--- title: "handy-ollama vs llm-app" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/datawhalechina-handy-ollama-vs-pathwaycom-llm-app" tools: ["datawhalechina-handy-ollama", "pathwaycom-llm-app"] --- # handy-ollama vs llm-app *GraphCanon updated Sep 20, 2026* ## Verdict Pick handy-ollama if handy-ollama is a guide for deploying large language models using Ollama on CPU systems via Jupyter Notebooks; pick llm-app if llm-app offers cloud templates for RAG, AI pipelines, and enterprise search, supporting integration with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs. [handy-ollama](https://datawhalechina.github.io/handy-ollama/) reports 2.5k GitHub stars, 321 forks, and 8 open issues, last pushed Jan 15, 2026. [llm-app](https://pathway.com/developers/templates/) has 59k stars, 1.5k forks, and 8 open issues, last pushed Jul 5, 2026. Figures are from public GitHub metadata via [handy-ollama's repository](https://github.com/datawhalechina/handy-ollama) and [llm-app's repository](https://github.com/pathwaycom/llm-app). | | [handy-ollama](/tools/datawhalechina-handy-ollama.md) | [llm-app](/tools/pathwaycom-llm-app.md) | | --- | --- | --- | | Tagline | Hands-On Ollama with CPU for Large Model Deployment | Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data | | Stars | 2,532 | 58,920 | | Forks | 321 | 1,498 | | Open issues | 8 | 8 | | Language | Jupyter Notebook | Jupyter Notebook | | Adopt for | handy-ollama is a guide for deploying large language models using Ollama on CPU systems via Jupyter Notebooks. | llm-app offers cloud templates for RAG, AI pipelines, and enterprise search, supporting integration with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs. | | Persona | - | - | | Runtime | - | - | | License | handy-ollama is released under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). | MIT License | | Categories | Inference & Serving, Model Training | Data & Retrieval, Evaluation & Observability, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [handy-ollama](/tools/datawhalechina-handy-ollama.md) | [llm-app](/tools/pathwaycom-llm-app.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Steady (60%) | | Days since push | 247d | 74d | | Stars delta | +33 (30d) | -117 (30d) | | Full report | [trust report](/tools/datawhalechina-handy-ollama/trust.md) | [trust report](/tools/pathwaycom-llm-app/trust.md) | ## Decision facts: handy-ollama - **Requirements:** Requires Ollama library for operations. - **Adopt for:** handy-ollama is a guide for deploying large language models using Ollama on CPU systems via Jupyter Notebooks. - **License detail:** handy-ollama is released under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). ## Decision facts: llm-app - **Pricing:** freemium - The repository is open-source under the MIT License, but additional services or support might incur costs. - **Requirements:** Min 4 GB RAM; Requires Docker; Requires Docker for running the cloud templates.; Supports integration with a variety of data sources including Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs. - **Adopt for:** llm-app offers cloud templates for RAG, AI pipelines, and enterprise search, supporting integration with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs. - **License detail:** MIT License ## Choose when ### Choose handy-ollama if… - License: handy-ollama is Other, llm-app is MIT. - Requirements: Requires Ollama library for operations.. - Tags unique to handy-ollama: agent, gguf, langchain, large-language-models. - Use handy-ollama when you require specific guidance on deploying large models with the Ollama library exclusively on CPUs, as opposed to GPU-based alternatives. ### Choose llm-app if… - License: llm-app is MIT, handy-ollama is Other. - Pricing: The repository is open-source under the MIT License, but additional services or support might incur costs.. - Requirements: Min 4 GB RAM; Requires Docker; Requires Docker for running the cloud templates.; Supports integration with a variety of data sources including Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs.. - Tags unique to llm-app: chatbot, hugging-face, llm-local, llm-prompting. - Also covers Data & Retrieval, Evaluation & Observability. - When you need ready-to-run cloud templates for RAG, AI pipelines, and enterprise search that integrate seamlessly with data sources such as Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-ti ## When NOT to use handy-ollama - Avoid handy-ollama if you need support for deploying models on GPU or other hardware that is not specifically CPUs. - Do not use this guide if comprehensive tutorials in languages other than English are necessary, as the content appears to be primarily in Chinese and English. ## When NOT to use llm-app - Avoid using llm-app if your project does not require integration with specific data sources like Sharepoint or Google Drive, as the tool's strength lies in its broad data source support. - Do not use llm-app if you are looking for a tool that focuses solely on model training or inference without the need for cloud templates or enterprise search capabilities. ## Common questions ### What is the difference between handy-ollama and llm-app? handy-ollama: Hands-On Ollama with CPU for Large Model Deployment. llm-app: Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. See the comparison table for live GitHub stats and shared categories. ### When should I choose handy-ollama over llm-app? Choose handy-ollama over llm-app when License: handy-ollama is Other, llm-app is MIT; Requirements: Requires Ollama library for operations.; Tags unique to handy-ollama: agent, gguf, langchain, large-language-models; Use handy-ollama when you require specific guidance on deploying large models with the Ollama library exclusively on CPUs, as opposed to GPU-based alternatives. ### When should I choose llm-app over handy-ollama? Choose llm-app over handy-ollama when License: llm-app is MIT, handy-ollama is Other; Pricing: The repository is open-source under the MIT License, but additional services or support might incur costs.; Requirements: Min 4 GB RAM; Requires Docker; Requires Docker for running the cloud templates.; Supports integration with a variety of data sources including Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs.; Tags unique to llm-app: chatbot, hugging-face, llm-local, llm-prompting; Also covers Data & Retrieval, Evaluation & Observability; When you need ready-to-run cloud templates for RAG, AI pipelines, and enterprise search that integrate seamlessly with data sources such as Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-ti. ### When should I avoid handy-ollama? Avoid handy-ollama if you need support for deploying models on GPU or other hardware that is not specifically CPUs. Do not use this guide if comprehensive tutorials in languages other than English are necessary, as the content appears to be primarily in Chinese and English. ### When should I avoid llm-app? Avoid using llm-app if your project does not require integration with specific data sources like Sharepoint or Google Drive, as the tool's strength lies in its broad data source support. Do not use llm-app if you are looking for a tool that focuses solely on model training or inference without the need for cloud templates or enterprise search capabilities. ### Is handy-ollama or llm-app more popular on GitHub? llm-app has more GitHub stars (58,920 vs 2,532). Stars measure visibility, not whether either tool fits your constraints. ### Are handy-ollama and llm-app open source? Yes - both are open-source projects on GitHub (handy-ollama: Other, llm-app: MIT). ### Where can I find alternatives to handy-ollama or llm-app? GraphCanon lists graph-backed alternatives at [handy-ollama alternatives](/tools/datawhalechina-handy-ollama/alternatives) and [llm-app alternatives](/tools/pathwaycom-llm-app/alternatives) ([handy-ollama markdown twin](/tools/datawhalechina-handy-ollama/alternatives.md), [llm-app markdown twin](/tools/pathwaycom-llm-app/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/datawhalechina-handy-ollama-vs-pathwaycom-llm-app.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, handy-ollama or llm-app? handy-ollama: Slowing. llm-app: 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 handy-ollama and llm-app? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [handy-ollama trust report](/tools/datawhalechina-handy-ollama/trust); [llm-app trust report](/tools/pathwaycom-llm-app/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=datawhalechina-handy-ollama`](/api/graphcanon/graph?tool=datawhalechina-handy-ollama) - 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/_