--- title: "automl-gs vs llm-app" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/minimaxir-automl-gs-vs-pathwaycom-llm-app" tools: ["minimaxir-automl-gs", "pathwaycom-llm-app"] --- # automl-gs vs llm-app *GraphCanon updated Sep 30, 2026* ## Verdict Pick automl-gs if automl-gs: Python tool for automated machine-learning model creation from CSV data; pick llm-app if llm-app offers cloud templates for RAG, AI pipelines, and enterprise search, supporting integration with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs. [automl-gs](https://github.com/minimaxir/automl-gs) reports 1.9k GitHub stars, 178 forks, and 28 open issues, last pushed Oct 22, 2019. [llm-app](https://pathway.com/developers/templates/) has 59k stars, 1.5k forks, and 8 open issues, last pushed Jul 5, 2026. Figures are from public GitHub metadata via [automl-gs's repository](https://github.com/minimaxir/automl-gs) and [llm-app's repository](https://github.com/pathwaycom/llm-app). | | [automl-gs](/tools/minimaxir-automl-gs.md) | [llm-app](/tools/pathwaycom-llm-app.md) | | --- | --- | --- | | Tagline | Automatically generate machine-learning models and code with input CSV and target field | Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data | | Stars | 1,867 | 58,920 | | Forks | 178 | 1,498 | | Open issues | 28 | 8 | | Language | Python | Jupyter Notebook | | Adopt for | automl-gs: Python tool for automated machine-learning model creation from CSV data | llm-app offers cloud templates for RAG, AI pipelines, and enterprise search, supporting integration with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT License | | Categories | Data & Retrieval, Model Training | Data & Retrieval, Evaluation & Observability, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [automl-gs](/tools/minimaxir-automl-gs.md) | [llm-app](/tools/pathwaycom-llm-app.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Steady (60%) | | Days since push | 2534d | 74d | | Open issues (now) | 28 | 8 | | Stars delta | -2 (30d) | -117 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/minimaxir-automl-gs/trust.md) | [trust report](/tools/pathwaycom-llm-app/trust.md) | ## Decision facts: automl-gs - **Adopt for:** automl-gs: Python tool for automated machine-learning model creation from CSV data ## Decision facts: llm-app - **Pricing:** freemium - The repository is open-source under the MIT License, but additional services or support might incur costs. - **Requirements:** Min 4 GB RAM; Requires Docker; Requires Docker for running the cloud templates.; Supports integration with a variety of data sources including Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs. - **Adopt for:** llm-app offers cloud templates for RAG, AI pipelines, and enterprise search, supporting integration with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs. - **License detail:** MIT License ## Choose when ### Choose automl-gs if… - automl-gs is primarily Python; llm-app is Jupyter Notebook. - Tags unique to automl-gs: automl, keras, python, tensorflow. - Need to rapidly prototype models with limited ML expertise ### Choose llm-app if… - llm-app is primarily Jupyter Notebook; automl-gs is Python. - Pricing: The repository is open-source under the MIT License, but additional services or support might incur costs.. - Requirements: Min 4 GB RAM; Requires Docker; Requires Docker for running the cloud templates.; Supports integration with a variety of data sources including Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs.. - Tags unique to llm-app: chatbot, hugging-face, llm, llm-local. - Also covers Evaluation & Observability, Inference & Serving. - When you need ready-to-run cloud templates for RAG, AI pipelines, and enterprise search that integrate seamlessly with data sources such as Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-ti ## When NOT to use automl-gs - Complex feature engineering or non-standard data inputs required - Sensitive about licensing of the generated code ## When NOT to use llm-app - Avoid using llm-app if your project does not require integration with specific data sources like Sharepoint or Google Drive, as the tool's strength lies in its broad data source support. - Do not use llm-app if you are looking for a tool that focuses solely on model training or inference without the need for cloud templates or enterprise search capabilities. ## Common questions ### What is the difference between automl-gs and llm-app? automl-gs: Automatically generate machine-learning models and code with input CSV and target field. llm-app: Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. See the comparison table for live GitHub stats and shared categories. ### When should I choose automl-gs over llm-app? Choose automl-gs over llm-app when automl-gs is primarily Python; llm-app is Jupyter Notebook; Tags unique to automl-gs: automl, keras, python, tensorflow; Need to rapidly prototype models with limited ML expertise. ### When should I choose llm-app over automl-gs? Choose llm-app over automl-gs when llm-app is primarily Jupyter Notebook; automl-gs is Python; Pricing: The repository is open-source under the MIT License, but additional services or support might incur costs.; Requirements: Min 4 GB RAM; Requires Docker; Requires Docker for running the cloud templates.; Supports integration with a variety of data sources including Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs.; Tags unique to llm-app: chatbot, hugging-face, llm, llm-local; Also covers Evaluation & Observability, Inference & Serving; When you need ready-to-run cloud templates for RAG, AI pipelines, and enterprise search that integrate seamlessly with data sources such as Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-ti. ### When should I avoid automl-gs? Complex feature engineering or non-standard data inputs required Sensitive about licensing of the generated code ### When should I avoid llm-app? Avoid using llm-app if your project does not require integration with specific data sources like Sharepoint or Google Drive, as the tool's strength lies in its broad data source support. Do not use llm-app if you are looking for a tool that focuses solely on model training or inference without the need for cloud templates or enterprise search capabilities. ### Is automl-gs or llm-app more popular on GitHub? llm-app has more GitHub stars (58,920 vs 1,867). Stars measure visibility, not whether either tool fits your constraints. ### Are automl-gs and llm-app open source? Yes - both are open-source projects on GitHub (automl-gs: MIT, llm-app: MIT). ### Where can I find alternatives to automl-gs or llm-app? GraphCanon lists graph-backed alternatives at [automl-gs alternatives](/tools/minimaxir-automl-gs/alternatives) and [llm-app alternatives](/tools/pathwaycom-llm-app/alternatives) ([automl-gs markdown twin](/tools/minimaxir-automl-gs/alternatives.md), [llm-app markdown twin](/tools/pathwaycom-llm-app/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/minimaxir-automl-gs-vs-pathwaycom-llm-app.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, automl-gs or llm-app? automl-gs: Dormant. llm-app: Steady. 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 automl-gs and llm-app? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [automl-gs trust report](/tools/minimaxir-automl-gs/trust); [llm-app trust report](/tools/pathwaycom-llm-app/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=minimaxir-automl-gs`](/api/graphcanon/graph?tool=minimaxir-automl-gs) - 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/_