--- title: "Hands-On-Large-Language-Models vs codespaces-langchain" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/handsonllm-hands-on-large-language-models-vs-lostintangent-codespaces-langchain" tools: ["handsonllm-hands-on-large-language-models", "lostintangent-codespaces-langchain"] --- # Hands-On-Large-Language-Models vs codespaces-langchain *GraphCanon updated Sep 20, 2026* ## Verdict 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; pick codespaces-langchain if codespaces-langchain is tailored for streamlined integration of LangChain within the GitHub Codespaces environment. [Hands-On-Large-Language-Models](https://www.llm-book.com/) reports 29k GitHub stars, 6.7k forks, and 40 open issues, last pushed Apr 24, 2026. [codespaces-langchain](https://github.com/lostintangent/codespaces-langchain) has 113 stars, 22 forks, and 5 open issues, last pushed Mar 22, 2023. Figures are from public GitHub metadata via [Hands-On-Large-Language-Models's repository](https://github.com/HandsOnLLM/Hands-On-Large-Language-Models) and [codespaces-langchain's repository](https://github.com/lostintangent/codespaces-langchain). | | [Hands-On-Large-Language-Models](/tools/handsonllm-hands-on-large-language-models.md) | [codespaces-langchain](/tools/lostintangent-codespaces-langchain.md) | | --- | --- | --- | | Tagline | Code for the book 'Hands-On Large Language Models' | A Codespaces template for getting up-and-running with LangChain in seconds | | Stars | 29,162 | 113 | | Forks | 6,662 | 22 | | Open issues | 40 | 5 | | Language | Jupyter Notebook | - | | 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 | codespaces-langchain is tailored for streamlined integration of LangChain within the GitHub Codespaces environment. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | - | | Categories | Developer Tools, LLM Frameworks | Developer Tools, LLM Frameworks | ## 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) | [codespaces-langchain](/tools/lostintangent-codespaces-langchain.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 146d | 1277d | | Open issues (now) | 40 | 5 | | Stars delta | +910 (30d) | 0 (30d) | | Open issues delta | +2 (30d) | 0 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/handsonllm-hands-on-large-language-models/trust.md) | [trust report](/tools/lostintangent-codespaces-langchain/trust.md) | ## 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 ## Decision facts: codespaces-langchain - **Requirements:** API keys from OpenAI (and optionally SerpAPI) are necessary to operate this tool. - **Adopt for:** codespaces-langchain is tailored for streamlined integration of LangChain within the GitHub Codespaces environment. ## Choose when ### Choose Hands-On-Large-Language-Models if… - Tags unique to Hands-On-Large-Language-Models: artificial-intelligence, book, large-language-models, llms. - Use this tool if you are looking for practical, code-based learning resources that align with the book 'Hands-On Large Language Models'. - More GitHub stars (29k vs 113) - visibility, not fit. ### Choose codespaces-langchain if… - Requirements: API keys from OpenAI (and optionally SerpAPI) are necessary to operate this tool.. - Tags unique to codespaces-langchain: codespaces, langchain, notebooks, openai-api. - This tool is ideal when working specifically with LangChain and wanting to leverage GitHub Codespaces for a seamless setup experience. ## 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. ## When NOT to use codespaces-langchain - Avoid this if your project requires customization beyond what's provided by default, as this template may not cover all specific needs without significant modification. - They might be less suitable for users new to both LangChain and GitHub Codespaces, who need more detailed onboarding support than the repository README provides. ## Common questions ### What is the difference between Hands-On-Large-Language-Models and codespaces-langchain? Hands-On-Large-Language-Models: Code for the book 'Hands-On Large Language Models'. codespaces-langchain: A Codespaces template for getting up-and-running with LangChain in seconds. See the comparison table for live GitHub stats and shared categories. ### When should I choose Hands-On-Large-Language-Models over codespaces-langchain? Choose Hands-On-Large-Language-Models over codespaces-langchain when Tags unique to Hands-On-Large-Language-Models: artificial-intelligence, book, large-language-models, llms; Use this tool if you are looking for practical, code-based learning resources that align with the book 'Hands-On Large Language Models'; More GitHub stars (29k vs 113) - visibility, not fit. ### When should I choose codespaces-langchain over Hands-On-Large-Language-Models? Choose codespaces-langchain over Hands-On-Large-Language-Models when Requirements: API keys from OpenAI (and optionally SerpAPI) are necessary to operate this tool.; Tags unique to codespaces-langchain: codespaces, langchain, notebooks, openai-api; This tool is ideal when working specifically with LangChain and wanting to leverage GitHub Codespaces for a seamless setup experience. ### 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. ### When should I avoid codespaces-langchain? Avoid this if your project requires customization beyond what's provided by default, as this template may not cover all specific needs without significant modification. They might be less suitable for users new to both LangChain and GitHub Codespaces, who need more detailed onboarding support than the repository README provides. ### Is Hands-On-Large-Language-Models or codespaces-langchain more popular on GitHub? Hands-On-Large-Language-Models has more GitHub stars (29,162 vs 113). Stars measure visibility, not whether either tool fits your constraints. ### Are Hands-On-Large-Language-Models and codespaces-langchain open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to Hands-On-Large-Language-Models or codespaces-langchain? GraphCanon lists graph-backed alternatives at [Hands-On-Large-Language-Models alternatives](/tools/handsonllm-hands-on-large-language-models/alternatives) and [codespaces-langchain alternatives](/tools/lostintangent-codespaces-langchain/alternatives) ([Hands-On-Large-Language-Models markdown twin](/tools/handsonllm-hands-on-large-language-models/alternatives.md), [codespaces-langchain markdown twin](/tools/lostintangent-codespaces-langchain/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/handsonllm-hands-on-large-language-models-vs-lostintangent-codespaces-langchain.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 codespaces-langchain? Hands-On-Large-Language-Models: Slowing. codespaces-langchain: 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 codespaces-langchain? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Hands-On-Large-Language-Models trust report](/tools/handsonllm-hands-on-large-language-models/trust); [codespaces-langchain trust report](/tools/lostintangent-codespaces-langchain/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/_