--- title: "rig vs llm-applications" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/0xplaygrounds-rig-vs-ray-project-llm-applications" tools: ["0xplaygrounds-rig", "ray-project-llm-applications"] --- # rig vs llm-applications *GraphCanon updated Aug 20, 2026* ## Verdict Pick rig if rig is a Rust library designed to create modular and scalable LLM applications with extensive support for agentic workflows, multi-turn streaming, full compatibility with GenAI conventions, and integration capabilities; 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. [rig](https://rig.rs) reports 8.3k GitHub stars, 937 forks, and 113 open issues, last pushed Aug 20, 2026. [llm-applications](https://github.com/ray-project/llm-applications) has 1.9k stars, 255 forks, and 13 open issues, last pushed Aug 2, 2024. Figures are from public GitHub metadata via [rig's repository](https://github.com/0xPlaygrounds/rig) and [llm-applications's repository](https://github.com/ray-project/llm-applications). | | [rig](/tools/0xplaygrounds-rig.md) | [llm-applications](/tools/ray-project-llm-applications.md) | | --- | --- | --- | | Tagline | Build modular and scalable LLM Applications in Rust | Comprehensive guide to building RAG-based LLM applications for production | | Stars | 8,328 | 1,857 | | Forks | 937 | 255 | | Open issues | 113 | 13 | | Language | Rust | Jupyter Notebook | | Adopt for | Rig is a Rust library designed to create modular and scalable LLM applications with extensive support for agentic workflows, multi-turn streaming, full compatibility with GenAI conventions, and integration capabilities. | The llm-applications repository offers focused guidance on deploying RAG-based LLM apps in production environments with an emphasis on using Ray. | | Persona | - | - | | Runtime | - | - | | License | MIT | CC-BY-4.0 | | Categories | AI Agents, LLM Frameworks | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [rig](/tools/0xplaygrounds-rig.md) | [llm-applications](/tools/ray-project-llm-applications.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 721d | | Open issues (now) | 113 | 13 | | Stars delta | +333 (30d) | Unknown | | Open issues delta | +17 (30d) | Unknown | | Full report | [trust report](/tools/0xplaygrounds-rig/trust.md) | [trust report](/tools/ray-project-llm-applications/trust.md) | ## Decision facts: rig - **Hosting:** self hosted - Self-hosted as a Rust library. - **Pricing:** freemium - Free to use under MIT license with potential premium support options. - **Adopt for:** Rig is a Rust library designed to create modular and scalable LLM applications with extensive support for agentic workflows, multi-turn streaming, full compatibility with GenAI conventions, and integration capabilities. - **License detail:** MIT ## 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. ## Choose when ### Choose rig if… - rig is primarily Rust; llm-applications is Jupyter Notebook. - License: rig is MIT, llm-applications is CC-BY-4.0. - Self-hosted as a Rust library. - Pricing: Free to use under MIT license with potential premium support options.. - Tags unique to rig: agent, ai, artificial-intelligence, automation. - Also covers AI Agents. - You should use Rig when you need to work with LLM applications in Rust and want full WASM (core library) compatibility. ### Choose llm-applications if… - llm-applications is primarily Jupyter Notebook; rig is Rust. - License: llm-applications is CC-BY-4.0, rig is MIT. - Tags unique to llm-applications: anyscale, fine-tuning, llama2, machin-learning. - Also covers Inference & Serving. - You require a detailed guide specifically tailored to the development and deployment of RAG-based applications, leveraging Ray for performance and scalability. ## When NOT to use rig - Avoid using Rig if you are working on applications that do not require or support Rust as it is specifically built to facilitate LLM operations within a Rust environment. - Rig may not be suitable if your project cannot handle potential breaking changes, which are expected due to its rapidly evolving nature and upcoming feature updates. ## 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. ## Common questions ### What is the difference between rig and llm-applications? rig: Build modular and scalable LLM Applications in Rust. llm-applications: Comprehensive guide to building RAG-based LLM applications for production. See the comparison table for live GitHub stats and shared categories. ### When should I choose rig over llm-applications? Choose rig over llm-applications when rig is primarily Rust; llm-applications is Jupyter Notebook; License: rig is MIT, llm-applications is CC-BY-4.0; Self-hosted as a Rust library; Pricing: Free to use under MIT license with potential premium support options.; Tags unique to rig: agent, ai, artificial-intelligence, automation; Also covers AI Agents; You should use Rig when you need to work with LLM applications in Rust and want full WASM (core library) compatibility. ### When should I choose llm-applications over rig? Choose llm-applications over rig when llm-applications is primarily Jupyter Notebook; rig is Rust; License: llm-applications is CC-BY-4.0, rig is MIT; Tags unique to llm-applications: anyscale, fine-tuning, llama2, machin-learning; Also covers Inference & Serving; 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 avoid rig? Avoid using Rig if you are working on applications that do not require or support Rust as it is specifically built to facilitate LLM operations within a Rust environment. Rig may not be suitable if your project cannot handle potential breaking changes, which are expected due to its rapidly evolving nature and upcoming feature updates. ### 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. ### Is rig or llm-applications more popular on GitHub? rig has more GitHub stars (8,328 vs 1,857). Stars measure visibility, not whether either tool fits your constraints. ### Are rig and llm-applications open source? Yes - both are open-source projects on GitHub (rig: MIT, llm-applications: CC-BY-4.0). ### Where can I find alternatives to rig or llm-applications? GraphCanon lists graph-backed alternatives at [rig alternatives](/tools/0xplaygrounds-rig/alternatives) and [llm-applications alternatives](/tools/ray-project-llm-applications/alternatives) ([rig markdown twin](/tools/0xplaygrounds-rig/alternatives.md), [llm-applications markdown twin](/tools/ray-project-llm-applications/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/0xplaygrounds-rig-vs-ray-project-llm-applications.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, rig or llm-applications? rig: Very active. llm-applications: 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 rig and llm-applications? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [rig trust report](/tools/0xplaygrounds-rig/trust); [llm-applications trust report](/tools/ray-project-llm-applications/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=0xplaygrounds-rig`](/api/graphcanon/graph?tool=0xplaygrounds-rig) - 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/_