---
title: "Hands-On-Large-Language-Models vs llm-books"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/handsonllm-hands-on-large-language-models-vs-morsoli-llm-books"
tools: ["handsonllm-hands-on-large-language-models", "morsoli-llm-books"]
---

# Hands-On-Large-Language-Models vs llm-books

Neutral, constraint-first comparison with live GitHub stats.

| | [Hands-On-Large-Language-Models](/tools/handsonllm-hands-on-large-language-models.md) | [llm-books](/tools/morsoli-llm-books.md) |
| --- | --- | --- |
| Tagline | Official code repo for the O'Reilly Book - 'Hands-On Large Language Models' | A book repository for practical notes on building applications with LLMs |
| Stars | 27,427 | 767 |
| Forks | 6,389 | 53 |
| Open issues | 37 | 6 |
| Language | Jupyter Notebook | Python |
| Adopt for | The 'Hands-On Large Language Models' repository, backed by Jay Alammar and Maarten Grootendorst, is a comprehensive collection of code examples from their book on large language models. It's designed to simplify the use, | llm-books is a comprehensive book repository focused on the practical aspects of building applications using Large Language Models (LLMs). |
| Persona | - | - |
| Runtime | - | - |
| License | Apache-2.0 | - |
| Categories | LLM Frameworks, Developer Tools | Evaluation & Observability, LLM Frameworks, Model Training, AI Agents, Inference & Serving |

## Trust and health

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

| | [Hands-On-Large-Language-Models](/tools/handsonllm-hands-on-large-language-models.md) | [llm-books](/tools/morsoli-llm-books.md) |
| --- | --- | --- |
| Maintenance | Steady (60%) | Dormant (18%) |
| Days since push | 75d | 585d |
| Open issues (now) | 37 | 6 |
| Owner type | Organization | User |
| Security scan | 96 low (96 low) | No lockfile |
| Full report | [trust report](/tools/handsonllm-hands-on-large-language-models/trust.md) | [trust report](/tools/morsoli-llm-books/trust.md) |

**Typed relationship:** Hands-On-Large-Language-Models _(alternative)_ llm-books

Both are book repositories with practical notes and resources on building applications with LLMs.

## Decision facts: Hands-On-Large-Language-Models

- **Adopt for:** The 'Hands-On Large Language Models' repository, backed by Jay Alammar and Maarten Grootendorst, is a comprehensive collection of code examples from their book on large language models. It's designed to simplify the use,

## Decision facts: llm-books

- **Adopt for:** llm-books is a comprehensive book repository focused on the practical aspects of building applications using Large Language Models (LLMs).

## Choose when

### Choose Hands-On-Large-Language-Models if…

- Hands-On-Large-Language-Models is primarily Jupyter Notebook; llm-books is Python.
- Both are book repositories with practical notes and resources on building applications with LLMs.
- Tags unique to Hands-On-Large-Language-Models: artificial-intelligence, large-language-models, book.
- Also covers Developer Tools.
- When you seek practical insights into LLMs complemented with nearly 300 custom-made figures for educational clarity;

### Choose llm-books if…

- llm-books is primarily Python; Hands-On-Large-Language-Models is Jupyter Notebook.
- Both are book repositories with practical notes and resources on building applications with LLMs.
- Tags unique to llm-books: llmops, chatgpt-api, agents, rag.
- Also covers Evaluation & Observability, Model Training, AI Agents, Inference & Serving.
- llm-books ships Docker support for self-hosted deployment.
- - When you need detailed guides and practical notes on specific LLM frameworks like LangChain, LlamaIndex, or HuggingFace's transformers.

## When NOT to use Hands-On-Large-Language-Models

- If your workflow does not include hands-on coding within Jupyter Notebooks and you do not require the visual educational elements provided by custom figures.
- When you need support or solutions using platforms other than Google Colab as setup examples and stability assurances are specifically tailored for Google Colab.
- If advanced theoretical insights beyond practical usage of LLMs are your priority, since this tool focuses more on hands-on application rather than deep theory.
- In scenarios where immediate access to the latest technical support from a wide community is essential, as this repository’s community might be more niche compared to broader, more generic developer L

## When NOT to use llm-books

- - If you are seeking purely theoretical knowledge without practical implementation details for building or deploying real-world applications.
- - Avoid using this resource if comprehensive insights into non-Chinese-speaking markets and model vendors are necessary.
- - For projects requiring advanced research-oriented information rather than practical application guidance.
- - If the project strictly requires up-to-date and well-curated open-source material under a recognized license, as the license status of this repository is unknown.

## Common questions

### What is the difference between Hands-On-Large-Language-Models and llm-books?

Hands-On-Large-Language-Models: Official code repo for the O'Reilly Book - 'Hands-On Large Language Models'. llm-books: A book repository for practical notes on building applications with LLMs. See the comparison table for live GitHub stats and shared categories.

### When should I choose Hands-On-Large-Language-Models over llm-books?

Choose Hands-On-Large-Language-Models over llm-books when Hands-On-Large-Language-Models is primarily Jupyter Notebook; llm-books is Python; Both are book repositories with practical notes and resources on building applications with LLMs; Tags unique to Hands-On-Large-Language-Models: artificial-intelligence, large-language-models, book; Also covers Developer Tools; When you seek practical insights into LLMs complemented with nearly 300 custom-made figures for educational clarity;.

### When should I choose llm-books over Hands-On-Large-Language-Models?

Choose llm-books over Hands-On-Large-Language-Models when llm-books is primarily Python; Hands-On-Large-Language-Models is Jupyter Notebook; Both are book repositories with practical notes and resources on building applications with LLMs; Tags unique to llm-books: llmops, chatgpt-api, agents, rag; Also covers Evaluation & Observability, Model Training, AI Agents, Inference & Serving; llm-books ships Docker support for self-hosted deployment; - When you need detailed guides and practical notes on specific LLM frameworks like LangChain, LlamaIndex, or HuggingFace's transformers.

### When should I avoid Hands-On-Large-Language-Models?

If your workflow does not include hands-on coding within Jupyter Notebooks and you do not require the visual educational elements provided by custom figures. When you need support or solutions using platforms other than Google Colab as setup examples and stability assurances are specifically tailored for Google Colab. If advanced theoretical insights beyond practical usage of LLMs are your priority, since this tool focuses more on hands-on application rather than deep theory. In scenarios where immediate access to the latest technical support from a wide community is essential, as this repository’s community might be more niche compared to broader, more generic developer L

### When should I avoid llm-books?

- If you are seeking purely theoretical knowledge without practical implementation details for building or deploying real-world applications. - Avoid using this resource if comprehensive insights into non-Chinese-speaking markets and model vendors are necessary. - For projects requiring advanced research-oriented information rather than practical application guidance. - If the project strictly requires up-to-date and well-curated open-source material under a recognized license, as the license status of this repository is unknown.

### Is Hands-On-Large-Language-Models or llm-books more popular on GitHub?

Hands-On-Large-Language-Models has more GitHub stars (27,427 vs 767). Stars measure visibility, not whether either tool fits your constraints.

### Are Hands-On-Large-Language-Models and llm-books open source?

Yes - both are open-source projects on GitHub.

### Where can I find alternatives to Hands-On-Large-Language-Models or llm-books?

GraphCanon lists graph-backed alternatives at /tools/handsonllm-hands-on-large-language-models/alternatives and /tools/morsoli-llm-books/alternatives (/tools/handsonllm-hands-on-large-language-models/alternatives.md, /tools/morsoli-llm-books/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 /compare/handsonllm-hands-on-large-language-models-vs-morsoli-llm-books.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, Hands-On-Large-Language-Models or llm-books?

Hands-On-Large-Language-Models: Steady. llm-books: Dormant. 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 Hands-On-Large-Language-Models and llm-books?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Hands-On-Large-Language-Models: /tools/handsonllm-hands-on-large-language-models/trust; llm-books: /tools/morsoli-llm-books/trust.

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=handsonllm-hands-on-large-language-models`](/api/graphcanon/graph?tool=handsonllm-hands-on-large-language-models)
- 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/_