--- title: "OpenRLHF vs ray-llm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/openrlhf-openrlhf-vs-ray-project-ray-llm" tools: ["openrlhf-openrlhf", "ray-project-ray-llm"] --- # OpenRLHF vs ray-llm *GraphCanon updated Aug 7, 2026* ## Verdict Pick OpenRLHF if openRLHF is a reinforcement learning framework designed for efficient distributed scheduling and large-scale model training up to 70B+ parameters, leveraging Ray for resource management and integrating vLLM, DeepSpeed, H; pick ray-llm if archived LLM deployment tool integrated into Ray; now focus on built-in APIs (`ray.serve.llm` & `ray.data.llm`). [OpenRLHF](https://openrlhf.readthedocs.io/) reports 9.9k GitHub stars, 996 forks, and 367 open issues, last pushed Jul 14, 2026. [ray-llm](https://docs.ray.io/en/latest/) has 1.3k stars, 90 forks, and 0 open issues, last pushed Mar 13, 2025. Figures are from public GitHub metadata via [OpenRLHF's repository](https://github.com/OpenRLHF/OpenRLHF) and [ray-llm's repository](https://github.com/ray-project/ray-llm). | | [OpenRLHF](/tools/openrlhf-openrlhf.md) | [ray-llm](/tools/ray-project-ray-llm.md) | | --- | --- | --- | | Tagline | Easy-to-use, Scalable and High-performance Agentic RL Framework based on Ray | Archived repository; LLM serving APIs integrated into the Ray project | | Stars | 9,891 | 1,261 | | Forks | 996 | 90 | | Open issues | 367 | 0 | | Language | Python | - | | Adopt for | OpenRLHF is a reinforcement learning framework designed for efficient distributed scheduling and large-scale model training up to 70B+ parameters, leveraging Ray for resource management and integrating vLLM, DeepSpeed, H | Archived LLM deployment tool integrated into Ray; now focus on built-in APIs (`ray.serve.llm` & `ray.data.llm`). | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | - | | Categories | Inference & Serving, Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [OpenRLHF](/tools/openrlhf-openrlhf.md) | [ray-llm](/tools/ray-project-ray-llm.md) | | --- | --- | --- | | Maintenance | Active (82%) | Archived (8%) | | Days since push | 24d | 507d | | Archived on GitHub | No | Yes | | Open issues (now) | 367 | 0 | | Stars delta | +132 (30d) | Unknown | | Open issues delta | +25 (30d) | Unknown | | Full report | [trust report](/tools/openrlhf-openrlhf/trust.md) | [trust report](/tools/ray-project-ray-llm/trust.md) | ## Decision facts: OpenRLHF - **Pricing:** freemium - OpenRLHF is primarily available free of cost under the Apache-2.0 license, but users might incur costs based on their infrastructure usage for distributed training setups (e.g., cloud GPU instances). - **Adopt for:** OpenRLHF is a reinforcement learning framework designed for efficient distributed scheduling and large-scale model training up to 70B+ parameters, leveraging Ray for resource management and integrating vLLM, DeepSpeed, H ## Decision facts: ray-llm - **Adopt for:** Archived LLM deployment tool integrated into Ray; now focus on built-in APIs (`ray.serve.llm` & `ray.data.llm`). ## Choose when ### Choose OpenRLHF if… - Pricing: OpenRLHF is primarily available free of cost under the Apache-2.0 license, but users might incur costs based on their infrastructure usage for distributed training setups (e.g., cloud GPU instances).. - Tags unique to OpenRLHF: large language models, proximal-policy-optimization, raylib, reinforcement-learning. - When you require high-throughput sample generation with minimal idle time on limited hardware due to its hybrid engine scheduling that enables sharing of GPU resources between models and vLLM engines. ### Choose ray-llm if… - Tags unique to ray-llm: llm-serving, ray. - For deploying LLMs with new Ray-integrated APIs, ensuring direct support and updates from the Ray team. - Leaner open-issue backlog (0). ## When NOT to use OpenRLHF - If your project does not require distributed training or large-scale model parameters (above 70B), as OpenRLHF is specifically optimized for scenarios where efficient distribution across multiple GPUs - When your environment cannot support Ray or vLLM, as these are crucial components of the framework for scheduling and high-performance sample generation, respectively. - If minimal Docker setup and hardware requirements with GPU constraints are not acceptable in your scenario. ## When NOT to use ray-llm - If seeking a standalone solution distinct from Ray’s main project, preferring specialized tools. - For needs requiring active maintenance and development in this specific repository; consider alternative up-to-date projects. ## Common questions ### What is the difference between OpenRLHF and ray-llm? OpenRLHF: Easy-to-use, Scalable and High-performance Agentic RL Framework based on Ray. ray-llm: Archived repository; LLM serving APIs integrated into the Ray project. See the comparison table for live GitHub stats and shared categories. ### When should I choose OpenRLHF over ray-llm? Choose OpenRLHF over ray-llm when Pricing: OpenRLHF is primarily available free of cost under the Apache-2.0 license, but users might incur costs based on their infrastructure usage for distributed training setups (e.g., cloud GPU instances).; Tags unique to OpenRLHF: large language models, proximal-policy-optimization, raylib, reinforcement-learning; When you require high-throughput sample generation with minimal idle time on limited hardware due to its hybrid engine scheduling that enables sharing of GPU resources between models and vLLM engines. ### When should I choose ray-llm over OpenRLHF? Choose ray-llm over OpenRLHF when Tags unique to ray-llm: llm-serving, ray; For deploying LLMs with new Ray-integrated APIs, ensuring direct support and updates from the Ray team; Leaner open-issue backlog (0). ### When should I avoid OpenRLHF? If your project does not require distributed training or large-scale model parameters (above 70B), as OpenRLHF is specifically optimized for scenarios where efficient distribution across multiple GPUs When your environment cannot support Ray or vLLM, as these are crucial components of the framework for scheduling and high-performance sample generation, respectively. If minimal Docker setup and hardware requirements with GPU constraints are not acceptable in your scenario. ### When should I avoid ray-llm? If seeking a standalone solution distinct from Ray’s main project, preferring specialized tools. For needs requiring active maintenance and development in this specific repository; consider alternative up-to-date projects. ### Is OpenRLHF or ray-llm more popular on GitHub? OpenRLHF has more GitHub stars (9,891 vs 1,261). Stars measure visibility, not whether either tool fits your constraints. ### Are OpenRLHF and ray-llm open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to OpenRLHF or ray-llm? GraphCanon lists graph-backed alternatives at [OpenRLHF alternatives](/tools/openrlhf-openrlhf/alternatives) and [ray-llm alternatives](/tools/ray-project-ray-llm/alternatives) ([OpenRLHF markdown twin](/tools/openrlhf-openrlhf/alternatives.md), [ray-llm markdown twin](/tools/ray-project-ray-llm/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-llm.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, OpenRLHF or ray-llm? OpenRLHF: Active. ray-llm: Archived. 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-llm? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [OpenRLHF trust report](/tools/openrlhf-openrlhf/trust); [ray-llm trust report](/tools/ray-project-ray-llm/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/_