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Comparison

ml-surveys vs awesome-LLM-resources

Verdict

Pick ml-surveys if ml-surveys is a collection of detailed review papers summarizing advancements in various AI domains such as deep learning, NLP, CV, graphs, reinforcement learning, and recommendation systems; pick awesome-LLM-resources if awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as.

Markdown twin · ml-surveys alternatives · awesome-LLM-resources alternatives

GraphCanon updated 2d

ml-surveys logo

ml-surveys

eugeneyan/ml-surveys

★ 2.9kpushed Mar 17, 2023
vs
awesome-LLM-resources logo

awesome-LLM-resources

WangRongsheng/awesome-LLM-resources

★ 8.8kpushed Aug 14, 2026

Trust & integrity

Signalml-surveysawesome-LLM-resources
Maintenance
Dormant (1254d since push)
As of 2d · github_public_v1
Very active (2d since push)
As of 1w · github_public_v1
Provenance
Not a fork · Personal account
As of 2d · github_public_v1
Not a fork · Personal account
As of 1w · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of 1mo · osv@v1
No lockfile (source not queried)
As of 1mo · 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
ml-surveys trust reportawesome-LLM-resources trust reportTrust methodology

Tagline

ml-surveys
Survey papers summarizing advances in various AI domains
awesome-LLM-resources
Summary of the world's best LLM resources.

Stars

ml-surveys
2.9k
awesome-LLM-resources
8.8k

Forks

ml-surveys
292
awesome-LLM-resources
950

Open issues

ml-surveys
2
awesome-LLM-resources
23

Language

ml-surveys
-
awesome-LLM-resources
-

Adopt for

ml-surveys
ml-surveys is a collection of detailed review papers summarizing advancements in various AI domains such as deep learning, NLP, CV, graphs, reinforcement learning, and recommendation systems.
awesome-LLM-resources
awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a

Persona

ml-surveys
-
awesome-LLM-resources
-

Runtime

ml-surveys
-
awesome-LLM-resources
-

License

ml-surveys
MIT
awesome-LLM-resources
Apache-2.0

Last pushed

ml-surveys
Mar 17, 2023
awesome-LLM-resources
Aug 14, 2026

Categories

ml-surveys
Computer Vision, Evaluation & Observability, Model Training
awesome-LLM-resources
AI Agents, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training

Trust and health

Maintenance

ml-surveys
Dormant (18%)
awesome-LLM-resources
Very active (96%)

Days since push

ml-surveys
1254d
awesome-LLM-resources
2d

Open issues (now)

ml-surveys
2
awesome-LLM-resources
23

Stars delta

ml-surveys
0 (30d)
awesome-LLM-resources
+142 (30d)

Open issues delta

ml-surveys
0 (30d)
awesome-LLM-resources
-13 (30d)

Full report

ml-surveys
Trust report
awesome-LLM-resources
Trust report

Choose ml-surveys if…

  • License: ml-surveys is MIT, awesome-LLM-resources is Apache-2.0.
  • Tags unique to ml-surveys: computer-vision, deep-learning, embeddings, machine-learning.
  • Also covers Computer Vision.
  • When you need comprehensive overviews and summaries of the latest research trends in multiple areas within machine learning

When NOT to use ml-surveys

  • If you are seeking detailed technical details, original experiments, or specific algorithm implementations as ml-surveys focuses more on synthesis and summary
  • In cases where deep-dive analysis is required into a single niche topic, as ml-surveys provides broad overviews rather than in-depth coverage of individual niches
GitHubDetails

Choose awesome-LLM-resources if…

  • License: awesome-LLM-resources is Apache-2.0, ml-surveys is MIT.
  • Tags unique to awesome-LLM-resources: awesome-list, book, course, large language models.
  • Also covers AI Agents, Developer Tools, Inference & Serving, LLM Frameworks.
  • - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies.

When NOT to use awesome-LLM-resources

  • - Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage.
  • - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content.
GitHubDetails

Explore

ml-surveys trust report →awesome-LLM-resources trust report →Computer Vision category →Evaluation & Observability category →Model Training category →AI Agents category →Developer Tools category →Inference & Serving category →LLM Frameworks 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 on cards: ml-surveys 2.9k · awesome-LLM-resources 8.8k (synced Aug 22, 2026).

Common questions

What is the difference between ml-surveys and awesome-LLM-resources?
ml-surveys: Survey papers summarizing advances in various AI domains. awesome-LLM-resources: Summary of the world's best LLM resources.. See the comparison table for live GitHub stats and shared categories.
When should I choose ml-surveys over awesome-LLM-resources?
Choose ml-surveys over awesome-LLM-resources when License: ml-surveys is MIT, awesome-LLM-resources is Apache-2.0; Tags unique to ml-surveys: computer-vision, deep-learning, embeddings, machine-learning; Also covers Computer Vision; When you need comprehensive overviews and summaries of the latest research trends in multiple areas within machine learning.
When should I choose awesome-LLM-resources over ml-surveys?
Choose awesome-LLM-resources over ml-surveys when License: awesome-LLM-resources is Apache-2.0, ml-surveys is MIT; Tags unique to awesome-LLM-resources: awesome-list, book, course, large language models; Also covers AI Agents, Developer Tools, Inference & Serving, LLM Frameworks; - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies.
When should I avoid ml-surveys?
If you are seeking detailed technical details, original experiments, or specific algorithm implementations as ml-surveys focuses more on synthesis and summary In cases where deep-dive analysis is required into a single niche topic, as ml-surveys provides broad overviews rather than in-depth coverage of individual niches
When should I avoid awesome-LLM-resources?
- Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage. - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content.
Is ml-surveys or awesome-LLM-resources more popular on GitHub?
awesome-LLM-resources has more GitHub stars (8,845 vs 2,902). Stars measure visibility, not whether either tool fits your constraints.
Are ml-surveys and awesome-LLM-resources open source?
Yes - both are open-source projects on GitHub (ml-surveys: MIT, awesome-LLM-resources: Apache-2.0).
Where can I find alternatives to ml-surveys or awesome-LLM-resources?
GraphCanon lists graph-backed alternatives at ml-surveys alternatives and awesome-LLM-resources alternatives (ml-surveys markdown twin, awesome-LLM-resources 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, ml-surveys or awesome-LLM-resources?
ml-surveys: Dormant. awesome-LLM-resources: 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 ml-surveys and awesome-LLM-resources?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: ml-surveys trust report; awesome-LLM-resources trust report.

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