--- title: "quivr vs R2R" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/quivrhq-quivr-vs-sciphi-ai-r2r" tools: ["quivrhq-quivr", "sciphi-ai-r2r"] --- # quivr vs R2R Neutral, constraint-first comparison with live GitHub stats. | | [quivr](/tools/quivrhq-quivr.md) | [R2R](/tools/sciphi-ai-r2r.md) | | --- | --- | --- | | Tagline | Opiniated RAG for integrating GenAI in your apps | SoTA production-ready AI retrieval system. | | Stars | 39,190 | 7,921 | | Forks | 3,719 | 644 | | Open issues | 29 | 121 | | Language | Python | Python | | Adopt for | Quivr is an opinionated RAG framework for integrating Generative AI into apps, emphasizing customizability and compatibility with multiple LLMs and vectorstores. It allows for quick setup and customization to meet varied | R2R is designed for developers aiming to integrate state-of-the-art retrieval abilities into their applications via a RESTful API. | | Persona | - | - | | Runtime | - | - | | License | Other | MIT | | Categories | Data & Retrieval, LLM Frameworks | Data & Retrieval, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [quivr](/tools/quivrhq-quivr.md) | [R2R](/tools/sciphi-ai-r2r.md) | | --- | --- | --- | | Days since push | 363d | 244d | | Open issues (now) | 29 | 121 | | Full report | [trust report](/tools/quivrhq-quivr/trust.md) | [trust report](/tools/sciphi-ai-r2r/trust.md) | **Typed relationship:** quivr _(alternative)_ R2R Both R2R and Quivr are focused on integrating GenAI into apps with a RESTful API, but they have different architectures and configurations. ## Shared compatibility - **Python**: [quivr](/tools/quivrhq-quivr.md) - Python runtime; [R2R](/tools/sciphi-ai-r2r.md) - Python runtime ## Decision facts: quivr - **Adopt for:** Quivr is an opinionated RAG framework for integrating Generative AI into apps, emphasizing customizability and compatibility with multiple LLMs and vectorstores. It allows for quick setup and customization to meet varied ## Decision facts: R2R - **Pricing:** unknown - Details on pricing are not available; the license is MIT, allowing for free use in both open-source and commercial projects. - **Requirements:** Min 8 GB RAM; Requires Docker - **Adopt for:** R2R is designed for developers aiming to integrate state-of-the-art retrieval abilities into their applications via a RESTful API. ## Choose when ### Choose quivr if… - License: quivr is Other, R2R is MIT. - Both R2R and Quivr are focused on integrating GenAI into apps with a RESTful API, but they have different architectures and configurations. - Tags unique to quivr: llm, ai, vector, api. - Also covers LLM Frameworks. - You need a customizable RAG solution that supports multiple types of files and can integrate easily with different LLMs. ### Choose R2R if… - License: R2R is MIT, quivr is Other. - Pricing: Details on pricing are not available; the license is MIT, allowing for free use in both open-source and commercial projects.. - Requirements: Min 8 GB RAM; Requires Docker. - Both R2R and Quivr are focused on integrating GenAI into apps with a RESTful API, but they have different architectures and configurations. - Tags unique to R2R: search, artificial-intelligence, python, large-language-models. - Also covers Inference & Serving. - - When your application requires precise and advanced retrieval capabilities that can be easily integrated via a RESTful interface. ## When NOT to use quivr - If your application strictly demands a non-opinionated approach to RAG where every detail must be manually configured from scratch. - When you require proprietary or highly restricted licensing terms, as Quivr has a 'Other' license that may not align with these needs. - Your project is limited to only specific LLMs not compatible with Quivr's broad support, such as certain bespoke models not covered by its wide umbrella. ## When NOT to use R2R - - If the project does not require high-level retrieval or generation abilities, as R2R is more suited for comprehensive integration in applications demanding advanced AI services. - - When a simpler or lighter integration is needed, as R2R might offer more features than required leading to unnecessary complexity. ## Common questions ### What is the difference between quivr and R2R? quivr: Opiniated RAG for integrating GenAI in your apps. R2R: SoTA production-ready AI retrieval system.. See the comparison table for live GitHub stats and shared categories. ### When should I choose quivr over R2R? Choose quivr over R2R when License: quivr is Other, R2R is MIT; Both R2R and Quivr are focused on integrating GenAI into apps with a RESTful API, but they have different architectures and configurations; Tags unique to quivr: llm, ai, vector, api; Also covers LLM Frameworks; You need a customizable RAG solution that supports multiple types of files and can integrate easily with different LLMs. ### When should I choose R2R over quivr? Choose R2R over quivr when License: R2R is MIT, quivr is Other; Pricing: Details on pricing are not available; the license is MIT, allowing for free use in both open-source and commercial projects.; Requirements: Min 8 GB RAM; Requires Docker; Both R2R and Quivr are focused on integrating GenAI into apps with a RESTful API, but they have different architectures and configurations; Tags unique to R2R: search, artificial-intelligence, python, large-language-models; Also covers Inference & Serving; - When your application requires precise and advanced retrieval capabilities that can be easily integrated via a RESTful interface. ### When should I avoid quivr? If your application strictly demands a non-opinionated approach to RAG where every detail must be manually configured from scratch. When you require proprietary or highly restricted licensing terms, as Quivr has a 'Other' license that may not align with these needs. Your project is limited to only specific LLMs not compatible with Quivr's broad support, such as certain bespoke models not covered by its wide umbrella. ### When should I avoid R2R? - If the project does not require high-level retrieval or generation abilities, as R2R is more suited for comprehensive integration in applications demanding advanced AI services. - When a simpler or lighter integration is needed, as R2R might offer more features than required leading to unnecessary complexity. ### Is quivr or R2R more popular on GitHub? quivr has more GitHub stars (39,190 vs 7,921). Stars measure visibility, not whether either tool fits your constraints. ### Are quivr and R2R open source? Yes - both are open-source projects on GitHub (quivr: Other, R2R: MIT). ### Where can I find alternatives to quivr or R2R? GraphCanon lists graph-backed alternatives at /tools/quivrhq-quivr/alternatives and /tools/sciphi-ai-r2r/alternatives (/tools/quivrhq-quivr/alternatives.md, /tools/sciphi-ai-r2r/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 /compare/quivrhq-quivr-vs-sciphi-ai-r2r.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, quivr or R2R? quivr: Slowing. R2R: 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 quivr and R2R? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: quivr: /tools/quivrhq-quivr/trust; R2R: /tools/sciphi-ai-r2r/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=quivrhq-quivr`](/api/graphcanon/graph?tool=quivrhq-quivr) - 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/_