---
title: "Awesome-LLM-Reasoning vs tree-of-thought-llm"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/atfortes-awesome-llm-reasoning-vs-princeton-nlp-tree-of-thought-llm"
tools: ["atfortes-awesome-llm-reasoning", "princeton-nlp-tree-of-thought-llm"]
---

# Awesome-LLM-Reasoning vs tree-of-thought-llm

*GraphCanon updated Aug 17, 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-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.

[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-thought-llm](https://arxiv.org/abs/2305.10601) has 6.0k stars, 624 forks, and 8 open issues, last pushed Jan 16, 2025. Figures are from public GitHub metadata via [Awesome-LLM-Reasoning's repository](https://github.com/atfortes/Awesome-LLM-Reasoning) and [tree-of-thought-llm's repository](https://github.com/princeton-nlp/tree-of-thought-llm).

| | [Awesome-LLM-Reasoning](/tools/atfortes-awesome-llm-reasoning.md) | [tree-of-thought-llm](/tools/princeton-nlp-tree-of-thought-llm.md) |
| --- | --- | --- |
| Tagline | Compiles resources on chain-of-thought prompting to advanced reasoning systems like OpenAI o1 and DeepSeek-R1. | [NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models |
| Stars | 3,657 | 6,048 |
| Forks | 212 | 624 |
| Open issues | 26 | 8 |
| 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. | 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 | - | - |
| Runtime | - | - |
| License | MIT | MIT |
| Categories | LLM Frameworks, Model Training | LLM Frameworks, 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-thought-llm](/tools/princeton-nlp-tree-of-thought-llm.md) |
| --- | --- | --- |
| Maintenance | Slowing (36%) | Dormant (18%) |
| Days since push | 99d | 577d |
| Open issues (now) | 26 | 8 |
| Stars delta | Unknown | +18 (30d) |
| Open issues delta | Unknown | 0 (30d) |
| Owner type | User | Organization |
| Full report | [trust report](/tools/atfortes-awesome-llm-reasoning/trust.md) | [trust report](/tools/princeton-nlp-tree-of-thought-llm/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-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.

## Choose when

### 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.

### 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 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-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.

## 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](/tools/atfortes-awesome-llm-reasoning/alternatives) and [tree-of-thought-llm alternatives](/tools/princeton-nlp-tree-of-thought-llm/alternatives) ([Awesome-LLM-Reasoning markdown twin](/tools/atfortes-awesome-llm-reasoning/alternatives.md), [tree-of-thought-llm markdown twin](/tools/princeton-nlp-tree-of-thought-llm/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-princeton-nlp-tree-of-thought-llm.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-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](/tools/atfortes-awesome-llm-reasoning/trust); [tree-of-thought-llm trust report](/tools/princeton-nlp-tree-of-thought-llm/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/_