--- title: "autogluon vs AutoGL" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/autogluon-autogluon-vs-thumnlab-autogl" tools: ["autogluon-autogluon", "thumnlab-autogl"] --- # autogluon vs AutoGL *GraphCanon updated Aug 4, 2026* ## Verdict Pick autogluon if autoGluon: an automated ML library for Python that promises accuracy in model training with minimal effort, supporting tabular data, time-series forecasting, vision tasks, and NLP; pick AutoGL if autoGL is an AutoML framework for machine learning on graphs, specializing in automated hyperparameter optimization and neural architecture search for various graph data tasks. [autogluon](https://auto.gluon.ai/) reports 11k GitHub stars, 1.2k forks, and 388 open issues, last pushed Aug 3, 2026. [AutoGL](http://mn.cs.tsinghua.edu.cn/AutoGL/) has 1.1k stars, 123 forks, and 20 open issues, last pushed Nov 20, 2025. Figures are from public GitHub metadata via [autogluon's repository](https://github.com/autogluon/autogluon) and [AutoGL's repository](https://github.com/THUMNLab/AutoGL). | | [autogluon](/tools/autogluon-autogluon.md) | [AutoGL](/tools/thumnlab-autogl.md) | | --- | --- | --- | | Tagline | Fast and Accurate ML in 3 Lines of Code | AutoML framework & toolkit for machine learning on graphs | | Stars | 10,576 | 1,138 | | Forks | 1,171 | 123 | | Open issues | 388 | 20 | | Language | Python | Python | | Adopt for | AutoGluon: an automated ML library for Python that promises accuracy in model training with minimal effort, supporting tabular data, time-series forecasting, vision tasks, and NLP. | AutoGL is an AutoML framework for machine learning on graphs, specializing in automated hyperparameter optimization and neural architecture search for various graph data tasks. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 License allows for both commercial and private use with attribution required but no warranty provided by contributors or authors. | Apache-2.0 | | Categories | Developer Tools, Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [autogluon](/tools/autogluon-autogluon.md) | [AutoGL](/tools/thumnlab-autogl.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 256d | | Open issues (now) | 388 | 20 | | Full report | [trust report](/tools/autogluon-autogluon/trust.md) | [trust report](/tools/thumnlab-autogl/trust.md) | ## Shared compatibility - **Python**: [autogluon](/tools/autogluon-autogluon.md) - Python runtime; [AutoGL](/tools/thumnlab-autogl.md) - Python runtime ## Decision facts: autogluon - **Adopt for:** AutoGluon: an automated ML library for Python that promises accuracy in model training with minimal effort, supporting tabular data, time-series forecasting, vision tasks, and NLP. - **License detail:** Apache-2.0 License allows for both commercial and private use with attribution required but no warranty provided by contributors or authors. ## Decision facts: AutoGL - **Requirements:** Min 8 GB RAM; Requires Python version >= 3.6.0.; Must include a backend library for graph processing; either PyTorch Geometric (>=1.7.0) or Deep Graph Library (DGL, >=0.7.0).; PyTorch version should be >=1.6.0. - **Adopt for:** AutoGL is an AutoML framework for machine learning on graphs, specializing in automated hyperparameter optimization and neural architecture search for various graph data tasks. ## Choose when ### Choose autogluon if… - Tags unique to autogluon: automated-machine-learning, computer-vision, data-science, ensemble-learning. - Also covers Developer Tools. - When you need quick setup of complex ML workflows involving CV, NLP, or structured data analysis. ### Choose AutoGL if… - Requirements: Min 8 GB RAM; Requires Python version >= 3.6.0.; Must include a backend library for graph processing; either PyTorch Geometric (>=1.7.0) or Deep Graph Library (DGL, >=0.7.0).; PyTorch version should be >=1.6.0.. - Tags unique to AutoGL: graph-neural-networks, hyper-parameter-optimization, machine-learning, neural-architecture-search. - When you need to automate the process of optimizing hyperparameters and searching through different neural architectures for complex graph-based datasets. ## When NOT to use autogluon - If your environment does not support Python versions 3.10-3.13 as AutoGluon requires these specific versions for operation. - For custom model developments where low-level control over every aspect of the ML process is a priority, given that AutoGluon automates significant parts of this. ## When NOT to use AutoGL - For scenarios where the dataset does not involve graph structures, as AutoGL is specifically designed to handle such data types, potentially leading to suboptimal results on non-graph datasets. - If your project relies heavily on frameworks other than PyTorch or backends outside of PyTorch Geometric or Deep Graph Library, considering it may pose integration challenges or inefficiencies. ## Common questions ### What is the difference between autogluon and AutoGL? autogluon: Fast and Accurate ML in 3 Lines of Code. AutoGL: AutoML framework & toolkit for machine learning on graphs. See the comparison table for live GitHub stats and shared categories. ### When should I choose autogluon over AutoGL? Choose autogluon over AutoGL when Tags unique to autogluon: automated-machine-learning, computer-vision, data-science, ensemble-learning; Also covers Developer Tools; When you need quick setup of complex ML workflows involving CV, NLP, or structured data analysis. ### When should I choose AutoGL over autogluon? Choose AutoGL over autogluon when Requirements: Min 8 GB RAM; Requires Python version >= 3.6.0.; Must include a backend library for graph processing; either PyTorch Geometric (>=1.7.0) or Deep Graph Library (DGL, >=0.7.0).; PyTorch version should be >=1.6.0.; Tags unique to AutoGL: graph-neural-networks, hyper-parameter-optimization, machine-learning, neural-architecture-search; When you need to automate the process of optimizing hyperparameters and searching through different neural architectures for complex graph-based datasets. ### When should I avoid autogluon? If your environment does not support Python versions 3.10-3.13 as AutoGluon requires these specific versions for operation. For custom model developments where low-level control over every aspect of the ML process is a priority, given that AutoGluon automates significant parts of this. ### When should I avoid AutoGL? For scenarios where the dataset does not involve graph structures, as AutoGL is specifically designed to handle such data types, potentially leading to suboptimal results on non-graph datasets. If your project relies heavily on frameworks other than PyTorch or backends outside of PyTorch Geometric or Deep Graph Library, considering it may pose integration challenges or inefficiencies. ### Is autogluon or AutoGL more popular on GitHub? autogluon has more GitHub stars (10,576 vs 1,138). Stars measure visibility, not whether either tool fits your constraints. ### Are autogluon and AutoGL open source? Yes - both are open-source projects on GitHub (autogluon: Apache-2.0, AutoGL: Apache-2.0). ### Where can I find alternatives to autogluon or AutoGL? GraphCanon lists graph-backed alternatives at [autogluon alternatives](/tools/autogluon-autogluon/alternatives) and [AutoGL alternatives](/tools/thumnlab-autogl/alternatives) ([autogluon markdown twin](/tools/autogluon-autogluon/alternatives.md), [AutoGL markdown twin](/tools/thumnlab-autogl/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/autogluon-autogluon-vs-thumnlab-autogl.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, autogluon or AutoGL? autogluon: Very active. AutoGL: 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 autogluon and AutoGL? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [autogluon trust report](/tools/autogluon-autogluon/trust); [AutoGL trust report](/tools/thumnlab-autogl/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=autogluon-autogluon`](/api/graphcanon/graph?tool=autogluon-autogluon) - 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/_