--- title: "evalml vs FLAML" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/alteryx-evalml-vs-microsoft-flaml" tools: ["alteryx-evalml", "microsoft-flaml"] --- # evalml vs FLAML *GraphCanon updated Aug 4, 2026* ## 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. [evalml](https://evalml.alteryx.com) reports 852 GitHub stars, 93 forks, and 324 open issues, last pushed Jan 14, 2026. [FLAML](https://microsoft.github.io/FLAML/) has 4.4k stars, 559 forks, and 180 open issues, last pushed Aug 3, 2026. Figures are from public GitHub metadata via [evalml's repository](https://github.com/alteryx/evalml) and [FLAML's repository](https://github.com/microsoft/FLAML). | | [evalml](/tools/alteryx-evalml.md) | [FLAML](/tools/microsoft-flaml.md) | | --- | --- | --- | | Tagline | An AutoML library written in Python | A fast library for AutoML and tuning | | Stars | 852 | 4,385 | | Forks | 93 | 559 | | Open issues | 324 | 180 | | Language | Python | Jupyter Notebook | | Adopt for | 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 streamlines AutoML and tuning tasks with optimized algorithms for model selection and hyperparameter optimization across classification, regression, NLP, and time-series forecasting. | | Persona | - | - | | Runtime | - | - | | License | EvalML uses the BSD-3-Clause license which allows free use, modification, and distribution but requires preservation of copyright notices. | MIT | | Categories | Evaluation & Observability, Model Training | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [evalml](/tools/alteryx-evalml.md) | [FLAML](/tools/microsoft-flaml.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 201d | 0d | | Open issues (now) | 324 | 180 | | Full report | [trust report](/tools/alteryx-evalml/trust.md) | [trust report](/tools/microsoft-flaml/trust.md) | ## Shared compatibility - **Python**: [evalml](/tools/alteryx-evalml.md) - Python runtime; [FLAML](/tools/microsoft-flaml.md) - Python runtime ## Decision facts: evalml - **Pricing:** freemium - Access to features comes at no cost due to its open-source nature; however, premium support can be purchased. - **Requirements:** Min 2 GB RAM - **Adopt for:** EvalML serves Python users seeking automated machine learning services with streamlined feature engineering, selection, and hyperparameter tuning, underpinned by the BSD-3-Clause license. - **License detail:** EvalML uses the BSD-3-Clause license which allows free use, modification, and distribution but requires preservation of copyright notices. ## Decision facts: FLAML - **Adopt for:** FLAML streamlines AutoML and tuning tasks with optimized algorithms for model selection and hyperparameter optimization across classification, regression, NLP, and time-series forecasting. ## Choose when ### 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. ### 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 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 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. ## 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](/tools/alteryx-evalml/alternatives) and [FLAML alternatives](/tools/microsoft-flaml/alternatives) ([evalml markdown twin](/tools/alteryx-evalml/alternatives.md), [FLAML markdown twin](/tools/microsoft-flaml/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/alteryx-evalml-vs-microsoft-flaml.md) 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](/tools/alteryx-evalml/trust); [FLAML trust report](/tools/microsoft-flaml/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=alteryx-evalml`](/api/graphcanon/graph?tool=alteryx-evalml) - 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/_