--- title: "Awesome-LLM-Reasoning vs reasoning-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/atfortes-awesome-llm-reasoning-vs-rasbt-reasoning-from-scratch" tools: ["atfortes-awesome-llm-reasoning", "rasbt-reasoning-from-scratch"] --- # Awesome-LLM-Reasoning vs reasoning-from-scratch *GraphCanon updated Aug 17, 2026* ## Verdict Pick Awesome-LLM-Reasoning if awesome-LLM-Reasoning is designed for developers and researchers focused on advanced reasoning capabilities in language models using chain-of-thought prompting techniques and multimodal learning; pick reasoning-from-scratch if a step-by-step guide to building a reasoning large language model (LLM) using PyTorch, suitable for running on consumer hardware with automatic GPU utilization. [Awesome-LLM-Reasoning](https://github.com/atfortes/Awesome-LLM-Reasoning) reports 3.7k GitHub stars, 212 forks, and 26 open issues, last pushed Apr 20, 2026. [reasoning-from-scratch](https://mng.bz/lZ5B) has 5.0k stars, 759 forks, and 2 open issues, last pushed Aug 4, 2026. Figures are from public GitHub metadata via [Awesome-LLM-Reasoning's repository](https://github.com/atfortes/Awesome-LLM-Reasoning) and [reasoning-from-scratch's repository](https://github.com/rasbt/reasoning-from-scratch). | | [Awesome-LLM-Reasoning](/tools/atfortes-awesome-llm-reasoning.md) | [reasoning-from-scratch](/tools/rasbt-reasoning-from-scratch.md) | | --- | --- | --- | | Tagline | Compiles resources on chain-of-thought prompting to advanced reasoning systems like OpenAI o1 and DeepSeek-R1. | Implement a reasoning LLM in PyTorch from scratch, step by step | | Stars | 3,657 | 4,998 | | Forks | 212 | 759 | | Open issues | 26 | 2 | | Language | - | Jupyter Notebook | | Adopt for | Awesome-LLM-Reasoning is designed for developers and researchers focused on advanced reasoning capabilities in language models using chain-of-thought prompting techniques and multimodal learning. | A step-by-step guide to building a reasoning large language model (LLM) using PyTorch, suitable for running on consumer hardware with automatic GPU utilization. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 License | | Categories | LLM Frameworks, Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-LLM-Reasoning](/tools/atfortes-awesome-llm-reasoning.md) | [reasoning-from-scratch](/tools/rasbt-reasoning-from-scratch.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Active (82%) | | Days since push | 99d | 12d | | Open issues (now) | 26 | 2 | | Stars delta | Unknown | +252 (30d) | | Open issues delta | Unknown | 0 (30d) | | Full report | [trust report](/tools/atfortes-awesome-llm-reasoning/trust.md) | [trust report](/tools/rasbt-reasoning-from-scratch/trust.md) | ## Decision facts: Awesome-LLM-Reasoning - **Pricing:** freemium - Freely available under the MIT license; resources linked within may have separate access costs, particularly proprietary models. - **Adopt for:** Awesome-LLM-Reasoning is designed for developers and researchers focused on advanced reasoning capabilities in language models using chain-of-thought prompting techniques and multimodal learning. ## Decision facts: reasoning-from-scratch - **Requirements:** Automatic GPU utilization where available, though not strictly necessary for the early chapters. - **Adopt for:** A step-by-step guide to building a reasoning large language model (LLM) using PyTorch, suitable for running on consumer hardware with automatic GPU utilization. - **License detail:** Apache-2.0 License ## Choose when ### Choose Awesome-LLM-Reasoning if… - License: Awesome-LLM-Reasoning is MIT, reasoning-from-scratch is Apache-2.0. - Pricing: Freely available under the MIT license; resources linked within may have separate access costs, particularly proprietary models.. - Tags unique to Awesome-LLM-Reasoning: chatgpt, cot, deepseek-r1, gpt-4o. - Use when developing projects that integrate OpenAI's o1 or DeepSeek-R1 advanced reasoning systems as these resources are specifically referenced within the repository. ### Choose reasoning-from-scratch if… - License: reasoning-from-scratch is Apache-2.0, Awesome-LLM-Reasoning is MIT. - Requirements: Automatic GPU utilization where available, though not strictly necessary for the early chapters.. - Tags unique to reasoning-from-scratch: ai, artificial-intelligence, deep-learning, distillation. - When you have intermediate knowledge of PyTorch and want detailed insights into the implementation process of reasoning LLMS. ## When NOT to use Awesome-LLM-Reasoning - Avoid if your project does not require or involve advanced reasoning systems from specific providers such as OpenAI's o1, instead relying on general-purpose models. - Not recommended for those working exclusively with non-language-model AI applications that do not focus on in-context learning or multimodal capabilities. ## When NOT to use reasoning-from-scratch - Avoid this tool if you are looking for rapid prototyping or quick model deployment; it focuses more on understanding and building the LLM from scratch rather than providing prebuilt components. - If specialized server hardware is available and preferred for the entire project, as chapters 5 and 6 recommend GPU use but earlier sections can be completed with just a CPU. ## Common questions ### What is the difference between Awesome-LLM-Reasoning and reasoning-from-scratch? Awesome-LLM-Reasoning: Compiles resources on chain-of-thought prompting to advanced reasoning systems like OpenAI o1 and DeepSeek-R1.. reasoning-from-scratch: Implement a reasoning LLM in PyTorch from scratch, step by step. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-LLM-Reasoning over reasoning-from-scratch? Choose Awesome-LLM-Reasoning over reasoning-from-scratch when License: Awesome-LLM-Reasoning is MIT, reasoning-from-scratch is Apache-2.0; Pricing: Freely available under the MIT license; resources linked within may have separate access costs, particularly proprietary models.; Tags unique to Awesome-LLM-Reasoning: chatgpt, cot, deepseek-r1, gpt-4o; Use when developing projects that integrate OpenAI's o1 or DeepSeek-R1 advanced reasoning systems as these resources are specifically referenced within the repository. ### When should I choose reasoning-from-scratch over Awesome-LLM-Reasoning? Choose reasoning-from-scratch over Awesome-LLM-Reasoning when License: reasoning-from-scratch is Apache-2.0, Awesome-LLM-Reasoning is MIT; Requirements: Automatic GPU utilization where available, though not strictly necessary for the early chapters.; Tags unique to reasoning-from-scratch: ai, artificial-intelligence, deep-learning, distillation; When you have intermediate knowledge of PyTorch and want detailed insights into the implementation process of reasoning LLMS. ### When should I avoid Awesome-LLM-Reasoning? Avoid if your project does not require or involve advanced reasoning systems from specific providers such as OpenAI's o1, instead relying on general-purpose models. Not recommended for those working exclusively with non-language-model AI applications that do not focus on in-context learning or multimodal capabilities. ### When should I avoid reasoning-from-scratch? Avoid this tool if you are looking for rapid prototyping or quick model deployment; it focuses more on understanding and building the LLM from scratch rather than providing prebuilt components. If specialized server hardware is available and preferred for the entire project, as chapters 5 and 6 recommend GPU use but earlier sections can be completed with just a CPU. ### Is Awesome-LLM-Reasoning or reasoning-from-scratch more popular on GitHub? reasoning-from-scratch has more GitHub stars (4,998 vs 3,657). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-LLM-Reasoning and reasoning-from-scratch open source? Yes - both are open-source projects on GitHub (Awesome-LLM-Reasoning: MIT, reasoning-from-scratch: Apache-2.0). ### Where can I find alternatives to Awesome-LLM-Reasoning or reasoning-from-scratch? GraphCanon lists graph-backed alternatives at [Awesome-LLM-Reasoning alternatives](/tools/atfortes-awesome-llm-reasoning/alternatives) and [reasoning-from-scratch alternatives](/tools/rasbt-reasoning-from-scratch/alternatives) ([Awesome-LLM-Reasoning markdown twin](/tools/atfortes-awesome-llm-reasoning/alternatives.md), [reasoning-from-scratch markdown twin](/tools/rasbt-reasoning-from-scratch/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/atfortes-awesome-llm-reasoning-vs-rasbt-reasoning-from-scratch.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Awesome-LLM-Reasoning or reasoning-from-scratch? Awesome-LLM-Reasoning: Slowing. reasoning-from-scratch: Active. 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 Awesome-LLM-Reasoning and reasoning-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-LLM-Reasoning trust report](/tools/atfortes-awesome-llm-reasoning/trust); [reasoning-from-scratch trust report](/tools/rasbt-reasoning-from-scratch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=atfortes-awesome-llm-reasoning`](/api/graphcanon/graph?tool=atfortes-awesome-llm-reasoning) - 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/_