Comparison
FEDOT vs AutoGL
Verdict
Pick FEDOT if fEDOT: auto-generates ML pipelines using evolutionary algorithms, supports various tasks including classification, regression, clustering, time series prediction; 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 · FEDOT alternatives · AutoGL alternatives
GraphCanon updated Sep 30, 2026
11views this month
Trust & integrity
| Signal | FEDOT | AutoGL |
|---|---|---|
| Maintenance | Very active (0d 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 | Published findings 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
- FEDOT
- Automated modeling and machine learning framework FEDOT
- AutoGL
- AutoML framework & toolkit for machine learning on graphs
Stars
- FEDOT
- 711
- AutoGL
- 1.1k
Forks
- FEDOT
- 98
- AutoGL
- 124
Open issues
- FEDOT
- 83
- AutoGL
- 22
Language
- FEDOT
- Python
- AutoGL
- Python
Adopt for
- FEDOT
- FEDOT: auto-generates ML pipelines using evolutionary algorithms, supports various tasks including classification, regression, clustering, time series prediction.
- 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
- FEDOT
- -
- AutoGL
- -
Runtime
- FEDOT
- -
- AutoGL
- -
License
- FEDOT
- BSD-3-Clause
- AutoGL
- Apache-2.0
Last pushed
- FEDOT
- Sep 29, 2026
- AutoGL
- Nov 20, 2025
Categories
- FEDOT
- Model Training
- AutoGL
- Model Training
Trust and health
Maintenance
- FEDOT
- Very active (96%)
- AutoGL
- Slowing (36%)
Days since push
- FEDOT
- 0d
- AutoGL
- 314d
Open issues (now)
- FEDOT
- 83
- AutoGL
- 22
Stars delta
- FEDOT
- +2 (30d)
- AutoGL
- +4 (30d)
Open issues delta
- FEDOT
- +1 (30d)
- AutoGL
- +2 (30d)
OSV dependency advisories
- FEDOT
- Published findings
- AutoGL
- No lockfile (source not queried)
Full report
- FEDOT
- Trust report
- AutoGL
- Trust report
Shared compatibility
- Python · FEDOT: Python runtime · AutoGL: Python runtime
Choose FEDOT if…
- License: FEDOT is BSD-3-Clause, AutoGL is Apache-2.0.
- Tags unique to FEDOT: evolutionary-algorithms, genetic-programming, hyperparameter-optimization, structural-learning.
- For projects requiring automated generative design of machine-learning pipelines suitable for a wide range of tasks and data types
When NOT to use FEDOT
- In scenarios with strict real-time requirements due to its optimization time
- For environments where only specific ML libraries are acceptable and FEDOT's integrations do not meet those needs
- When the project specifically requires a non-evolutionary approach for pipeline design
Choose AutoGL if…
- License: AutoGL is Apache-2.0, FEDOT is BSD-3-Clause.
- 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: deep-learning, graph-neural-networks, hyper-parameter-optimization, machine-learning.
- 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 (aimclub/FEDOT) · observed Sep 30, 2026
- GitHub forks (aimclub/FEDOT) · observed Sep 30, 2026
- Last push (aimclub/FEDOT) · observed Sep 29, 2026
- License file (BSD-3-Clause) · observed Sep 30, 2026
- Decision facts (enrichment) · observed Jul 17, 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: FEDOT 711 · AutoGL 1.1k (synced Sep 30, 2026).
Common questions
- What is the difference between FEDOT and AutoGL?
- FEDOT: Automated modeling and machine learning framework FEDOT. AutoGL: AutoML framework & toolkit for machine learning on graphs. See the comparison table for live GitHub stats and shared categories.
- When should I choose FEDOT over AutoGL?
- Choose FEDOT over AutoGL when License: FEDOT is BSD-3-Clause, AutoGL is Apache-2.0; Tags unique to FEDOT: evolutionary-algorithms, genetic-programming, hyperparameter-optimization, structural-learning; For projects requiring automated generative design of machine-learning pipelines suitable for a wide range of tasks and data types.
- When should I choose AutoGL over FEDOT?
- Choose AutoGL over FEDOT when License: AutoGL is Apache-2.0, FEDOT is BSD-3-Clause; 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: deep-learning, graph-neural-networks, hyper-parameter-optimization, machine-learning; 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 FEDOT?
- In scenarios with strict real-time requirements due to its optimization time For environments where only specific ML libraries are acceptable and FEDOT's integrations do not meet those needs When the project specifically requires a non-evolutionary approach for pipeline design
- 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 FEDOT or AutoGL more popular on GitHub?
- AutoGL has more GitHub stars (1,142 vs 711). Stars measure visibility, not whether either tool fits your constraints.
- Are FEDOT and AutoGL open source?
- Yes - both are open-source projects on GitHub (FEDOT: BSD-3-Clause, AutoGL: Apache-2.0).
- Where can I find alternatives to FEDOT or AutoGL?
- GraphCanon lists graph-backed alternatives at FEDOT alternatives and AutoGL alternatives (FEDOT 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, FEDOT or AutoGL?
- FEDOT: 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 FEDOT and AutoGL?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: FEDOT trust report; AutoGL trust report.