--- title: "AlignLLMHumanSurvey vs Awesome-LLMOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/garyyufei-alignllmhumansurvey-vs-tensorchord-awesome-llmops" tools: ["garyyufei-alignllmhumansurvey", "tensorchord-awesome-llmops"] --- # AlignLLMHumanSurvey vs Awesome-LLMOps *GraphCanon updated Jul 12, 2026* ## Verdict Pick AlignLLMHumanSurvey if alignLLMHumanSurvey is a survey repository aggregating resources and research on aligning large language models with human expectations through various methodologies like data collection, training techniques, and model评价; 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. [AlignLLMHumanSurvey](https://arxiv.org/abs/2307.12966) reports 742 GitHub stars, 30 forks, and 0 open issues, last pushed Sep 11, 2023. [Awesome-LLMOps](https://github.com/tensorchord/Awesome-LLMOps) has 5.9k stars, 901 forks, and 157 open issues, last pushed May 21, 2026. Figures are from public GitHub metadata via [AlignLLMHumanSurvey's repository](https://github.com/GaryYufei/AlignLLMHumanSurvey) and [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps). | | [AlignLLMHumanSurvey](/tools/garyyufei-alignllmhumansurvey.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Tagline | A survey on aligning large language models with human expectations | An awesome & curated list of best LLMOps tools for developers | | Stars | 742 | 5,877 | | Forks | 30 | 901 | | Open issues | 0 | 157 | | Language | - | Shell | | Adopt for | AlignLLMHumanSurvey is a survey repository aggregating resources and research on aligning large language models with human expectations through various methodologies like data collection, training techniques, and model评价 | 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 | - | CC0-1.0 | | Categories | Evaluation & Observability, Model Training | LLM Frameworks, Model Training, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [AlignLLMHumanSurvey](/tools/garyyufei-alignllmhumansurvey.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Steady (60%) | | Days since push | 1034d | 51d | | Open issues (now) | 0 | 157 | | Owner type | User | Organization | | Full report | [trust report](/tools/garyyufei-alignllmhumansurvey/trust.md) | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | ## Decision facts: AlignLLMHumanSurvey - **Adopt for:** AlignLLMHumanSurvey is a survey repository aggregating resources and research on aligning large language models with human expectations through various methodologies like data collection, training techniques, and model评价 ## 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 AlignLLMHumanSurvey if… - Tags unique to AlignLLMHumanSurvey: awesome, chatgpt, chinese-llama, gpt-4. - Also covers Evaluation & Observability. - 当您需要全面了解将大型语言模型与人类期望对齐的技术和方法时，可以使用AlignLLMHumanSurvey，它涵盖了数据收集、训练方法和模型评估等多个方面。 ### Choose Awesome-LLMOps if… - Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops. - Also covers LLM Frameworks, Vector Databases. - - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ## When NOT to use AlignLLMHumanSurvey - 当您需要具体的技术实现代码或工具箱时，请勿使用AlignLLMHumanSurvey，因为它仅仅是一个汇总知识的调研文档，不提供具体的编程实现细节。 - 如果您正在寻找特定模型的性能评估数据或者准备在实际项目中直接应用某种对齐技术，则这个资源可能不会满足您的需求，因为它更侧重于理论和调查研究。 ## 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 AlignLLMHumanSurvey and Awesome-LLMOps? AlignLLMHumanSurvey: A survey on aligning large language models with human expectations. 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 AlignLLMHumanSurvey over Awesome-LLMOps? Choose AlignLLMHumanSurvey over Awesome-LLMOps when Tags unique to AlignLLMHumanSurvey: awesome, chatgpt, chinese-llama, gpt-4; Also covers Evaluation & Observability; 当您需要全面了解将大型语言模型与人类期望对齐的技术和方法时，可以使用AlignLLMHumanSurvey，它涵盖了数据收集、训练方法和模型评估等多个方面。. ### When should I choose Awesome-LLMOps over AlignLLMHumanSurvey? Choose Awesome-LLMOps over AlignLLMHumanSurvey when Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops; Also covers LLM Frameworks, Vector Databases; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ### When should I avoid AlignLLMHumanSurvey? 当您需要具体的技术实现代码或工具箱时，请勿使用AlignLLMHumanSurvey，因为它仅仅是一个汇总知识的调研文档，不提供具体的编程实现细节。 如果您正在寻找特定模型的性能评估数据或者准备在实际项目中直接应用某种对齐技术，则这个资源可能不会满足您的需求，因为它更侧重于理论和调查研究。 ### 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 AlignLLMHumanSurvey or Awesome-LLMOps more popular on GitHub? Awesome-LLMOps has more GitHub stars (5,877 vs 742). Stars measure visibility, not whether either tool fits your constraints. ### Are AlignLLMHumanSurvey and Awesome-LLMOps open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to AlignLLMHumanSurvey or Awesome-LLMOps? GraphCanon lists graph-backed alternatives at [AlignLLMHumanSurvey alternatives](/tools/garyyufei-alignllmhumansurvey/alternatives) and [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) ([AlignLLMHumanSurvey markdown twin](/tools/garyyufei-alignllmhumansurvey/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/garyyufei-alignllmhumansurvey-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, AlignLLMHumanSurvey or Awesome-LLMOps? AlignLLMHumanSurvey: Dormant. Awesome-LLMOps: Steady. 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 AlignLLMHumanSurvey and Awesome-LLMOps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [AlignLLMHumanSurvey trust report](/tools/garyyufei-alignllmhumansurvey/trust); [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=garyyufei-alignllmhumansurvey`](/api/graphcanon/graph?tool=garyyufei-alignllmhumansurvey) - 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/_