--- title: "awesome-RLHF vs Awesome-LLMOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/opendilab-awesome-rlhf-vs-tensorchord-awesome-llmops" tools: ["opendilab-awesome-rlhf", "tensorchord-awesome-llmops"] --- # awesome-RLHF vs Awesome-LLMOps *GraphCanon updated Aug 20, 2026* ## Verdict Pick awesome-RLHF if awesome-RLHF is a curated resource list focusing on reinforcement learning with human feedback (RLHF), which is crucial for refining large language models through interactive training methods; pick Awesome-LLMOps if awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more. [awesome-RLHF](https://github.com/opendilab/awesome-RLHF) reports 4.4k GitHub stars, 258 forks, and 6 open issues, last pushed May 20, 2026. [Awesome-LLMOps](https://github.com/tensorchord/Awesome-LLMOps) has 5.9k stars, 993 forks, and 247 open issues, last pushed May 21, 2026. Figures are from public GitHub metadata via [awesome-RLHF's repository](https://github.com/opendilab/awesome-RLHF) and [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps). | | [awesome-RLHF](/tools/opendilab-awesome-rlhf.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Tagline | A curated list of reinforcement learning with human feedback resources (continually updated) | An awesome & curated list of best LLMOps tools for developers | | Stars | 4,422 | 5,915 | | Forks | 258 | 993 | | Open issues | 6 | 247 | | Language | - | Shell | | Adopt for | awesome-RLHF is a curated resource list focusing on reinforcement learning with human feedback (RLHF), which is crucial for refining large language models through interactive training methods. | Awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | CC0-1.0 | | Categories | Evaluation & Observability, Model Training | Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-RLHF](/tools/opendilab-awesome-rlhf.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Slowing (36%) | | Days since push | 89d | 91d | | Open issues (now) | 6 | 247 | | Stars delta | +9 (30d) | +28 (30d) | | Open issues delta | 0 (30d) | +66 (30d) | | Full report | [trust report](/tools/opendilab-awesome-rlhf/trust.md) | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | **Typed relationship:** awesome-RLHF _(related)_ Awesome-LLMOps 'awesome-RLHF' and 'Awesome-LLMOps' both offer curated lists relevant for LLM development, with the former focusing on RLHF resources and the latter on LLMOps tools. They address different aspects of the same field. ## Decision facts: awesome-RLHF - **Adopt for:** awesome-RLHF is a curated resource list focusing on reinforcement learning with human feedback (RLHF), which is crucial for refining large language models through interactive training methods. ## Decision facts: Awesome-LLMOps - **Adopt for:** Awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more. ## Choose when ### Choose awesome-RLHF if… - License: awesome-RLHF is Apache-2.0, Awesome-LLMOps is CC0-1.0. - 'awesome-RLHF' and 'Awesome-LLMOps' both offer curated lists relevant for LLM development, with the former focusing on RLHF resources and the latter on LLMOps tools. They address different aspects of the same field. - Tags unique to awesome-RLHF: deep-learning, depth-reinforcement-learning, human-feedback, large language models. - When you are specifically interested in the resources that pertain to enhancing reinforcement learning algorithms with human feedback for developing advanced AI systems. ### Choose Awesome-LLMOps if… - License: Awesome-LLMOps is CC0-1.0, awesome-RLHF is Apache-2.0. - 'awesome-RLHF' and 'Awesome-LLMOps' both offer curated lists relevant for LLM development, with the former focusing on RLHF resources and the latter on LLMOps tools. They address different aspects of the same field. - Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops. - Also covers Computer Vision, Data & Retrieval, Inference & Serving, LLM Frameworks, Speech & Audio. - - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ## When NOT to use awesome-RLHF - If your focus is exclusively on generic deep-learning or reinforcement-learning resources without the aspect of integrating human feedback into the training process. ## When NOT to use Awesome-LLMOps - - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list. - - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources. ## Common questions ### What is the difference between awesome-RLHF and Awesome-LLMOps? awesome-RLHF: A curated list of reinforcement learning with human feedback resources (continually updated). Awesome-LLMOps: An awesome & curated list of best LLMOps tools for developers. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-RLHF over Awesome-LLMOps? Choose awesome-RLHF over Awesome-LLMOps when License: awesome-RLHF is Apache-2.0, Awesome-LLMOps is CC0-1.0; 'awesome-RLHF' and 'Awesome-LLMOps' both offer curated lists relevant for LLM development, with the former focusing on RLHF resources and the latter on LLMOps tools. They address different aspects of the same field; Tags unique to awesome-RLHF: deep-learning, depth-reinforcement-learning, human-feedback, large language models; When you are specifically interested in the resources that pertain to enhancing reinforcement learning algorithms with human feedback for developing advanced AI systems. ### When should I choose Awesome-LLMOps over awesome-RLHF? Choose Awesome-LLMOps over awesome-RLHF when License: Awesome-LLMOps is CC0-1.0, awesome-RLHF is Apache-2.0; 'awesome-RLHF' and 'Awesome-LLMOps' both offer curated lists relevant for LLM development, with the former focusing on RLHF resources and the latter on LLMOps tools. They address different aspects of the same field; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops; Also covers Computer Vision, Data & Retrieval, Inference & Serving, LLM Frameworks, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ### When should I avoid awesome-RLHF? If your focus is exclusively on generic deep-learning or reinforcement-learning resources without the aspect of integrating human feedback into the training process. ### When should I avoid Awesome-LLMOps? - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list. - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources. ### Is awesome-RLHF or Awesome-LLMOps more popular on GitHub? Awesome-LLMOps has more GitHub stars (5,915 vs 4,422). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-RLHF and Awesome-LLMOps open source? Yes - both are open-source projects on GitHub (awesome-RLHF: Apache-2.0, Awesome-LLMOps: CC0-1.0). ### Where can I find alternatives to awesome-RLHF or Awesome-LLMOps? GraphCanon lists graph-backed alternatives at [awesome-RLHF alternatives](/tools/opendilab-awesome-rlhf/alternatives) and [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) ([awesome-RLHF markdown twin](/tools/opendilab-awesome-rlhf/alternatives.md), [Awesome-LLMOps markdown twin](/tools/tensorchord-awesome-llmops/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/opendilab-awesome-rlhf-vs-tensorchord-awesome-llmops.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-RLHF or Awesome-LLMOps? awesome-RLHF: Steady. Awesome-LLMOps: Slowing. 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 awesome-RLHF and Awesome-LLMOps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-RLHF trust report](/tools/opendilab-awesome-rlhf/trust); [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=opendilab-awesome-rlhf`](/api/graphcanon/graph?tool=opendilab-awesome-rlhf) - 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/_