--- title: "open-r1 vs ai-engineering-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/huggingface-open-r1-vs-rohitg00-ai-engineering-from-scratch" tools: ["huggingface-open-r1", "rohitg00-ai-engineering-from-scratch"] --- # open-r1 vs ai-engineering-from-scratch *GraphCanon updated Oct 10, 2026* ## Verdict Pick open-r1 if open-R1 is an open-source effort to replicate DeepSeek-R1's models and training pipelines involving model distillation, RL pipeline replication, and multi-stage training; pick ai-engineering-from-scratch if ai-engineering-from-scratch is a comprehensive course that teaches AI engineering skills from foundational math to advanced AI agents and machine learning techniques, using Python and Node.js for interactive learning and. [open-r1](https://github.com/huggingface/open-r1) reports 26k GitHub stars, 2.5k forks, and 0 open issues, last pushed Oct 2, 2026. [ai-engineering-from-scratch](https://aiengineeringfromscratch.com) has 55k stars, 9.6k forks, and 114 open issues, last pushed Sep 7, 2026. Figures are from public GitHub metadata via [open-r1's repository](https://github.com/huggingface/open-r1) and [ai-engineering-from-scratch's repository](https://github.com/rohitg00/ai-engineering-from-scratch). | | [open-r1](/tools/huggingface-open-r1.md) | [ai-engineering-from-scratch](/tools/rohitg00-ai-engineering-from-scratch.md) | | --- | --- | --- | | Tagline | Fully open reproduction of DeepSeek-R1 | Learn, build, and deploy AI engineering skills from scratch. | | Stars | 26,477 | 54,935 | | Forks | 2,452 | 9,649 | | Open issues | 0 | 114 | | Language | Python | Python | | Adopt for | Open-R1 is an open-source effort to replicate DeepSeek-R1's models and training pipelines involving model distillation, RL pipeline replication, and multi-stage training. | ai-engineering-from-scratch is a comprehensive course that teaches AI engineering skills from foundational math to advanced AI agents and machine learning techniques, using Python and Node.js for interactive learning and | | Persona | - | - | | Runtime | - | - | | License | The project is licensed under Apache-2.0, providing a permissive license that allows for free use, modification, and distribution. | MIT | | Categories | Inference & Serving, Model Training | AI Agents, Computer Vision, Developer Tools, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [open-r1](/tools/huggingface-open-r1.md) | [ai-engineering-from-scratch](/tools/rohitg00-ai-engineering-from-scratch.md) | | --- | --- | --- | | Days since push | 8d | 10d | | Open issues (now) | 0 | 114 | | Stars delta | +28 (30d) | +8.1k (30d) | | Open issues delta | -340 (30d) | +7 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/huggingface-open-r1/trust.md) | [trust report](/tools/rohitg00-ai-engineering-from-scratch/trust.md) | ## Shared compatibility - **Python**: [open-r1](/tools/huggingface-open-r1.md) - Python runtime; [ai-engineering-from-scratch](/tools/rohitg00-ai-engineering-from-scratch.md) - Python runtime ## Decision facts: open-r1 - **Requirements:** Min 8 GB RAM; Installation requires CUDA version 12.4 and PyTorch v2.6.0, with specific dependencies like vLLM and FlashAttention that are critical. - **Adopt for:** Open-R1 is an open-source effort to replicate DeepSeek-R1's models and training pipelines involving model distillation, RL pipeline replication, and multi-stage training. - **License detail:** The project is licensed under Apache-2.0, providing a permissive license that allows for free use, modification, and distribution. ## Decision facts: ai-engineering-from-scratch - **Adopt for:** ai-engineering-from-scratch is a comprehensive course that teaches AI engineering skills from foundational math to advanced AI agents and machine learning techniques, using Python and Node.js for interactive learning and ## Choose when ### Choose open-r1 if… - License: open-r1 is Apache-2.0, ai-engineering-from-scratch is MIT. - Requirements: Min 8 GB RAM; Installation requires CUDA version 12.4 and PyTorch v2.6.0, with specific dependencies like vLLM and FlashAttention that are critical.. - Tags unique to open-r1: cuda, deepseek-r1, flashattention, model distillation. - Also covers Inference & Serving. - Use Open-R1 when you need a detailed understanding of how DeepSeek-R1 operates, considering the project closely mirrors its architecture and processes. ### Choose ai-engineering-from-scratch if… - License: ai-engineering-from-scratch is MIT, open-r1 is Apache-2.0. - Tags unique to ai-engineering-from-scratch: agents, ai-engineering, computer-vision, deep-learning. - Also covers AI Agents, Computer Vision, Developer Tools. - when you need a structured course that covers a wide range of AI engineering topics from scratch, including foundational math, deep learning, and reinforcement learning. ## When NOT to use open-r1 - Avoid Open-R1 if your hardware does not support CUDA 12.4 or cannot run PyTorch `v2.6.0`, as this may lead to errors. - Do not use it if the need for rapid experimentation outweighs the value of detailed replication, since the multi-stage training and datasets curation process can be time-consuming. ## When NOT to use ai-engineering-from-scratch - if you are looking for a tool that focuses solely on theoretical knowledge without practical application, as this course emphasizes hands-on learning. - when you do not have access to Node.js or Python, as these are required for running the course and its interactive components. - if you prefer a more traditional learning approach without the use of terminal-based learning tools, as the course is designed for interactive terminal sessions. - when you are working with a development environment that does not support skill-capable hosts, as the course is optimized for such environments. ## Common questions ### What is the difference between open-r1 and ai-engineering-from-scratch? open-r1: Fully open reproduction of DeepSeek-R1. ai-engineering-from-scratch: Learn, build, and deploy AI engineering skills from scratch.. See the comparison table for live GitHub stats and shared categories. ### When should I choose open-r1 over ai-engineering-from-scratch? Choose open-r1 over ai-engineering-from-scratch when License: open-r1 is Apache-2.0, ai-engineering-from-scratch is MIT; Requirements: Min 8 GB RAM; Installation requires CUDA version 12.4 and PyTorch v2.6.0, with specific dependencies like vLLM and FlashAttention that are critical.; Tags unique to open-r1: cuda, deepseek-r1, flashattention, model distillation; Also covers Inference & Serving; Use Open-R1 when you need a detailed understanding of how DeepSeek-R1 operates, considering the project closely mirrors its architecture and processes. ### When should I choose ai-engineering-from-scratch over open-r1? Choose ai-engineering-from-scratch over open-r1 when License: ai-engineering-from-scratch is MIT, open-r1 is Apache-2.0; Tags unique to ai-engineering-from-scratch: agents, ai-engineering, computer-vision, deep-learning; Also covers AI Agents, Computer Vision, Developer Tools; when you need a structured course that covers a wide range of AI engineering topics from scratch, including foundational math, deep learning, and reinforcement learning. ### When should I avoid open-r1? Avoid Open-R1 if your hardware does not support CUDA 12.4 or cannot run PyTorch `v2.6.0`, as this may lead to errors. Do not use it if the need for rapid experimentation outweighs the value of detailed replication, since the multi-stage training and datasets curation process can be time-consuming. ### When should I avoid ai-engineering-from-scratch? if you are looking for a tool that focuses solely on theoretical knowledge without practical application, as this course emphasizes hands-on learning. when you do not have access to Node.js or Python, as these are required for running the course and its interactive components. if you prefer a more traditional learning approach without the use of terminal-based learning tools, as the course is designed for interactive terminal sessions. when you are working with a development environment that does not support skill-capable hosts, as the course is optimized for such environments. ### Is open-r1 or ai-engineering-from-scratch more popular on GitHub? ai-engineering-from-scratch has more GitHub stars (54,935 vs 26,477). Stars measure visibility, not whether either tool fits your constraints. ### Are open-r1 and ai-engineering-from-scratch open source? Yes - both are open-source projects on GitHub (open-r1: Apache-2.0, ai-engineering-from-scratch: MIT). ### Where can I find alternatives to open-r1 or ai-engineering-from-scratch? GraphCanon lists graph-backed alternatives at [open-r1 alternatives](/tools/huggingface-open-r1/alternatives) and [ai-engineering-from-scratch alternatives](/tools/rohitg00-ai-engineering-from-scratch/alternatives) ([open-r1 markdown twin](/tools/huggingface-open-r1/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/huggingface-open-r1-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, open-r1 or ai-engineering-from-scratch? open-r1: Active. 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 open-r1 and ai-engineering-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [open-r1 trust report](/tools/huggingface-open-r1/trust); [ai-engineering-from-scratch trust report](/tools/rohitg00-ai-engineering-from-scratch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=huggingface-open-r1`](/api/graphcanon/graph?tool=huggingface-open-r1) - 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/_