--- title: "langchain-tutorials vs Hands-On-Large-Language-Models" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/gkamradt-langchain-tutorials-vs-handsonllm-hands-on-large-language-models" tools: ["gkamradt-langchain-tutorials", "handsonllm-hands-on-large-language-models"] --- # langchain-tutorials vs Hands-On-Large-Language-Models *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 Hands-On-Large-Language-Models if hands-On-Large-Language-Models offers code examples and educational content for large language models, as detailed in the O'Reilly book 'Hands-On Large Language Models'. The repository is in Jupyter Notebook format and,遵. [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. [Hands-On-Large-Language-Models](https://www.llm-book.com/) has 29k stars, 6.7k forks, and 40 open issues, last pushed Apr 24, 2026. Figures are from public GitHub metadata via [langchain-tutorials's repository](https://github.com/gkamradt/langchain-tutorials) and [Hands-On-Large-Language-Models's repository](https://github.com/HandsOnLLM/Hands-On-Large-Language-Models). | | [langchain-tutorials](/tools/gkamradt-langchain-tutorials.md) | [Hands-On-Large-Language-Models](/tools/handsonllm-hands-on-large-language-models.md) | | --- | --- | --- | | Tagline | Overview and tutorial of the LangChain Library | Code for the book 'Hands-On Large Language Models' | | Stars | 7,494 | 29,162 | | Forks | 2,010 | 6,662 | | Open issues | 15 | 40 | | Language | Jupyter Notebook | Jupyter Notebook | | Adopt for | langchain-tutorials offers educational material to aid in understanding and applying the LangChain library via Jupyter Notebooks. | Hands-On-Large-Language-Models offers code examples and educational content for large language models, as detailed in the O'Reilly book 'Hands-On Large Language Models'. The repository is in Jupyter Notebook format and,遵 | | Persona | - | - | | Runtime | - | - | | License | The license details for this tool are unknown. | Apache-2.0 | | Categories | Developer Tools, Model Training | Developer Tools, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [langchain-tutorials](/tools/gkamradt-langchain-tutorials.md) | [Hands-On-Large-Language-Models](/tools/handsonllm-hands-on-large-language-models.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 775d | 146d | | Open issues (now) | 15 | 40 | | Stars delta | +14 (30d) | +910 (30d) | | Open issues delta | 0 (30d) | +2 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/gkamradt-langchain-tutorials/trust.md) | [trust report](/tools/handsonllm-hands-on-large-language-models/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: Hands-On-Large-Language-Models - **Adopt for:** Hands-On-Large-Language-Models offers code examples and educational content for large language models, as detailed in the O'Reilly book 'Hands-On Large Language Models'. The repository is in Jupyter Notebook format and,遵 ## 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. - Also covers Model Training. - - When you're interested in hands-on learning through Jupyter Notebooks and want a structured approach to mastering LangChain with guided examples. ### Choose Hands-On-Large-Language-Models if… - Tags unique to Hands-On-Large-Language-Models: artificial-intelligence, book, large-language-models, llm. - Also covers LLM Frameworks. - Use this tool if you are looking for practical, code-based learning resources that align with the book 'Hands-On Large Language Models'. ## 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 Hands-On-Large-Language-Models - Avoid this tool if you are seeking a comprehensive theoretical background without practical examples, as the focus is on hands-on learning. - Not recommended for users who prefer a different platform for running code examples, as the repository is optimized for Google Colab with a T4 GPU and 16GB VRAM. - If you are looking for a tool that covers a broader range of AI topics beyond large language models, this repository may not be the best fit. ## Common questions ### What is the difference between langchain-tutorials and Hands-On-Large-Language-Models? langchain-tutorials: Overview and tutorial of the LangChain Library. Hands-On-Large-Language-Models: Code for the book 'Hands-On Large Language Models'. See the comparison table for live GitHub stats and shared categories. ### When should I choose langchain-tutorials over Hands-On-Large-Language-Models? Choose langchain-tutorials over Hands-On-Large-Language-Models 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; Also covers Model Training; - 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 Hands-On-Large-Language-Models over langchain-tutorials? Choose Hands-On-Large-Language-Models over langchain-tutorials when Tags unique to Hands-On-Large-Language-Models: artificial-intelligence, book, large-language-models, llm; Also covers LLM Frameworks; Use this tool if you are looking for practical, code-based learning resources that align with the book 'Hands-On Large Language Models'. ### 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 Hands-On-Large-Language-Models? Avoid this tool if you are seeking a comprehensive theoretical background without practical examples, as the focus is on hands-on learning. Not recommended for users who prefer a different platform for running code examples, as the repository is optimized for Google Colab with a T4 GPU and 16GB VRAM. If you are looking for a tool that covers a broader range of AI topics beyond large language models, this repository may not be the best fit. ### Is langchain-tutorials or Hands-On-Large-Language-Models more popular on GitHub? Hands-On-Large-Language-Models has more GitHub stars (29,162 vs 7,494). Stars measure visibility, not whether either tool fits your constraints. ### Are langchain-tutorials and Hands-On-Large-Language-Models open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to langchain-tutorials or Hands-On-Large-Language-Models? GraphCanon lists graph-backed alternatives at [langchain-tutorials alternatives](/tools/gkamradt-langchain-tutorials/alternatives) and [Hands-On-Large-Language-Models alternatives](/tools/handsonllm-hands-on-large-language-models/alternatives) ([langchain-tutorials markdown twin](/tools/gkamradt-langchain-tutorials/alternatives.md), [Hands-On-Large-Language-Models markdown twin](/tools/handsonllm-hands-on-large-language-models/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-handsonllm-hands-on-large-language-models.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, langchain-tutorials or Hands-On-Large-Language-Models? langchain-tutorials: Dormant. Hands-On-Large-Language-Models: 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 Hands-On-Large-Language-Models? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [langchain-tutorials trust report](/tools/gkamradt-langchain-tutorials/trust); [Hands-On-Large-Language-Models trust report](/tools/handsonllm-hands-on-large-language-models/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/_