--- title: "paper-qa vs graphrag" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/future-house-paper-qa-vs-microsoft-graphrag" tools: ["future-house-paper-qa", "microsoft-graphrag"] --- # paper-qa vs graphrag Neutral, constraint-first comparison with live GitHub stats. | | [paper-qa](/tools/future-house-paper-qa.md) | [graphrag](/tools/microsoft-graphrag.md) | | --- | --- | --- | | Tagline | High accuracy RAG for answering questions from scientific documents with citations | A modular graph-based Retrieval-Augmented Generation (RAG) system | | Stars | 8,837 | 34,249 | | Forks | 887 | 3,621 | | Open issues | 140 | 158 | | Language | Python | Python | | Adopt for | PaperQA2 is a specialized high-accuracy RAG tool for answering questions from scientific documents with citations. | GraphRAG offers a specialized graph-based approach to Retrieval-Augmented Generation (RAG) using the power of Large Language Models (LLMs) for enhancing unstructured data transformation and reasoning over private data. | | 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. | MIT | | Categories | Data & Retrieval | Data & Retrieval, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [paper-qa](/tools/future-house-paper-qa.md) | [graphrag](/tools/microsoft-graphrag.md) | | --- | --- | --- | | Days since push | 9d | 16d | | Open issues (now) | 140 | 158 | | Security scan | Not scanned | No lockfile | | Full report | [trust report](/tools/future-house-paper-qa/trust.md) | [trust report](/tools/microsoft-graphrag/trust.md) | **Typed relationship:** paper-qa _(alternative)_ graphrag PaperQA2 and GraphRAG both serve as modular systems for RAG, focusing on retrieval from structured sources to enhance machine learning models. ## 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: graphrag - **Pricing:** unknown - **Adopt for:** GraphRAG offers a specialized graph-based approach to Retrieval-Augmented Generation (RAG) using the power of Large Language Models (LLMs) for enhancing unstructured data transformation and reasoning over private data. ## Choose when ### Choose paper-qa if… - License: paper-qa is Apache-2.0, graphrag is MIT. - 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.. - PaperQA2 and GraphRAG both serve as modular systems for RAG, focusing on retrieval from structured sources to enhance machine learning models. - Tags unique to paper-qa: science, search, ai. - - You require precise and high-quality responses from scientific literature. ### Choose graphrag if… - License: graphrag is MIT, paper-qa is Apache-2.0. - PaperQA2 and GraphRAG both serve as modular systems for RAG, focusing on retrieval from structured sources to enhance machine learning models. - Tags unique to graphrag: llm, gpt-4, gpt, graph-based rag system. - Also covers Model Training. - - When you need to extract structured information from narrative or private data using LLMs and require a modular, graph-based system. - For projects that involve handling sensitive datasets where the ## 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 graphrag - - Avoid GraphRAG if your project requires minimal setup and cost since GraphRAG's indexing process can be resource-intensive. - - Not recommended for scenarios with extremely large datasets or when low latency is critical as this tool may pose significant computational demands. ## Common questions ### What is the difference between paper-qa and graphrag? paper-qa: High accuracy RAG for answering questions from scientific documents with citations. graphrag: A modular graph-based Retrieval-Augmented Generation (RAG) system. See the comparison table for live GitHub stats and shared categories. ### When should I choose paper-qa over graphrag? Choose paper-qa over graphrag when License: paper-qa is Apache-2.0, graphrag is MIT; 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.; PaperQA2 and GraphRAG both serve as modular systems for RAG, focusing on retrieval from structured sources to enhance machine learning models; Tags unique to paper-qa: science, search, ai; - You require precise and high-quality responses from scientific literature. ### When should I choose graphrag over paper-qa? Choose graphrag over paper-qa when License: graphrag is MIT, paper-qa is Apache-2.0; PaperQA2 and GraphRAG both serve as modular systems for RAG, focusing on retrieval from structured sources to enhance machine learning models; Tags unique to graphrag: llm, gpt-4, gpt, graph-based rag system; Also covers Model Training; - When you need to extract structured information from narrative or private data using LLMs and require a modular, graph-based system. - For projects that involve handling sensitive datasets where the. ### 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 graphrag? - Avoid GraphRAG if your project requires minimal setup and cost since GraphRAG's indexing process can be resource-intensive. - Not recommended for scenarios with extremely large datasets or when low latency is critical as this tool may pose significant computational demands. ### Is paper-qa or graphrag more popular on GitHub? graphrag has more GitHub stars (34,249 vs 8,837). Stars measure visibility, not whether either tool fits your constraints. ### Are paper-qa and graphrag open source? Yes - both are open-source projects on GitHub (paper-qa: Apache-2.0, graphrag: MIT). ### Where can I find alternatives to paper-qa or graphrag? GraphCanon lists graph-backed alternatives at /tools/future-house-paper-qa/alternatives and /tools/microsoft-graphrag/alternatives (/tools/future-house-paper-qa/alternatives.md, /tools/microsoft-graphrag/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-microsoft-graphrag.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, paper-qa or graphrag? paper-qa: Active. graphrag: Active. 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 graphrag? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: paper-qa: /tools/future-house-paper-qa/trust; graphrag: /tools/microsoft-graphrag/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/_