Comparison
rig vs ragas
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 ragas if ragas is a Python-based tool designed to enhance the evaluation process of Large Language Model (LLM) applications through specialized workflows and performance insights.
Markdown twin · rig alternatives · ragas alternatives
GraphCanon updated 1mo
Trust & integrity
| Signal | rig | ragas |
|---|---|---|
| Maintenance | Very active (0d since push) As of 1mo · github_public_v1 | Slowing (146d since push) As of 1mo · github_public_v1 |
| Provenance | Not a fork · Organization account As of 1mo · github_public_v1 | Not a fork · Organization account As of 1mo · 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
- ragas
- Supercharge Your LLM Application Evaluations 🚀
Stars
- rig
- 8.0k
- ragas
- 15k
Forks
- rig
- 894
- ragas
- 1.6k
Open issues
- rig
- 96
- ragas
- 517
Language
- rig
- Rust
- ragas
- Python
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.
- ragas
- Ragas is a Python-based tool designed to enhance the evaluation process of Large Language Model (LLM) applications through specialized workflows and performance insights.
Persona
- rig
- -
- ragas
- developer harness
Runtime
- rig
- -
- ragas
- -
License
- rig
- MIT
- ragas
- Apache-2.0
Last pushed
- rig
- Jul 20, 2026
- ragas
- Feb 24, 2026
Categories
- rig
- AI Agents, LLM Frameworks
- ragas
- Evaluation & Observability
Trust and health
Maintenance
- rig
- Very active (96%)
- ragas
- Slowing (36%)
Days since push
- rig
- 0d
- ragas
- 146d
Open issues (now)
- rig
- 96
- ragas
- 517
Full report
- rig
- Trust report
- ragas
- Trust report
Choose rig if…
- rig is primarily Rust; ragas is Python.
- License: rig is MIT, ragas is Apache-2.0.
- 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, LLM Frameworks.
- 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 ragas if…
- ragas is primarily Python; rig is Rust.
- License: ragas is Apache-2.0, rig is MIT.
- Requirements: Min 4 GB RAM.
- Tags unique to ragas: evaluation.
- Also covers Evaluation & Observability.
- When you need advanced tools tailored for evaluating LLM applications, as RAGAS offers specific optimizations not found in generic testing frameworks.
When NOT to use ragas
- If your application does not involve Large Language Models or if the evaluation needs are basic; RAGAS is optimized for LLM-specific evaluations which may be overkill for simpler systems.
- For projects that require real-time monitoring or continuous testing of live models where more dynamic observability tools might offer better support.
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 Jul 21, 2026
- GitHub forks (0xPlaygrounds/rig) · observed Jul 21, 2026
- Last push (0xPlaygrounds/rig) · observed Jul 20, 2026
- License file (MIT) · observed Jul 21, 2026
- Decision facts (enrichment) · observed Jul 9, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (vibrantlabsai/ragas) · observed Jul 21, 2026
- GitHub forks (vibrantlabsai/ragas) · observed Jul 21, 2026
- Last push (vibrantlabsai/ragas) · observed Feb 24, 2026
- License file (Apache-2.0) · observed Jul 21, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: rig 8.0k · ragas 15k (synced Jul 21, 2026).
Common questions
- What is the difference between rig and ragas?
- rig: Build modular and scalable LLM Applications in Rust. ragas: Supercharge Your LLM Application Evaluations 🚀. See the comparison table for live GitHub stats and shared categories.
- When should I choose rig over ragas?
- Choose rig over ragas when rig is primarily Rust; ragas is Python; License: rig is MIT, ragas is Apache-2.0; 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, LLM Frameworks; 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 ragas over rig?
- Choose ragas over rig when ragas is primarily Python; rig is Rust; License: ragas is Apache-2.0, rig is MIT; Requirements: Min 4 GB RAM; Tags unique to ragas: evaluation; Also covers Evaluation & Observability; When you need advanced tools tailored for evaluating LLM applications, as RAGAS offers specific optimizations not found in generic testing frameworks.
- 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 ragas?
- If your application does not involve Large Language Models or if the evaluation needs are basic; RAGAS is optimized for LLM-specific evaluations which may be overkill for simpler systems. For projects that require real-time monitoring or continuous testing of live models where more dynamic observability tools might offer better support.
- Is rig or ragas more popular on GitHub?
- ragas has more GitHub stars (14,918 vs 7,995). Stars measure visibility, not whether either tool fits your constraints.
- Are rig and ragas open source?
- Yes - both are open-source projects on GitHub (rig: MIT, ragas: Apache-2.0).
- Where can I find alternatives to rig or ragas?
- GraphCanon lists graph-backed alternatives at rig alternatives and ragas alternatives (rig markdown twin, ragas 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 ragas?
- rig: Very active. ragas: Slowing. 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 ragas?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: rig trust report; ragas trust report.