--- title: "transformers vs sacred" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/huggingface-transformers-vs-idsia-sacred" tools: ["huggingface-transformers", "idsia-sacred"] --- # transformers vs sacred *GraphCanon updated Jul 11, 2026* ## Verdict Pick transformers when license: transformers is Apache-2.0, sacred is MIT; pick sacred when license: sacred is MIT, transformers is Apache-2.0. [transformers](https://huggingface.co/transformers) reports 162k GitHub stars, 34k forks, and 2.5k open issues, last pushed Jul 11, 2026. [sacred](https://github.com/IDSIA/sacred) has 4.4k stars, 392 forks, and 107 open issues, last pushed Oct 22, 2025. Figures are from public GitHub metadata via [transformers's repository](https://github.com/huggingface/transformers) and [sacred's repository](https://github.com/IDSIA/sacred). | | [transformers](/tools/huggingface-transformers.md) | [sacred](/tools/idsia-sacred.md) | | --- | --- | --- | | Tagline | Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models | Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA. | | Stars | 162,482 | 4,367 | | Forks | 33,865 | 392 | | Open issues | 2,475 | 107 | | Language | Python | Python | | Adopt for | Transformers is a versatile library for training and deploying state-of-the-art models across various domains such as NLP, computer vision, speech recognition, and multi-modal tasks. It supports PyTorch 2.4+ and Python 3 | - | | Persona | - | - | | Runtime | - | - | | License | Transformers is distributed under the Apache-2.0 license, ensuring wide permissions for use in both open-source and proprietary systems. | MIT | | Categories | Computer Vision, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [transformers](/tools/huggingface-transformers.md) | [sacred](/tools/idsia-sacred.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 262d | | Open issues (now) | 2.5k | 107 | | Security scan | No lockfile | No criticals | | Full report | [trust report](/tools/huggingface-transformers/trust.md) | [trust report](/tools/idsia-sacred/trust.md) | ## Decision facts: transformers - **Requirements:** Min 4 GB RAM; Works with Python 3.10+ and PyTorch 2.4+ - **Adopt for:** Transformers is a versatile library for training and deploying state-of-the-art models across various domains such as NLP, computer vision, speech recognition, and multi-modal tasks. It supports PyTorch 2.4+ and Python 3 - **License detail:** Transformers is distributed under the Apache-2.0 license, ensuring wide permissions for use in both open-source and proprietary systems. ## Choose when ### Choose transformers if… - License: transformers is Apache-2.0, sacred is MIT. - Requirements: Min 4 GB RAM; Works with Python 3.10+ and PyTorch 2.4+. - Tags unique to transformers: audio, deep-learning, natural-language-processing, pretrained models. - Also covers Computer Vision, Inference & Serving, Speech & Audio. - The library excels in scenarios where you need highly optimized and pre-trained models available for a wide range of data types including text, vision, audio, and multimodal inputs. ### Choose sacred if… - License: sacred is MIT, transformers is Apache-2.0. - Tags unique to sacred: infrastructure, mongodb, reproducibility, reproducible-research. - Leaner open-issue backlog (107). ## When NOT to use transformers - If the specific task or dataset size does not benefit from state-of-the-art models due to computational inefficiency or overfitting, alternatives may be more suitable. - It might not be the best choice for projects that strictly require compatibility with frameworks other than PyTorch and Python versions older than 3.10. ## When NOT to use sacred - Last GitHub push was 263 days ago (slowing maintenance, Oct 22, 2025). Validate activity before betting a new project on sacred. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## Common questions ### What is the difference between transformers and sacred? transformers: Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models. sacred: Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.. See the comparison table for live GitHub stats and shared categories. ### When should I choose transformers over sacred? Choose transformers over sacred when License: transformers is Apache-2.0, sacred is MIT; Requirements: Min 4 GB RAM; Works with Python 3.10+ and PyTorch 2.4+; Tags unique to transformers: audio, deep-learning, natural-language-processing, pretrained models; Also covers Computer Vision, Inference & Serving, Speech & Audio; The library excels in scenarios where you need highly optimized and pre-trained models available for a wide range of data types including text, vision, audio, and multimodal inputs. ### When should I choose sacred over transformers? Choose sacred over transformers when License: sacred is MIT, transformers is Apache-2.0; Tags unique to sacred: infrastructure, mongodb, reproducibility, reproducible-research; Leaner open-issue backlog (107). ### When should I avoid transformers? If the specific task or dataset size does not benefit from state-of-the-art models due to computational inefficiency or overfitting, alternatives may be more suitable. It might not be the best choice for projects that strictly require compatibility with frameworks other than PyTorch and Python versions older than 3.10. ### When should I avoid sacred? Last GitHub push was 263 days ago (slowing maintenance, Oct 22, 2025). Validate activity before betting a new project on sacred. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### Is transformers or sacred more popular on GitHub? transformers has more GitHub stars (162,482 vs 4,367). Stars measure visibility, not whether either tool fits your constraints. ### Are transformers and sacred open source? Yes - both are open-source projects on GitHub (transformers: Apache-2.0, sacred: MIT). ### Where can I find alternatives to transformers or sacred? GraphCanon lists graph-backed alternatives at [transformers alternatives](/tools/huggingface-transformers/alternatives) and [sacred alternatives](/tools/idsia-sacred/alternatives) ([transformers markdown twin](/tools/huggingface-transformers/alternatives.md), [sacred markdown twin](/tools/idsia-sacred/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-transformers-vs-idsia-sacred.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, transformers or sacred? transformers: Very active. sacred: 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 transformers and sacred? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [transformers trust report](/tools/huggingface-transformers/trust); [sacred trust report](/tools/idsia-sacred/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=huggingface-transformers`](/api/graphcanon/graph?tool=huggingface-transformers) - 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/_