models across different GPUs. - Tags unique to ray: data-science, deep-learning, deployment, distributed. - Also covers Developer Tools, Evaluation & Observability. - When you need to perform distributed training, as Ray's Train library simplifies the setup and management of distributed training jobs. ## When NOT to use OpenRLHF - If your project does not require support for large models or distributed training, as OpenRLHF's complexity and resource requirements may be unnecessary. - When you prefer a framework that does not rely on Ray for distributed scheduling, as OpenRLHF's architecture is tightly coupled with Ray for its distributed capabilities. ## When NOT to use ray - If your project is limited to single-node development and does not require scaling across multiple machines or clusters. - When the specific requirements of your project are better met by specialized tools that offer more tailored functionality for a particular task, such as TensorFlow for deep learning or PyTorch for GPU - If you are working in a non-Python environment, as Ray is primarily designed for Python applications. ## Common questions ### What is the difference between OpenRLHF and ray? OpenRLHF: Scalable and High-performance Agentic RL Framework based on Ray. ray: Unified framework for scaling AI and Python applications. See the comparison table for live GitHub stats and shared categories. ### When should I choose OpenRLHF over ray? Choose OpenRLHF over ray when OpenRLHF leverages Ray for efficient distributed scheduling, separating Actor, Reward, Reference, and Critic models across different GPUs; Tags unique to OpenRLHF: proximal-policy-optimization, raylib, reinforcement learning, reinforcement-learning-from-human-feedba; When you need to train large models up to 70B+ parameters with efficient distributed scheduling and high-performance inference. ### When should I choose ray over OpenRLHF? Choose ray over OpenRLHF when OpenRLHF leverages Ray for efficient distributed scheduling, separating Actor, Reward, Reference, and Critic models across different GPUs; Tags unique to ray: data-science, deep-learning, deployment, distributed; Also covers Developer Tools, Evaluation & Observability; When you need to perform distributed training, as Ray's Train library simplifies the setup and management of distributed training jobs. ### When should I avoid OpenRLHF? If your project does not require support for large models or distributed training, as OpenRLHF's complexity and resource requirements may be unnecessary. When you prefer a framework that does not rely on Ray for distributed scheduling, as OpenRLHF's architecture is tightly coupled with Ray for its distributed capabilities. ### When should I avoid ray? If your project is limited to single-node development and does not require scaling across multiple machines or clusters. When the specific requirements of your project are better met by specialized tools that offer more tailored functionality for a particular task, such as TensorFlow for deep learning or PyTorch for GPU If you are working in a non-Python environment, as Ray is primarily designed for Python applications. ### Is OpenRLHF or ray more popular on GitHub? ray has more GitHub stars (43,863 vs 10,014). Stars measure visibility, not whether either tool fits your constraints. ### Are OpenRLHF and ray open source? Yes - both are open-source projects on GitHub (OpenRLHF: Apache-2.0, ray: Apache-2.0). ### Where can I find alternatives to OpenRLHF or ray? GraphCanon lists graph-backed alternatives at [OpenRLHF alternatives](/tools/openrlhf-openrlhf/alternatives) and [ray alternatives](/tools/ray-project-ray/alternatives) ([OpenRLHF markdown twin](/tools/openrlhf-openrlhf/alternatives.md), [ray markdown twin](/tools/ray-project-ray/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/openrlhf-openrlhf-vs-ray-project-ray.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, OpenRLHF or ray? OpenRLHF: Very active. ray: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for OpenRLHF and ray? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [OpenRLHF trust report](/tools/openrlhf-openrlhf/trust); [ray trust report](/tools/ray-project-ray/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=openrlhf-openrlhf`](/api/graphcanon/graph?tool=openrlhf-openrlhf) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_