--- title: "Awesome-LLM-Reasoning vs ai-engineering-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/atfortes-awesome-llm-reasoning-vs-rohitg00-ai-engineering-from-scratch" tools: ["atfortes-awesome-llm-reasoning", "rohitg00-ai-engineering-from-scratch"] --- # Awesome-LLM-Reasoning vs ai-engineering-from-scratch *GraphCanon updated Jul 11, 2026* ## Verdict Pick Awesome-LLM-Reasoning if awesome-LLM-Reasoning is a curated collection of papers and resources dedicated to enhancing the reasoning abilities of large language models (LLMs) and multimodal large language models (MLLMs). Specifically, it delves深入; pick ai-engineering-from-scratch if specifically designed for individuals looking to build a comprehensive understanding of AI tools and frameworks from the ground up. [Awesome-LLM-Reasoning](https://github.com/atfortes/Awesome-LLM-Reasoning) reports 3.6k GitHub stars, 212 forks, and 27 open issues, last pushed Apr 20, 2026. [ai-engineering-from-scratch](https://aiengineeringfromscratch.com) has 38k stars, 6.3k forks, and 96 open issues, last pushed Jun 25, 2026. Figures are from public GitHub metadata via [Awesome-LLM-Reasoning's repository](https://github.com/atfortes/Awesome-LLM-Reasoning) and [ai-engineering-from-scratch's repository](https://github.com/rohitg00/ai-engineering-from-scratch). | | [Awesome-LLM-Reasoning](/tools/atfortes-awesome-llm-reasoning.md) | [ai-engineering-from-scratch](/tools/rohitg00-ai-engineering-from-scratch.md) | | --- | --- | --- | | Tagline | From Chain-of-Thought prompting to OpenAI o1 and DeepSeek-R1 🍓 | Learn it. Build it. Ship it for others. | | Stars | 3,648 | 37,922 | | Forks | 212 | 6,329 | | Open issues | 27 | 96 | | Language | - | Python | | Adopt for | Awesome-LLM-Reasoning is a curated collection of papers and resources dedicated to enhancing the reasoning abilities of large language models (LLMs) and multimodal large language models (MLLMs). Specifically, it delves深入 | Specifically designed for individuals looking to build a comprehensive understanding of AI tools and frameworks from the ground up. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | LLM Frameworks | AI Agents, Computer Vision, Developer Tools, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-LLM-Reasoning](/tools/atfortes-awesome-llm-reasoning.md) | [ai-engineering-from-scratch](/tools/rohitg00-ai-engineering-from-scratch.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Active (82%) | | Days since push | 82d | 15d | | Open issues (now) | 27 | 96 | | Security scan | No lockfile | No MCP manifest | | Full report | [trust report](/tools/atfortes-awesome-llm-reasoning/trust.md) | [trust report](/tools/rohitg00-ai-engineering-from-scratch/trust.md) | ## Decision facts: Awesome-LLM-Reasoning - **Adopt for:** Awesome-LLM-Reasoning is a curated collection of papers and resources dedicated to enhancing the reasoning abilities of large language models (LLMs) and multimodal large language models (MLLMs). Specifically, it delves深入 ## Decision facts: ai-engineering-from-scratch - **Pricing:** freemium - The `ai-engineering-from-scratch` repository is free and open-source under an MIT license, but for full access to additional resources or support, a paid option may be provided. Consult official or up - **Adopt for:** Specifically designed for individuals looking to build a comprehensive understanding of AI tools and frameworks from the ground up. ## Choose when ### Choose Awesome-LLM-Reasoning if… - Tags unique to Awesome-LLM-Reasoning: awesome, chain-of-thought, chatgpt, cot. - 你正在寻找关于如何解锁和增强大语言模型（LLMs）和多模态大型语言模型（MLLMs）推理能力的论文和资源时。例如，如果你对理解和测试这些模型的符号推理能力感兴趣，这一资源将非常有用。 - Leaner open-issue backlog (27). ### Choose ai-engineering-from-scratch if… - Pricing: The `ai-engineering-from-scratch` repository is free and open-source under an MIT license, but for full access to additional resources or support, a paid option may be provided. Consult official or up. - Tags unique to ai-engineering-from-scratch: agents, ai-engineering, computer-vision, deep-learning. - Also covers AI Agents, Computer Vision, Developer Tools. - When you want to start with foundational knowledge and learn the intricacies behind AI systems. ## When NOT to use Awesome-LLM-Reasoning - 如果你正在寻找具体的工具或平台来直接进行LLM的训练或推理实现，而不是想要了解技术背后的理论和最近的研究成果。 - 当你寻求的是特定项目的代码库或者实际的应用实例，而非纯粹的研究性和理论性的文献收集和分析时。Awesome-LLM-Reasoning主要聚焦于提供最新的调研文章和资源链接，并不涉及具体的项目实现内容。 ## When NOT to use ai-engineering-from-scratch - If you are looking for a quick setup or ready-to-go solution without diving into the foundational understanding. - When your project requires immediate practical application with less emphasis on self-implemented solutions from scratch. ## Common questions ### What is the difference between Awesome-LLM-Reasoning and ai-engineering-from-scratch? Awesome-LLM-Reasoning: From Chain-of-Thought prompting to OpenAI o1 and DeepSeek-R1 🍓. ai-engineering-from-scratch: Learn it. Build it. Ship it for others.. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-LLM-Reasoning over ai-engineering-from-scratch? Choose Awesome-LLM-Reasoning over ai-engineering-from-scratch when Tags unique to Awesome-LLM-Reasoning: awesome, chain-of-thought, chatgpt, cot; 你正在寻找关于如何解锁和增强大语言模型（LLMs）和多模态大型语言模型（MLLMs）推理能力的论文和资源时。例如，如果你对理解和测试这些模型的符号推理能力感兴趣，这一资源将非常有用。; Leaner open-issue backlog (27). ### When should I choose ai-engineering-from-scratch over Awesome-LLM-Reasoning? Choose ai-engineering-from-scratch over Awesome-LLM-Reasoning when Pricing: The `ai-engineering-from-scratch` repository is free and open-source under an MIT license, but for full access to additional resources or support, a paid option may be provided. Consult official or up; Tags unique to ai-engineering-from-scratch: agents, ai-engineering, computer-vision, deep-learning; Also covers AI Agents, Computer Vision, Developer Tools; When you want to start with foundational knowledge and learn the intricacies behind AI systems. ### When should I avoid Awesome-LLM-Reasoning? 如果你正在寻找具体的工具或平台来直接进行LLM的训练或推理实现，而不是想要了解技术背后的理论和最近的研究成果。 当你寻求的是特定项目的代码库或者实际的应用实例，而非纯粹的研究性和理论性的文献收集和分析时。Awesome-LLM-Reasoning主要聚焦于提供最新的调研文章和资源链接，并不涉及具体的项目实现内容。 ### When should I avoid ai-engineering-from-scratch? If you are looking for a quick setup or ready-to-go solution without diving into the foundational understanding. When your project requires immediate practical application with less emphasis on self-implemented solutions from scratch. ### Is Awesome-LLM-Reasoning or ai-engineering-from-scratch more popular on GitHub? ai-engineering-from-scratch has more GitHub stars (37,922 vs 3,648). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-LLM-Reasoning and ai-engineering-from-scratch open source? Yes - both are open-source projects on GitHub (Awesome-LLM-Reasoning: MIT, ai-engineering-from-scratch: MIT). ### Where can I find alternatives to Awesome-LLM-Reasoning or ai-engineering-from-scratch? GraphCanon lists graph-backed alternatives at [Awesome-LLM-Reasoning alternatives](/tools/atfortes-awesome-llm-reasoning/alternatives) and [ai-engineering-from-scratch alternatives](/tools/rohitg00-ai-engineering-from-scratch/alternatives) ([Awesome-LLM-Reasoning markdown twin](/tools/atfortes-awesome-llm-reasoning/alternatives.md), [ai-engineering-from-scratch markdown twin](/tools/rohitg00-ai-engineering-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-rohitg00-ai-engineering-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 ai-engineering-from-scratch? Awesome-LLM-Reasoning: Steady. ai-engineering-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 ai-engineering-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); [ai-engineering-from-scratch trust report](/tools/rohitg00-ai-engineering-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/_