--- title: "quivr vs rags" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/quivrhq-quivr-vs-run-llama-rags" tools: ["quivrhq-quivr", "run-llama-rags"] --- # quivr vs rags Neutral, constraint-first comparison with live GitHub stats. | | [quivr](/tools/quivrhq-quivr.md) | [rags](/tools/run-llama-rags.md) | | --- | --- | --- | | Tagline | Opiniated RAG for integrating GenAI in your apps | Build ChatGPT over your data using natural language with RAGs | | Stars | 39,190 | 6,546 | | Forks | 3,719 | 660 | | Open issues | 29 | 38 | | 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 | RAGs is a Python-based Streamlit app designed to build Retriever-Augmented Generation pipelines using natural language instructions and configurations. | | Persona | - | - | | Runtime | - | - | | License | Other | MIT | | Categories | Data & Retrieval, LLM Frameworks | AI Agents, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [quivr](/tools/quivrhq-quivr.md) | [rags](/tools/run-llama-rags.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 363d | 824d | | Open issues (now) | 29 | 38 | | Security scan | No lockfile | Not scanned | | Full report | [trust report](/tools/quivrhq-quivr/trust.md) | [trust report](/tools/run-llama-rags/trust.md) | **Typed relationship:** quivr _(alternative)_ rags Quivr and RAGs both offer retrieval-augmented generation (RAG) solutions, but they have different design philosophies with Quivr being more opinionated. ## Shared compatibility - **Python**: [quivr](/tools/quivrhq-quivr.md) - Python runtime; [rags](/tools/run-llama-rags.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: rags - **Pricing:** freemium - RAGs is open-source under MIT license. Costs arise from any third-party API usage such as OpenAI and are not covered by RAGs itself. - **Requirements:** Min 4 GB RAM; RAGs requires an internet connection to interact with external APIs like OpenAI.; Ensure you configure your environment with the necessary API keys and secrets as per the installation guide. - **Adopt for:** RAGs is a Python-based Streamlit app designed to build Retriever-Augmented Generation pipelines using natural language instructions and configurations. ## Choose when ### Choose quivr if… - License: quivr is Other, rags is MIT. - Quivr and RAGs both offer retrieval-augmented generation (RAG) solutions, but they have different design philosophies with Quivr being more opinionated. - Tags unique to quivr: ai, vector, api, framework. - Also covers LLM Frameworks. - You need a customizable RAG solution that supports multiple types of files and can integrate easily with different LLMs. ### Choose rags if… - License: rags is MIT, quivr is Other. - Pricing: RAGs is open-source under MIT license. Costs arise from any third-party API usage such as OpenAI and are not covered by RAGs itself.. - Requirements: Min 4 GB RAM; RAGs requires an internet connection to interact with external APIs like OpenAI.; Ensure you configure your environment with the necessary API keys and secrets as per the installation guide.. - Quivr and RAGs both offer retrieval-augmented generation (RAG) solutions, but they have different design philosophies with Quivr being more opinionated. - Tags unique to rags: streamlit, agent. - Also covers AI Agents. - Use RAGs if you want an interactive way to configure and query your data with simple textual instructions through an intuitive UI in a Streamlit app. ## 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 rags - Avoid RAGs if you need full customization of the backend logic and don't want the constraints imposed by the Streamlit interface. - Not recommended for environments with strict security policies that forbid the use of external APIs like OpenAI, unless you have the capability to replace those services. ## Common questions ### What is the difference between quivr and rags? quivr: Opiniated RAG for integrating GenAI in your apps. rags: Build ChatGPT over your data using natural language with RAGs. See the comparison table for live GitHub stats and shared categories. ### When should I choose quivr over rags? Choose quivr over rags when License: quivr is Other, rags is MIT; Quivr and RAGs both offer retrieval-augmented generation (RAG) solutions, but they have different design philosophies with Quivr being more opinionated; Tags unique to quivr: ai, vector, api, framework; 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 rags over quivr? Choose rags over quivr when License: rags is MIT, quivr is Other; Pricing: RAGs is open-source under MIT license. Costs arise from any third-party API usage such as OpenAI and are not covered by RAGs itself.; Requirements: Min 4 GB RAM; RAGs requires an internet connection to interact with external APIs like OpenAI.; Ensure you configure your environment with the necessary API keys and secrets as per the installation guide.; Quivr and RAGs both offer retrieval-augmented generation (RAG) solutions, but they have different design philosophies with Quivr being more opinionated; Tags unique to rags: streamlit, agent; Also covers AI Agents; Use RAGs if you want an interactive way to configure and query your data with simple textual instructions through an intuitive UI in a Streamlit app. ### 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 rags? Avoid RAGs if you need full customization of the backend logic and don't want the constraints imposed by the Streamlit interface. Not recommended for environments with strict security policies that forbid the use of external APIs like OpenAI, unless you have the capability to replace those services. ### Is quivr or rags more popular on GitHub? quivr has more GitHub stars (39,190 vs 6,546). Stars measure visibility, not whether either tool fits your constraints. ### Are quivr and rags open source? Yes - both are open-source projects on GitHub (quivr: Other, rags: MIT). ### Where can I find alternatives to quivr or rags? GraphCanon lists graph-backed alternatives at /tools/quivrhq-quivr/alternatives and /tools/run-llama-rags/alternatives (/tools/quivrhq-quivr/alternatives.md, /tools/run-llama-rags/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-run-llama-rags.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, quivr or rags? quivr: Slowing. rags: 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 quivr and rags? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: quivr: /tools/quivrhq-quivr/trust; rags: /tools/run-llama-rags/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/_