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Comparison

evalml vs FLAML

Verdict

Pick evalml if evalML serves Python users seeking automated machine learning services with streamlined feature engineering, selection, and hyperparameter tuning, underpinned by the BSD-3-Clause license; pick FLAML if fLAML streamlines AutoML and tuning tasks with optimized algorithms for model selection and hyperparameter optimization across classification, regression, NLP, and time-series forecasting.

Markdown twin · evalml alternatives · FLAML alternatives

GraphCanon updated 2w

evalml logo

evalml

alteryx/evalml

★ 852pushed Jan 14, 2026
vs
FLAML logo

FLAML

microsoft/FLAML

★ 4.4kpushed Aug 3, 2026

Trust & integrity

SignalevalmlFLAML
Maintenance
Slowing (201d since push)
As of 2w · github_public_v1
Very active (0d 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
No lockfile (source not queried)
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
evalml trust reportFLAML trust reportTrust methodology

Tagline

evalml
An AutoML library written in Python
FLAML
A fast library for AutoML and tuning

Stars

evalml
852
FLAML
4.4k

Forks

evalml
93
FLAML
559

Open issues

evalml
324
FLAML
180

Language

evalml
Python
FLAML
Jupyter Notebook

Adopt for

evalml
EvalML serves Python users seeking automated machine learning services with streamlined feature engineering, selection, and hyperparameter tuning, underpinned by the BSD-3-Clause license.
FLAML
FLAML streamlines AutoML and tuning tasks with optimized algorithms for model selection and hyperparameter optimization across classification, regression, NLP, and time-series forecasting.

Persona

evalml
-
FLAML
-

Runtime

evalml
-
FLAML
-

License

evalml
EvalML uses the BSD-3-Clause license which allows free use, modification, and distribution but requires preservation of copyright notices.
FLAML
MIT

Last pushed

evalml
Jan 14, 2026
FLAML
Aug 3, 2026

Categories

evalml
Evaluation & Observability, Model Training
FLAML
Evaluation & Observability, Model Training

Trust and health

Maintenance

evalml
Slowing (36%)
FLAML
Very active (96%)

Days since push

evalml
201d
FLAML
0d

Open issues (now)

evalml
324
FLAML
180

Full report

evalml
Trust report
FLAML
Trust report

Shared compatibility

  • Python · evalml: Python runtime · FLAML: Python runtime

Choose evalml if…

  • evalml is primarily Python; FLAML is Jupyter Notebook.
  • License: evalml is BSD-3-Clause, FLAML is MIT.
  • Pricing: Access to features comes at no cost due to its open-source nature; however, premium support can be purchased..
  • Requirements: Min 2 GB RAM.
  • Tags unique to evalml: automl, feature-engineering, feature-selection, hyperparameter-tuning.
  • You value an intuitive API for automating model training processes in Python contexts where feature engineering and selection are critical.

When NOT to use evalml

  • You require deep customization of feature engineering processes that go beyond what EvalML automates out-of-the-box.
  • Your team prefers tools that offer more advanced explainability features for model decisions and behavior analysis, as this is a focus area lacking specific mention in EvalML's capabilities.
GitHubDetails

Choose FLAML if…

  • FLAML is primarily Jupyter Notebook; evalml is Python.
  • License: FLAML is MIT, evalml is BSD-3-Clause.
  • Tags unique to FLAML: automated-machine-learning, classification, deep-learning, finetuning.
  • FLAML ships Docker support for self-hosted deployment.
  • When working with Python >= 3.10 and < 3.14 to ensure full support of all models in FLAML.

When NOT to use FLAML

  • When your project is restricted to using Python versions below 3.10 or equal to and above 3.14, as FLAML's full feature support may not be available.
  • If the scope of work does not include AutoML tasks such as hyperparameter optimization or model selection for classification, regression, NLP, or time-series forecasting.
  • For users who need cross-language compatibility beyond Python and Jupyter Notebook environments; FLAML primarily supports Python-based operations.
GitHubDetails

Explore

evalml trust report →FLAML trust report →Evaluation & Observability category →Model Training category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: evalml 852 · FLAML 4.4k (synced Aug 4, 2026).

Common questions

What is the difference between evalml and FLAML?
evalml: An AutoML library written in Python. FLAML: A fast library for AutoML and tuning. See the comparison table for live GitHub stats and shared categories.
When should I choose evalml over FLAML?
Choose evalml over FLAML when evalml is primarily Python; FLAML is Jupyter Notebook; License: evalml is BSD-3-Clause, FLAML is MIT; Pricing: Access to features comes at no cost due to its open-source nature; however, premium support can be purchased.; Requirements: Min 2 GB RAM; Tags unique to evalml: automl, feature-engineering, feature-selection, hyperparameter-tuning; You value an intuitive API for automating model training processes in Python contexts where feature engineering and selection are critical.
When should I choose FLAML over evalml?
Choose FLAML over evalml when FLAML is primarily Jupyter Notebook; evalml is Python; License: FLAML is MIT, evalml is BSD-3-Clause; Tags unique to FLAML: automated-machine-learning, classification, deep-learning, finetuning; FLAML ships Docker support for self-hosted deployment; When working with Python >= 3.10 and < 3.14 to ensure full support of all models in FLAML.
When should I avoid evalml?
You require deep customization of feature engineering processes that go beyond what EvalML automates out-of-the-box. Your team prefers tools that offer more advanced explainability features for model decisions and behavior analysis, as this is a focus area lacking specific mention in EvalML's capabilities.
When should I avoid FLAML?
When your project is restricted to using Python versions below 3.10 or equal to and above 3.14, as FLAML's full feature support may not be available. If the scope of work does not include AutoML tasks such as hyperparameter optimization or model selection for classification, regression, NLP, or time-series forecasting. For users who need cross-language compatibility beyond Python and Jupyter Notebook environments; FLAML primarily supports Python-based operations.
Is evalml or FLAML more popular on GitHub?
FLAML has more GitHub stars (4,385 vs 852). Stars measure visibility, not whether either tool fits your constraints.
Are evalml and FLAML open source?
Yes - both are open-source projects on GitHub (evalml: BSD-3-Clause, FLAML: MIT).
Where can I find alternatives to evalml or FLAML?
GraphCanon lists graph-backed alternatives at evalml alternatives and FLAML alternatives (evalml markdown twin, FLAML 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, evalml or FLAML?
evalml: Slowing. FLAML: Very active. 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 evalml and FLAML?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: evalml trust report; FLAML trust report.

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