Comparison
rig vs ruby_llm
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.
Markdown twin · rig alternatives · ruby_llm alternatives
GraphCanon updated Sep 30, 2026
Trust & integrity
| Signal | rig | ruby_llm |
|---|---|---|
| Maintenance | Very active (0d since push) As of Sep 20, 2026 · github_public_v1 | Very active (2d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- rig
- Build modular and scalable LLM Applications in Rust
- ruby_llm
- A Ruby framework for building AI agents and applications
Stars
- rig
- 8.7k
- ruby_llm
- 4.4k
Forks
- rig
- 974
- ruby_llm
- 504
Open issues
- rig
- 102
- ruby_llm
- 10
Language
- rig
- Rust
- ruby_llm
- Ruby
Adopt for
- rig
- 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
- ruby_llm: A Ruby framework for interacting with major AI providers through a Ruby interface.
Persona
- rig
- -
- ruby_llm
- -
Runtime
- rig
- -
- ruby_llm
- -
License
- rig
- MIT
- ruby_llm
- MIT License
Last pushed
- rig
- Sep 19, 2026
- ruby_llm
- Sep 18, 2026
Categories
- rig
- AI Agents, LLM Frameworks
- ruby_llm
- AI Agents, LLM Frameworks
Trust and health
Days since push
- rig
- 0d
- ruby_llm
- 2d
Open issues (now)
- rig
- 102
- ruby_llm
- 10
Stars delta
- rig
- +351 (30d)
- ruby_llm
- +131 (30d)
Open issues delta
- rig
- -11 (30d)
- ruby_llm
- -38 (30d)
Owner type
- rig
- Organization
- ruby_llm
- User
Full report
- rig
- Trust report
- ruby_llm
- Trust report
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.
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.
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 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).
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (0xPlaygrounds/rig) · observed Sep 20, 2026
- GitHub forks (0xPlaygrounds/rig) · observed Sep 20, 2026
- Last push (0xPlaygrounds/rig) · observed Sep 19, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 9, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (crmne/ruby_llm) · observed Sep 20, 2026
- GitHub forks (crmne/ruby_llm) · observed Sep 20, 2026
- Last push (crmne/ruby_llm) · observed Sep 18, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: rig 8.7k · ruby_llm 4.4k (synced Sep 20, 2026).
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 and ruby_llm alternatives (rig markdown twin, ruby_llm markdown twin), 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 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; ruby_llm trust report.