Comparison
automl-gs vs llm-app
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.
Markdown twin · automl-gs alternatives · llm-app alternatives
GraphCanon updated Sep 30, 2026
7views this month
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Trust & integrity
| Signal | automl-gs | llm-app |
|---|---|---|
| Maintenance | Dormant (2534d since push) As of Sep 30, 2026 · github_public_v1 | Steady (74d since push) As of Sep 18, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 30, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Sep 18, 2026 · 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 |
Tagline
- 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
Stars
- automl-gs
- 1.9k
- llm-app
- 59k
Forks
- automl-gs
- 178
- llm-app
- 1.5k
Open issues
- automl-gs
- 28
- llm-app
- 8
Language
- automl-gs
- Python
- llm-app
- Jupyter Notebook
Adopt for
- automl-gs
- automl-gs: Python tool for automated machine-learning model creation from CSV data
- llm-app
- 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
- automl-gs
- -
- llm-app
- -
Runtime
- automl-gs
- -
- llm-app
- -
License
- automl-gs
- MIT
- llm-app
- MIT License
Last pushed
- automl-gs
- Oct 22, 2019
- llm-app
- Jul 5, 2026
Categories
- automl-gs
- Data & Retrieval, Model Training
- llm-app
- Data & Retrieval, Evaluation & Observability, Inference & Serving, Model Training
Trust and health
Maintenance
- automl-gs
- Dormant (18%)
- llm-app
- Steady (60%)
Days since push
- automl-gs
- 2534d
- llm-app
- 74d
Open issues (now)
- automl-gs
- 28
- llm-app
- 8
Stars delta
- automl-gs
- -2 (30d)
- llm-app
- -117 (30d)
Owner type
- automl-gs
- User
- llm-app
- Organization
OSV dependency advisories
- automl-gs
- Published findings
- llm-app
- No lockfile (source not queried)
Full report
- automl-gs
- Trust report
- llm-app
- Trust report
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
When NOT to use automl-gs
- Complex feature engineering or non-standard data inputs required
- Sensitive about licensing of the generated code
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (minimaxir/automl-gs) · observed Sep 30, 2026
- GitHub forks (minimaxir/automl-gs) · observed Sep 30, 2026
- Last push (minimaxir/automl-gs) · observed Oct 22, 2019
- License file (MIT) · observed Sep 30, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (pathwaycom/llm-app) · observed Sep 20, 2026
- GitHub forks (pathwaycom/llm-app) · observed Sep 20, 2026
- Last push (pathwaycom/llm-app) · observed Jul 5, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
GitHub stars on cards: automl-gs 1.9k · llm-app 59k (synced Sep 30, 2026).
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 and llm-app alternatives (automl-gs markdown twin, llm-app 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, 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; llm-app trust report.