--- title: "rig vs ruby_llm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/0xplaygrounds-rig-vs-crmne-ruby-llm" tools: ["0xplaygrounds-rig", "crmne-ruby-llm"] --- # rig vs ruby_llm *GraphCanon updated Sep 30, 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 ruby_llm if ruby_llm: A Ruby framework for interacting with major AI providers through a Ruby interface. [rig](https://rig.rs) reports 8.7k GitHub stars, 974 forks, and 102 open issues, last pushed Sep 19, 2026. [ruby_llm](https://rubyllm.com/) has 4.4k stars, 504 forks, and 10 open issues, last pushed Sep 18, 2026. Figures are from public GitHub metadata via [rig's repository](https://github.com/0xPlaygrounds/rig) and [ruby_llm's repository](https://github.com/crmne/ruby_llm). | | [rig](/tools/0xplaygrounds-rig.md) | [ruby_llm](/tools/crmne-ruby-llm.md) | | --- | --- | --- | | Tagline | Build modular and scalable LLM Applications in Rust | A Ruby framework for building AI agents and applications | | Stars | 8,679 | 4,390 | | Forks | 974 | 504 | | Open issues | 102 | 10 | | Language | Rust | Ruby | | 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. | ruby_llm: A Ruby framework for interacting with major AI providers through a Ruby interface. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT License | | Categories | AI Agents, LLM Frameworks | AI Agents, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [rig](/tools/0xplaygrounds-rig.md) | [ruby_llm](/tools/crmne-ruby-llm.md) | | --- | --- | --- | | Days since push | 0d | 2d | | Open issues (now) | 102 | 10 | | Stars delta | +351 (30d) | +131 (30d) | | Open issues delta | -11 (30d) | -38 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/0xplaygrounds-rig/trust.md) | [trust report](/tools/crmne-ruby-llm/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: ruby_llm - **Pricing:** freemium - The library is free and open-source under the MIT license, but some of its functionalities will depend on third-party AI service costs. - **Requirements:** Min 4 GB RAM; Requires Ruby runtime environment - **Adopt for:** ruby_llm: A Ruby framework for interacting with major AI providers through a Ruby interface. - **License detail:** MIT License ## Choose when ### Choose rig if… - rig is primarily Rust; ruby_llm is Ruby. - Self-hosted as a Rust library. - Pricing: Free to use under MIT license with potential premium support options.. - Tags unique to rig: agent, artificial-intelligence, automation, generative-ai. - You should use Rig when you need to work with LLM applications in Rust and want full WASM (core library) compatibility. ### Choose ruby_llm if… - ruby_llm is primarily Ruby; rig is Rust. - Pricing: The library is free and open-source under the MIT license, but some of its functionalities will depend on third-party AI service costs.. - Requirements: Min 4 GB RAM; Requires Ruby runtime environment. - Tags unique to ruby_llm: agents, anthropic, chatgpt, claude. - When your application is built in Ruby and you want to use multiple AI services from different providers (such as Anthropic, OpenAI, etc.) without rewriting the integration code for each. ## 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 ruby_llm - If you are working in an environment where Ruby is not supported or preferred, and your primary requirement is to use a different programming language ecosystem. - In scenarios where the specific AI providers you're interested in do not have good support within ruby_llm (check provider compatibility before starting a project). ## Common questions ### What is the difference between rig and ruby_llm? rig: Build modular and scalable LLM Applications in Rust. ruby_llm: A Ruby framework for building AI agents and applications. See the comparison table for live GitHub stats and shared categories. ### When should I choose rig over ruby_llm? Choose rig over ruby_llm when rig is primarily Rust; ruby_llm is Ruby; Self-hosted as a Rust library; Pricing: Free to use under MIT license with potential premium support options.; Tags unique to rig: agent, artificial-intelligence, automation, generative-ai; 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 ruby_llm over rig? Choose ruby_llm over rig when ruby_llm is primarily Ruby; rig is Rust; Pricing: The library is free and open-source under the MIT license, but some of its functionalities will depend on third-party AI service costs.; Requirements: Min 4 GB RAM; Requires Ruby runtime environment; Tags unique to ruby_llm: agents, anthropic, chatgpt, claude; When your application is built in Ruby and you want to use multiple AI services from different providers (such as Anthropic, OpenAI, etc.) without rewriting the integration code for each. ### 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 ruby_llm? If you are working in an environment where Ruby is not supported or preferred, and your primary requirement is to use a different programming language ecosystem. In scenarios where the specific AI providers you're interested in do not have good support within ruby_llm (check provider compatibility before starting a project). ### Is rig or ruby_llm more popular on GitHub? rig has more GitHub stars (8,679 vs 4,390). Stars measure visibility, not whether either tool fits your constraints. ### Are rig and ruby_llm open source? Yes - both are open-source projects on GitHub (rig: MIT, ruby_llm: MIT). ### Where can I find alternatives to rig or ruby_llm? GraphCanon lists graph-backed alternatives at [rig alternatives](/tools/0xplaygrounds-rig/alternatives) and [ruby_llm alternatives](/tools/crmne-ruby-llm/alternatives) ([rig markdown twin](/tools/0xplaygrounds-rig/alternatives.md), [ruby_llm markdown twin](/tools/crmne-ruby-llm/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-crmne-ruby-llm.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, rig or ruby_llm? rig: Very active. ruby_llm: Very active. 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 ruby_llm? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [rig trust report](/tools/0xplaygrounds-rig/trust); [ruby_llm trust report](/tools/crmne-ruby-llm/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/_