---
title: "llm-course vs open-dungeon"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/mlabonne-llm-course-vs-newideas99-open-dungeon"
tools: ["mlabonne-llm-course", "newideas99-open-dungeon"]
---

# llm-course vs open-dungeon

*GraphCanon updated Jul 15, 2026*

## Verdict

Pick llm-course when license: llm-course is Apache-2.0, open-dungeon is MIT; pick open-dungeon when license: open-dungeon is MIT, llm-course is Apache-2.0.

[llm-course](https://mlabonne.github.io/blog/) reports 81k GitHub stars, 9.4k forks, and 85 open issues, last pushed Feb 5, 2026. [open-dungeon](https://github.com/newideas99/open-dungeon) has 211 stars, 19 forks, and 0 open issues, last pushed Jul 6, 2026. Figures are from public GitHub metadata via [llm-course's repository](https://github.com/mlabonne/llm-course) and [open-dungeon's repository](https://github.com/newideas99/open-dungeon).

| | [llm-course](/tools/mlabonne-llm-course.md) | [open-dungeon](/tools/newideas99-open-dungeon.md) |
| --- | --- | --- |
| Tagline | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. | Open Dungeon, the first easy-to-use, fully local AI roleplay app. Story and inline scene images generated 100% on your machine (Gemma 4 QAT via Ollama(and others) + FLUX). No accounts, no API keys, no |
| Stars | 80,904 | 211 |
| Forks | 9,424 | 19 |
| Open issues | 85 | 0 |
| Language | - | TypeScript |
| Adopt for | The llm-course provides a comprehensive guided course on Large Language Models (LLMs), divided into three parts: LLM Fundamentals, The LLM Scientist, and The LLM Engineer. It includes resources such as Colab notebooks to | - |
| Persona | - | - |
| Runtime | - | - |
| License | Apache-2.0 | MIT |
| Categories | Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | Computer Vision, Inference & Serving, LLM Frameworks |

## Trust and health

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

| | [llm-course](/tools/mlabonne-llm-course.md) | [open-dungeon](/tools/newideas99-open-dungeon.md) |
| --- | --- | --- |
| Maintenance | Slowing (36%) | Active (82%) |
| Days since push | 159d | 9d |
| Open issues (now) | 85 | 0 |
| Full report | [trust report](/tools/mlabonne-llm-course/trust.md) | [trust report](/tools/newideas99-open-dungeon/trust.md) |

## Decision facts: llm-course

- **Requirements:** Course materials are available in Colab notebooks; access requires a Google account
- **Adopt for:** The llm-course provides a comprehensive guided course on Large Language Models (LLMs), divided into three parts: LLM Fundamentals, The LLM Scientist, and The LLM Engineer. It includes resources such as Colab notebooks to
- **License detail:** Apache-2.0

## Choose when

### Choose llm-course if…

- License: llm-course is Apache-2.0, open-dungeon is MIT.
- Requirements: Course materials are available in Colab notebooks; access requires a Google account.
- Tags unique to llm-course: colab-notebooks, course, large-language-models, machine-learning.
- Also covers Evaluation & Observability, Model Training.
- - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge

### Choose open-dungeon if…

- License: open-dungeon is MIT, llm-course is Apache-2.0.
- Tags unique to open-dungeon: gemma, image-generation, interactive-fiction, local-llm.
- Also covers Computer Vision.

## When NOT to use llm-course

- - If you only require a quick introduction to LLMs without deep dive into core components
- - When you prefer working directly with commercial platforms that provide complete services rather than following detailed steps on building and deploying models yourself through this course's open,DI

## When NOT to use open-dungeon

- Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.

## Common questions

### What is the difference between llm-course and open-dungeon?

llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.. open-dungeon: Open Dungeon, the first easy-to-use, fully local AI roleplay app. Story and inline scene images generated 100% on your machine (Gemma 4 QAT via Ollama(and others) + FLUX). No accounts, no API keys, no. See the comparison table for live GitHub stats and shared categories.

### When should I choose llm-course over open-dungeon?

Choose llm-course over open-dungeon when License: llm-course is Apache-2.0, open-dungeon is MIT; Requirements: Course materials are available in Colab notebooks; access requires a Google account; Tags unique to llm-course: colab-notebooks, course, large-language-models, machine-learning; Also covers Evaluation & Observability, Model Training; - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge.

### When should I choose open-dungeon over llm-course?

Choose open-dungeon over llm-course when License: open-dungeon is MIT, llm-course is Apache-2.0; Tags unique to open-dungeon: gemma, image-generation, interactive-fiction, local-llm; Also covers Computer Vision.

### When should I avoid llm-course?

- If you only require a quick introduction to LLMs without deep dive into core components - When you prefer working directly with commercial platforms that provide complete services rather than following detailed steps on building and deploying models yourself through this course's open,DI

### When should I avoid open-dungeon?

Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.

### Is llm-course or open-dungeon more popular on GitHub?

llm-course has more GitHub stars (80,904 vs 211). Stars measure visibility, not whether either tool fits your constraints.

### Are llm-course and open-dungeon open source?

Yes - both are open-source projects on GitHub (llm-course: Apache-2.0, open-dungeon: MIT).

### Where can I find alternatives to llm-course or open-dungeon?

GraphCanon lists graph-backed alternatives at [llm-course alternatives](/tools/mlabonne-llm-course/alternatives) and [open-dungeon alternatives](/tools/newideas99-open-dungeon/alternatives) ([llm-course markdown twin](/tools/mlabonne-llm-course/alternatives.md), [open-dungeon markdown twin](/tools/newideas99-open-dungeon/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-newideas99-open-dungeon.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, llm-course or open-dungeon?

llm-course: Slowing. open-dungeon: Active. 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 open-dungeon?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm-course trust report](/tools/mlabonne-llm-course/trust); [open-dungeon trust report](/tools/newideas99-open-dungeon/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/_