--- title: "tree-of-thought-llm vs LLMSurvey" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/princeton-nlp-tree-of-thought-llm-vs-rucaibox-llmsurvey" tools: ["princeton-nlp-tree-of-thought-llm", "rucaibox-llmsurvey"] --- # tree-of-thought-llm vs LLMSurvey *GraphCanon updated Sep 20, 2026* ## Verdict Pick tree-of-thought-llm if the 'Tree of Thoughts' approach provides a structured way to deliberate problem-solving using large language models and is well-suited for tasks requiring exploration through a tree-like structure; 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. [tree-of-thought-llm](https://arxiv.org/abs/2305.10601) reports 6.0k GitHub stars, 624 forks, and 8 open issues, last pushed Jan 16, 2025. [LLMSurvey](https://arxiv.org/abs/2303.18223) has 12k stars, 930 forks, and 30 open issues, last pushed Mar 11, 2025. Figures are from public GitHub metadata via [tree-of-thought-llm's repository](https://github.com/princeton-nlp/tree-of-thought-llm) and [LLMSurvey's repository](https://github.com/RUCAIBox/LLMSurvey). | | [tree-of-thought-llm](/tools/princeton-nlp-tree-of-thought-llm.md) | [LLMSurvey](/tools/rucaibox-llmsurvey.md) | | --- | --- | --- | | Tagline | [NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models | A collection of papers and resources related to Large Language Models | | Stars | 6,048 | 12,216 | | Forks | 624 | 930 | | Open issues | 8 | 30 | | Language | Python | Python | | Adopt for | The 'Tree of Thoughts' approach provides a structured way to deliberate problem-solving using large language models and is well-suited for tasks requiring exploration through a tree-like structure. | 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. | | Persona | - | - | | Runtime | - | - | | License | MIT | The license information for LLMSurvey is unknown. | | Categories | LLM Frameworks, Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [tree-of-thought-llm](/tools/princeton-nlp-tree-of-thought-llm.md) | [LLMSurvey](/tools/rucaibox-llmsurvey.md) | | --- | --- | --- | | Days since push | 577d | 555d | | Open issues (now) | 8 | 30 | | Stars delta | +18 (30d) | +11 (30d) | | Full report | [trust report](/tools/princeton-nlp-tree-of-thought-llm/trust.md) | [trust report](/tools/rucaibox-llmsurvey/trust.md) | ## Decision facts: tree-of-thought-llm - **Requirements:** Min 4 GB RAM - **Adopt for:** The 'Tree of Thoughts' approach provides a structured way to deliberate problem-solving using large language models and is well-suited for tasks requiring exploration through a tree-like structure. ## 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. ## Choose when ### Choose tree-of-thought-llm if… - Requirements: Min 4 GB RAM. - Tags unique to tree-of-thought-llm: prompting, tree-of-thoughts, tree-search. - - Use 'tree-of-thought-llm' when you need an approach that handles deliberative reasoning problems, like the game of 24, leveraging large language models. ### 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. ## When NOT to use tree-of-thought-llm - - Avoid using 'tree-of-thought-llm' for problems that do not benefit from tree-like exploration or where the solution does not involve deliberate reasoning or step-by-step evaluation. - - If real-time decision-making is critical and computational resources are limited, this tool might be too slow due to its reliance on large language models like GPT-4 which may introduce latency. ## 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. ## Common questions ### What is the difference between tree-of-thought-llm and LLMSurvey? tree-of-thought-llm: [NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models. LLMSurvey: A collection of papers and resources related to Large Language Models. See the comparison table for live GitHub stats and shared categories. ### When should I choose tree-of-thought-llm over LLMSurvey? Choose tree-of-thought-llm over LLMSurvey when Requirements: Min 4 GB RAM; Tags unique to tree-of-thought-llm: prompting, tree-of-thoughts, tree-search; - Use 'tree-of-thought-llm' when you need an approach that handles deliberative reasoning problems, like the game of 24, leveraging large language models. ### When should I choose LLMSurvey over tree-of-thought-llm? Choose LLMSurvey over tree-of-thought-llm 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 avoid tree-of-thought-llm? - Avoid using 'tree-of-thought-llm' for problems that do not benefit from tree-like exploration or where the solution does not involve deliberate reasoning or step-by-step evaluation. - If real-time decision-making is critical and computational resources are limited, this tool might be too slow due to its reliance on large language models like GPT-4 which may introduce latency. ### 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. ### Is tree-of-thought-llm or LLMSurvey more popular on GitHub? LLMSurvey has more GitHub stars (12,216 vs 6,048). Stars measure visibility, not whether either tool fits your constraints. ### Are tree-of-thought-llm and LLMSurvey open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to tree-of-thought-llm or LLMSurvey? GraphCanon lists graph-backed alternatives at [tree-of-thought-llm alternatives](/tools/princeton-nlp-tree-of-thought-llm/alternatives) and [LLMSurvey alternatives](/tools/rucaibox-llmsurvey/alternatives) ([tree-of-thought-llm markdown twin](/tools/princeton-nlp-tree-of-thought-llm/alternatives.md), [LLMSurvey markdown twin](/tools/rucaibox-llmsurvey/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/princeton-nlp-tree-of-thought-llm-vs-rucaibox-llmsurvey.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, tree-of-thought-llm or LLMSurvey? tree-of-thought-llm: Dormant. LLMSurvey: Dormant. 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 tree-of-thought-llm and LLMSurvey? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [tree-of-thought-llm trust report](/tools/princeton-nlp-tree-of-thought-llm/trust); [LLMSurvey trust report](/tools/rucaibox-llmsurvey/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=princeton-nlp-tree-of-thought-llm`](/api/graphcanon/graph?tool=princeton-nlp-tree-of-thought-llm) - 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/_