--- title: "archai vs AutoGL" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/microsoft-archai-vs-thumnlab-autogl" tools: ["microsoft-archai", "thumnlab-autogl"] --- # archai vs AutoGL *GraphCanon updated Aug 4, 2026* ## Verdict Pick archai if archai expedites Neural Architecture Search (NAS) research by providing fast, reproducible, modular tools for automated machine learning and hyperparameter optimization with Python and PyTorch; 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. [archai](https://microsoft.github.io/archai) reports 485 GitHub stars, 93 forks, and 4 open issues, last pushed Nov 24, 2025. [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 [archai's repository](https://github.com/microsoft/archai) and [AutoGL's repository](https://github.com/THUMNLab/AutoGL). | | [archai](/tools/microsoft-archai.md) | [AutoGL](/tools/thumnlab-autogl.md) | | --- | --- | --- | | Tagline | Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research. | AutoML framework & toolkit for machine learning on graphs | | Stars | 485 | 1,138 | | Forks | 93 | 123 | | Open issues | 4 | 20 | | Language | Python | Python | | Adopt for | Archai expedites Neural Architecture Search (NAS) research by providing fast, reproducible, modular tools for automated machine learning and hyperparameter optimization with Python and PyTorch. | 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 | MIT | Apache-2.0 | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [archai](/tools/microsoft-archai.md) | [AutoGL](/tools/thumnlab-autogl.md) | | --- | --- | --- | | Days since push | 252d | 256d | | Open issues (now) | 4 | 20 | | Full report | [trust report](/tools/microsoft-archai/trust.md) | [trust report](/tools/thumnlab-autogl/trust.md) | ## Shared compatibility - **Python**: [archai](/tools/microsoft-archai.md) - Python runtime; [AutoGL](/tools/thumnlab-autogl.md) - Python runtime ## Decision facts: archai - **Adopt for:** Archai expedites Neural Architecture Search (NAS) research by providing fast, reproducible, modular tools for automated machine learning and hyperparameter optimization with Python and PyTorch. ## 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 archai if… - License: archai is MIT, AutoGL is Apache-2.0. - Tags unique to archai: automated-machine-learning, darts, hyperparameter-optimization, model-compression. - Need rapid iteration in NAS projects while ensuring reproducibility ### Choose AutoGL if… - License: AutoGL is Apache-2.0, archai is MIT. - 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, pytorch. - 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 archai - Project requires specific GPU support not aligned with PyTorch 1.7.0+ versions - Development occurs outside Python 3.8+, limiting the application of Archai tools ## 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 archai and AutoGL? archai: Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.. AutoGL: AutoML framework & toolkit for machine learning on graphs. See the comparison table for live GitHub stats and shared categories. ### When should I choose archai over AutoGL? Choose archai over AutoGL when License: archai is MIT, AutoGL is Apache-2.0; Tags unique to archai: automated-machine-learning, darts, hyperparameter-optimization, model-compression; Need rapid iteration in NAS projects while ensuring reproducibility. ### When should I choose AutoGL over archai? Choose AutoGL over archai when License: AutoGL is Apache-2.0, archai is MIT; 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, pytorch; 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 archai? Project requires specific GPU support not aligned with PyTorch 1.7.0+ versions Development occurs outside Python 3.8+, limiting the application of Archai tools ### 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 archai or AutoGL more popular on GitHub? AutoGL has more GitHub stars (1,138 vs 485). Stars measure visibility, not whether either tool fits your constraints. ### Are archai and AutoGL open source? Yes - both are open-source projects on GitHub (archai: MIT, AutoGL: Apache-2.0). ### Where can I find alternatives to archai or AutoGL? GraphCanon lists graph-backed alternatives at [archai alternatives](/tools/microsoft-archai/alternatives) and [AutoGL alternatives](/tools/thumnlab-autogl/alternatives) ([archai markdown twin](/tools/microsoft-archai/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/microsoft-archai-vs-thumnlab-autogl.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, archai or AutoGL? archai: Slowing. 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 archai and AutoGL? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [archai trust report](/tools/microsoft-archai/trust); [AutoGL trust report](/tools/thumnlab-autogl/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=microsoft-archai`](/api/graphcanon/graph?tool=microsoft-archai) - 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/_