---
title: "rag-time vs llm-course"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/microsoft-rag-time-vs-mlabonne-llm-course"
tools: ["microsoft-rag-time", "mlabonne-llm-course"]
---

# rag-time vs llm-course

*GraphCanon updated Jul 11, 2026*

## Verdict

Pick rag-time when license: rag-time is MIT, llm-course is Apache-2.0; pick llm-course when license: llm-course is Apache-2.0, rag-time is MIT.

[rag-time](https://github.com/microsoft/rag-time) reports 894 GitHub stars, 316 forks, and 4 open issues, last pushed Jun 17, 2025. [llm-course](https://mlabonne.github.io/blog/) has 81k stars, 9.4k forks, and 84 open issues, last pushed Feb 5, 2026. Figures are from public GitHub metadata via [rag-time's repository](https://github.com/microsoft/rag-time) and [llm-course's repository](https://github.com/mlabonne/llm-course).

| | [rag-time](/tools/microsoft-rag-time.md) | [llm-course](/tools/mlabonne-llm-course.md) |
| --- | --- | --- |
| Tagline | RAG Time: A 5-week Learning Journey to Mastering RAG | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. |
| Stars | 894 | 80,839 |
| Forks | 316 | 9,421 |
| Open issues | 4 | 84 |
| Language | Jupyter Notebook | - |
| 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 | MIT | Apache-2.0 |
| Categories | LLM Frameworks, Vector Databases, Inference & Serving | LLM Frameworks, Model Training, Inference & Serving, Evaluation & Observability |

## Trust and health

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

| | [rag-time](/tools/microsoft-rag-time.md) | [llm-course](/tools/mlabonne-llm-course.md) |
| --- | --- | --- |
| Maintenance | Dormant (18%) | Slowing (36%) |
| Days since push | 388d | 155d |
| Open issues (now) | 4 | 84 |
| Owner type | Organization | User |
| Full report | [trust report](/tools/microsoft-rag-time/trust.md) | [trust report](/tools/mlabonne-llm-course/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 rag-time if…

- License: rag-time is MIT, llm-course is Apache-2.0.
- Tags unique to rag-time: ai, binary-quantization, generative-ai, gpt.
- Also covers Vector Databases.

### Choose llm-course if…

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

## When NOT to use rag-time

- Last GitHub push was 389 days ago (dormant maintenance, Jun 17, 2025). Validate activity before betting a new project on rag-time.
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
- Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.

## 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

## Common questions

### What is the difference between rag-time and llm-course?

rag-time: RAG Time: A 5-week Learning Journey to Mastering RAG. 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 rag-time over llm-course?

Choose rag-time over llm-course when License: rag-time is MIT, llm-course is Apache-2.0; Tags unique to rag-time: ai, binary-quantization, generative-ai, gpt; Also covers Vector Databases.

### When should I choose llm-course over rag-time?

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

### When should I avoid rag-time?

Last GitHub push was 389 days ago (dormant maintenance, Jun 17, 2025). Validate activity before betting a new project on rag-time. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.

### 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

### Is rag-time or llm-course more popular on GitHub?

llm-course has more GitHub stars (80,839 vs 894). Stars measure visibility, not whether either tool fits your constraints.

### Are rag-time and llm-course open source?

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

### Where can I find alternatives to rag-time or llm-course?

GraphCanon lists graph-backed alternatives at [rag-time alternatives](/tools/microsoft-rag-time/alternatives) and [llm-course alternatives](/tools/mlabonne-llm-course/alternatives) ([rag-time markdown twin](/tools/microsoft-rag-time/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/microsoft-rag-time-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, rag-time or llm-course?

rag-time: 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 rag-time and llm-course?

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

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=microsoft-rag-time`](/api/graphcanon/graph?tool=microsoft-rag-time)
- 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/_