--- title: "llm-applications vs llmflows" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ray-project-llm-applications-vs-stoyan-stoyanov-llmflows" tools: ["ray-project-llm-applications", "stoyan-stoyanov-llmflows"] --- # llm-applications vs llmflows *GraphCanon updated Aug 24, 2026* ## Verdict Pick llm-applications if the llm-applications repository offers focused guidance on deploying RAG-based LLM apps in production environments with an emphasis on using Ray; pick llmflows if lLMFlows is designed for developers seeking a streamlined way to build and deploy language model applications with an emphasis on transparency and simplicity. [llm-applications](https://github.com/ray-project/llm-applications) reports 1.9k GitHub stars, 256 forks, and 13 open issues, last pushed Aug 15, 2026. [llmflows](https://llmflows.readthedocs.io) has 707 stars, 35 forks, and 19 open issues, last pushed Feb 20, 2025. Figures are from public GitHub metadata via [llm-applications's repository](https://github.com/ray-project/llm-applications) and [llmflows's repository](https://github.com/stoyan-stoyanov/llmflows). | | [llm-applications](/tools/ray-project-llm-applications.md) | [llmflows](/tools/stoyan-stoyanov-llmflows.md) | | --- | --- | --- | | Tagline | Comprehensive guide to building RAG-based LLM applications for production | Simple Explicit Transparent LLM Apps | | Stars | 1,855 | 707 | | Forks | 256 | 35 | | Open issues | 13 | 19 | | Language | Jupyter Notebook | Python | | Adopt for | The llm-applications repository offers focused guidance on deploying RAG-based LLM apps in production environments with an emphasis on using Ray. | LLMFlows is designed for developers seeking a streamlined way to build and deploy language model applications with an emphasis on transparency and simplicity. | | Persona | - | - | | Runtime | - | - | | License | CC-BY-4.0 | MIT | | Categories | Inference & Serving, LLM Frameworks | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llm-applications](/tools/ray-project-llm-applications.md) | [llmflows](/tools/stoyan-stoyanov-llmflows.md) | | --- | --- | --- | | Maintenance | Active (82%) | Dormant (18%) | | Days since push | 8d | 541d | | Open issues (now) | 13 | 19 | | Stars delta | -2 (30d) | +2 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/ray-project-llm-applications/trust.md) | [trust report](/tools/stoyan-stoyanov-llmflows/trust.md) | ## Shared compatibility - **Python**: [llm-applications](/tools/ray-project-llm-applications.md) - Python runtime; [llmflows](/tools/stoyan-stoyanov-llmflows.md) - Python runtime ## Decision facts: llm-applications - **Adopt for:** The llm-applications repository offers focused guidance on deploying RAG-based LLM apps in production environments with an emphasis on using Ray. ## Decision facts: llmflows - **Adopt for:** LLMFlows is designed for developers seeking a streamlined way to build and deploy language model applications with an emphasis on transparency and simplicity. ## Choose when ### Choose llm-applications if… - llm-applications is primarily Jupyter Notebook; llmflows is Python. - License: llm-applications is CC-BY-4.0, llmflows is MIT. - Tags unique to llm-applications: anyscale, fine-tuning, llama2, machin-learning. - You require a detailed guide specifically tailored to the development and deployment of RAG-based applications, leveraging Ray for performance and scalability. ### Choose llmflows if… - llmflows is primarily Python; llm-applications is Jupyter Notebook. - License: llmflows is MIT, llm-applications is CC-BY-4.0. - Tags unique to llmflows: ai, chatgpt, gpt-4, llm. - If you need a Python framework that prioritizes the clarity and ease of use in developing language model apps. ## When NOT to use llm-applications - If you are looking for a more generalized approach to LLM application development that does not specifically cater to RAG-based designs and Ray optimizations. - When your project workflow is incompatible with or cannot support Jupyter Notebook dependencies and the resources assume. ## When NOT to use llmflows - Do not choose LLMFlows if advanced customization or integration with more complex frameworks is required for your project. - Avoid using this tool in scenarios where you need real-time adaptive features that are highly dynamic, as LLMFlows emphasizes explicitness which can limit flexibility. ## Common questions ### What is the difference between llm-applications and llmflows? llm-applications: Comprehensive guide to building RAG-based LLM applications for production. llmflows: Simple Explicit Transparent LLM Apps. See the comparison table for live GitHub stats and shared categories. ### When should I choose llm-applications over llmflows? Choose llm-applications over llmflows when llm-applications is primarily Jupyter Notebook; llmflows is Python; License: llm-applications is CC-BY-4.0, llmflows is MIT; Tags unique to llm-applications: anyscale, fine-tuning, llama2, machin-learning; You require a detailed guide specifically tailored to the development and deployment of RAG-based applications, leveraging Ray for performance and scalability. ### When should I choose llmflows over llm-applications? Choose llmflows over llm-applications when llmflows is primarily Python; llm-applications is Jupyter Notebook; License: llmflows is MIT, llm-applications is CC-BY-4.0; Tags unique to llmflows: ai, chatgpt, gpt-4, llm; If you need a Python framework that prioritizes the clarity and ease of use in developing language model apps. ### When should I avoid llm-applications? If you are looking for a more generalized approach to LLM application development that does not specifically cater to RAG-based designs and Ray optimizations. When your project workflow is incompatible with or cannot support Jupyter Notebook dependencies and the resources assume. ### When should I avoid llmflows? Do not choose LLMFlows if advanced customization or integration with more complex frameworks is required for your project. Avoid using this tool in scenarios where you need real-time adaptive features that are highly dynamic, as LLMFlows emphasizes explicitness which can limit flexibility. ### Is llm-applications or llmflows more popular on GitHub? llm-applications has more GitHub stars (1,855 vs 707). Stars measure visibility, not whether either tool fits your constraints. ### Are llm-applications and llmflows open source? Yes - both are open-source projects on GitHub (llm-applications: CC-BY-4.0, llmflows: MIT). ### Where can I find alternatives to llm-applications or llmflows? GraphCanon lists graph-backed alternatives at [llm-applications alternatives](/tools/ray-project-llm-applications/alternatives) and [llmflows alternatives](/tools/stoyan-stoyanov-llmflows/alternatives) ([llm-applications markdown twin](/tools/ray-project-llm-applications/alternatives.md), [llmflows markdown twin](/tools/stoyan-stoyanov-llmflows/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/ray-project-llm-applications-vs-stoyan-stoyanov-llmflows.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, llm-applications or llmflows? llm-applications: Active. llmflows: Dormant. 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-applications and llmflows? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm-applications trust report](/tools/ray-project-llm-applications/trust); [llmflows trust report](/tools/stoyan-stoyanov-llmflows/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=ray-project-llm-applications`](/api/graphcanon/graph?tool=ray-project-llm-applications) - 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/_