--- title: "gpt_academic vs paper-qa" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/binary-husky-gpt-academic-vs-future-house-paper-qa" tools: ["binary-husky-gpt-academic", "future-house-paper-qa"] --- # gpt_academic vs paper-qa Neutral, constraint-first comparison with live GitHub stats. | | [gpt_academic](/tools/binary-husky-gpt-academic.md) | [paper-qa](/tools/future-house-paper-qa.md) | | --- | --- | --- | | Tagline | 为GPT/GLM等LLM大语言模型提供实用化交互接口，特别优化论文阅读/润色/写作体验 | High accuracy RAG for answering questions from scientific documents with citations | | Stars | 71,049 | 8,837 | | Forks | 8,352 | 887 | | Open issues | 329 | 140 | | Language | Python | Python | | Adopt for | gpt_academic is an interactive interface for GPT and GLM large language models specifically tailored to enhance the academic experience of paper reading, polishing, and writing. It supports customization through shortcut | PaperQA2 is a specialized high-accuracy RAG tool for answering questions from scientific documents with citations. | | Persona | - | - | | Runtime | - | - | | License | GPL-3.0 | Apache-2.0 - The software can be used for any purpose, including commercial purposes, and modified to comply with the Apache License terms. | | Categories | LLM Frameworks, Developer Tools | Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [gpt_academic](/tools/binary-husky-gpt-academic.md) | [paper-qa](/tools/future-house-paper-qa.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Active (82%) | | Days since push | 164d | 9d | | Open issues (now) | 329 | 140 | | Owner type | User | Organization | | Security scan | 67 low (67 low) | Not scanned | | Full report | [trust report](/tools/binary-husky-gpt-academic/trust.md) | [trust report](/tools/future-house-paper-qa/trust.md) | **Typed relationship:** gpt_academic _(alternative)_ paper-qa Both PaperQA2 and gpt_academic are specialized for handling academic content, enabling enhanced interactions with scientific documents through RAG. ## Shared compatibility - **Python**: [gpt_academic](/tools/binary-husky-gpt-academic.md) - Python runtime; [paper-qa](/tools/future-house-paper-qa.md) - Python runtime ## Decision facts: gpt_academic - **Requirements:** Min 8 GB RAM; Requires Docker; Requires Docker for efficient setup.; Supports customization through shortcut buttons and function plugins. - **Adopt for:** gpt_academic is an interactive interface for GPT and GLM large language models specifically tailored to enhance the academic experience of paper reading, polishing, and writing. It supports customization through shortcut ## 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. ## Choose when ### Choose gpt_academic if… - License: gpt_academic is GPL-3.0, paper-qa is Apache-2.0. - Requirements: Min 8 GB RAM; Requires Docker; Requires Docker for efficient setup.; Supports customization through shortcut buttons and function plugins.. - Both PaperQA2 and gpt_academic are specialized for handling academic content, enabling enhanced interactions with scientific documents through RAG. - Tags unique to gpt_academic: translation, pdf, latex, large-language-models. - Also covers LLM Frameworks, Developer Tools. - gpt_academic ships Docker support for self-hosted deployment. - - Use when you need specialized support in academic tasks such as reading,润色, and writing papers with integration optimized for Chinese LLMs like Qwen, GLM. ### Choose paper-qa if… - License: paper-qa is Apache-2.0, gpt_academic is GPL-3.0. - 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.. - Both PaperQA2 and gpt_academic are specialized for handling academic content, enabling enhanced interactions with scientific documents through RAG. - Tags unique to paper-qa: science, search, ai, rag. - Also covers Data & Retrieval. - - You require precise and high-quality responses from scientific literature. ## When NOT to use gpt_academic - - Not recommended if your focus is outside of the academic sector or tasks unrelated to paper processing. - - Avoid using it if you do not require local model support (like chatglm3) or specific integration with LLMs like Qwen, GLM4 that are emphasized in gpt_academic. ## 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. ## Common questions ### What is the difference between gpt_academic and paper-qa? gpt_academic: 为GPT/GLM等LLM大语言模型提供实用化交互接口，特别优化论文阅读/润色/写作体验. paper-qa: High accuracy RAG for answering questions from scientific documents with citations. See the comparison table for live GitHub stats and shared categories. ### When should I choose gpt_academic over paper-qa? Choose gpt_academic over paper-qa when License: gpt_academic is GPL-3.0, paper-qa is Apache-2.0; Requirements: Min 8 GB RAM; Requires Docker; Requires Docker for efficient setup.; Supports customization through shortcut buttons and function plugins.; Both PaperQA2 and gpt_academic are specialized for handling academic content, enabling enhanced interactions with scientific documents through RAG; Tags unique to gpt_academic: translation, pdf, latex, large-language-models; Also covers LLM Frameworks, Developer Tools; gpt_academic ships Docker support for self-hosted deployment; - Use when you need specialized support in academic tasks such as reading,润色, and writing papers with integration optimized for Chinese LLMs like Qwen, GLM. ### When should I choose paper-qa over gpt_academic? Choose paper-qa over gpt_academic when License: paper-qa is Apache-2.0, gpt_academic is GPL-3.0; 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.; Both PaperQA2 and gpt_academic are specialized for handling academic content, enabling enhanced interactions with scientific documents through RAG; Tags unique to paper-qa: science, search, ai, rag; Also covers Data & Retrieval; - You require precise and high-quality responses from scientific literature. ### When should I avoid gpt_academic? - Not recommended if your focus is outside of the academic sector or tasks unrelated to paper processing. - Avoid using it if you do not require local model support (like chatglm3) or specific integration with LLMs like Qwen, GLM4 that are emphasized in gpt_academic. ### 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. ### Is gpt_academic or paper-qa more popular on GitHub? gpt_academic has more GitHub stars (71,049 vs 8,837). Stars measure visibility, not whether either tool fits your constraints. ### Are gpt_academic and paper-qa open source? Yes - both are open-source projects on GitHub (gpt_academic: GPL-3.0, paper-qa: Apache-2.0). ### Where can I find alternatives to gpt_academic or paper-qa? GraphCanon lists graph-backed alternatives at /tools/binary-husky-gpt-academic/alternatives and /tools/future-house-paper-qa/alternatives (/tools/binary-husky-gpt-academic/alternatives.md, /tools/future-house-paper-qa/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/binary-husky-gpt-academic-vs-future-house-paper-qa.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, gpt_academic or paper-qa? gpt_academic: Slowing. paper-qa: 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 gpt_academic and paper-qa? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: gpt_academic: /tools/binary-husky-gpt-academic/trust; paper-qa: /tools/future-house-paper-qa/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=binary-husky-gpt-academic`](/api/graphcanon/graph?tool=binary-husky-gpt-academic) - 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/_