Comparison
rig vs llm-applications
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.
Markdown twin · rig alternatives · llm-applications alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | rig | llm-applications |
|---|---|---|
| Maintenance | Very active (0d since push) As of today · github_public_v1 | Dormant (721d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Organization account As of 3w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · 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
- llm-applications
- Comprehensive guide to building RAG-based LLM applications for production
Stars
- rig
- 8.3k
- llm-applications
- 1.9k
Forks
- rig
- 937
- llm-applications
- 255
Open issues
- rig
- 113
- llm-applications
- 13
Language
- rig
- Rust
- llm-applications
- Jupyter Notebook
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.
- llm-applications
- The llm-applications repository offers focused guidance on deploying RAG-based LLM apps in production environments with an emphasis on using Ray.
Persona
- rig
- -
- llm-applications
- -
Runtime
- rig
- -
- llm-applications
- -
License
- rig
- MIT
- llm-applications
- CC-BY-4.0
Last pushed
- rig
- Aug 20, 2026
- llm-applications
- Aug 2, 2024
Categories
- rig
- AI Agents, LLM Frameworks
- llm-applications
- Inference & Serving, LLM Frameworks
Trust and health
Maintenance
- rig
- Very active (96%)
- llm-applications
- Dormant (18%)
Days since push
- rig
- 0d
- llm-applications
- 721d
Open issues (now)
- rig
- 113
- llm-applications
- 13
Stars delta
- rig
- +333 (30d)
- llm-applications
- Unknown
Open issues delta
- rig
- +17 (30d)
- llm-applications
- Unknown
Full report
- rig
- Trust report
- llm-applications
- Trust report
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.
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 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 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.
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 Aug 20, 2026
- GitHub forks (0xPlaygrounds/rig) · observed Aug 20, 2026
- Last push (0xPlaygrounds/rig) · observed Aug 20, 2026
- License file (MIT) · observed Aug 20, 2026
- Decision facts (enrichment) · observed Jul 9, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (ray-project/llm-applications) · observed Jul 24, 2026
- GitHub forks (ray-project/llm-applications) · observed Jul 24, 2026
- Last push (ray-project/llm-applications) · observed Aug 2, 2024
- License file (CC-BY-4.0) · observed Jul 24, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: rig 8.3k · llm-applications 1.9k (synced Aug 20, 2026).
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 and llm-applications alternatives (rig markdown twin, llm-applications 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 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; llm-applications trust report.