--- title: "awesome-automl-papers vs FLAML" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hibayesian-awesome-automl-papers-vs-microsoft-flaml" tools: ["hibayesian-awesome-automl-papers", "microsoft-flaml"] --- # awesome-automl-papers vs FLAML *GraphCanon updated Aug 4, 2026* ## Verdict Pick awesome-automl-papers if awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search; 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. [awesome-automl-papers](https://github.com/hibayesian/awesome-automl-papers) reports 4.2k GitHub stars, 678 forks, and 2 open issues, last pushed Jun 11, 2024. [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 [awesome-automl-papers's repository](https://github.com/hibayesian/awesome-automl-papers) and [FLAML's repository](https://github.com/microsoft/FLAML). | | [awesome-automl-papers](/tools/hibayesian-awesome-automl-papers.md) | [FLAML](/tools/microsoft-flaml.md) | | --- | --- | --- | | Tagline | A curated list of automated machine learning papers and resources. | A fast library for AutoML and tuning | | Stars | 4,155 | 4,385 | | Forks | 678 | 559 | | Open issues | 2 | 180 | | Language | - | Jupyter Notebook | | Adopt for | awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search. | 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 | Apache-2.0 | MIT | | Categories | Evaluation & Observability, Model Training | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-automl-papers](/tools/hibayesian-awesome-automl-papers.md) | [FLAML](/tools/microsoft-flaml.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 784d | 0d | | Open issues (now) | 2 | 180 | | Owner type | User | Organization | | Full report | [trust report](/tools/hibayesian-awesome-automl-papers/trust.md) | [trust report](/tools/microsoft-flaml/trust.md) | ## Decision facts: awesome-automl-papers - **Adopt for:** awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search. ## 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 awesome-automl-papers if… - License: awesome-automl-papers is Apache-2.0, FLAML is MIT. - Tags unique to awesome-automl-papers: automl, feature-engineering, hyperparameter-optimization, neural-architecture-search. - When you need a curated list of academic materials to research or learn about AutoML technologies ### Choose FLAML if… - License: FLAML is MIT, awesome-automl-papers is Apache-2.0. - Tags unique to FLAML: automated-machine-learning, classification, data-science, deep-learning. - 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 awesome-automl-papers - If looking for direct integration with commercial AutoML systems, as the tool provides only a list of academic papers and resources - When seeking practical AutoML solutions to directly apply in production settings without extensive customization or interpretation from papers ## 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 awesome-automl-papers and FLAML? awesome-automl-papers: A curated list of automated machine learning papers and resources.. FLAML: A fast library for AutoML and tuning. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-automl-papers over FLAML? Choose awesome-automl-papers over FLAML when License: awesome-automl-papers is Apache-2.0, FLAML is MIT; Tags unique to awesome-automl-papers: automl, feature-engineering, hyperparameter-optimization, neural-architecture-search; When you need a curated list of academic materials to research or learn about AutoML technologies. ### When should I choose FLAML over awesome-automl-papers? Choose FLAML over awesome-automl-papers when License: FLAML is MIT, awesome-automl-papers is Apache-2.0; Tags unique to FLAML: automated-machine-learning, classification, data-science, deep-learning; 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 awesome-automl-papers? If looking for direct integration with commercial AutoML systems, as the tool provides only a list of academic papers and resources When seeking practical AutoML solutions to directly apply in production settings without extensive customization or interpretation from papers ### 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 awesome-automl-papers or FLAML more popular on GitHub? FLAML has more GitHub stars (4,385 vs 4,155). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-automl-papers and FLAML open source? Yes - both are open-source projects on GitHub (awesome-automl-papers: Apache-2.0, FLAML: MIT). ### Where can I find alternatives to awesome-automl-papers or FLAML? GraphCanon lists graph-backed alternatives at [awesome-automl-papers alternatives](/tools/hibayesian-awesome-automl-papers/alternatives) and [FLAML alternatives](/tools/microsoft-flaml/alternatives) ([awesome-automl-papers markdown twin](/tools/hibayesian-awesome-automl-papers/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/hibayesian-awesome-automl-papers-vs-microsoft-flaml.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-automl-papers or FLAML? awesome-automl-papers: Dormant. 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 awesome-automl-papers and FLAML? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-automl-papers trust report](/tools/hibayesian-awesome-automl-papers/trust); [FLAML trust report](/tools/microsoft-flaml/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=hibayesian-awesome-automl-papers`](/api/graphcanon/graph?tool=hibayesian-awesome-automl-papers) - 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/_