--- title: "ml-surveys vs awesome-LLM-resources" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/eugeneyan-ml-surveys-vs-wangrongsheng-awesome-llm-resources" tools: ["eugeneyan-ml-surveys", "wangrongsheng-awesome-llm-resources"] --- # ml-surveys vs awesome-LLM-resources *GraphCanon updated Aug 22, 2026* ## Verdict Pick ml-surveys if ml-surveys is a collection of detailed review papers summarizing advancements in various AI domains such as deep learning, NLP, CV, graphs, reinforcement learning, and recommendation systems; pick awesome-LLM-resources if awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as. [ml-surveys](https://github.com/eugeneyan/ml-surveys) reports 2.9k GitHub stars, 292 forks, and 2 open issues, last pushed Mar 17, 2023. [awesome-LLM-resources](https://github.com/WangRongsheng/awesome-LLM-resources) has 8.8k stars, 950 forks, and 23 open issues, last pushed Aug 14, 2026. Figures are from public GitHub metadata via [ml-surveys's repository](https://github.com/eugeneyan/ml-surveys) and [awesome-LLM-resources's repository](https://github.com/WangRongsheng/awesome-LLM-resources). | | [ml-surveys](/tools/eugeneyan-ml-surveys.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Tagline | Survey papers summarizing advances in various AI domains | Summary of the world's best LLM resources. | | Stars | 2,902 | 8,845 | | Forks | 292 | 950 | | Open issues | 2 | 23 | | Language | - | - | | Adopt for | ml-surveys is a collection of detailed review papers summarizing advancements in various AI domains such as deep learning, NLP, CV, graphs, reinforcement learning, and recommendation systems. | awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Computer Vision, Evaluation & Observability, Model Training | AI Agents, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ml-surveys](/tools/eugeneyan-ml-surveys.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 1254d | 2d | | Open issues (now) | 2 | 23 | | Stars delta | 0 (30d) | +142 (30d) | | Open issues delta | 0 (30d) | -13 (30d) | | Full report | [trust report](/tools/eugeneyan-ml-surveys/trust.md) | [trust report](/tools/wangrongsheng-awesome-llm-resources/trust.md) | ## Decision facts: ml-surveys - **Adopt for:** ml-surveys is a collection of detailed review papers summarizing advancements in various AI domains such as deep learning, NLP, CV, graphs, reinforcement learning, and recommendation systems. ## Decision facts: awesome-LLM-resources - **Adopt for:** awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a ## Choose when ### Choose ml-surveys if… - License: ml-surveys is MIT, awesome-LLM-resources is Apache-2.0. - Tags unique to ml-surveys: computer-vision, deep-learning, embeddings, machine-learning. - Also covers Computer Vision. - When you need comprehensive overviews and summaries of the latest research trends in multiple areas within machine learning ### Choose awesome-LLM-resources if… - License: awesome-LLM-resources is Apache-2.0, ml-surveys is MIT. - Tags unique to awesome-LLM-resources: awesome-list, book, course, large language models. - Also covers AI Agents, Developer Tools, Inference & Serving, LLM Frameworks. - - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies. ## When NOT to use ml-surveys - If you are seeking detailed technical details, original experiments, or specific algorithm implementations as ml-surveys focuses more on synthesis and summary - In cases where deep-dive analysis is required into a single niche topic, as ml-surveys provides broad overviews rather than in-depth coverage of individual niches ## When NOT to use awesome-LLM-resources - - Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage. - - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content. ## Common questions ### What is the difference between ml-surveys and awesome-LLM-resources? ml-surveys: Survey papers summarizing advances in various AI domains. awesome-LLM-resources: Summary of the world's best LLM resources.. See the comparison table for live GitHub stats and shared categories. ### When should I choose ml-surveys over awesome-LLM-resources? Choose ml-surveys over awesome-LLM-resources when License: ml-surveys is MIT, awesome-LLM-resources is Apache-2.0; Tags unique to ml-surveys: computer-vision, deep-learning, embeddings, machine-learning; Also covers Computer Vision; When you need comprehensive overviews and summaries of the latest research trends in multiple areas within machine learning. ### When should I choose awesome-LLM-resources over ml-surveys? Choose awesome-LLM-resources over ml-surveys when License: awesome-LLM-resources is Apache-2.0, ml-surveys is MIT; Tags unique to awesome-LLM-resources: awesome-list, book, course, large language models; Also covers AI Agents, Developer Tools, Inference & Serving, LLM Frameworks; - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies. ### When should I avoid ml-surveys? If you are seeking detailed technical details, original experiments, or specific algorithm implementations as ml-surveys focuses more on synthesis and summary In cases where deep-dive analysis is required into a single niche topic, as ml-surveys provides broad overviews rather than in-depth coverage of individual niches ### When should I avoid awesome-LLM-resources? - Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage. - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content. ### Is ml-surveys or awesome-LLM-resources more popular on GitHub? awesome-LLM-resources has more GitHub stars (8,845 vs 2,902). Stars measure visibility, not whether either tool fits your constraints. ### Are ml-surveys and awesome-LLM-resources open source? Yes - both are open-source projects on GitHub (ml-surveys: MIT, awesome-LLM-resources: Apache-2.0). ### Where can I find alternatives to ml-surveys or awesome-LLM-resources? GraphCanon lists graph-backed alternatives at [ml-surveys alternatives](/tools/eugeneyan-ml-surveys/alternatives) and [awesome-LLM-resources alternatives](/tools/wangrongsheng-awesome-llm-resources/alternatives) ([ml-surveys markdown twin](/tools/eugeneyan-ml-surveys/alternatives.md), [awesome-LLM-resources markdown twin](/tools/wangrongsheng-awesome-llm-resources/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/eugeneyan-ml-surveys-vs-wangrongsheng-awesome-llm-resources.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ml-surveys or awesome-LLM-resources? ml-surveys: Dormant. awesome-LLM-resources: 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 ml-surveys and awesome-LLM-resources? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ml-surveys trust report](/tools/eugeneyan-ml-surveys/trust); [awesome-LLM-resources trust report](/tools/wangrongsheng-awesome-llm-resources/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=eugeneyan-ml-surveys`](/api/graphcanon/graph?tool=eugeneyan-ml-surveys) - 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/_