--- title: "autoai vs Awesome-AutoDL" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/blobcity-autoai-vs-d-x-y-awesome-autodl" tools: ["blobcity-autoai", "d-x-y-awesome-autodl"] --- # autoai vs Awesome-AutoDL *GraphCanon updated Aug 4, 2026* ## Verdict Pick autoai if python based framework for automated machine learning focused on numerical data, providing model search, hyper-parameter tuning, and Jupyter Notebook code generation; pick Awesome-AutoDL if a curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques. [autoai](https://github.com/blobcity/autoai) reports 186 GitHub stars, 46 forks, and 9 open issues, last pushed Mar 25, 2025. [Awesome-AutoDL](https://github.com/D-X-Y/Awesome-AutoDL) has 2.3k stars, 319 forks, and 2 open issues, last pushed Sep 26, 2022. Figures are from public GitHub metadata via [autoai's repository](https://github.com/blobcity/autoai) and [Awesome-AutoDL's repository](https://github.com/D-X-Y/Awesome-AutoDL). | | [autoai](/tools/blobcity-autoai.md) | [Awesome-AutoDL](/tools/d-x-y-awesome-autodl.md) | | --- | --- | --- | | Tagline | Python based framework for Automatic AI enabling model search, hyper-parameter tuning and Jupyter Notebook code generation | Curated list of automated deep learning resources covering AutoDL, NAS, HPO | | Stars | 186 | 2,339 | | Forks | 46 | 319 | | Open issues | 9 | 2 | | Language | Python | Python | | Adopt for | Python based framework for automated machine learning focused on numerical data, providing model search, hyper-parameter tuning, and Jupyter Notebook code generation. | A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice. | | Categories | Model Training | Developer Tools, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [autoai](/tools/blobcity-autoai.md) | [Awesome-AutoDL](/tools/d-x-y-awesome-autodl.md) | | --- | --- | --- | | Days since push | 496d | 1408d | | Open issues (now) | 9 | 2 | | Owner type | Organization | User | | Full report | [trust report](/tools/blobcity-autoai/trust.md) | [trust report](/tools/d-x-y-awesome-autodl/trust.md) | ## Decision facts: autoai - **Adopt for:** Python based framework for automated machine learning focused on numerical data, providing model search, hyper-parameter tuning, and Jupyter Notebook code generation. ## Decision facts: Awesome-AutoDL - **Adopt for:** A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques. - **License detail:** MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice. ## Choose when ### Choose autoai if… - License: autoai is Apache-2.0, Awesome-AutoDL is MIT. - Tags unique to autoai: ai, autoai, codegen, machine-learning. - Use AutoAI when you need a tool that can handle both regression and classification tasks specifically over numerical datasets. ### Choose Awesome-AutoDL if… - License: Awesome-AutoDL is MIT, autoai is Apache-2.0. - Tags unique to Awesome-AutoDL: autodl, awesome, hyper-parameter-optimization, nas. - Also covers Developer Tools. - Use this resource when you require an exhaustive compilation of AutoDL tools that include Hyper-parameter Optimization (HPO) and Neural Architecture Search (NAS). ## When NOT to use autoai - Avoid using AutoAI if your dataset includes non-numerical data exclusively as the framework is tailored for numerical data processing. - Do not use if generating model training scripts in formats other than Jupyter Notebooks is required, as this tool only supports Python code output within a Jupyter format. ## When NOT to use Awesome-AutoDL - Avoid using Awesome-AutoDL if you are looking for hands-on code implementation examples or tutorials specific to each tool mentioned. - Do not rely on this repository alone for practical use cases in AutoDL without further investigation into the individual libraries listed, as it primarily serves as a reference guide. ## Common questions ### What is the difference between autoai and Awesome-AutoDL? autoai: Python based framework for Automatic AI enabling model search, hyper-parameter tuning and Jupyter Notebook code generation. Awesome-AutoDL: Curated list of automated deep learning resources covering AutoDL, NAS, HPO. See the comparison table for live GitHub stats and shared categories. ### When should I choose autoai over Awesome-AutoDL? Choose autoai over Awesome-AutoDL when License: autoai is Apache-2.0, Awesome-AutoDL is MIT; Tags unique to autoai: ai, autoai, codegen, machine-learning; Use AutoAI when you need a tool that can handle both regression and classification tasks specifically over numerical datasets. ### When should I choose Awesome-AutoDL over autoai? Choose Awesome-AutoDL over autoai when License: Awesome-AutoDL is MIT, autoai is Apache-2.0; Tags unique to Awesome-AutoDL: autodl, awesome, hyper-parameter-optimization, nas; Also covers Developer Tools; Use this resource when you require an exhaustive compilation of AutoDL tools that include Hyper-parameter Optimization (HPO) and Neural Architecture Search (NAS). ### When should I avoid autoai? Avoid using AutoAI if your dataset includes non-numerical data exclusively as the framework is tailored for numerical data processing. Do not use if generating model training scripts in formats other than Jupyter Notebooks is required, as this tool only supports Python code output within a Jupyter format. ### When should I avoid Awesome-AutoDL? Avoid using Awesome-AutoDL if you are looking for hands-on code implementation examples or tutorials specific to each tool mentioned. Do not rely on this repository alone for practical use cases in AutoDL without further investigation into the individual libraries listed, as it primarily serves as a reference guide. ### Is autoai or Awesome-AutoDL more popular on GitHub? Awesome-AutoDL has more GitHub stars (2,339 vs 186). Stars measure visibility, not whether either tool fits your constraints. ### Are autoai and Awesome-AutoDL open source? Yes - both are open-source projects on GitHub (autoai: Apache-2.0, Awesome-AutoDL: MIT). ### Where can I find alternatives to autoai or Awesome-AutoDL? GraphCanon lists graph-backed alternatives at [autoai alternatives](/tools/blobcity-autoai/alternatives) and [Awesome-AutoDL alternatives](/tools/d-x-y-awesome-autodl/alternatives) ([autoai markdown twin](/tools/blobcity-autoai/alternatives.md), [Awesome-AutoDL markdown twin](/tools/d-x-y-awesome-autodl/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/blobcity-autoai-vs-d-x-y-awesome-autodl.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, autoai or Awesome-AutoDL? autoai: Dormant. Awesome-AutoDL: Dormant. 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 autoai and Awesome-AutoDL? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [autoai trust report](/tools/blobcity-autoai/trust); [Awesome-AutoDL trust report](/tools/d-x-y-awesome-autodl/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=blobcity-autoai`](/api/graphcanon/graph?tool=blobcity-autoai) - 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/_