Comparison
llama-hub vs Awesome-LLMOps
Verdict
Pick llama-hub if community-driven data loaders for LlamaIndex/LangChain; 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 · llama-hub alternatives · Awesome-LLMOps alternatives
GraphCanon updated Sep 20, 2026
7views this month
vs
Trust & integrity
| Signal | llama-hub | Awesome-LLMOps |
|---|---|---|
| Maintenance | Archived (920d since push) As of Sep 8, 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 8, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings 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
- llama-hub
- A library of data loaders for LLMs made by the community
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- llama-hub
- 3.5k
- Awesome-LLMOps
- 5.9k
Forks
- llama-hub
- 721
- Awesome-LLMOps
- 1.1k
Open issues
- llama-hub
- 96
- Awesome-LLMOps
- 317
Language
- llama-hub
- Jupyter Notebook
- Awesome-LLMOps
- Shell
Adopt for
- llama-hub
- community-driven data loaders for LlamaIndex/LangChain
- 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
- llama-hub
- -
- Awesome-LLMOps
- -
Runtime
- llama-hub
- -
- Awesome-LLMOps
- -
License
- llama-hub
- MIT
- Awesome-LLMOps
- CC0-1.0
Last pushed
- llama-hub
- Mar 1, 2024
- Awesome-LLMOps
- May 21, 2026
Categories
- llama-hub
- Data & Retrieval, Model Training
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Maintenance
- llama-hub
- Archived (8%)
- Awesome-LLMOps
- Slowing (36%)
Days since push
- llama-hub
- 920d
- Awesome-LLMOps
- 121d
Archived on GitHub
- llama-hub
- Yes
- Awesome-LLMOps
- No
Open issues (now)
- llama-hub
- 96
- Awesome-LLMOps
- 317
Stars delta
- llama-hub
- -8 (30d)
- Awesome-LLMOps
- +26 (30d)
Open issues delta
- llama-hub
- 0 (30d)
- Awesome-LLMOps
- +70 (30d)
OSV dependency advisories
- llama-hub
- Published findings
- Awesome-LLMOps
- No lockfile (source not queried)
Full report
- llama-hub
- Trust report
- Awesome-LLMOps
- Trust report
Choose llama-hub if…
- llama-hub is primarily Jupyter Notebook; Awesome-LLMOps is Shell.
- License: llama-hub is MIT, Awesome-LLMOps is CC0-1.0.
- Tags unique to llama-hub: community-driven, jupyter-notebook, langchain, llamaindex.
- Community-specific features require engagement with community
When NOT to use llama-hub
- Limited support if the community lacks activity
- Not suitable without familiarity with Poetry for dependency management
Choose Awesome-LLMOps if…
- Awesome-LLMOps is primarily Shell; llama-hub is Jupyter Notebook.
- License: Awesome-LLMOps is CC0-1.0, llama-hub is MIT.
- Tags unique to Awesome-LLMOps: ai development tools, awesome-list, llmops, mlops.
- Also covers Computer Vision, Evaluation & Observability, 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
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (run-llama/llama-hub) · observed Sep 20, 2026
- GitHub forks (run-llama/llama-hub) · observed Sep 20, 2026
- Last push (run-llama/llama-hub) · observed Mar 1, 2024
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 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: llama-hub 3.5k · Awesome-LLMOps 5.9k (synced Sep 20, 2026).
Common questions
- What is the difference between llama-hub and Awesome-LLMOps?
- llama-hub: A library of data loaders for LLMs made by the community. 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 llama-hub over Awesome-LLMOps?
- Choose llama-hub over Awesome-LLMOps when llama-hub is primarily Jupyter Notebook; Awesome-LLMOps is Shell; License: llama-hub is MIT, Awesome-LLMOps is CC0-1.0; Tags unique to llama-hub: community-driven, jupyter-notebook, langchain, llamaindex; Community-specific features require engagement with community.
- When should I choose Awesome-LLMOps over llama-hub?
- Choose Awesome-LLMOps over llama-hub when Awesome-LLMOps is primarily Shell; llama-hub is Jupyter Notebook; License: Awesome-LLMOps is CC0-1.0, llama-hub is MIT; Tags unique to Awesome-LLMOps: ai development tools, awesome-list, llmops, mlops; Also covers Computer Vision, Evaluation & Observability, 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 llama-hub?
- Limited support if the community lacks activity Not suitable without familiarity with Poetry for dependency management
- 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 llama-hub or Awesome-LLMOps more popular on GitHub?
- Awesome-LLMOps has more GitHub stars (5,941 vs 3,461). Stars measure visibility, not whether either tool fits your constraints.
- Are llama-hub and Awesome-LLMOps open source?
- Yes - both are open-source projects on GitHub (llama-hub: MIT, Awesome-LLMOps: CC0-1.0).
- Where can I find alternatives to llama-hub or Awesome-LLMOps?
- GraphCanon lists graph-backed alternatives at llama-hub alternatives and Awesome-LLMOps alternatives (llama-hub 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, llama-hub or Awesome-LLMOps?
- llama-hub: Archived. 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 llama-hub and Awesome-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: llama-hub trust report; Awesome-LLMOps trust report.