---
title: "langchain-tutorials vs llm-course"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/gkamradt-langchain-tutorials-vs-mlabonne-llm-course"
tools: ["gkamradt-langchain-tutorials", "mlabonne-llm-course"]
---

# langchain-tutorials vs llm-course

*GraphCanon updated Sep 20, 2026*

## Verdict

Pick langchain-tutorials if langchain-tutorials offers educational material to aid in understanding and applying the LangChain library via Jupyter Notebooks; 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.

[langchain-tutorials](https://github.com/gkamradt/langchain-tutorials) reports 7.5k GitHub stars, 2.0k forks, and 15 open issues, last pushed Aug 5, 2024. [llm-course](https://mlabonne.github.io/blog/) has 83k stars, 9.7k forks, and 90 open issues, last pushed Feb 5, 2026. Figures are from public GitHub metadata via [langchain-tutorials's repository](https://github.com/gkamradt/langchain-tutorials) and [llm-course's repository](https://github.com/mlabonne/llm-course).

| | [langchain-tutorials](/tools/gkamradt-langchain-tutorials.md) | [llm-course](/tools/mlabonne-llm-course.md) |
| --- | --- | --- |
| Tagline | Overview and tutorial of the LangChain Library | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. |
| Stars | 7,494 | 83,011 |
| Forks | 2,010 | 9,657 |
| Open issues | 15 | 90 |
| Language | Jupyter Notebook | - |
| Adopt for | langchain-tutorials offers educational material to aid in understanding and applying the LangChain library via Jupyter Notebooks. | llm-course provides a comprehensive curriculum on Large Language Models, including fundamental knowledge, building and deploying LLMs, and hands-on Colab notebooks. |
| Persona | - | - |
| Runtime | - | - |
| License | The license details for this tool are unknown. | Apache-2.0 |
| Categories | Developer Tools, Model Training | Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training |

## Trust and health

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

| | [langchain-tutorials](/tools/gkamradt-langchain-tutorials.md) | [llm-course](/tools/mlabonne-llm-course.md) |
| --- | --- | --- |
| Maintenance | Dormant (18%) | Slowing (36%) |
| Days since push | 775d | 224d |
| Open issues (now) | 15 | 90 |
| Stars delta | +14 (30d) | +1.5k (30d) |
| Open issues delta | 0 (30d) | +4 (30d) |
| Full report | [trust report](/tools/gkamradt-langchain-tutorials/trust.md) | [trust report](/tools/mlabonne-llm-course/trust.md) |

## Decision facts: langchain-tutorials

- **Pricing:** freemium - The repository is freely accessible with no stated fees; however, specific services or advanced features (if any) may require payment and aren't detailed in the provided data.
- **Adopt for:** langchain-tutorials offers educational material to aid in understanding and applying the LangChain library via Jupyter Notebooks.
- **License detail:** The license details for this tool are unknown.

## 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.

## Choose when

### Choose langchain-tutorials if…

- Pricing: The repository is freely accessible with no stated fees; however, specific services or advanced features (if any) may require payment and aren't detailed in the provided data..
- Tags unique to langchain-tutorials: jupyter-notebook, langchain, prompt-engineering, tutorials.
- - When you're interested in hands-on learning through Jupyter Notebooks and want a structured approach to mastering LangChain with guided examples.

### Choose llm-course if…

- Tags unique to llm-course: course, large-language-models, llm, machine-learning.
- Also covers Evaluation & Observability, Inference & Serving, LLM Frameworks.
- 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 NOT to use langchain-tutorials

- - When you prefer video tutorials or written articles over interactive notebooks; although the repository links to supplementary videos and online resources, its primary medium is Jupyter Notebooks.
- - If your goal is immediate application without foundational knowledge, since langchain-tutorials emphasizes a learning path from basics up, which may add time before practical applications.

## 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.

## Common questions

### What is the difference between langchain-tutorials and llm-course?

langchain-tutorials: Overview and tutorial of the LangChain Library. llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.. See the comparison table for live GitHub stats and shared categories.

### When should I choose langchain-tutorials over llm-course?

Choose langchain-tutorials over llm-course when Pricing: The repository is freely accessible with no stated fees; however, specific services or advanced features (if any) may require payment and aren't detailed in the provided data.; Tags unique to langchain-tutorials: jupyter-notebook, langchain, prompt-engineering, tutorials; - When you're interested in hands-on learning through Jupyter Notebooks and want a structured approach to mastering LangChain with guided examples.

### When should I choose llm-course over langchain-tutorials?

Choose llm-course over langchain-tutorials when Tags unique to llm-course: course, large-language-models, llm, machine-learning; Also covers Evaluation & Observability, Inference & Serving, LLM Frameworks; 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 avoid langchain-tutorials?

- When you prefer video tutorials or written articles over interactive notebooks; although the repository links to supplementary videos and online resources, its primary medium is Jupyter Notebooks. - If your goal is immediate application without foundational knowledge, since langchain-tutorials emphasizes a learning path from basics up, which may add time before practical applications.

### 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.

### Is langchain-tutorials or llm-course more popular on GitHub?

llm-course has more GitHub stars (83,011 vs 7,494). Stars measure visibility, not whether either tool fits your constraints.

### Are langchain-tutorials and llm-course open source?

Yes - both are open-source projects on GitHub.

### Where can I find alternatives to langchain-tutorials or llm-course?

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

### Which is better maintained, langchain-tutorials or llm-course?

langchain-tutorials: Dormant. llm-course: 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 langchain-tutorials and llm-course?

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

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=gkamradt-langchain-tutorials`](/api/graphcanon/graph?tool=gkamradt-langchain-tutorials)
- 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/_