--- title: "rig vs ragas" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/0xplaygrounds-rig-vs-vibrantlabsai-ragas" tools: ["0xplaygrounds-rig", "vibrantlabsai-ragas"] --- # rig vs ragas *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 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. [rig](https://rig.rs) reports 8.3k GitHub stars, 937 forks, and 113 open issues, last pushed Aug 20, 2026. [ragas](https://docs.ragas.io) has 15k stars, 1.6k forks, and 562 open issues, last pushed Feb 24, 2026. Figures are from public GitHub metadata via [rig's repository](https://github.com/0xPlaygrounds/rig) and [ragas's repository](https://github.com/vibrantlabsai/ragas). | | [rig](/tools/0xplaygrounds-rig.md) | [ragas](/tools/vibrantlabsai-ragas.md) | | --- | --- | --- | | Tagline | Build modular and scalable LLM Applications in Rust | Supercharge Your LLM Application Evaluations 🚀 | | Stars | 8,328 | 15,388 | | Forks | 937 | 1,637 | | Open issues | 113 | 562 | | Language | Rust | Python | | 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. | 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 | - | developer harness | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | AI Agents, LLM Frameworks | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [rig](/tools/0xplaygrounds-rig.md) | [ragas](/tools/vibrantlabsai-ragas.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 176d | | Open issues (now) | 113 | 562 | | Stars delta | +333 (30d) | +470 (30d) | | Open issues delta | +17 (30d) | +45 (30d) | | Full report | [trust report](/tools/0xplaygrounds-rig/trust.md) | [trust report](/tools/vibrantlabsai-ragas/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: ragas - **Requirements:** Min 4 GB RAM - **Adopt for:** 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:** developer harness ## Choose when ### 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. ### 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 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 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. ## 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 (15,388 vs 8,328). 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](/tools/0xplaygrounds-rig/alternatives) and [ragas alternatives](/tools/vibrantlabsai-ragas/alternatives) ([rig markdown twin](/tools/0xplaygrounds-rig/alternatives.md), [ragas markdown twin](/tools/vibrantlabsai-ragas/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-vibrantlabsai-ragas.md) 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](/tools/0xplaygrounds-rig/trust); [ragas trust report](/tools/vibrantlabsai-ragas/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/_