--- title: "LLMSurvey vs graph-of-thoughts" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/rucaibox-llmsurvey-vs-spcl-graph-of-thoughts" tools: ["rucaibox-llmsurvey", "spcl-graph-of-thoughts"] --- # LLMSurvey vs graph-of-thoughts *GraphCanon updated Sep 20, 2026* ## Verdict Pick LLMSurvey if lLMSurvey is a repository that provides a comprehensive survey paper and resources on Large Language Models, including a beginner-friendly Chinese book and detailed content on long chain-of-thought reasoning; pick graph-of-thoughts if the Graph of Thoughts tool is designed for leveraging large language models and graph structures to solve elaborate problems. [LLMSurvey](https://arxiv.org/abs/2303.18223) reports 12k GitHub stars, 930 forks, and 30 open issues, last pushed Mar 11, 2025. [graph-of-thoughts](https://arxiv.org/pdf/2308.09687.pdf) has 2.8k stars, 216 forks, and 7 open issues, last pushed Mar 24, 2026. Figures are from public GitHub metadata via [LLMSurvey's repository](https://github.com/RUCAIBox/LLMSurvey) and [graph-of-thoughts's repository](https://github.com/spcl/graph-of-thoughts). | | [LLMSurvey](/tools/rucaibox-llmsurvey.md) | [graph-of-thoughts](/tools/spcl-graph-of-thoughts.md) | | --- | --- | --- | | Tagline | A collection of papers and resources related to Large Language Models | Implementation of Graph of Thoughts for large language models problem-solving | | Stars | 12,216 | 2,841 | | Forks | 930 | 216 | | Open issues | 30 | 7 | | Language | Python | Python | | Adopt for | LLMSurvey is a repository that provides a comprehensive survey paper and resources on Large Language Models, including a beginner-friendly Chinese book and detailed content on long chain-of-thought reasoning. | The Graph of Thoughts tool is designed for leveraging large language models and graph structures to solve elaborate problems. | | Persona | - | - | | Runtime | - | - | | License | The license information for LLMSurvey is unknown. | Other | | Categories | LLM Frameworks, Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LLMSurvey](/tools/rucaibox-llmsurvey.md) | [graph-of-thoughts](/tools/spcl-graph-of-thoughts.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 555d | 179d | | Open issues (now) | 30 | 7 | | Stars delta | +11 (30d) | +15 (30d) | | Full report | [trust report](/tools/rucaibox-llmsurvey/trust.md) | [trust report](/tools/spcl-graph-of-thoughts/trust.md) | ## Decision facts: LLMSurvey - **Pricing:** freemium - The repository is free to access, but the Chinese book is available for purchase. - **Requirements:** Min 4 GB RAM; A basic understanding of deep learning is recommended for the Chinese book. - **Adopt for:** LLMSurvey is a repository that provides a comprehensive survey paper and resources on Large Language Models, including a beginner-friendly Chinese book and detailed content on long chain-of-thought reasoning. - **License detail:** The license information for LLMSurvey is unknown. ## Decision facts: graph-of-thoughts - **Pricing:** freemium - **Requirements:** Min 8 GB RAM - **Adopt for:** The Graph of Thoughts tool is designed for leveraging large language models and graph structures to solve elaborate problems. - **License detail:** Other ## Choose when ### Choose LLMSurvey if… - Pricing: The repository is free to access, but the Chinese book is available for purchase.. - Requirements: Min 4 GB RAM; A basic understanding of deep learning is recommended for the Chinese book.. - Tags unique to LLMSurvey: chain-of-thought, chatgpt, in-context-learning, instruction-tuning. - Use LLMSurvey when you need a detailed survey paper and resources on Large Language Models, especially if you are interested in long chain-of-thought reasoning. ### Choose graph-of-thoughts if… - Requirements: Min 8 GB RAM. - Tags unique to graph-of-thoughts: graph-of-thoughts, graph-structures, prompt-engineering. - Use when you need to solve complex problem scenarios that require the interplay between advanced language understanding and structured problem decomposition capabilities. ## When NOT to use LLMSurvey - Avoid LLMSurvey if you are looking for a tool that provides hands-on training or implementation of LLMs, as it focuses more on theoretical resources and surveys. - Do not use LLMSurvey if you require real-time updates or interactive tools for LLM development, as it primarily offers static content and resources. ## When NOT to use graph-of-thoughts - Avoid using Graph of Thoughts for simpler, straightforward queries or when real-time performance is critical because it may introduce overhead due to its complex graph processing. - Do not use this tool where privacy and data security are paramount concerns if the official license does not sufficiently protect your needs. ## Common questions ### What is the difference between LLMSurvey and graph-of-thoughts? LLMSurvey: A collection of papers and resources related to Large Language Models. graph-of-thoughts: Implementation of Graph of Thoughts for large language models problem-solving. See the comparison table for live GitHub stats and shared categories. ### When should I choose LLMSurvey over graph-of-thoughts? Choose LLMSurvey over graph-of-thoughts when Pricing: The repository is free to access, but the Chinese book is available for purchase.; Requirements: Min 4 GB RAM; A basic understanding of deep learning is recommended for the Chinese book.; Tags unique to LLMSurvey: chain-of-thought, chatgpt, in-context-learning, instruction-tuning; Use LLMSurvey when you need a detailed survey paper and resources on Large Language Models, especially if you are interested in long chain-of-thought reasoning. ### When should I choose graph-of-thoughts over LLMSurvey? Choose graph-of-thoughts over LLMSurvey when Requirements: Min 8 GB RAM; Tags unique to graph-of-thoughts: graph-of-thoughts, graph-structures, prompt-engineering; Use when you need to solve complex problem scenarios that require the interplay between advanced language understanding and structured problem decomposition capabilities. ### When should I avoid LLMSurvey? Avoid LLMSurvey if you are looking for a tool that provides hands-on training or implementation of LLMs, as it focuses more on theoretical resources and surveys. Do not use LLMSurvey if you require real-time updates or interactive tools for LLM development, as it primarily offers static content and resources. ### When should I avoid graph-of-thoughts? Avoid using Graph of Thoughts for simpler, straightforward queries or when real-time performance is critical because it may introduce overhead due to its complex graph processing. Do not use this tool where privacy and data security are paramount concerns if the official license does not sufficiently protect your needs. ### Is LLMSurvey or graph-of-thoughts more popular on GitHub? LLMSurvey has more GitHub stars (12,216 vs 2,841). Stars measure visibility, not whether either tool fits your constraints. ### Are LLMSurvey and graph-of-thoughts open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to LLMSurvey or graph-of-thoughts? GraphCanon lists graph-backed alternatives at [LLMSurvey alternatives](/tools/rucaibox-llmsurvey/alternatives) and [graph-of-thoughts alternatives](/tools/spcl-graph-of-thoughts/alternatives) ([LLMSurvey markdown twin](/tools/rucaibox-llmsurvey/alternatives.md), [graph-of-thoughts markdown twin](/tools/spcl-graph-of-thoughts/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-spcl-graph-of-thoughts.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LLMSurvey or graph-of-thoughts? LLMSurvey: Dormant. graph-of-thoughts: 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 graph-of-thoughts? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLMSurvey trust report](/tools/rucaibox-llmsurvey/trust); [graph-of-thoughts trust report](/tools/spcl-graph-of-thoughts/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/_