Comparison
Awesome-LLM-Reasoning vs tree-of-thought-llm
Verdict
Pick Awesome-LLM-Reasoning if awesome-LLM-Reasoning is designed for developers and researchers focused on advanced reasoning capabilities in language models using chain-of-thought prompting techniques and multimodal learning; 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.
Markdown twin · Awesome-LLM-Reasoning alternatives · tree-of-thought-llm alternatives
GraphCanon updated 4d
vs
Trust & integrity
| Signal | Awesome-LLM-Reasoning | tree-of-thought-llm |
|---|---|---|
| Maintenance | Slowing (99d since push) As of 3w · github_public_v1 | Dormant (577d since push) As of 4d · github_public_v1 |
| Provenance | Not a fork · Personal account As of 3w · github_public_v1 | Not a fork · Organization account As of 4d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | Published findings As of 1mo · 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 |
Tagline
- Awesome-LLM-Reasoning
- Compiles resources on chain-of-thought prompting to advanced reasoning systems like OpenAI o1 and DeepSeek-R1.
- tree-of-thought-llm
- [NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models
Stars
- Awesome-LLM-Reasoning
- 3.7k
- tree-of-thought-llm
- 6.0k
Forks
- Awesome-LLM-Reasoning
- 212
- tree-of-thought-llm
- 624
Open issues
- Awesome-LLM-Reasoning
- 26
- tree-of-thought-llm
- 8
Language
- Awesome-LLM-Reasoning
- -
- tree-of-thought-llm
- Python
Adopt for
- Awesome-LLM-Reasoning
- Awesome-LLM-Reasoning is designed for developers and researchers focused on advanced reasoning capabilities in language models using chain-of-thought prompting techniques and multimodal learning.
- 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.
Persona
- Awesome-LLM-Reasoning
- -
- tree-of-thought-llm
- -
Runtime
- Awesome-LLM-Reasoning
- -
- tree-of-thought-llm
- -
License
- Awesome-LLM-Reasoning
- MIT
- tree-of-thought-llm
- MIT
Last pushed
- Awesome-LLM-Reasoning
- Apr 20, 2026
- tree-of-thought-llm
- Jan 16, 2025
Categories
- Awesome-LLM-Reasoning
- LLM Frameworks, Model Training
- tree-of-thought-llm
- LLM Frameworks, Model Training
Trust and health
Maintenance
- Awesome-LLM-Reasoning
- Slowing (36%)
- tree-of-thought-llm
- Dormant (18%)
Days since push
- Awesome-LLM-Reasoning
- 99d
- tree-of-thought-llm
- 577d
Open issues (now)
- Awesome-LLM-Reasoning
- 26
- tree-of-thought-llm
- 8
Stars delta
- Awesome-LLM-Reasoning
- Unknown
- tree-of-thought-llm
- +18 (30d)
Open issues delta
- Awesome-LLM-Reasoning
- Unknown
- tree-of-thought-llm
- 0 (30d)
Owner type
- Awesome-LLM-Reasoning
- User
- tree-of-thought-llm
- Organization
OSV dependency advisories
- Awesome-LLM-Reasoning
- No lockfile (source not queried)
- tree-of-thought-llm
- Published findings
Full report
- Awesome-LLM-Reasoning
- Trust report
- tree-of-thought-llm
- Trust report
Choose Awesome-LLM-Reasoning if…
- Pricing: Freely available under the MIT license; resources linked within may have separate access costs, particularly proprietary models..
- Tags unique to Awesome-LLM-Reasoning: chain-of-thought, chatgpt, cot, deepseek-r1.
- Use when developing projects that integrate OpenAI's o1 or DeepSeek-R1 advanced reasoning systems as these resources are specifically referenced within the repository.
When NOT to use Awesome-LLM-Reasoning
- Avoid if your project does not require or involve advanced reasoning systems from specific providers such as OpenAI's o1, instead relying on general-purpose models.
- Not recommended for those working exclusively with non-language-model AI applications that do not focus on in-context learning or multimodal capabilities.
Choose tree-of-thought-llm if…
- Requirements: Min 4 GB RAM.
- Tags unique to tree-of-thought-llm: large language models, llm, prompting, tree-of-thoughts.
- - 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (atfortes/Awesome-LLM-Reasoning) · observed Jul 28, 2026
- GitHub forks (atfortes/Awesome-LLM-Reasoning) · observed Jul 28, 2026
- Last push (atfortes/Awesome-LLM-Reasoning) · observed Apr 20, 2026
- License file (MIT) · observed Jul 28, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (princeton-nlp/tree-of-thought-llm) · observed Aug 17, 2026
- GitHub forks (princeton-nlp/tree-of-thought-llm) · observed Aug 17, 2026
- Last push (princeton-nlp/tree-of-thought-llm) · observed Jan 16, 2025
- License file (MIT) · observed Aug 17, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Awesome-LLM-Reasoning 3.7k · tree-of-thought-llm 6.0k (synced Jul 28, 2026).
Common questions
- What is the difference between Awesome-LLM-Reasoning and tree-of-thought-llm?
- Awesome-LLM-Reasoning: Compiles resources on chain-of-thought prompting to advanced reasoning systems like OpenAI o1 and DeepSeek-R1.. tree-of-thought-llm: [NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models. See the comparison table for live GitHub stats and shared categories.
- When should I choose Awesome-LLM-Reasoning over tree-of-thought-llm?
- Choose Awesome-LLM-Reasoning over tree-of-thought-llm when Pricing: Freely available under the MIT license; resources linked within may have separate access costs, particularly proprietary models.; Tags unique to Awesome-LLM-Reasoning: chain-of-thought, chatgpt, cot, deepseek-r1; Use when developing projects that integrate OpenAI's o1 or DeepSeek-R1 advanced reasoning systems as these resources are specifically referenced within the repository.
- When should I choose tree-of-thought-llm over Awesome-LLM-Reasoning?
- Choose tree-of-thought-llm over Awesome-LLM-Reasoning when Requirements: Min 4 GB RAM; Tags unique to tree-of-thought-llm: large language models, llm, prompting, tree-of-thoughts; - 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 avoid Awesome-LLM-Reasoning?
- Avoid if your project does not require or involve advanced reasoning systems from specific providers such as OpenAI's o1, instead relying on general-purpose models. Not recommended for those working exclusively with non-language-model AI applications that do not focus on in-context learning or multimodal capabilities.
- 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.
- Is Awesome-LLM-Reasoning or tree-of-thought-llm more popular on GitHub?
- tree-of-thought-llm has more GitHub stars (6,048 vs 3,657). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-LLM-Reasoning and tree-of-thought-llm open source?
- Yes - both are open-source projects on GitHub (Awesome-LLM-Reasoning: MIT, tree-of-thought-llm: MIT).
- Where can I find alternatives to Awesome-LLM-Reasoning or tree-of-thought-llm?
- GraphCanon lists graph-backed alternatives at Awesome-LLM-Reasoning alternatives and tree-of-thought-llm alternatives (Awesome-LLM-Reasoning markdown twin, tree-of-thought-llm 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, Awesome-LLM-Reasoning or tree-of-thought-llm?
- Awesome-LLM-Reasoning: Slowing. tree-of-thought-llm: 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 Awesome-LLM-Reasoning and tree-of-thought-llm?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-LLM-Reasoning trust report; tree-of-thought-llm trust report.