---
title: "agentic-rag-for-dummies vs llm-course"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/giovannipasq-agentic-rag-for-dummies-vs-mlabonne-llm-course"
tools: ["giovannipasq-agentic-rag-for-dummies", "mlabonne-llm-course"]
---

# agentic-rag-for-dummies vs llm-course

*GraphCanon updated Jul 15, 2026*

## Verdict

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

[agentic-rag-for-dummies](https://github.com/GiovanniPasq/agentic-rag-for-dummies) reports 3.7k GitHub stars, 473 forks, and 0 open issues, last pushed Jun 21, 2026. [llm-course](https://mlabonne.github.io/blog/) has 81k stars, 9.4k forks, and 85 open issues, last pushed Feb 5, 2026. Figures are from public GitHub metadata via [agentic-rag-for-dummies's repository](https://github.com/GiovanniPasq/agentic-rag-for-dummies) and [llm-course's repository](https://github.com/mlabonne/llm-course).

| | [agentic-rag-for-dummies](/tools/giovannipasq-agentic-rag-for-dummies.md) | [llm-course](/tools/mlabonne-llm-course.md) |
| --- | --- | --- |
| Tagline | A modular Agentic RAG built with LangGraph, learn Retrieval-Augmented Generation Agents in minutes. | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. |
| Stars | 3,659 | 80,904 |
| Forks | 473 | 9,424 |
| Open issues | 0 | 85 |
| 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 | AI Agents, Inference & Serving, LLM Frameworks | Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training |

## Trust and health

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

| | [agentic-rag-for-dummies](/tools/giovannipasq-agentic-rag-for-dummies.md) | [llm-course](/tools/mlabonne-llm-course.md) |
| --- | --- | --- |
| Maintenance | Active (82%) | Slowing (36%) |
| Days since push | 23d | 159d |
| Open issues (now) | 0 | 85 |
| Full report | [trust report](/tools/giovannipasq-agentic-rag-for-dummies/trust.md) | [trust report](/tools/mlabonne-llm-course/trust.md) |

## Shared compatibility

- **Python**: [agentic-rag-for-dummies](/tools/giovannipasq-agentic-rag-for-dummies.md) - Python runtime; [llm-course](/tools/mlabonne-llm-course.md) - Python runtime

## 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 agentic-rag-for-dummies if…

- License: agentic-rag-for-dummies is MIT, llm-course is Apache-2.0.
- Tags unique to agentic-rag-for-dummies: agent, agentic-ai, agentic-rag, agents.
- Also covers AI Agents.

### Choose llm-course if…

- License: llm-course is Apache-2.0, agentic-rag-for-dummies 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 NOT to use agentic-rag-for-dummies

- AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism.
- 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.

## 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 agentic-rag-for-dummies and llm-course?

agentic-rag-for-dummies: A modular Agentic RAG built with LangGraph, learn Retrieval-Augmented Generation Agents in minutes.. 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 agentic-rag-for-dummies over llm-course?

Choose agentic-rag-for-dummies over llm-course when License: agentic-rag-for-dummies is MIT, llm-course is Apache-2.0; Tags unique to agentic-rag-for-dummies: agent, agentic-ai, agentic-rag, agents; Also covers AI Agents.

### When should I choose llm-course over agentic-rag-for-dummies?

Choose llm-course over agentic-rag-for-dummies when License: llm-course is Apache-2.0, agentic-rag-for-dummies 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 avoid agentic-rag-for-dummies?

AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. 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.

### 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 agentic-rag-for-dummies or llm-course more popular on GitHub?

llm-course has more GitHub stars (80,904 vs 3,659). Stars measure visibility, not whether either tool fits your constraints.

### Are agentic-rag-for-dummies and llm-course open source?

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

### Where can I find alternatives to agentic-rag-for-dummies or llm-course?

GraphCanon lists graph-backed alternatives at [agentic-rag-for-dummies alternatives](/tools/giovannipasq-agentic-rag-for-dummies/alternatives) and [llm-course alternatives](/tools/mlabonne-llm-course/alternatives) ([agentic-rag-for-dummies markdown twin](/tools/giovannipasq-agentic-rag-for-dummies/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/giovannipasq-agentic-rag-for-dummies-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, agentic-rag-for-dummies or llm-course?

agentic-rag-for-dummies: Active. 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 agentic-rag-for-dummies and llm-course?

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

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=giovannipasq-agentic-rag-for-dummies`](/api/graphcanon/graph?tool=giovannipasq-agentic-rag-for-dummies)
- 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/_