Comparison
ml-surveys vs awesome-ai-tools
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-ai-tools if awesome AI Tools provides a curated list of top-notch AI resources across various domains from text generation to marketing.
Markdown twin · ml-surveys alternatives · awesome-ai-tools alternatives
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Trust & integrity
| Signal | ml-surveys | awesome-ai-tools |
|---|---|---|
| Maintenance | Dormant (1254d since push) As of today · github_public_v1 | Slowing (221d since push) As of 1w · github_public_v1 |
| Provenance | Not a fork · Personal account As of today · 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 |
Tagline
- ml-surveys
- Survey papers summarizing advances in various AI domains
- awesome-ai-tools
- A curated list of Artificial Intelligence Top Tools
Stars
- ml-surveys
- 2.9k
- awesome-ai-tools
- 5.9k
Forks
- ml-surveys
- 292
- awesome-ai-tools
- 2.0k
Open issues
- ml-surveys
- 2
- awesome-ai-tools
- 1.2k
Language
- ml-surveys
- -
- awesome-ai-tools
- -
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-ai-tools
- Awesome AI Tools provides a curated list of top-notch AI resources across various domains from text generation to marketing.
Persona
- ml-surveys
- -
- awesome-ai-tools
- -
Runtime
- ml-surveys
- -
- awesome-ai-tools
- -
License
- ml-surveys
- MIT
- awesome-ai-tools
- MIT
Last pushed
- ml-surveys
- Mar 17, 2023
- awesome-ai-tools
- Dec 31, 2025
Categories
- ml-surveys
- Computer Vision, Evaluation & Observability, Model Training
- awesome-ai-tools
- AI Agents, Computer Vision, Data & Retrieval, Developer Tools, Evaluation & Observability, Inference & Serving, Model Training, Speech & Audio
Trust and health
Maintenance
- ml-surveys
- Dormant (18%)
- awesome-ai-tools
- Slowing (36%)
Days since push
- ml-surveys
- 1254d
- awesome-ai-tools
- 221d
Open issues (now)
- ml-surveys
- 2
- awesome-ai-tools
- 1.2k
Stars delta
- ml-surveys
- 0 (30d)
- awesome-ai-tools
- Unknown
Open issues delta
- ml-surveys
- 0 (30d)
- awesome-ai-tools
- Unknown
Full report
- ml-surveys
- Trust report
- awesome-ai-tools
- Trust report
Choose ml-surveys if…
- Tags unique to ml-surveys: computer-vision, deep-learning, embeddings, machine-learning.
- When you need comprehensive overviews and summaries of the latest research trends in multiple areas within machine learning
- Leaner open-issue backlog (2).
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
Choose awesome-ai-tools if…
- Tags unique to awesome-ai-tools: ai-tools-list, awesome-ai-tools, code-ai, editor-choice.
- Also covers AI Agents, Data & Retrieval, Developer Tools, Inference & Serving, Speech & Audio.
- When in need of a wide range of categorized AI tools for varied tasks like text generation, audio and video creation, or email management
When NOT to use awesome-ai-tools
- If you seek in-depth technical documentation on each tool since the repository mainly lists tools without comprehensive descriptions
- When you are exclusively interested in AI tools focusing only on one niche domain as there is a broad spectrum of choices presented here
Explore
ml-surveys trust report →awesome-ai-tools trust report →Computer Vision category →Evaluation & Observability category →Model Training category →AI Agents category →Data & Retrieval category →Developer Tools category →Inference & Serving 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 (eugeneyan/ml-surveys) · observed Aug 22, 2026
- GitHub forks (eugeneyan/ml-surveys) · observed Aug 22, 2026
- Last push (eugeneyan/ml-surveys) · observed Mar 17, 2023
- License file (MIT) · observed Aug 22, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (mahseema/awesome-ai-tools) · observed Aug 10, 2026
- GitHub forks (mahseema/awesome-ai-tools) · observed Aug 10, 2026
- Last push (mahseema/awesome-ai-tools) · observed Dec 31, 2025
- License file (MIT) · observed Aug 10, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
GitHub stars on cards: ml-surveys 2.9k · awesome-ai-tools 5.9k (synced Aug 22, 2026).
Common questions
- What is the difference between ml-surveys and awesome-ai-tools?
- ml-surveys: Survey papers summarizing advances in various AI domains. awesome-ai-tools: A curated list of Artificial Intelligence Top Tools. See the comparison table for live GitHub stats and shared categories.
- When should I choose ml-surveys over awesome-ai-tools?
- Choose ml-surveys over awesome-ai-tools when Tags unique to ml-surveys: computer-vision, deep-learning, embeddings, machine-learning; When you need comprehensive overviews and summaries of the latest research trends in multiple areas within machine learning; Leaner open-issue backlog (2).
- When should I choose awesome-ai-tools over ml-surveys?
- Choose awesome-ai-tools over ml-surveys when Tags unique to awesome-ai-tools: ai-tools-list, awesome-ai-tools, code-ai, editor-choice; Also covers AI Agents, Data & Retrieval, Developer Tools, Inference & Serving, Speech & Audio; When in need of a wide range of categorized AI tools for varied tasks like text generation, audio and video creation, or email management.
- 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-ai-tools?
- If you seek in-depth technical documentation on each tool since the repository mainly lists tools without comprehensive descriptions When you are exclusively interested in AI tools focusing only on one niche domain as there is a broad spectrum of choices presented here
- Is ml-surveys or awesome-ai-tools more popular on GitHub?
- awesome-ai-tools has more GitHub stars (5,912 vs 2,902). Stars measure visibility, not whether either tool fits your constraints.
- Are ml-surveys and awesome-ai-tools open source?
- Yes - both are open-source projects on GitHub (ml-surveys: MIT, awesome-ai-tools: MIT).
- Where can I find alternatives to ml-surveys or awesome-ai-tools?
- GraphCanon lists graph-backed alternatives at ml-surveys alternatives and awesome-ai-tools alternatives (ml-surveys markdown twin, awesome-ai-tools 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-ai-tools?
- ml-surveys: Dormant. awesome-ai-tools: 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 ml-surveys and awesome-ai-tools?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: ml-surveys trust report; awesome-ai-tools trust report.