--- title: "Hands-On-Large-Language-Models vs ReAct" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/handsonllm-hands-on-large-language-models-vs-ysymyth-react" tools: ["handsonllm-hands-on-large-language-models", "ysymyth-react"] --- # Hands-On-Large-Language-Models vs ReAct *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 ReAct if reAct enhances large language models by improving reasoning and executing actions through specific tasks using GPT-3. [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. [ReAct](https://github.com/ysymyth/ReAct) has 4.1k stars, 396 forks, and 5 open issues, last pushed Feb 6, 2024. Figures are from public GitHub metadata via [Hands-On-Large-Language-Models's repository](https://github.com/HandsOnLLM/Hands-On-Large-Language-Models) and [ReAct's repository](https://github.com/ysymyth/ReAct). | | [Hands-On-Large-Language-Models](/tools/handsonllm-hands-on-large-language-models.md) | [ReAct](/tools/ysymyth-react.md) | | --- | --- | --- | | Tagline | Code for the book 'Hands-On Large Language Models' | ReAct Prompting for decision-making with language models | | Stars | 29,162 | 4,109 | | Forks | 6,662 | 396 | | Open issues | 40 | 5 | | Language | Jupyter Notebook | 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 | ReAct enhances large language models by improving reasoning and executing actions through specific tasks using GPT-3. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Developer Tools, LLM Frameworks | AI Agents, 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) | [ReAct](/tools/ysymyth-react.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 146d | 923d | | Open issues (now) | 40 | 5 | | Stars delta | +910 (30d) | +50 (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/ysymyth-react/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: ReAct - **Adopt for:** ReAct enhances large language models by improving reasoning and executing actions through specific tasks using GPT-3. ## Choose when ### Choose Hands-On-Large-Language-Models if… - License: Hands-On-Large-Language-Models is Apache-2.0, ReAct is MIT. - Tags unique to Hands-On-Large-Language-Models: artificial-intelligence, book, llms, oreilly. - Also covers Developer Tools. - Use this tool if you are looking for practical, code-based learning resources that align with the book 'Hands-On Large Language Models'. ### Choose ReAct if… - License: ReAct is MIT, Hands-On-Large-Language-Models is Apache-2.0. - Tags unique to ReAct: decision-making, prompting, reasoning. - Also covers AI Agents. - When aiming for better decision-making in HotpotQA, alfworld environments, or WebShop scenarios with GPT-3 ## 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 ReAct - If requiring extensive custom task integration beyond provided notebooks, LangChain's zero-shot ReAct agent may be more preferable - When PaLM outperforms GPT-3 on specific tasks or if an alternative model is preferred ## Common questions ### What is the difference between Hands-On-Large-Language-Models and ReAct? Hands-On-Large-Language-Models: Code for the book 'Hands-On Large Language Models'. ReAct: ReAct Prompting for decision-making with language models. See the comparison table for live GitHub stats and shared categories. ### When should I choose Hands-On-Large-Language-Models over ReAct? Choose Hands-On-Large-Language-Models over ReAct when License: Hands-On-Large-Language-Models is Apache-2.0, ReAct is MIT; Tags unique to Hands-On-Large-Language-Models: artificial-intelligence, book, llms, oreilly; Also covers Developer Tools; 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 choose ReAct over Hands-On-Large-Language-Models? Choose ReAct over Hands-On-Large-Language-Models when License: ReAct is MIT, Hands-On-Large-Language-Models is Apache-2.0; Tags unique to ReAct: decision-making, prompting, reasoning; Also covers AI Agents; When aiming for better decision-making in HotpotQA, alfworld environments, or WebShop scenarios with GPT-3. ### 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 ReAct? If requiring extensive custom task integration beyond provided notebooks, LangChain's zero-shot ReAct agent may be more preferable When PaLM outperforms GPT-3 on specific tasks or if an alternative model is preferred ### Is Hands-On-Large-Language-Models or ReAct more popular on GitHub? Hands-On-Large-Language-Models has more GitHub stars (29,162 vs 4,109). Stars measure visibility, not whether either tool fits your constraints. ### Are Hands-On-Large-Language-Models and ReAct open source? Yes - both are open-source projects on GitHub (Hands-On-Large-Language-Models: Apache-2.0, ReAct: MIT). ### Where can I find alternatives to Hands-On-Large-Language-Models or ReAct? GraphCanon lists graph-backed alternatives at [Hands-On-Large-Language-Models alternatives](/tools/handsonllm-hands-on-large-language-models/alternatives) and [ReAct alternatives](/tools/ysymyth-react/alternatives) ([Hands-On-Large-Language-Models markdown twin](/tools/handsonllm-hands-on-large-language-models/alternatives.md), [ReAct markdown twin](/tools/ysymyth-react/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-ysymyth-react.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 ReAct? Hands-On-Large-Language-Models: Slowing. ReAct: 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 ReAct? 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); [ReAct trust report](/tools/ysymyth-react/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/_