--- title: "paper-qa vs quivr" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/future-house-paper-qa-vs-quivrhq-quivr" tools: ["future-house-paper-qa", "quivrhq-quivr"] --- # paper-qa vs quivr Neutral, constraint-first comparison with live GitHub stats. | | [paper-qa](/tools/future-house-paper-qa.md) | [quivr](/tools/quivrhq-quivr.md) | | --- | --- | --- | | Tagline | High accuracy RAG for answering questions from scientific documents with citations | Opiniated RAG for integrating GenAI in your apps | | Stars | 8,837 | 39,190 | | Forks | 887 | 3,719 | | Open issues | 140 | 29 | | Language | Python | Python | | Adopt for | PaperQA2 is a specialized high-accuracy RAG tool for answering questions from scientific documents with citations. | 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 | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 - The software can be used for any purpose, including commercial purposes, and modified to comply with the Apache License terms. | Other | | Categories | Data & Retrieval | Data & Retrieval, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [paper-qa](/tools/future-house-paper-qa.md) | [quivr](/tools/quivrhq-quivr.md) | | --- | --- | --- | | Maintenance | Active (82%) | Slowing (36%) | | Days since push | 9d | 363d | | Open issues (now) | 140 | 29 | | Full report | [trust report](/tools/future-house-paper-qa/trust.md) | [trust report](/tools/quivrhq-quivr/trust.md) | **Typed relationship:** paper-qa _(alternative)_ quivr Quivr provides an opiniated RAG framework which could be seen as an alternative approach to PaperQA2, both aiming at integrating generative AI into apps for document interaction. ## Shared compatibility - **Python**: [paper-qa](/tools/future-house-paper-qa.md) - Python runtime; [quivr](/tools/quivrhq-quivr.md) - Python runtime ## Decision facts: paper-qa - **Requirements:** Min 4 GB RAM; Supports multiple document types like PDFs, text files, Microsoft Office documents, and source code.; Requires a setup for various document indexing and handling. - **Adopt for:** PaperQA2 is a specialized high-accuracy RAG tool for answering questions from scientific documents with citations. - **License detail:** Apache-2.0 - The software can be used for any purpose, including commercial purposes, and modified to comply with the Apache License terms. ## 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 ## Choose when ### Choose paper-qa if… - License: paper-qa is Apache-2.0, quivr is Other. - Requirements: Min 4 GB RAM; Supports multiple document types like PDFs, text files, Microsoft Office documents, and source code.; Requires a setup for various document indexing and handling.. - Quivr provides an opiniated RAG framework which could be seen as an alternative approach to PaperQA2, both aiming at integrating generative AI into apps for document interaction. - Tags unique to paper-qa: science, search. - - You require precise and high-quality responses from scientific literature. ### Choose quivr if… - License: quivr is Other, paper-qa is Apache-2.0. - Quivr provides an opiniated RAG framework which could be seen as an alternative approach to PaperQA2, both aiming at integrating generative AI into apps for document interaction. - Tags unique to quivr: llm, 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 NOT to use paper-qa - - You are working in a domain beyond scientific literature where the emphasis on accuracy for scientific tasks is less critical. - - If you need features not covered by PaperQA2 such as real-time data retrieval or analysis from non-scientific documents, look elsewhere. - - For users who require open-source options and prefer licenses other than Apache-2.0. ## 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. ## Common questions ### What is the difference between paper-qa and quivr? paper-qa: High accuracy RAG for answering questions from scientific documents with citations. quivr: Opiniated RAG for integrating GenAI in your apps. See the comparison table for live GitHub stats and shared categories. ### When should I choose paper-qa over quivr? Choose paper-qa over quivr when License: paper-qa is Apache-2.0, quivr is Other; Requirements: Min 4 GB RAM; Supports multiple document types like PDFs, text files, Microsoft Office documents, and source code.; Requires a setup for various document indexing and handling.; Quivr provides an opiniated RAG framework which could be seen as an alternative approach to PaperQA2, both aiming at integrating generative AI into apps for document interaction; Tags unique to paper-qa: science, search; - You require precise and high-quality responses from scientific literature. ### When should I choose quivr over paper-qa? Choose quivr over paper-qa when License: quivr is Other, paper-qa is Apache-2.0; Quivr provides an opiniated RAG framework which could be seen as an alternative approach to PaperQA2, both aiming at integrating generative AI into apps for document interaction; Tags unique to quivr: llm, 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 avoid paper-qa? - You are working in a domain beyond scientific literature where the emphasis on accuracy for scientific tasks is less critical. - If you need features not covered by PaperQA2 such as real-time data retrieval or analysis from non-scientific documents, look elsewhere. - For users who require open-source options and prefer licenses other than Apache-2.0. ### 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. ### Is paper-qa or quivr more popular on GitHub? quivr has more GitHub stars (39,190 vs 8,837). Stars measure visibility, not whether either tool fits your constraints. ### Are paper-qa and quivr open source? Yes - both are open-source projects on GitHub (paper-qa: Apache-2.0, quivr: Other). ### Where can I find alternatives to paper-qa or quivr? GraphCanon lists graph-backed alternatives at /tools/future-house-paper-qa/alternatives and /tools/quivrhq-quivr/alternatives (/tools/future-house-paper-qa/alternatives.md, /tools/quivrhq-quivr/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/future-house-paper-qa-vs-quivrhq-quivr.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, paper-qa or quivr? paper-qa: Active. quivr: 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 paper-qa and quivr? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: paper-qa: /tools/future-house-paper-qa/trust; quivr: /tools/quivrhq-quivr/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=future-house-paper-qa`](/api/graphcanon/graph?tool=future-house-paper-qa) - 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/_