--- title: "LLMSurvey vs awesome-LLM-resources" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/rucaibox-llmsurvey-vs-wangrongsheng-awesome-llm-resources" tools: ["rucaibox-llmsurvey", "wangrongsheng-awesome-llm-resources"] --- # LLMSurvey vs awesome-LLM-resources *GraphCanon updated Aug 17, 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-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. [LLMSurvey](https://arxiv.org/abs/2303.18223) reports 12k GitHub stars, 931 forks, and 30 open issues, last pushed Mar 11, 2025. [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 [LLMSurvey's repository](https://github.com/RUCAIBox/LLMSurvey) and [awesome-LLM-resources's repository](https://github.com/WangRongsheng/awesome-LLM-resources). | | [LLMSurvey](/tools/rucaibox-llmsurvey.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Tagline | A comprehensive collection of papers and resources related to Large Language Models. | Summary of the world's best LLM resources. | | Stars | 12,205 | 8,845 | | Forks | 931 | 950 | | Open issues | 30 | 23 | | Language | Python | - | | 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-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 | The license for LLMSurvey is unknown based on the provided repository information. | Apache-2.0 | | Categories | Evaluation & Observability, LLM Frameworks | AI Agents, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LLMSurvey](/tools/rucaibox-llmsurvey.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 523d | 2d | | Open issues (now) | 30 | 23 | | Stars delta | +18 (30d) | +142 (30d) | | Open issues delta | 0 (30d) | -13 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/rucaibox-llmsurvey/trust.md) | [trust report](/tools/wangrongsheng-awesome-llm-resources/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-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 LLMSurvey if… - 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, natural-language-processing. - 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-LLM-resources if… - Tags unique to awesome-LLM-resources: awesome-list, book, course, llama. - Also covers AI Agents, Developer Tools, Inference & Serving, Model Training. - - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies. ## 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-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 LLMSurvey and awesome-LLM-resources? LLMSurvey: A comprehensive collection of papers and resources related to Large Language Models.. 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 LLMSurvey over awesome-LLM-resources? Choose LLMSurvey over awesome-LLM-resources when 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, natural-language-processing; 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-LLM-resources over LLMSurvey? Choose awesome-LLM-resources over LLMSurvey when Tags unique to awesome-LLM-resources: awesome-list, book, course, llama; Also covers AI Agents, Developer Tools, Inference & Serving, Model Training; - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies. ### 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-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 LLMSurvey or awesome-LLM-resources more popular on GitHub? LLMSurvey has more GitHub stars (12,205 vs 8,845). Stars measure visibility, not whether either tool fits your constraints. ### Are LLMSurvey and awesome-LLM-resources open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to LLMSurvey or awesome-LLM-resources? GraphCanon lists graph-backed alternatives at [LLMSurvey alternatives](/tools/rucaibox-llmsurvey/alternatives) and [awesome-LLM-resources alternatives](/tools/wangrongsheng-awesome-llm-resources/alternatives) ([LLMSurvey markdown twin](/tools/rucaibox-llmsurvey/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/rucaibox-llmsurvey-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, LLMSurvey or awesome-LLM-resources? LLMSurvey: 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 LLMSurvey and awesome-LLM-resources? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLMSurvey trust report](/tools/rucaibox-llmsurvey/trust); [awesome-LLM-resources trust report](/tools/wangrongsheng-awesome-llm-resources/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/_