---
title: "chain-of-thought-hub vs tree-of-thoughts"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/franxyao-chain-of-thought-hub-vs-kyegomez-tree-of-thoughts"
tools: ["franxyao-chain-of-thought-hub", "kyegomez-tree-of-thoughts"]
---

# chain-of-thought-hub vs tree-of-thoughts

*GraphCanon updated Aug 6, 2026*

## Verdict

Pick chain-of-thought-hub if chain-of-Thought Hub measures the performance of large language models (LLMs) on complex tasks by using carefully selected datasets across various domains such as math, science, coding, and knowledge. It evaluates if LLM; pick tree-of-thoughts if (Tree-of-Thoughts) Plug in and Play Implementation of Tree of Thoughts for Elevated Model Reasoning.

[chain-of-thought-hub](https://github.com/FranxYao/chain-of-thought-hub) reports 2.8k GitHub stars, 144 forks, and 27 open issues, last pushed Aug 4, 2024. [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 [chain-of-thought-hub's repository](https://github.com/FranxYao/chain-of-thought-hub) and [tree-of-thoughts's repository](https://github.com/kyegomez/tree-of-thoughts).

| | [chain-of-thought-hub](/tools/franxyao-chain-of-thought-hub.md) | [tree-of-thoughts](/tools/kyegomez-tree-of-thoughts.md) |
| --- | --- | --- |
| Tagline | Benchmarking large language models' complex reasoning ability with chain-of-thought prompting | Plug in and Play Implementation of Tree of Thoughts for Elevated Model Reasoning |
| Stars | 2,774 | 4,590 |
| Forks | 144 | 374 |
| Open issues | 27 | 21 |
| Language | Jupyter Notebook | Python |
| Adopt for | Chain-of-Thought Hub measures the performance of large language models (LLMs) on complex tasks by using carefully selected datasets across various domains such as math, science, coding, and knowledge. It evaluates if LLM | (Tree-of-Thoughts) Plug in and Play Implementation of Tree of Thoughts for Elevated Model Reasoning |
| Persona | - | - |
| Runtime | - | - |
| License | The MIT license permits the use of Chain-of-Thought Hub in both open source and commercial projects with acknowledgment. | Licensed under Apache-2.0, allowing for wide usage but requires preservation of copyright and license notices |
| Categories | Evaluation & Observability | Evaluation & Observability, Model Training |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [chain-of-thought-hub](/tools/franxyao-chain-of-thought-hub.md) | [tree-of-thoughts](/tools/kyegomez-tree-of-thoughts.md) |
| --- | --- | --- |
| Maintenance | Dormant (18%) | Slowing (36%) |
| Days since push | 732d | 364d |
| Open issues (now) | 27 | 21 |
| Full report | [trust report](/tools/franxyao-chain-of-thought-hub/trust.md) | [trust report](/tools/kyegomez-tree-of-thoughts/trust.md) |

## Decision facts: chain-of-thought-hub

- **Requirements:** Min 8 GB RAM; Chain-of-Thought Hub is designed to be integrated into environments for evaluating LLMs using Jupyter Notebooks
- **Adopt for:** Chain-of-Thought Hub measures the performance of large language models (LLMs) on complex tasks by using carefully selected datasets across various domains such as math, science, coding, and knowledge. It evaluates if LLM
- **License detail:** The MIT license permits the use of Chain-of-Thought Hub in both open source and commercial projects with acknowledgment.

## 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 chain-of-thought-hub if…

- chain-of-thought-hub is primarily Jupyter Notebook; tree-of-thoughts is Python.
- License: chain-of-thought-hub is MIT, tree-of-thoughts is Apache-2.0.
- Requirements: Min 8 GB RAM; Chain-of-Thought Hub is designed to be integrated into environments for evaluating LLMs using Jupyter Notebooks.
- Tags unique to chain-of-thought-hub: chain-of-thought prompting, complex reasoning, llm-benchmarking.
- Use Chain-of-Thought Hub when you need to benchmark smaller LLMs against larger ones for complex reasoning abilities.

### Choose tree-of-thoughts if…

- tree-of-thoughts is primarily Python; chain-of-thought-hub is Jupyter Notebook.
- License: tree-of-thoughts is Apache-2.0, chain-of-thought-hub 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, chatgpt, deep-learning, gpt4.
- Also covers Model Training.
- - When you require enhanced reasoning capabilities from large language models through structured problem-solving techniques

## When NOT to use chain-of-thought-hub

- Do not use Chain-of-Thought Hub if your focus is on general conversational capabilities rather than specific, challenging problem-solving tasks.
- Avoid this tool if you are primarily interested in simpler language processing tasks that do not involve chain-of-thought prompting or complex datasets.

## 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 chain-of-thought-hub and tree-of-thoughts?

chain-of-thought-hub: Benchmarking large language models' complex reasoning ability with chain-of-thought prompting. 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 chain-of-thought-hub over tree-of-thoughts?

Choose chain-of-thought-hub over tree-of-thoughts when chain-of-thought-hub is primarily Jupyter Notebook; tree-of-thoughts is Python; License: chain-of-thought-hub is MIT, tree-of-thoughts is Apache-2.0; Requirements: Min 8 GB RAM; Chain-of-Thought Hub is designed to be integrated into environments for evaluating LLMs using Jupyter Notebooks; Tags unique to chain-of-thought-hub: chain-of-thought prompting, complex reasoning, llm-benchmarking; Use Chain-of-Thought Hub when you need to benchmark smaller LLMs against larger ones for complex reasoning abilities.

### When should I choose tree-of-thoughts over chain-of-thought-hub?

Choose tree-of-thoughts over chain-of-thought-hub when tree-of-thoughts is primarily Python; chain-of-thought-hub is Jupyter Notebook; License: tree-of-thoughts is Apache-2.0, chain-of-thought-hub 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, chatgpt, deep-learning, gpt4; Also covers Model Training; - When you require enhanced reasoning capabilities from large language models through structured problem-solving techniques.

### When should I avoid chain-of-thought-hub?

Do not use Chain-of-Thought Hub if your focus is on general conversational capabilities rather than specific, challenging problem-solving tasks. Avoid this tool if you are primarily interested in simpler language processing tasks that do not involve chain-of-thought prompting or complex datasets.

### 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 chain-of-thought-hub or tree-of-thoughts more popular on GitHub?

tree-of-thoughts has more GitHub stars (4,590 vs 2,774). Stars measure visibility, not whether either tool fits your constraints.

### Are chain-of-thought-hub and tree-of-thoughts open source?

Yes - both are open-source projects on GitHub (chain-of-thought-hub: MIT, tree-of-thoughts: Apache-2.0).

### Where can I find alternatives to chain-of-thought-hub or tree-of-thoughts?

GraphCanon lists graph-backed alternatives at [chain-of-thought-hub alternatives](/tools/franxyao-chain-of-thought-hub/alternatives) and [tree-of-thoughts alternatives](/tools/kyegomez-tree-of-thoughts/alternatives) ([chain-of-thought-hub markdown twin](/tools/franxyao-chain-of-thought-hub/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/franxyao-chain-of-thought-hub-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, chain-of-thought-hub or tree-of-thoughts?

chain-of-thought-hub: Dormant. 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 chain-of-thought-hub and tree-of-thoughts?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [chain-of-thought-hub trust report](/tools/franxyao-chain-of-thought-hub/trust); [tree-of-thoughts trust report](/tools/kyegomez-tree-of-thoughts/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=franxyao-chain-of-thought-hub`](/api/graphcanon/graph?tool=franxyao-chain-of-thought-hub)
- 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/_