--- title: "reasoning-from-scratch vs graph-of-thoughts" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/rasbt-reasoning-from-scratch-vs-spcl-graph-of-thoughts" tools: ["rasbt-reasoning-from-scratch", "spcl-graph-of-thoughts"] --- # reasoning-from-scratch vs graph-of-thoughts *GraphCanon updated Aug 17, 2026* ## Verdict 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; pick graph-of-thoughts if the Graph of Thoughts tool is designed for leveraging large language models and graph structures to solve elaborate problems. [reasoning-from-scratch](https://mng.bz/lZ5B) reports 5.0k GitHub stars, 759 forks, and 2 open issues, last pushed Aug 4, 2026. [graph-of-thoughts](https://arxiv.org/pdf/2308.09687.pdf) has 2.8k stars, 217 forks, and 7 open issues, last pushed Mar 24, 2026. Figures are from public GitHub metadata via [reasoning-from-scratch's repository](https://github.com/rasbt/reasoning-from-scratch) and [graph-of-thoughts's repository](https://github.com/spcl/graph-of-thoughts). | | [reasoning-from-scratch](/tools/rasbt-reasoning-from-scratch.md) | [graph-of-thoughts](/tools/spcl-graph-of-thoughts.md) | | --- | --- | --- | | Tagline | Implement a reasoning LLM in PyTorch from scratch, step by step | Implementation of Graph of Thoughts for large language models problem-solving | | Stars | 4,998 | 2,826 | | Forks | 759 | 217 | | Open issues | 2 | 7 | | Language | Jupyter Notebook | Python | | 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. | The Graph of Thoughts tool is designed for leveraging large language models and graph structures to solve elaborate problems. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 License | Other | | Categories | LLM Frameworks, Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [reasoning-from-scratch](/tools/rasbt-reasoning-from-scratch.md) | [graph-of-thoughts](/tools/spcl-graph-of-thoughts.md) | | --- | --- | --- | | Maintenance | Active (82%) | Slowing (36%) | | Days since push | 12d | 125d | | Open issues (now) | 2 | 7 | | Stars delta | +252 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Owner type | User | Organization | | Full report | [trust report](/tools/rasbt-reasoning-from-scratch/trust.md) | [trust report](/tools/spcl-graph-of-thoughts/trust.md) | ## Shared compatibility - **Python**: [reasoning-from-scratch](/tools/rasbt-reasoning-from-scratch.md) - Python runtime; [graph-of-thoughts](/tools/spcl-graph-of-thoughts.md) - Python runtime ## 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 ## Decision facts: graph-of-thoughts - **Pricing:** freemium - **Requirements:** Min 8 GB RAM - **Adopt for:** The Graph of Thoughts tool is designed for leveraging large language models and graph structures to solve elaborate problems. - **License detail:** Other ## Choose when ### Choose reasoning-from-scratch if… - reasoning-from-scratch is primarily Jupyter Notebook; graph-of-thoughts is Python. - License: reasoning-from-scratch is Apache-2.0, graph-of-thoughts is Other. - Requirements: Automatic GPU utilization where available, though not strictly necessary for the early chapters.. - Tags unique to reasoning-from-scratch: ai, artificial-intelligence, chain-of-thought, deep-learning. - When you have intermediate knowledge of PyTorch and want detailed insights into the implementation process of reasoning LLMS. ### Choose graph-of-thoughts if… - graph-of-thoughts is primarily Python; reasoning-from-scratch is Jupyter Notebook. - License: graph-of-thoughts is Other, reasoning-from-scratch is Apache-2.0. - Requirements: Min 8 GB RAM. - Tags unique to graph-of-thoughts: graph-of-thoughts, graph-structures, prompt-engineering. - Use when you need to solve complex problem scenarios that require the interplay between advanced language understanding and structured problem decomposition 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. ## When NOT to use graph-of-thoughts - Avoid using Graph of Thoughts for simpler, straightforward queries or when real-time performance is critical because it may introduce overhead due to its complex graph processing. - Do not use this tool where privacy and data security are paramount concerns if the official license does not sufficiently protect your needs. ## Common questions ### What is the difference between reasoning-from-scratch and graph-of-thoughts? reasoning-from-scratch: Implement a reasoning LLM in PyTorch from scratch, step by step. graph-of-thoughts: Implementation of Graph of Thoughts for large language models problem-solving. See the comparison table for live GitHub stats and shared categories. ### When should I choose reasoning-from-scratch over graph-of-thoughts? Choose reasoning-from-scratch over graph-of-thoughts when reasoning-from-scratch is primarily Jupyter Notebook; graph-of-thoughts is Python; License: reasoning-from-scratch is Apache-2.0, graph-of-thoughts is Other; Requirements: Automatic GPU utilization where available, though not strictly necessary for the early chapters.; Tags unique to reasoning-from-scratch: ai, artificial-intelligence, chain-of-thought, deep-learning; When you have intermediate knowledge of PyTorch and want detailed insights into the implementation process of reasoning LLMS. ### When should I choose graph-of-thoughts over reasoning-from-scratch? Choose graph-of-thoughts over reasoning-from-scratch when graph-of-thoughts is primarily Python; reasoning-from-scratch is Jupyter Notebook; License: graph-of-thoughts is Other, reasoning-from-scratch is Apache-2.0; Requirements: Min 8 GB RAM; Tags unique to graph-of-thoughts: graph-of-thoughts, graph-structures, prompt-engineering; Use when you need to solve complex problem scenarios that require the interplay between advanced language understanding and structured problem decomposition 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. ### When should I avoid graph-of-thoughts? Avoid using Graph of Thoughts for simpler, straightforward queries or when real-time performance is critical because it may introduce overhead due to its complex graph processing. Do not use this tool where privacy and data security are paramount concerns if the official license does not sufficiently protect your needs. ### Is reasoning-from-scratch or graph-of-thoughts more popular on GitHub? reasoning-from-scratch has more GitHub stars (4,998 vs 2,826). Stars measure visibility, not whether either tool fits your constraints. ### Are reasoning-from-scratch and graph-of-thoughts open source? Yes - both are open-source projects on GitHub (reasoning-from-scratch: Apache-2.0, graph-of-thoughts: Other). ### Where can I find alternatives to reasoning-from-scratch or graph-of-thoughts? GraphCanon lists graph-backed alternatives at [reasoning-from-scratch alternatives](/tools/rasbt-reasoning-from-scratch/alternatives) and [graph-of-thoughts alternatives](/tools/spcl-graph-of-thoughts/alternatives) ([reasoning-from-scratch markdown twin](/tools/rasbt-reasoning-from-scratch/alternatives.md), [graph-of-thoughts markdown twin](/tools/spcl-graph-of-thoughts/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/rasbt-reasoning-from-scratch-vs-spcl-graph-of-thoughts.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, reasoning-from-scratch or graph-of-thoughts? reasoning-from-scratch: Active. graph-of-thoughts: 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 reasoning-from-scratch and graph-of-thoughts? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [reasoning-from-scratch trust report](/tools/rasbt-reasoning-from-scratch/trust); [graph-of-thoughts trust report](/tools/spcl-graph-of-thoughts/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=rasbt-reasoning-from-scratch`](/api/graphcanon/graph?tool=rasbt-reasoning-from-scratch) - 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/_