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

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

*GraphCanon updated Jul 11, 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-thought-llm if tree-of-thought-llm is a NeurIPS 2023 methodology using large language models for deliberate problem solving, often exemplified through game-solving algorithms. It's implemented with.

[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-thought-llm](https://arxiv.org/abs/2305.10601) has 6.0k stars, 620 forks, and 8 open issues, last pushed Jan 16, 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-thought-llm's repository](https://github.com/princeton-nlp/tree-of-thought-llm).

| | [chain-of-thought-hub](/tools/franxyao-chain-of-thought-hub.md) | [tree-of-thought-llm](/tools/princeton-nlp-tree-of-thought-llm.md) |
| --- | --- | --- |
| Tagline | Benchmarking large language models' complex reasoning ability with chain-of-thought prompting | [NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models |
| Stars | 2,777 | 6,025 |
| Forks | 144 | 620 |
| Open issues | 27 | 8 |
| 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-thought-llm is a NeurIPS 2023 methodology using large language models for deliberate problem solving, often exemplified through game-solving algorithms. It's implemented with Python and open-sourced under the MIT |
| Persona | - | - |
| Runtime | - | - |
| License | The MIT license permits the use of Chain-of-Thought Hub in both open source and commercial projects with acknowledgment. | MIT |
| Categories | Evaluation & Observability | LLM Frameworks, Evaluation & Observability |

## 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-thought-llm](/tools/princeton-nlp-tree-of-thought-llm.md) |
| --- | --- | --- |
| Days since push | 706d | 540d |
| Open issues (now) | 27 | 8 |
| Owner type | User | Organization |
| Security scan | No lockfile | 90 low (90 low) |
| Full report | [trust report](/tools/franxyao-chain-of-thought-hub/trust.md) | [trust report](/tools/princeton-nlp-tree-of-thought-llm/trust.md) |

## Shared compatibility

- **Python**: [chain-of-thought-hub](/tools/franxyao-chain-of-thought-hub.md) - Python runtime; [tree-of-thought-llm](/tools/princeton-nlp-tree-of-thought-llm.md) - Python runtime

## 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-thought-llm

- **Pricing:** freemium - Open-source implementation under MIT License with no direct cost. However, dependencies such as GPT-4 backend access might incur costs associated with API usage.
- **Requirements:** Min 8 GB RAM
- **Adopt for:** Tree-of-thought-llm is a NeurIPS 2023 methodology using large language models for deliberate problem solving, often exemplified through game-solving algorithms. It's implemented with Python and open-sourced under the MIT

## Choose when

### Choose chain-of-thought-hub if…

- chain-of-thought-hub is primarily Jupyter Notebook; tree-of-thought-llm is Python.
- 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: complex reasoning, chain-of-thought prompting, llm-benchmarking.
- Use Chain-of-Thought Hub when you need to benchmark smaller LLMs against larger ones for complex reasoning abilities.

### Choose tree-of-thought-llm if…

- tree-of-thought-llm is primarily Python; chain-of-thought-hub is Jupyter Notebook.
- Pricing: Open-source implementation under MIT License with no direct cost. However, dependencies such as GPT-4 backend access might incur costs associated with API usage..
- Requirements: Min 8 GB RAM.
- Tags unique to tree-of-thought-llm: tree-search, llm, python, large-language-models.
- Also covers LLM Frameworks.
- - This tool should be used when you need to deliberately solve problems with LLMs, especially if your application scenario involves strategic or game-like decision-making processes.

## 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-thought-llm

- - Avoid using this tool if real-time or near-real-time responses are crucial as the methodology can be slow due to its deliberate problem-solving approach (notably with backends like GPT-4).
- - Not recommended when the application requires deterministic outcomes. The output of Tree-of-thought-llm, especially in game scenarios, might not always be accurate given it's a probabilistic process
- - If your project doesn't have dedicated resources to tune and understand how LLMs reason through problems, this tool may require more setup effort compared to more straightforward application tools.

## Common questions

### What is the difference between chain-of-thought-hub and tree-of-thought-llm?

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

Choose chain-of-thought-hub over tree-of-thought-llm when chain-of-thought-hub is primarily Jupyter Notebook; tree-of-thought-llm is Python; 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: complex reasoning, chain-of-thought prompting, 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-thought-llm over chain-of-thought-hub?

Choose tree-of-thought-llm over chain-of-thought-hub when tree-of-thought-llm is primarily Python; chain-of-thought-hub is Jupyter Notebook; Pricing: Open-source implementation under MIT License with no direct cost. However, dependencies such as GPT-4 backend access might incur costs associated with API usage.; Requirements: Min 8 GB RAM; Tags unique to tree-of-thought-llm: tree-search, llm, python, large-language-models; Also covers LLM Frameworks; - This tool should be used when you need to deliberately solve problems with LLMs, especially if your application scenario involves strategic or game-like decision-making processes.

### 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-thought-llm?

- Avoid using this tool if real-time or near-real-time responses are crucial as the methodology can be slow due to its deliberate problem-solving approach (notably with backends like GPT-4). - Not recommended when the application requires deterministic outcomes. The output of Tree-of-thought-llm, especially in game scenarios, might not always be accurate given it's a probabilistic process - If your project doesn't have dedicated resources to tune and understand how LLMs reason through problems, this tool may require more setup effort compared to more straightforward application tools.

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

tree-of-thought-llm has more GitHub stars (6,025 vs 2,777). Stars measure visibility, not whether either tool fits your constraints.

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

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

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

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

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

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-thought-llm trust report](/tools/princeton-nlp-tree-of-thought-llm/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/_