--- title: "llm-app vs llama-hub" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/pathwaycom-llm-app-vs-run-llama-llama-hub" tools: ["pathwaycom-llm-app", "run-llama-llama-hub"] --- # llm-app vs llama-hub *GraphCanon updated Jul 12, 2026* ## Verdict Pick llm-app when requirements: Requires Docker; The tool is Docker-friendly and designed to ensure synchronization with cloud-based storage solutions among others.; pick llama-hub when tags unique to llama-hub: jupyter notebook. [llm-app](https://pathway.com/developers/templates/) reports 59k GitHub stars, 1.4k forks, and 10 open issues, last pushed Jul 5, 2026. [llama-hub](https://llamahub.ai/) has 3.5k stars, 719 forks, and 96 open issues, last pushed Mar 1, 2024. Figures are from public GitHub metadata via [llm-app's repository](https://github.com/pathwaycom/llm-app) and [llama-hub's repository](https://github.com/run-llama/llama-hub). | | [llm-app](/tools/pathwaycom-llm-app.md) | [llama-hub](/tools/run-llama-llama-hub.md) | | --- | --- | --- | | Tagline | Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. | A library of data loaders for LLMs made by the community -- to be used with LlamaIndex and/or LangChain | | Stars | 59,068 | 3,473 | | Forks | 1,432 | 719 | | Open issues | 10 | 96 | | Language | Jupyter Notebook | Jupyter Notebook | | Adopt for | llm-app offers pre-configured cloud deployment templates designed specifically for creating AI-driven applications such as chatbots and machine learning projects leveraging Hugging Face models. It supports direct integrz | - | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | Data & Retrieval, LLM Frameworks, Vector Databases | Data & Retrieval, Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llm-app](/tools/pathwaycom-llm-app.md) | [llama-hub](/tools/run-llama-llama-hub.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Archived (8%) | | Days since push | 5d | 861d | | Archived on GitHub | No | Yes | | Open issues (now) | 10 | 96 | | Security scan | No lockfile | 121 low (121 low) | | Full report | [trust report](/tools/pathwaycom-llm-app/trust.md) | [trust report](/tools/run-llama-llama-hub/trust.md) | ## Decision facts: llm-app - **Requirements:** Requires Docker; The tool is Docker-friendly and designed to ensure synchronization with cloud-based storage solutions among others. - **Adopt for:** llm-app offers pre-configured cloud deployment templates designed specifically for creating AI-driven applications such as chatbots and machine learning projects leveraging Hugging Face models. It supports direct integrz ## Choose when ### Choose llm-app if… - Requirements: Requires Docker; The tool is Docker-friendly and designed to ensure synchronization with cloud-based storage solutions among others.. - Tags unique to llm-app: chatbot, hugging-face, llm, retrieval-augmented-generation. - Also covers Vector Databases. - - You need a ready-to-run solution that directly integrates with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and live APIs. ### Choose llama-hub if… - Tags unique to llama-hub: jupyter notebook. - Also covers Evaluation & Observability. ## When NOT to use llm-app - - You require custom deployment configurations that extend beyond the pre-set cloud templates available through llm-app. - - There’s a need for tightly integrated support with data sources or APIs not explicitly mentioned, such as specialized CRM systems (Salesforce), which may lack direct template support in llm-app. ## When NOT to use llama-hub - llama-hub is archived on GitHub. Prefer an active alternative unless you maintain a private fork or need a frozen dependency. - Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## Common questions ### What is the difference between llm-app and llama-hub? llm-app: Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data.. llama-hub: A library of data loaders for LLMs made by the community -- to be used with LlamaIndex and/or LangChain. See the comparison table for live GitHub stats and shared categories. ### When should I choose llm-app over llama-hub? Choose llm-app over llama-hub when Requirements: Requires Docker; The tool is Docker-friendly and designed to ensure synchronization with cloud-based storage solutions among others.; Tags unique to llm-app: chatbot, hugging-face, llm, retrieval-augmented-generation; Also covers Vector Databases; - You need a ready-to-run solution that directly integrates with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and live APIs. ### When should I choose llama-hub over llm-app? Choose llama-hub over llm-app when Tags unique to llama-hub: jupyter notebook; Also covers Evaluation & Observability. ### When should I avoid llm-app? - You require custom deployment configurations that extend beyond the pre-set cloud templates available through llm-app. - There’s a need for tightly integrated support with data sources or APIs not explicitly mentioned, such as specialized CRM systems (Salesforce), which may lack direct template support in llm-app. ### When should I avoid llama-hub? llama-hub is archived on GitHub. Prefer an active alternative unless you maintain a private fork or need a frozen dependency. Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### Is llm-app or llama-hub more popular on GitHub? llm-app has more GitHub stars (59,068 vs 3,473). Stars measure visibility, not whether either tool fits your constraints. ### Are llm-app and llama-hub open source? Yes - both are open-source projects on GitHub (llm-app: MIT, llama-hub: MIT). ### Where can I find alternatives to llm-app or llama-hub? GraphCanon lists graph-backed alternatives at [llm-app alternatives](/tools/pathwaycom-llm-app/alternatives) and [llama-hub alternatives](/tools/run-llama-llama-hub/alternatives) ([llm-app markdown twin](/tools/pathwaycom-llm-app/alternatives.md), [llama-hub markdown twin](/tools/run-llama-llama-hub/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/pathwaycom-llm-app-vs-run-llama-llama-hub.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, llm-app or llama-hub? llm-app: Very active. llama-hub: Archived. 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 llm-app and llama-hub? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm-app trust report](/tools/pathwaycom-llm-app/trust); [llama-hub trust report](/tools/run-llama-llama-hub/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=pathwaycom-llm-app`](/api/graphcanon/graph?tool=pathwaycom-llm-app) - 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/_