--- title: "Awesome-LLM-Reasoning vs tree-of-thoughts" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/atfortes-awesome-llm-reasoning-vs-kyegomez-tree-of-thoughts" tools: ["atfortes-awesome-llm-reasoning", "kyegomez-tree-of-thoughts"] --- # Awesome-LLM-Reasoning vs tree-of-thoughts *GraphCanon updated Jul 28, 2026* ## 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-thoughts if (Tree-of-Thoughts) Plug in and Play Implementation of Tree of Thoughts for Elevated Model Reasoning. [Awesome-LLM-Reasoning](https://github.com/atfortes/Awesome-LLM-Reasoning) reports 3.7k GitHub stars, 212 forks, and 26 open issues, last pushed Apr 20, 2026. [tree-of-thoughts](https://discord.gg/qUtxnK2NMf) has 4.6k stars, 374 forks, and 21 open issues, last pushed Jul 29, 2025. Figures are from public GitHub metadata via [Awesome-LLM-Reasoning's repository](https://github.com/atfortes/Awesome-LLM-Reasoning) and [tree-of-thoughts's repository](https://github.com/kyegomez/tree-of-thoughts). | | [Awesome-LLM-Reasoning](/tools/atfortes-awesome-llm-reasoning.md) | [tree-of-thoughts](/tools/kyegomez-tree-of-thoughts.md) | | --- | --- | --- | | Tagline | Compiles resources on chain-of-thought prompting to advanced reasoning systems like OpenAI o1 and DeepSeek-R1. | Plug in and Play Implementation of Tree of Thoughts for Elevated Model Reasoning | | Stars | 3,657 | 4,590 | | Forks | 212 | 374 | | Open issues | 26 | 21 | | Language | - | Python | | Adopt for | 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-Thoughts) Plug in and Play Implementation of Tree of Thoughts for Elevated Model Reasoning | | Persona | - | - | | Runtime | - | - | | License | MIT | Licensed under Apache-2.0, allowing for wide usage but requires preservation of copyright and license notices | | Categories | LLM Frameworks, Model Training | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-LLM-Reasoning](/tools/atfortes-awesome-llm-reasoning.md) | [tree-of-thoughts](/tools/kyegomez-tree-of-thoughts.md) | | --- | --- | --- | | Days since push | 99d | 364d | | Open issues (now) | 26 | 21 | | Full report | [trust report](/tools/atfortes-awesome-llm-reasoning/trust.md) | [trust report](/tools/kyegomez-tree-of-thoughts/trust.md) | ## Decision facts: Awesome-LLM-Reasoning - **Pricing:** freemium - Freely available under the MIT license; resources linked within may have separate access costs, particularly proprietary models. - **Adopt for:** 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. ## Decision facts: tree-of-thoughts - **Pricing:** freemium - Free to use due to open-source nature; potential costs associated with hosting and any paid models it interfaces with - **Requirements:** Min 4 GB RAM - **Adopt for:** (Tree-of-Thoughts) Plug in and Play Implementation of Tree of Thoughts for Elevated Model Reasoning - **License detail:** Licensed under Apache-2.0, allowing for wide usage but requires preservation of copyright and license notices ## Choose when ### Choose Awesome-LLM-Reasoning if… - License: Awesome-LLM-Reasoning is MIT, tree-of-thoughts is Apache-2.0. - 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, cot, deepseek-r1, gpt-4o. - Also covers LLM Frameworks. - Use when developing projects that integrate OpenAI's o1 or DeepSeek-R1 advanced reasoning systems as these resources are specifically referenced within the repository. ### Choose tree-of-thoughts if… - License: tree-of-thoughts is Apache-2.0, Awesome-LLM-Reasoning is MIT. - Pricing: Free to use due to open-source nature; potential costs associated with hosting and any paid models it interfaces with. - Requirements: Min 4 GB RAM. - Tags unique to tree-of-thoughts: artificial-intelligence, deep-learning, gpt4, multimodal. - Also covers Evaluation & Observability. - - When you require enhanced reasoning capabilities from large language models through structured problem-solving techniques ## 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. ## When NOT to use tree-of-thoughts - - Avoid if you need solutions that are heavily customizable beyond what is provided, as it may not offer deep configuration options - - Should be avoided in scenarios where minimal dependency installations are critical, as this tool might come with broader package dependencies that could complicate setup ## Common questions ### What is the difference between Awesome-LLM-Reasoning and tree-of-thoughts? Awesome-LLM-Reasoning: Compiles resources on chain-of-thought prompting to advanced reasoning systems like OpenAI o1 and DeepSeek-R1.. tree-of-thoughts: Plug in and Play Implementation of Tree of Thoughts for Elevated Model Reasoning. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-LLM-Reasoning over tree-of-thoughts? Choose Awesome-LLM-Reasoning over tree-of-thoughts when License: Awesome-LLM-Reasoning is MIT, tree-of-thoughts is Apache-2.0; 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, cot, deepseek-r1, gpt-4o; Also covers LLM Frameworks; 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-thoughts over Awesome-LLM-Reasoning? Choose tree-of-thoughts over Awesome-LLM-Reasoning when License: tree-of-thoughts is Apache-2.0, Awesome-LLM-Reasoning is MIT; Pricing: Free to use due to open-source nature; potential costs associated with hosting and any paid models it interfaces with; Requirements: Min 4 GB RAM; Tags unique to tree-of-thoughts: artificial-intelligence, deep-learning, gpt4, multimodal; Also covers Evaluation & Observability; - When you require enhanced reasoning capabilities from large language models through structured problem-solving techniques. ### 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-thoughts? - Avoid if you need solutions that are heavily customizable beyond what is provided, as it may not offer deep configuration options - Should be avoided in scenarios where minimal dependency installations are critical, as this tool might come with broader package dependencies that could complicate setup ### Is Awesome-LLM-Reasoning or tree-of-thoughts more popular on GitHub? tree-of-thoughts has more GitHub stars (4,590 vs 3,657). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-LLM-Reasoning and tree-of-thoughts open source? Yes - both are open-source projects on GitHub (Awesome-LLM-Reasoning: MIT, tree-of-thoughts: Apache-2.0). ### Where can I find alternatives to Awesome-LLM-Reasoning or tree-of-thoughts? GraphCanon lists graph-backed alternatives at [Awesome-LLM-Reasoning alternatives](/tools/atfortes-awesome-llm-reasoning/alternatives) and [tree-of-thoughts alternatives](/tools/kyegomez-tree-of-thoughts/alternatives) ([Awesome-LLM-Reasoning markdown twin](/tools/atfortes-awesome-llm-reasoning/alternatives.md), [tree-of-thoughts markdown twin](/tools/kyegomez-tree-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/atfortes-awesome-llm-reasoning-vs-kyegomez-tree-of-thoughts.md) 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-thoughts? Awesome-LLM-Reasoning: Slowing. tree-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 Awesome-LLM-Reasoning and tree-of-thoughts? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-LLM-Reasoning trust report](/tools/atfortes-awesome-llm-reasoning/trust); [tree-of-thoughts trust report](/tools/kyegomez-tree-of-thoughts/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=atfortes-awesome-llm-reasoning`](/api/graphcanon/graph?tool=atfortes-awesome-llm-reasoning) - 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/_