--- title: "paper-qa vs knowledge-gpt" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/future-house-paper-qa-vs-geeks-of-data-knowledge-gpt" tools: ["future-house-paper-qa", "geeks-of-data-knowledge-gpt"] --- # paper-qa vs knowledge-gpt *GraphCanon updated Aug 18, 2026* ## Verdict Pick paper-qa if paperQA2 version 5 is a retrieval-augmented generation (RAG) system optimized for extracting information from scientific documents, enhancing user queries with citations; pick knowledge-gpt if knowledge-gpt: Python toolkit for indexing and Q&A sessions with info sources using GPT & transformers. [paper-qa](https://futurehouse.gitbook.io/futurehouse-cookbook) reports 9.0k GitHub stars, 907 forks, and 141 open issues, last pushed Aug 12, 2026. [knowledge-gpt](https://pypi.org/project/knowledgegpt/) has 291 stars, 52 forks, and 8 open issues, last pushed Apr 25, 2023. Figures are from public GitHub metadata via [paper-qa's repository](https://github.com/Future-House/paper-qa) and [knowledge-gpt's repository](https://github.com/geeks-of-data/knowledge-gpt). | | [paper-qa](/tools/future-house-paper-qa.md) | [knowledge-gpt](/tools/geeks-of-data-knowledge-gpt.md) | | --- | --- | --- | | Tagline | High accuracy RAG for answering questions from scientific documents with citations | Extract knowledge from all information sources using GPT and other language models. Index and conduct Q&A sessions with information sources. | | Stars | 9,048 | 291 | | Forks | 907 | 52 | | Open issues | 141 | 8 | | Language | Python | Python | | Adopt for | PaperQA2 version 5 is a retrieval-augmented generation (RAG) system optimized for extracting information from scientific documents, enhancing user queries with citations. | knowledge-gpt: Python toolkit for indexing and Q&A sessions with info sources using GPT & transformers. | | Persona | - | - | | Runtime | - | - | | License | 'Apache-2.0' - Permissive free software license that allows for both non-commercial use and commercial exploitation of the package. | MIT | | Categories | Data & Retrieval, Model Training | Data & Retrieval, Evaluation & Observability, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [paper-qa](/tools/future-house-paper-qa.md) | [knowledge-gpt](/tools/geeks-of-data-knowledge-gpt.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 5d | 1185d | | Open issues (now) | 141 | 8 | | Stars delta | +154 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Full report | [trust report](/tools/future-house-paper-qa/trust.md) | [trust report](/tools/geeks-of-data-knowledge-gpt/trust.md) | ## Shared compatibility - **Python**: [paper-qa](/tools/future-house-paper-qa.md) - Python runtime; [knowledge-gpt](/tools/geeks-of-data-knowledge-gpt.md) - Python runtime ## Decision facts: paper-qa - **Requirements:** Min 4 GB RAM - **Adopt for:** PaperQA2 version 5 is a retrieval-augmented generation (RAG) system optimized for extracting information from scientific documents, enhancing user queries with citations. - **License detail:** 'Apache-2.0' - Permissive free software license that allows for both non-commercial use and commercial exploitation of the package. ## Decision facts: knowledge-gpt - **Adopt for:** knowledge-gpt: Python toolkit for indexing and Q&A sessions with info sources using GPT & transformers. ## Choose when ### Choose paper-qa if… - License: paper-qa is Apache-2.0, knowledge-gpt is MIT. - Requirements: Min 4 GB RAM. - Tags unique to paper-qa: ai, rag, science, search. - Your project specifically requires processing and querying scientific documents, as PaperQA2 offers specialized capabilities tuned for this domain. ### Choose knowledge-gpt if… - License: knowledge-gpt is MIT, paper-qa is Apache-2.0. - Tags unique to knowledge-gpt: context, embedding-vectors, gpt, huggingface-transformers. - Also covers Evaluation & Observability, LLM Frameworks. - knowledge-gpt ships Docker support for self-hosted deployment. - When you need a flexible, model-agnostic approach for Q&A over diverse data sources ## When NOT to use paper-qa - If your use case does not involve scientific document processing, another RAG system better suited to your specific type of documents (e.g., legal, medical) might be more fitting. - In scenarios where real-time performance is critical and extensive indexing or access to external APIs for large-scale paper handling becomes a bottleneck. ## When NOT to use knowledge-gpt - Avoid if strictly needing real-time response performance without indexing capabilities - Not recommended if focusing solely on visual or multimedia content extraction ## Common questions ### What is the difference between paper-qa and knowledge-gpt? paper-qa: High accuracy RAG for answering questions from scientific documents with citations. knowledge-gpt: Extract knowledge from all information sources using GPT and other language models. Index and conduct Q&A sessions with information sources.. See the comparison table for live GitHub stats and shared categories. ### When should I choose paper-qa over knowledge-gpt? Choose paper-qa over knowledge-gpt when License: paper-qa is Apache-2.0, knowledge-gpt is MIT; Requirements: Min 4 GB RAM; Tags unique to paper-qa: ai, rag, science, search; Your project specifically requires processing and querying scientific documents, as PaperQA2 offers specialized capabilities tuned for this domain. ### When should I choose knowledge-gpt over paper-qa? Choose knowledge-gpt over paper-qa when License: knowledge-gpt is MIT, paper-qa is Apache-2.0; Tags unique to knowledge-gpt: context, embedding-vectors, gpt, huggingface-transformers; Also covers Evaluation & Observability, LLM Frameworks; knowledge-gpt ships Docker support for self-hosted deployment; When you need a flexible, model-agnostic approach for Q&A over diverse data sources. ### When should I avoid paper-qa? If your use case does not involve scientific document processing, another RAG system better suited to your specific type of documents (e.g., legal, medical) might be more fitting. In scenarios where real-time performance is critical and extensive indexing or access to external APIs for large-scale paper handling becomes a bottleneck. ### When should I avoid knowledge-gpt? Avoid if strictly needing real-time response performance without indexing capabilities Not recommended if focusing solely on visual or multimedia content extraction ### Is paper-qa or knowledge-gpt more popular on GitHub? paper-qa has more GitHub stars (9,048 vs 291). Stars measure visibility, not whether either tool fits your constraints. ### Are paper-qa and knowledge-gpt open source? Yes - both are open-source projects on GitHub (paper-qa: Apache-2.0, knowledge-gpt: MIT). ### Where can I find alternatives to paper-qa or knowledge-gpt? GraphCanon lists graph-backed alternatives at [paper-qa alternatives](/tools/future-house-paper-qa/alternatives) and [knowledge-gpt alternatives](/tools/geeks-of-data-knowledge-gpt/alternatives) ([paper-qa markdown twin](/tools/future-house-paper-qa/alternatives.md), [knowledge-gpt markdown twin](/tools/geeks-of-data-knowledge-gpt/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/future-house-paper-qa-vs-geeks-of-data-knowledge-gpt.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, paper-qa or knowledge-gpt? paper-qa: Very active. knowledge-gpt: 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 paper-qa and knowledge-gpt? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [paper-qa trust report](/tools/future-house-paper-qa/trust); [knowledge-gpt trust report](/tools/geeks-of-data-knowledge-gpt/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/_