Comparison
autoai vs AutoGL
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 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.
Markdown twin · autoai alternatives · AutoGL alternatives
GraphCanon updated 2w
Trust & integrity
| Signal | autoai | AutoGL |
|---|---|---|
| Maintenance | Dormant (496d since push) As of 2w · github_public_v1 | Slowing (256d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | Published findings As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- autoai
- Python based framework for Automatic AI enabling model search, hyper-parameter tuning and Jupyter Notebook code generation
- AutoGL
- AutoML framework & toolkit for machine learning on graphs
Stars
- autoai
- 186
- AutoGL
- 1.1k
Forks
- autoai
- 46
- AutoGL
- 123
Open issues
- autoai
- 9
- AutoGL
- 20
Language
- autoai
- Python
- AutoGL
- Python
Adopt for
- autoai
- Python based framework for automated machine learning focused on numerical data, providing model search, hyper-parameter tuning, and Jupyter Notebook code generation.
- AutoGL
- 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
- autoai
- -
- AutoGL
- -
Runtime
- autoai
- -
- AutoGL
- -
License
- autoai
- Apache-2.0
- AutoGL
- Apache-2.0
Last pushed
- autoai
- Mar 25, 2025
- AutoGL
- Nov 20, 2025
Categories
- autoai
- Model Training
- AutoGL
- Model Training
Trust and health
Maintenance
- autoai
- Dormant (18%)
- AutoGL
- Slowing (36%)
Days since push
- autoai
- 496d
- AutoGL
- 256d
Open issues (now)
- autoai
- 9
- AutoGL
- 20
OSV dependency advisories
- autoai
- Published findings
- AutoGL
- No lockfile (source not queried)
Full report
- autoai
- Trust report
- AutoGL
- Trust report
Shared compatibility
- Python · autoai: Python runtime · AutoGL: Python runtime
Choose autoai if…
- Tags unique to autoai: ai, autoai, codegen, ml.
- Use AutoAI when you need a tool that can handle both regression and classification tasks specifically over numerical datasets.
- Leaner open-issue backlog (9).
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.
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, neural-architecture-search, 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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (blobcity/autoai) · observed Aug 4, 2026
- GitHub forks (blobcity/autoai) · observed Aug 4, 2026
- Last push (blobcity/autoai) · observed Mar 25, 2025
- License file (Apache-2.0) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (THUMNLab/AutoGL) · observed Aug 4, 2026
- GitHub forks (THUMNLab/AutoGL) · observed Aug 4, 2026
- Last push (THUMNLab/AutoGL) · observed Nov 20, 2025
- License file (Apache-2.0) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: autoai 186 · AutoGL 1.1k (synced Aug 4, 2026).
Common questions
- What is the difference between autoai and AutoGL?
- autoai: Python based framework for Automatic AI enabling model search, hyper-parameter tuning and Jupyter Notebook code generation. AutoGL: AutoML framework & toolkit for machine learning on graphs. See the comparison table for live GitHub stats and shared categories.
- When should I choose autoai over AutoGL?
- Choose autoai over AutoGL when Tags unique to autoai: ai, autoai, codegen, ml; Use AutoAI when you need a tool that can handle both regression and classification tasks specifically over numerical datasets; Leaner open-issue backlog (9).
- When should I choose AutoGL over autoai?
- Choose AutoGL over autoai 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, neural-architecture-search, 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 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 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 autoai or AutoGL more popular on GitHub?
- AutoGL has more GitHub stars (1,138 vs 186). Stars measure visibility, not whether either tool fits your constraints.
- Are autoai and AutoGL open source?
- Yes - both are open-source projects on GitHub (autoai: Apache-2.0, AutoGL: Apache-2.0).
- Where can I find alternatives to autoai or AutoGL?
- GraphCanon lists graph-backed alternatives at autoai alternatives and AutoGL alternatives (autoai markdown twin, AutoGL markdown twin), 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 mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, autoai or AutoGL?
- autoai: Dormant. 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 autoai and AutoGL?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: autoai trust report; AutoGL trust report.