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Comparison

tree-of-thought-llm vs LLMSurvey

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.

Markdown twin · tree-of-thought-llm alternatives · LLMSurvey alternatives

GraphCanon updated Sep 20, 2026

9views this month

tree-of-thought-llm logo

tree-of-thought-llm

princeton-nlp/tree-of-thought-llm

★ 6.0kpushed Jan 16, 2025
vs
LLMSurvey logo

LLMSurvey

RUCAIBox/LLMSurvey

★ 12kpushed Mar 11, 2025

Trust & integrity

Signaltree-of-thought-llmLLMSurvey
Maintenance
Dormant (577d since push)
As of Aug 17, 2026 · github_public_v1
Dormant (555d since push)
As of Sep 18, 2026 · github_public_v1
Provenance
Not a fork · Organization account
As of Aug 17, 2026 · github_public_v1
Not a fork · Organization account
As of Sep 18, 2026 · github_public_v1
OSV dependency advisories
Published findings
As of Jul 11, 2026 · osv@v1
No lockfile (source not queried)
As of Sep 18, 2026 · osv@v1
deps.dev advisories
Not queried
deps.dev@v1
Not queried
deps.dev@v1
OpenSSF Scorecard
Not queried
openssf-scorecard@v1
Not queried
openssf-scorecard@v1
tree-of-thought-llm trust reportLLMSurvey trust reportTrust methodology

Tagline

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

Stars

tree-of-thought-llm
6.0k
LLMSurvey
12k

Forks

tree-of-thought-llm
624
LLMSurvey
930

Open issues

tree-of-thought-llm
8
LLMSurvey
30

Language

tree-of-thought-llm
Python
LLMSurvey
Python

Adopt for

tree-of-thought-llm
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
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

tree-of-thought-llm
-
LLMSurvey
-

Runtime

tree-of-thought-llm
-
LLMSurvey
-

License

tree-of-thought-llm
MIT
LLMSurvey
The license information for LLMSurvey is unknown.

Last pushed

tree-of-thought-llm
Jan 16, 2025
LLMSurvey
Mar 11, 2025

Categories

tree-of-thought-llm
LLM Frameworks, Model Training
LLMSurvey
LLM Frameworks, Model Training

Trust and health

Days since push

tree-of-thought-llm
577d
LLMSurvey
555d

Open issues (now)

tree-of-thought-llm
8
LLMSurvey
30

Stars delta

tree-of-thought-llm
+18 (30d)
LLMSurvey
+11 (30d)

OSV dependency advisories

tree-of-thought-llm
Published findings
LLMSurvey
No lockfile (source not queried)

Full report

tree-of-thought-llm
Trust report
LLMSurvey
Trust report

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.

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.
GitHubDetails

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 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.
GitHubDetails

Explore

tree-of-thought-llm trust report →LLMSurvey trust report →LLM Frameworks category →Model Training category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: tree-of-thought-llm 6.0k · LLMSurvey 12k (synced Sep 20, 2026).

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 and LLMSurvey alternatives (tree-of-thought-llm markdown twin, LLMSurvey markdown twin), 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 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; LLMSurvey trust report.

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