--- title: "sacred vs sagify" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/idsia-sacred-vs-kenza-ai-sagify" tools: ["idsia-sacred", "kenza-ai-sagify"] --- # sacred vs sagify *GraphCanon updated Aug 25, 2026* ## Verdict Pick sacred if sacred is an experiment management tool for machine learning that emphasizes configuration management, logging, and reproducibility; pick sagify if an accessible tool for managing large language models and other machine learning tasks in Python. [sacred](https://github.com/IDSIA/sacred) reports 4.4k GitHub stars, 393 forks, and 107 open issues, last pushed Oct 22, 2025. [sagify](https://kenza-ai.github.io/sagify/) has 442 stars, 68 forks, and 18 open issues, last pushed Feb 11, 2026. Figures are from public GitHub metadata via [sacred's repository](https://github.com/IDSIA/sacred) and [sagify's repository](https://github.com/Kenza-AI/sagify). | | [sacred](/tools/idsia-sacred.md) | [sagify](/tools/kenza-ai-sagify.md) | | --- | --- | --- | | Tagline | A tool for experiment configuration, logging, and reproducibility | LLMs and Machine Learning done easily | | Stars | 4,372 | 442 | | Forks | 393 | 68 | | Open issues | 107 | 18 | | Language | Python | Python | | Adopt for | Sacred is an experiment management tool for machine learning that emphasizes configuration management, logging, and reproducibility. | An accessible tool for managing large language models and other machine learning tasks in Python. | | Persona | - | - | | Runtime | - | - | | License | Sacred is open-source under the MIT license, providing broad permissiveness in its use and modification across various applications. | Offered under the MIT license, allowing broad use for both commercial and non-commercial purposes with few restrictions. | | Categories | Developer Tools, Model Training | Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [sacred](/tools/idsia-sacred.md) | [sagify](/tools/kenza-ai-sagify.md) | | --- | --- | --- | | Days since push | 284d | 195d | | Open issues (now) | 107 | 18 | | Stars delta | Unknown | 0 (30d) | | Open issues delta | Unknown | 0 (30d) | | Full report | [trust report](/tools/idsia-sacred/trust.md) | [trust report](/tools/kenza-ai-sagify/trust.md) | ## Shared compatibility - **Python**: [sacred](/tools/idsia-sacred.md) - Python runtime; [sagify](/tools/kenza-ai-sagify.md) - Python runtime ## Decision facts: sacred - **Pricing:** freemium - Being an open-source tool under the MIT license, Sacred can be used freely without any cost. - **Adopt for:** Sacred is an experiment management tool for machine learning that emphasizes configuration management, logging, and reproducibility. - **License detail:** Sacred is open-source under the MIT license, providing broad permissiveness in its use and modification across various applications. ## Decision facts: sagify - **Requirements:** Requires Docker; - Requires Docker to manage environments consistently across different platforms. - **Adopt for:** An accessible tool for managing large language models and other machine learning tasks in Python. - **License detail:** Offered under the MIT license, allowing broad use for both commercial and non-commercial purposes with few restrictions. ## Choose when ### Choose sacred if… - Pricing: Being an open-source tool under the MIT license, Sacred can be used freely without any cost.. - Tags unique to sacred: config injection, experiment management, logging, reproducibility. - Also covers Developer Tools. - When precise control over experiment configurations and their dependencies is required, allowing consistent reproduction of results. ### Choose sagify if… - Requirements: Requires Docker; - Requires Docker to manage environments consistently across different platforms.. - Tags unique to sagify: ai-gateway, anthropic, cohere, generative-ai. - Also covers Inference & Serving, LLM Frameworks. - - When you need an integrated solution for various aspects of working with LLMs and ML tasks that is easy to understand and use, without deep technical expertise. ## When NOT to use sacred - If your project does not require deep integration with MongoDB for logging purposes, as Sacred assumes this setup out-of-the-box without offering as much flexibility to other storage options. - When you need a tool with lightweight overhead, since Sacred's comprehensive feature set introduces more complexity suitable only for larger-scale projects. ## When NOT to use sagify - - When your focus is exclusively on advanced fine-tuning or customization of machine learning models which require deep configuration options tailored to specific needs. - - If you prioritize working within a highly specialized ML ecosystem that has its own set of tools and workflows, as Sagify might not integrate seamlessly with every specialized tool. ## Common questions ### What is the difference between sacred and sagify? sacred: A tool for experiment configuration, logging, and reproducibility. sagify: LLMs and Machine Learning done easily. See the comparison table for live GitHub stats and shared categories. ### When should I choose sacred over sagify? Choose sacred over sagify when Pricing: Being an open-source tool under the MIT license, Sacred can be used freely without any cost.; Tags unique to sacred: config injection, experiment management, logging, reproducibility; Also covers Developer Tools; When precise control over experiment configurations and their dependencies is required, allowing consistent reproduction of results. ### When should I choose sagify over sacred? Choose sagify over sacred when Requirements: Requires Docker; - Requires Docker to manage environments consistently across different platforms.; Tags unique to sagify: ai-gateway, anthropic, cohere, generative-ai; Also covers Inference & Serving, LLM Frameworks; - When you need an integrated solution for various aspects of working with LLMs and ML tasks that is easy to understand and use, without deep technical expertise. ### When should I avoid sacred? If your project does not require deep integration with MongoDB for logging purposes, as Sacred assumes this setup out-of-the-box without offering as much flexibility to other storage options. When you need a tool with lightweight overhead, since Sacred's comprehensive feature set introduces more complexity suitable only for larger-scale projects. ### When should I avoid sagify? - When your focus is exclusively on advanced fine-tuning or customization of machine learning models which require deep configuration options tailored to specific needs. - If you prioritize working within a highly specialized ML ecosystem that has its own set of tools and workflows, as Sagify might not integrate seamlessly with every specialized tool. ### Is sacred or sagify more popular on GitHub? sacred has more GitHub stars (4,372 vs 442). Stars measure visibility, not whether either tool fits your constraints. ### Are sacred and sagify open source? Yes - both are open-source projects on GitHub (sacred: MIT, sagify: MIT). ### Where can I find alternatives to sacred or sagify? GraphCanon lists graph-backed alternatives at [sacred alternatives](/tools/idsia-sacred/alternatives) and [sagify alternatives](/tools/kenza-ai-sagify/alternatives) ([sacred markdown twin](/tools/idsia-sacred/alternatives.md), [sagify markdown twin](/tools/kenza-ai-sagify/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/idsia-sacred-vs-kenza-ai-sagify.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, sacred or sagify? sacred: Slowing. sagify: 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 sacred and sagify? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [sacred trust report](/tools/idsia-sacred/trust); [sagify trust report](/tools/kenza-ai-sagify/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=idsia-sacred`](/api/graphcanon/graph?tool=idsia-sacred) - 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/_