--- title: "llama-hub vs Awesome-LLMOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/run-llama-llama-hub-vs-tensorchord-awesome-llmops" tools: ["run-llama-llama-hub", "tensorchord-awesome-llmops"] --- # llama-hub vs Awesome-LLMOps *GraphCanon updated Sep 20, 2026* ## 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. [llama-hub](https://llamahub.ai/) reports 3.5k GitHub stars, 721 forks, and 96 open issues, last pushed Mar 1, 2024. [Awesome-LLMOps](https://github.com/tensorchord/Awesome-LLMOps) has 5.9k stars, 1.1k forks, and 317 open issues, last pushed May 21, 2026. Figures are from public GitHub metadata via [llama-hub's repository](https://github.com/run-llama/llama-hub) and [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps). | | [llama-hub](/tools/run-llama-llama-hub.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Tagline | A library of data loaders for LLMs made by the community | An awesome & curated list of best LLMOps tools for developers | | Stars | 3,461 | 5,941 | | Forks | 721 | 1,058 | | Open issues | 96 | 317 | | Language | Jupyter Notebook | Shell | | Adopt for | community-driven data loaders for LlamaIndex/LangChain | 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 | - | - | | Runtime | - | - | | License | MIT | CC0-1.0 | | Categories | Data & Retrieval, Model Training | Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llama-hub](/tools/run-llama-llama-hub.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Maintenance | Archived (8%) | Slowing (36%) | | Days since push | 920d | 121d | | Archived on GitHub | Yes | No | | Open issues (now) | 96 | 317 | | Stars delta | -8 (30d) | +26 (30d) | | Open issues delta | 0 (30d) | +70 (30d) | | Full report | [trust report](/tools/run-llama-llama-hub/trust.md) | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | ## Decision facts: llama-hub - **Adopt for:** community-driven data loaders for LlamaIndex/LangChain ## Decision facts: Awesome-LLMOps - **Adopt for:** 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. ## Choose when ### 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 ### 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 llama-hub - Limited support if the community lacks activity - Not suitable without familiarity with Poetry for dependency 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. ## 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](/tools/run-llama-llama-hub/alternatives) and [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) ([llama-hub markdown twin](/tools/run-llama-llama-hub/alternatives.md), [Awesome-LLMOps markdown twin](/tools/tensorchord-awesome-llmops/alternatives.md)), 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](/compare/run-llama-llama-hub-vs-tensorchord-awesome-llmops.md) 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](/tools/run-llama-llama-hub/trust); [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=run-llama-llama-hub`](/api/graphcanon/graph?tool=run-llama-llama-hub) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_