--- title: "great_expectations vs automl-gs" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/fivetran-great-expectations-vs-minimaxir-automl-gs" tools: ["fivetran-great-expectations", "minimaxir-automl-gs"] --- # great_expectations vs automl-gs *GraphCanon updated Aug 4, 2026* ## Verdict Pick great_expectations if great Expectations is a Python library that helps maintain data quality through unit testing mechanisms known as expectations; pick automl-gs if automl-gs: Python tool for automated machine-learning model creation from CSV data. [great_expectations](https://docs.greatexpectations.io/) reports 12k GitHub stars, 1.8k forks, and 39 open issues, last pushed Aug 2, 2026. [automl-gs](https://github.com/minimaxir/automl-gs) has 1.9k stars, 181 forks, and 28 open issues, last pushed Oct 22, 2019. Figures are from public GitHub metadata via [great_expectations's repository](https://github.com/fivetran/great_expectations) and [automl-gs's repository](https://github.com/minimaxir/automl-gs). | | [great_expectations](/tools/fivetran-great-expectations.md) | [automl-gs](/tools/minimaxir-automl-gs.md) | | --- | --- | --- | | Tagline | Always know what to expect from your data | Automatically generate machine-learning models and code with input CSV and target field | | Stars | 11,690 | 1,869 | | Forks | 1,790 | 181 | | Open issues | 39 | 28 | | Language | Python | Python | | Adopt for | Great Expectations is a Python library that helps maintain data quality through unit testing mechanisms known as expectations. | automl-gs: Python tool for automated machine-learning model creation from CSV data | | Persona | - | - | | Runtime | - | - | | License | Great Expectations is available under the Apache-2.0 license. | MIT | | Categories | Data & Retrieval | Data & Retrieval, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [great_expectations](/tools/fivetran-great-expectations.md) | [automl-gs](/tools/minimaxir-automl-gs.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 2477d | | Open issues (now) | 39 | 28 | | Owner type | Organization | User | | Full report | [trust report](/tools/fivetran-great-expectations/trust.md) | [trust report](/tools/minimaxir-automl-gs/trust.md) | ## Decision facts: great_expectations - **Requirements:** Supports Python versions 3.10 through 3.13, with experimental support for Python 3.14 and later via an environment variable. - **Adopt for:** Great Expectations is a Python library that helps maintain data quality through unit testing mechanisms known as expectations. - **License detail:** Great Expectations is available under the Apache-2.0 license. ## Decision facts: automl-gs - **Adopt for:** automl-gs: Python tool for automated machine-learning model creation from CSV data ## Choose when ### Choose great_expectations if… - License: great_expectations is Apache-2.0, automl-gs is MIT. - Requirements: Supports Python versions 3.10 through 3.13, with experimental support for Python 3.14 and later via an environment variable.. - Tags unique to great_expectations: data-engineering, data-quality, exploratory-data-analysis, mlops. - When you need detailed and automated documentation for each set of validation results to simplify your data quality processes while preserving institutional knowledge. ### Choose automl-gs if… - License: automl-gs is MIT, great_expectations is Apache-2.0. - Tags unique to automl-gs: automl, keras, machine-learning, python. - Also covers Model Training. - Need to rapidly prototype models with limited ML expertise ## When NOT to use great_expectations - For environments that strictly require adherence to Python versions 3.9 or lower, since Great Expectations supports only 3.10 through 3.13 natively. - If your data integration requirements are not compatible with those listed in the Great Expectations compatibility reference. ## When NOT to use automl-gs - Complex feature engineering or non-standard data inputs required - Sensitive about licensing of the generated code ## Common questions ### What is the difference between great_expectations and automl-gs? great_expectations: Always know what to expect from your data. automl-gs: Automatically generate machine-learning models and code with input CSV and target field. See the comparison table for live GitHub stats and shared categories. ### When should I choose great_expectations over automl-gs? Choose great_expectations over automl-gs when License: great_expectations is Apache-2.0, automl-gs is MIT; Requirements: Supports Python versions 3.10 through 3.13, with experimental support for Python 3.14 and later via an environment variable.; Tags unique to great_expectations: data-engineering, data-quality, exploratory-data-analysis, mlops; When you need detailed and automated documentation for each set of validation results to simplify your data quality processes while preserving institutional knowledge. ### When should I choose automl-gs over great_expectations? Choose automl-gs over great_expectations when License: automl-gs is MIT, great_expectations is Apache-2.0; Tags unique to automl-gs: automl, keras, machine-learning, python; Also covers Model Training; Need to rapidly prototype models with limited ML expertise. ### When should I avoid great_expectations? For environments that strictly require adherence to Python versions 3.9 or lower, since Great Expectations supports only 3.10 through 3.13 natively. If your data integration requirements are not compatible with those listed in the Great Expectations compatibility reference. ### When should I avoid automl-gs? Complex feature engineering or non-standard data inputs required Sensitive about licensing of the generated code ### Is great_expectations or automl-gs more popular on GitHub? great_expectations has more GitHub stars (11,690 vs 1,869). Stars measure visibility, not whether either tool fits your constraints. ### Are great_expectations and automl-gs open source? Yes - both are open-source projects on GitHub (great_expectations: Apache-2.0, automl-gs: MIT). ### Where can I find alternatives to great_expectations or automl-gs? GraphCanon lists graph-backed alternatives at [great_expectations alternatives](/tools/fivetran-great-expectations/alternatives) and [automl-gs alternatives](/tools/minimaxir-automl-gs/alternatives) ([great_expectations markdown twin](/tools/fivetran-great-expectations/alternatives.md), [automl-gs markdown twin](/tools/minimaxir-automl-gs/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/fivetran-great-expectations-vs-minimaxir-automl-gs.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, great_expectations or automl-gs? great_expectations: Very active. automl-gs: Dormant. 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 great_expectations and automl-gs? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [great_expectations trust report](/tools/fivetran-great-expectations/trust); [automl-gs trust report](/tools/minimaxir-automl-gs/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=fivetran-great-expectations`](/api/graphcanon/graph?tool=fivetran-great-expectations) - 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/_