Comparison
archai vs AutoGL
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.
Markdown twin · archai alternatives · AutoGL alternatives
GraphCanon updated Sep 30, 2026
19views this month
Trust & integrity
| Signal | archai | AutoGL |
|---|---|---|
| Maintenance | Very active (1d since push) As of Sep 30, 2026 · github_public_v1 | Slowing (314d since push) As of Sep 30, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 30, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 30, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · 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
- archai
- Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.
- AutoGL
- AutoML framework & toolkit for machine learning on graphs
Stars
- archai
- 486
- AutoGL
- 1.1k
Forks
- archai
- 95
- AutoGL
- 124
Open issues
- archai
- 0
- AutoGL
- 22
Language
- archai
- Python
- AutoGL
- Python
Adopt for
- archai
- 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
- 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
- archai
- -
- AutoGL
- -
Runtime
- archai
- -
- AutoGL
- -
License
- archai
- MIT
- AutoGL
- Apache-2.0
Last pushed
- archai
- Sep 28, 2026
- AutoGL
- Nov 20, 2025
Categories
- archai
- Model Training
- AutoGL
- Model Training
Trust and health
Maintenance
- archai
- Very active (96%)
- AutoGL
- Slowing (36%)
Days since push
- archai
- 1d
- AutoGL
- 314d
Open issues (now)
- archai
- 0
- AutoGL
- 22
Stars delta
- archai
- +1 (30d)
- AutoGL
- +4 (30d)
Open issues delta
- archai
- -4 (30d)
- AutoGL
- +2 (30d)
Full report
- archai
- Trust report
- AutoGL
- Trust report
Shared compatibility
- Python · archai: Python runtime · AutoGL: Python runtime
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
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
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 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 (microsoft/archai) · observed Sep 30, 2026
- GitHub forks (microsoft/archai) · observed Sep 30, 2026
- Last push (microsoft/archai) · observed Sep 28, 2026
- License file (MIT) · observed Sep 30, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (THUMNLab/AutoGL) · observed Sep 30, 2026
- GitHub forks (THUMNLab/AutoGL) · observed Sep 30, 2026
- Last push (THUMNLab/AutoGL) · observed Nov 20, 2025
- License file (Apache-2.0) · observed Sep 30, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: archai 486 · AutoGL 1.1k (synced Sep 30, 2026).
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 (1,142 vs 486). 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 and AutoGL alternatives (archai 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, archai or AutoGL?
- archai: 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 archai and AutoGL?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: archai trust report; AutoGL trust report.