---
title: "llm-course vs graph-of-thoughts"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/mlabonne-llm-course-vs-spcl-graph-of-thoughts"
tools: ["mlabonne-llm-course", "spcl-graph-of-thoughts"]
---

# llm-course vs graph-of-thoughts

*GraphCanon updated Sep 20, 2026*

## Verdict

Pick llm-course if llm-course provides a comprehensive curriculum on Large Language Models, including fundamental knowledge, building and deploying LLMs, and hands-on Colab notebooks; pick graph-of-thoughts if the Graph of Thoughts tool is designed for leveraging large language models and graph structures to solve elaborate problems.

[llm-course](https://mlabonne.github.io/blog/) reports 83k GitHub stars, 9.7k forks, and 90 open issues, last pushed Feb 5, 2026. [graph-of-thoughts](https://arxiv.org/pdf/2308.09687.pdf) has 2.8k stars, 216 forks, and 7 open issues, last pushed Mar 24, 2026. Figures are from public GitHub metadata via [llm-course's repository](https://github.com/mlabonne/llm-course) and [graph-of-thoughts's repository](https://github.com/spcl/graph-of-thoughts).

| | [llm-course](/tools/mlabonne-llm-course.md) | [graph-of-thoughts](/tools/spcl-graph-of-thoughts.md) |
| --- | --- | --- |
| Tagline | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. | Implementation of Graph of Thoughts for large language models problem-solving |
| Stars | 83,011 | 2,841 |
| Forks | 9,657 | 216 |
| Open issues | 90 | 7 |
| Language | - | Python |
| Adopt for | llm-course provides a comprehensive curriculum on Large Language Models, including fundamental knowledge, building and deploying LLMs, and hands-on Colab notebooks. | The Graph of Thoughts tool is designed for leveraging large language models and graph structures to solve elaborate problems. |
| Persona | - | - |
| Runtime | - | - |
| License | Apache-2.0 | Other |
| Categories | Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | LLM Frameworks, Model Training |

## Trust and health

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

| | [llm-course](/tools/mlabonne-llm-course.md) | [graph-of-thoughts](/tools/spcl-graph-of-thoughts.md) |
| --- | --- | --- |
| Days since push | 224d | 179d |
| Open issues (now) | 90 | 7 |
| Stars delta | +1.5k (30d) | +15 (30d) |
| Open issues delta | +4 (30d) | 0 (30d) |
| Owner type | User | Organization |
| Full report | [trust report](/tools/mlabonne-llm-course/trust.md) | [trust report](/tools/spcl-graph-of-thoughts/trust.md) |

## Shared compatibility

- **Python**: [llm-course](/tools/mlabonne-llm-course.md) - Python runtime; [graph-of-thoughts](/tools/spcl-graph-of-thoughts.md) - Python runtime

## Decision facts: llm-course

- **Adopt for:** llm-course provides a comprehensive curriculum on Large Language Models, including fundamental knowledge, building and deploying LLMs, and hands-on Colab notebooks.

## Decision facts: graph-of-thoughts

- **Pricing:** freemium
- **Requirements:** Min 8 GB RAM
- **Adopt for:** The Graph of Thoughts tool is designed for leveraging large language models and graph structures to solve elaborate problems.
- **License detail:** Other

## Choose when

### Choose llm-course if…

- License: llm-course is Apache-2.0, graph-of-thoughts is Other.
- Tags unique to llm-course: course, llm, machine-learning, roadmap.
- Also covers Developer Tools, Evaluation & Observability, Inference & Serving.
- Use llm-course if you are looking for a structured learning path that includes both theoretical and practical aspects of LLMs, from fundamentals to deployment.

### Choose graph-of-thoughts if…

- License: graph-of-thoughts is Other, llm-course is Apache-2.0.
- Requirements: Min 8 GB RAM.
- Tags unique to graph-of-thoughts: graph-of-thoughts, graph-structures, prompt-engineering.
- Use when you need to solve complex problem scenarios that require the interplay between advanced language understanding and structured problem decomposition capabilities.

## When NOT to use llm-course

- Avoid llm-course if you are seeking a course that focuses solely on theoretical aspects without practical implementation.
- Do not use llm-course if you prefer a more formal certification program or a course that is part of a university curriculum.
- Skip llm-course if you are looking for a tool that provides only code snippets or pre-built models without a structured learning path.

## When NOT to use graph-of-thoughts

- Avoid using Graph of Thoughts for simpler, straightforward queries or when real-time performance is critical because it may introduce overhead due to its complex graph processing.
- Do not use this tool where privacy and data security are paramount concerns if the official license does not sufficiently protect your needs.

## Common questions

### What is the difference between llm-course and graph-of-thoughts?

llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.. graph-of-thoughts: Implementation of Graph of Thoughts for large language models problem-solving. See the comparison table for live GitHub stats and shared categories.

### When should I choose llm-course over graph-of-thoughts?

Choose llm-course over graph-of-thoughts when License: llm-course is Apache-2.0, graph-of-thoughts is Other; Tags unique to llm-course: course, llm, machine-learning, roadmap; Also covers Developer Tools, Evaluation & Observability, Inference & Serving; Use llm-course if you are looking for a structured learning path that includes both theoretical and practical aspects of LLMs, from fundamentals to deployment.

### When should I choose graph-of-thoughts over llm-course?

Choose graph-of-thoughts over llm-course when License: graph-of-thoughts is Other, llm-course is Apache-2.0; Requirements: Min 8 GB RAM; Tags unique to graph-of-thoughts: graph-of-thoughts, graph-structures, prompt-engineering; Use when you need to solve complex problem scenarios that require the interplay between advanced language understanding and structured problem decomposition capabilities.

### When should I avoid llm-course?

Avoid llm-course if you are seeking a course that focuses solely on theoretical aspects without practical implementation. Do not use llm-course if you prefer a more formal certification program or a course that is part of a university curriculum. Skip llm-course if you are looking for a tool that provides only code snippets or pre-built models without a structured learning path.

### When should I avoid graph-of-thoughts?

Avoid using Graph of Thoughts for simpler, straightforward queries or when real-time performance is critical because it may introduce overhead due to its complex graph processing. Do not use this tool where privacy and data security are paramount concerns if the official license does not sufficiently protect your needs.

### Is llm-course or graph-of-thoughts more popular on GitHub?

llm-course has more GitHub stars (83,011 vs 2,841). Stars measure visibility, not whether either tool fits your constraints.

### Are llm-course and graph-of-thoughts open source?

Yes - both are open-source projects on GitHub (llm-course: Apache-2.0, graph-of-thoughts: Other).

### Where can I find alternatives to llm-course or graph-of-thoughts?

GraphCanon lists graph-backed alternatives at [llm-course alternatives](/tools/mlabonne-llm-course/alternatives) and [graph-of-thoughts alternatives](/tools/spcl-graph-of-thoughts/alternatives) ([llm-course markdown twin](/tools/mlabonne-llm-course/alternatives.md), [graph-of-thoughts markdown twin](/tools/spcl-graph-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/mlabonne-llm-course-vs-spcl-graph-of-thoughts.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, llm-course or graph-of-thoughts?

llm-course: Slowing. graph-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 llm-course and graph-of-thoughts?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm-course trust report](/tools/mlabonne-llm-course/trust); [graph-of-thoughts trust report](/tools/spcl-graph-of-thoughts/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=mlabonne-llm-course`](/api/graphcanon/graph?tool=mlabonne-llm-course)
- 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/_