--- title: "OpenRLHF vs helm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/openrlhf-openrlhf-vs-stanford-crfm-helm" tools: ["openrlhf-openrlhf", "stanford-crfm-helm"] --- # OpenRLHF vs helm *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 helm if helm is an open-source Python framework for evaluating foundation models, including LLMs and multimodal models. It emphasizes holistic, reproducible, and transparent evaluation processes. [OpenRLHF](https://openrlhf.readthedocs.io/) reports 9.9k GitHub stars, 996 forks, and 367 open issues, last pushed Jul 14, 2026. [helm](https://crfm.stanford.edu/helm) has 2.9k stars, 406 forks, and 90 open issues, last pushed Aug 1, 2026. Figures are from public GitHub metadata via [OpenRLHF's repository](https://github.com/OpenRLHF/OpenRLHF) and [helm's repository](https://github.com/stanford-crfm/helm). | | [OpenRLHF](/tools/openrlhf-openrlhf.md) | [helm](/tools/stanford-crfm-helm.md) | | --- | --- | --- | | Tagline | Easy-to-use, Scalable and High-performance Agentic RL Framework based on Ray | Holistic, reproducible and transparent evaluation of foundation models | | Stars | 9,891 | 2,873 | | Forks | 996 | 406 | | Open issues | 367 | 90 | | Language | Python | 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 | Helm is an open-source Python framework for evaluating foundation models, including LLMs and multimodal models. It emphasizes holistic, reproducible, and transparent evaluation processes. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Inference & Serving, Model Training | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [OpenRLHF](/tools/openrlhf-openrlhf.md) | [helm](/tools/stanford-crfm-helm.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 24d | 5d | | Open issues (now) | 367 | 90 | | Stars delta | +132 (30d) | Unknown | | Open issues delta | +25 (30d) | Unknown | | Full report | [trust report](/tools/openrlhf-openrlhf/trust.md) | [trust report](/tools/stanford-crfm-helm/trust.md) | ## Shared compatibility - **Python**: [OpenRLHF](/tools/openrlhf-openrlhf.md) - Python runtime; [helm](/tools/stanford-crfm-helm.md) - Python runtime ## 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: helm - **Adopt for:** Helm is an open-source Python framework for evaluating foundation models, including LLMs and multimodal models. It emphasizes holistic, reproducible, and transparent evaluation processes. ## 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. - Also covers Inference & Serving, Model Training. - 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 helm if… - Tags unique to helm: evaluation, foundation-models, framework, language-models. - Also covers Evaluation & Observability. - When you need a comprehensive tool to evaluate the performance of large language models (LLMs) and other types of foundation models in a standardized way. ## 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 helm - Helm may not be suitable if you are working with smaller scale projects that do not require extensive, holistic evaluation capabilities associated with foundation models. - If your framework of choice already provides sufficient evaluation tools or processes for foundation models, adding Helm might introduce unnecessary complexity. ## Common questions ### What is the difference between OpenRLHF and helm? OpenRLHF: Easy-to-use, Scalable and High-performance Agentic RL Framework based on Ray. helm: Holistic, reproducible and transparent evaluation of foundation models. See the comparison table for live GitHub stats and shared categories. ### When should I choose OpenRLHF over helm? Choose OpenRLHF over helm 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; Also covers Inference & Serving, Model Training; 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 helm over OpenRLHF? Choose helm over OpenRLHF when Tags unique to helm: evaluation, foundation-models, framework, language-models; Also covers Evaluation & Observability; When you need a comprehensive tool to evaluate the performance of large language models (LLMs) and other types of foundation models in a standardized way. ### 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 helm? Helm may not be suitable if you are working with smaller scale projects that do not require extensive, holistic evaluation capabilities associated with foundation models. If your framework of choice already provides sufficient evaluation tools or processes for foundation models, adding Helm might introduce unnecessary complexity. ### Is OpenRLHF or helm more popular on GitHub? OpenRLHF has more GitHub stars (9,891 vs 2,873). Stars measure visibility, not whether either tool fits your constraints. ### Are OpenRLHF and helm open source? Yes - both are open-source projects on GitHub (OpenRLHF: Apache-2.0, helm: Apache-2.0). ### Where can I find alternatives to OpenRLHF or helm? GraphCanon lists graph-backed alternatives at [OpenRLHF alternatives](/tools/openrlhf-openrlhf/alternatives) and [helm alternatives](/tools/stanford-crfm-helm/alternatives) ([OpenRLHF markdown twin](/tools/openrlhf-openrlhf/alternatives.md), [helm markdown twin](/tools/stanford-crfm-helm/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-stanford-crfm-helm.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, OpenRLHF or helm? OpenRLHF: Active. helm: 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 helm? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [OpenRLHF trust report](/tools/openrlhf-openrlhf/trust); [helm trust report](/tools/stanford-crfm-helm/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/_