Comparison
awesome-automl-papers vs Awesome-LLMOps
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 Awesome-LLMOps if awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more.
Markdown twin · awesome-automl-papers alternatives · Awesome-LLMOps alternatives
GraphCanon updated Oct 4, 2026
8views this month
vs
Trust & integrity
| Signal | awesome-automl-papers | Awesome-LLMOps |
|---|---|---|
| Maintenance | Dormant (844d since push) As of Oct 4, 2026 · github_public_v1 | Slowing (121d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Oct 4, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 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
- awesome-automl-papers
- A curated list of automated machine learning papers and resources.
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- awesome-automl-papers
- 4.2k
- Awesome-LLMOps
- 5.9k
Forks
- awesome-automl-papers
- 677
- Awesome-LLMOps
- 1.1k
Open issues
- awesome-automl-papers
- 2
- Awesome-LLMOps
- 317
Language
- awesome-automl-papers
- -
- Awesome-LLMOps
- Shell
Adopt for
- awesome-automl-papers
- awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search.
- Awesome-LLMOps
- Awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more.
Persona
- awesome-automl-papers
- -
- Awesome-LLMOps
- -
Runtime
- awesome-automl-papers
- -
- Awesome-LLMOps
- -
License
- awesome-automl-papers
- Apache-2.0
- Awesome-LLMOps
- CC0-1.0
Last pushed
- awesome-automl-papers
- Jun 11, 2024
- Awesome-LLMOps
- May 21, 2026
Categories
- awesome-automl-papers
- Evaluation & Observability, Model Training
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Maintenance
- awesome-automl-papers
- Dormant (18%)
- Awesome-LLMOps
- Slowing (36%)
Days since push
- awesome-automl-papers
- 844d
- Awesome-LLMOps
- 121d
Open issues (now)
- awesome-automl-papers
- 2
- Awesome-LLMOps
- 317
Stars delta
- awesome-automl-papers
- +6 (30d)
- Awesome-LLMOps
- +26 (30d)
Open issues delta
- awesome-automl-papers
- 0 (30d)
- Awesome-LLMOps
- +70 (30d)
Owner type
- awesome-automl-papers
- User
- Awesome-LLMOps
- Organization
Full report
- awesome-automl-papers
- Trust report
- Awesome-LLMOps
- Trust report
Choose awesome-automl-papers if…
- License: awesome-automl-papers is Apache-2.0, Awesome-LLMOps is CC0-1.0.
- 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 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
Choose Awesome-LLMOps if…
- License: Awesome-LLMOps is CC0-1.0, awesome-automl-papers is Apache-2.0.
- Tags unique to Awesome-LLMOps: ai development tools, awesome-list, llmops, mlops.
- Also covers Computer Vision, Data & Retrieval, Inference & Serving, LLM Frameworks, Speech & Audio.
- - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
When NOT to use Awesome-LLMOps
- - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list.
- - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources.
Explore
awesome-automl-papers trust report →Awesome-LLMOps trust report →Evaluation & Observability category →Model Training category →Computer Vision category →Data & Retrieval category →Inference & Serving category →LLM Frameworks category →Speech & Audio category →All comparisonsStack workflowsTrending tools
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (hibayesian/awesome-automl-papers) · observed Oct 4, 2026
- GitHub forks (hibayesian/awesome-automl-papers) · observed Oct 4, 2026
- Last push (hibayesian/awesome-automl-papers) · observed Jun 11, 2024
- License file (Apache-2.0) · observed Oct 4, 2026
- Decision facts (enrichment) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (tensorchord/Awesome-LLMOps) · observed Sep 20, 2026
- GitHub forks (tensorchord/Awesome-LLMOps) · observed Sep 20, 2026
- Last push (tensorchord/Awesome-LLMOps) · observed May 21, 2026
- License file (CC0-1.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-automl-papers 4.2k · Awesome-LLMOps 5.9k (synced Oct 4, 2026).
Common questions
- What is the difference between awesome-automl-papers and Awesome-LLMOps?
- awesome-automl-papers: A curated list of automated machine learning papers and resources.. Awesome-LLMOps: An awesome & curated list of best LLMOps tools for developers. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-automl-papers over Awesome-LLMOps?
- Choose awesome-automl-papers over Awesome-LLMOps when License: awesome-automl-papers is Apache-2.0, Awesome-LLMOps is CC0-1.0; 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 Awesome-LLMOps over awesome-automl-papers?
- Choose Awesome-LLMOps over awesome-automl-papers when License: Awesome-LLMOps is CC0-1.0, awesome-automl-papers is Apache-2.0; Tags unique to Awesome-LLMOps: ai development tools, awesome-list, llmops, mlops; Also covers Computer Vision, Data & Retrieval, Inference & Serving, LLM Frameworks, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
- 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 Awesome-LLMOps?
- - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list. - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources.
- Is awesome-automl-papers or Awesome-LLMOps more popular on GitHub?
- Awesome-LLMOps has more GitHub stars (5,941 vs 4,161). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-automl-papers and Awesome-LLMOps open source?
- Yes - both are open-source projects on GitHub (awesome-automl-papers: Apache-2.0, Awesome-LLMOps: CC0-1.0).
- Where can I find alternatives to awesome-automl-papers or Awesome-LLMOps?
- GraphCanon lists graph-backed alternatives at awesome-automl-papers alternatives and Awesome-LLMOps alternatives (awesome-automl-papers markdown twin, Awesome-LLMOps 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, awesome-automl-papers or Awesome-LLMOps?
- awesome-automl-papers: Dormant. Awesome-LLMOps: Slowing. 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 Awesome-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-automl-papers trust report; Awesome-LLMOps trust report.