Comparison
LLMSurvey vs Awesome-LLMOps
Verdict
Pick LLMSurvey if lLMSurvey is a repository that provides a comprehensive survey paper and resources on Large Language Models, including a beginner-friendly Chinese book and detailed content on long chain-of-thought reasoning; 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 · LLMSurvey alternatives · Awesome-LLMOps alternatives
GraphCanon updated Sep 20, 2026
19views this month
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Trust & integrity
| Signal | LLMSurvey | Awesome-LLMOps |
|---|---|---|
| Maintenance | Dormant (555d since push) As of Sep 18, 2026 · github_public_v1 | Slowing (121d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 18, 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 Sep 18, 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
- LLMSurvey
- A collection of papers and resources related to Large Language Models
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- LLMSurvey
- 12k
- Awesome-LLMOps
- 5.9k
Forks
- LLMSurvey
- 930
- Awesome-LLMOps
- 1.1k
Open issues
- LLMSurvey
- 30
- Awesome-LLMOps
- 317
Language
- LLMSurvey
- Python
- Awesome-LLMOps
- Shell
Adopt for
- LLMSurvey
- LLMSurvey is a repository that provides a comprehensive survey paper and resources on Large Language Models, including a beginner-friendly Chinese book and detailed content on long chain-of-thought reasoning.
- 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
- LLMSurvey
- -
- Awesome-LLMOps
- -
Runtime
- LLMSurvey
- -
- Awesome-LLMOps
- -
License
- LLMSurvey
- The license information for LLMSurvey is unknown.
- Awesome-LLMOps
- CC0-1.0
Last pushed
- LLMSurvey
- Mar 11, 2025
- Awesome-LLMOps
- May 21, 2026
Categories
- LLMSurvey
- LLM Frameworks, Model Training
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Maintenance
- LLMSurvey
- Dormant (18%)
- Awesome-LLMOps
- Slowing (36%)
Days since push
- LLMSurvey
- 555d
- Awesome-LLMOps
- 121d
Open issues (now)
- LLMSurvey
- 30
- Awesome-LLMOps
- 317
Stars delta
- LLMSurvey
- +11 (30d)
- Awesome-LLMOps
- +26 (30d)
Open issues delta
- LLMSurvey
- 0 (30d)
- Awesome-LLMOps
- +70 (30d)
Full report
- LLMSurvey
- Trust report
- Awesome-LLMOps
- Trust report
Choose LLMSurvey if…
- LLMSurvey is primarily Python; Awesome-LLMOps is Shell.
- Pricing: The repository is free to access, but the Chinese book is available for purchase..
- Requirements: Min 4 GB RAM; A basic understanding of deep learning is recommended for the Chinese book..
- Tags unique to LLMSurvey: chain-of-thought, chatgpt, in-context-learning, instruction-tuning.
- Use LLMSurvey when you need a detailed survey paper and resources on Large Language Models, especially if you are interested in long chain-of-thought reasoning.
When NOT to use LLMSurvey
- Avoid LLMSurvey if you are looking for a tool that provides hands-on training or implementation of LLMs, as it focuses more on theoretical resources and surveys.
- Do not use LLMSurvey if you require real-time updates or interactive tools for LLM development, as it primarily offers static content and resources.
Choose Awesome-LLMOps if…
- Awesome-LLMOps is primarily Shell; LLMSurvey is Python.
- Tags unique to Awesome-LLMOps: ai development tools, awesome-list, llmops, mlops.
- Also covers Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, 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
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (RUCAIBox/LLMSurvey) · observed Sep 20, 2026
- GitHub forks (RUCAIBox/LLMSurvey) · observed Sep 20, 2026
- Last push (RUCAIBox/LLMSurvey) · observed Mar 11, 2025
- License file (unknown) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 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: LLMSurvey 12k · Awesome-LLMOps 5.9k (synced Sep 20, 2026).
Common questions
- What is the difference between LLMSurvey and Awesome-LLMOps?
- LLMSurvey: A collection of papers and resources related to Large Language Models. 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 LLMSurvey over Awesome-LLMOps?
- Choose LLMSurvey over Awesome-LLMOps when LLMSurvey is primarily Python; Awesome-LLMOps is Shell; Pricing: The repository is free to access, but the Chinese book is available for purchase.; Requirements: Min 4 GB RAM; A basic understanding of deep learning is recommended for the Chinese book.; Tags unique to LLMSurvey: chain-of-thought, chatgpt, in-context-learning, instruction-tuning; Use LLMSurvey when you need a detailed survey paper and resources on Large Language Models, especially if you are interested in long chain-of-thought reasoning.
- When should I choose Awesome-LLMOps over LLMSurvey?
- Choose Awesome-LLMOps over LLMSurvey when Awesome-LLMOps is primarily Shell; LLMSurvey is Python; Tags unique to Awesome-LLMOps: ai development tools, awesome-list, llmops, mlops; Also covers Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
- When should I avoid LLMSurvey?
- Avoid LLMSurvey if you are looking for a tool that provides hands-on training or implementation of LLMs, as it focuses more on theoretical resources and surveys. Do not use LLMSurvey if you require real-time updates or interactive tools for LLM development, as it primarily offers static content and resources.
- 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 LLMSurvey or Awesome-LLMOps more popular on GitHub?
- LLMSurvey has more GitHub stars (12,216 vs 5,941). Stars measure visibility, not whether either tool fits your constraints.
- Are LLMSurvey and Awesome-LLMOps open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to LLMSurvey or Awesome-LLMOps?
- GraphCanon lists graph-backed alternatives at LLMSurvey alternatives and Awesome-LLMOps alternatives (LLMSurvey 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, LLMSurvey or Awesome-LLMOps?
- LLMSurvey: 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 LLMSurvey and Awesome-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: LLMSurvey trust report; Awesome-LLMOps trust report.