Comparison
LLMSurvey vs Awesome-LLMOps
Verdict
Pick LLMSurvey if lLMSurvey is a comprehensive resource center dedicated to large language model research, collecting and organizing scholarly materials and resources relevant to chain-of-thought reasoning, in-context learning, RLHF, and训; 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 2d
vs
Trust & integrity
| Signal | LLMSurvey | Awesome-LLMOps |
|---|---|---|
| Maintenance | Dormant (523d since push) As of 2d · github_public_v1 | Steady (60d since push) As of 4w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2d · github_public_v1 | Not a fork · Organization account As of 4w · 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
- LLMSurvey
- A comprehensive 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
- 931
- Awesome-LLMOps
- 924
Open issues
- LLMSurvey
- 30
- Awesome-LLMOps
- 181
Language
- LLMSurvey
- Python
- Awesome-LLMOps
- Shell
Adopt for
- LLMSurvey
- LLMSurvey is a comprehensive resource center dedicated to large language model research, collecting and organizing scholarly materials and resources relevant to chain-of-thought reasoning, in-context learning, RLHF, and训
- 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 for LLMSurvey is unknown based on the provided repository information.
- Awesome-LLMOps
- CC0-1.0
Last pushed
- LLMSurvey
- Mar 11, 2025
- Awesome-LLMOps
- May 21, 2026
Categories
- LLMSurvey
- Evaluation & Observability, LLM Frameworks
- 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
- Steady (60%)
Days since push
- LLMSurvey
- 523d
- Awesome-LLMOps
- 60d
Open issues (now)
- LLMSurvey
- 30
- Awesome-LLMOps
- 181
Stars delta
- LLMSurvey
- +18 (30d)
- Awesome-LLMOps
- Unknown
Open issues delta
- LLMSurvey
- 0 (30d)
- Awesome-LLMOps
- Unknown
Full report
- LLMSurvey
- Trust report
- Awesome-LLMOps
- Trust report
Choose LLMSurvey if…
- LLMSurvey is primarily Python; Awesome-LLMOps is Shell.
- Pricing: Since no detailed pricing plan was specified in the repository contents, it can be inferred that access to the materials and resources of LLMSurvey might be free; however, specific details about usage.
- Tags unique to LLMSurvey: chain-of-thought, in-context-learning, instruction-tuning, large language models.
- You should use LLMSurvey if you are seeking deep insights into specific advancements such as long chain-of-thought (CoT) reasoning approaches used by DeepSeek-R1 or OpenAI's o-series models.
When NOT to use LLMSurvey
- You might not want to use LLMSurvey if you prefer tools that offer practical implementation details over a survey-style summary and organization of research papers.
- Consider other resources if your focus is on hands-on development rather than deep academic exploration, as LLMSurvey provides extensive academic coverage but fewer direct coding or implementation how
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, Inference & Serving, Model Training, 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 Aug 17, 2026
- GitHub forks (RUCAIBox/LLMSurvey) · observed Aug 17, 2026
- Last push (RUCAIBox/LLMSurvey) · observed Mar 11, 2025
- License file (unknown) · observed Aug 17, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (tensorchord/Awesome-LLMOps) · observed Jul 21, 2026
- GitHub forks (tensorchord/Awesome-LLMOps) · observed Jul 21, 2026
- Last push (tensorchord/Awesome-LLMOps) · observed May 21, 2026
- License file (CC0-1.0) · observed Jul 21, 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 Aug 17, 2026).
Common questions
- What is the difference between LLMSurvey and Awesome-LLMOps?
- LLMSurvey: A comprehensive 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: Since no detailed pricing plan was specified in the repository contents, it can be inferred that access to the materials and resources of LLMSurvey might be free; however, specific details about usage; Tags unique to LLMSurvey: chain-of-thought, in-context-learning, instruction-tuning, large language models; You should use LLMSurvey if you are seeking deep insights into specific advancements such as long chain-of-thought (CoT) reasoning approaches used by DeepSeek-R1 or OpenAI's o-series models.
- 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, Inference & Serving, Model Training, 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?
- You might not want to use LLMSurvey if you prefer tools that offer practical implementation details over a survey-style summary and organization of research papers. Consider other resources if your focus is on hands-on development rather than deep academic exploration, as LLMSurvey provides extensive academic coverage but fewer direct coding or implementation how
- 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 (12,205 vs 5,887). 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: 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 LLMSurvey and Awesome-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: LLMSurvey trust report; Awesome-LLMOps trust report.