--- title: "LLMSurvey vs Awesome-LLMOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/rucaibox-llmsurvey-vs-tensorchord-awesome-llmops" tools: ["rucaibox-llmsurvey", "tensorchord-awesome-llmops"] --- # LLMSurvey vs Awesome-LLMOps *GraphCanon updated Aug 20, 2026* ## Verdict Pick LLMSurvey if lLMSurvey is a comprehensive resource center dedicated to large language model research, collecting and organizing scholarly materials and resources relevant to chain-of-thought reasoning, in-context learning, RLHF, and训; 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. [LLMSurvey](https://arxiv.org/abs/2303.18223) reports 12k GitHub stars, 931 forks, and 30 open issues, last pushed Mar 11, 2025. [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 [LLMSurvey's repository](https://github.com/RUCAIBox/LLMSurvey) and [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps). | | [LLMSurvey](/tools/rucaibox-llmsurvey.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Tagline | A comprehensive collection of papers and resources related to Large Language Models. | An awesome & curated list of best LLMOps tools for developers | | Stars | 12,205 | 5,915 | | Forks | 931 | 993 | | Open issues | 30 | 247 | | Language | Python | Shell | | Adopt for | LLMSurvey is a comprehensive resource center dedicated to large language model research, collecting and organizing scholarly materials and resources relevant to chain-of-thought reasoning, in-context learning, RLHF, and训 | 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 | The license for LLMSurvey is unknown based on the provided repository information. | CC0-1.0 | | Categories | Evaluation & Observability, LLM Frameworks | 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._ | | [LLMSurvey](/tools/rucaibox-llmsurvey.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 523d | 91d | | Open issues (now) | 30 | 247 | | Stars delta | +18 (30d) | +28 (30d) | | Open issues delta | 0 (30d) | +66 (30d) | | Full report | [trust report](/tools/rucaibox-llmsurvey/trust.md) | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | ## Decision facts: LLMSurvey - **Pricing:** freemium - Since no detailed pricing plan was specified in the repository contents, it can be inferred that access to the materials and resources of LLMSurvey might be free; however, specific details about usage - **Adopt for:** LLMSurvey is a comprehensive resource center dedicated to large language model research, collecting and organizing scholarly materials and resources relevant to chain-of-thought reasoning, in-context learning, RLHF, and训 - **License detail:** The license for LLMSurvey is unknown based on the provided repository information. ## 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 LLMSurvey if… - LLMSurvey is primarily Python; Awesome-LLMOps is Shell. - Pricing: Since no detailed pricing plan was specified in the repository contents, it can be inferred that access to the materials and resources of LLMSurvey might be free; however, specific details about usage. - Tags unique to LLMSurvey: chain-of-thought, in-context-learning, instruction-tuning, large language models. - You should use LLMSurvey if you are seeking deep insights into specific advancements such as long chain-of-thought (CoT) reasoning approaches used by DeepSeek-R1 or OpenAI's o-series models. ### Choose Awesome-LLMOps if… - Awesome-LLMOps is primarily Shell; LLMSurvey is Python. - Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops. - Also covers Computer Vision, Data & Retrieval, Inference & Serving, Model Training, Speech & Audio. - - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ## When NOT to use LLMSurvey - You might not want to use LLMSurvey if you prefer tools that offer practical implementation details over a survey-style summary and organization of research papers. - Consider other resources if your focus is on hands-on development rather than deep academic exploration, as LLMSurvey provides extensive academic coverage but fewer direct coding or implementation how ## 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 LLMSurvey and Awesome-LLMOps? LLMSurvey: A comprehensive collection of papers and resources related to Large Language Models.. 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 LLMSurvey over Awesome-LLMOps? Choose LLMSurvey over Awesome-LLMOps when LLMSurvey is primarily Python; Awesome-LLMOps is Shell; Pricing: Since no detailed pricing plan was specified in the repository contents, it can be inferred that access to the materials and resources of LLMSurvey might be free; however, specific details about usage; Tags unique to LLMSurvey: chain-of-thought, in-context-learning, instruction-tuning, large language models; You should use LLMSurvey if you are seeking deep insights into specific advancements such as long chain-of-thought (CoT) reasoning approaches used by DeepSeek-R1 or OpenAI's o-series models. ### When should I choose Awesome-LLMOps over LLMSurvey? Choose Awesome-LLMOps over LLMSurvey when Awesome-LLMOps is primarily Shell; LLMSurvey is Python; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops; Also covers Computer Vision, Data & Retrieval, Inference & Serving, Model Training, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ### When should I avoid LLMSurvey? You might not want to use LLMSurvey if you prefer tools that offer practical implementation details over a survey-style summary and organization of research papers. Consider other resources if your focus is on hands-on development rather than deep academic exploration, as LLMSurvey provides extensive academic coverage but fewer direct coding or implementation how ### 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 LLMSurvey or Awesome-LLMOps more popular on GitHub? LLMSurvey has more GitHub stars (12,205 vs 5,915). Stars measure visibility, not whether either tool fits your constraints. ### Are LLMSurvey and Awesome-LLMOps open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to LLMSurvey or Awesome-LLMOps? GraphCanon lists graph-backed alternatives at [LLMSurvey alternatives](/tools/rucaibox-llmsurvey/alternatives) and [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) ([LLMSurvey markdown twin](/tools/rucaibox-llmsurvey/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/rucaibox-llmsurvey-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, LLMSurvey or Awesome-LLMOps? LLMSurvey: Dormant. 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 LLMSurvey and Awesome-LLMOps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLMSurvey trust report](/tools/rucaibox-llmsurvey/trust); [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=rucaibox-llmsurvey`](/api/graphcanon/graph?tool=rucaibox-llmsurvey) - 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/_