--- title: "paper-qa vs txtai" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/future-house-paper-qa-vs-neuml-txtai" tools: ["future-house-paper-qa", "neuml-txtai"] --- # paper-qa vs txtai *GraphCanon updated Sep 20, 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 txtai if txtai is an all-in-one AI framework that supports semantic search, LLM orchestration, and language model workflows, making it suitable for projects that require comprehensive AI capabilities in Python. [paper-qa](https://futurehouse.gitbook.io/futurehouse-cookbook) reports 9.0k GitHub stars, 907 forks, and 141 open issues, last pushed Aug 12, 2026. [txtai](https://neuml.github.io/txtai) has 13k stars, 891 forks, and 10 open issues, last pushed Sep 15, 2026. Figures are from public GitHub metadata via [paper-qa's repository](https://github.com/Future-House/paper-qa) and [txtai's repository](https://github.com/neuml/txtai). | | [paper-qa](/tools/future-house-paper-qa.md) | [txtai](/tools/neuml-txtai.md) | | --- | --- | --- | | Tagline | High accuracy RAG for answering questions from scientific documents with citations | All-in-one AI framework for semantic search, LLM orchestration and language model workflows | | Stars | 9,048 | 12,959 | | Forks | 907 | 891 | | Open issues | 141 | 10 | | 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. | txtai is an all-in-one AI framework that supports semantic search, LLM orchestration, and language model workflows, making it suitable for projects that require comprehensive AI capabilities in Python. | | Persona | - | - | | Runtime | - | - | | License | 'Apache-2.0' - Permissive free software license that allows for both non-commercial use and commercial exploitation of the package. | Apache-2.0 | | Categories | Data & Retrieval, Model Training | AI Agents, Data & Retrieval, Evaluation & Observability, Inference & Serving, Model Training, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [paper-qa](/tools/future-house-paper-qa.md) | [txtai](/tools/neuml-txtai.md) | | --- | --- | --- | | Days since push | 5d | 2d | | Open issues (now) | 141 | 10 | | Stars delta | +154 (30d) | +69 (30d) | | Full report | [trust report](/tools/future-house-paper-qa/trust.md) | [trust report](/tools/neuml-txtai/trust.md) | ## Shared compatibility - **Python**: [paper-qa](/tools/future-house-paper-qa.md) - Python runtime; [txtai](/tools/neuml-txtai.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: txtai - **Adopt for:** txtai is an all-in-one AI framework that supports semantic search, LLM orchestration, and language model workflows, making it suitable for projects that require comprehensive AI capabilities in Python. ## Choose when ### Choose paper-qa if… - Requirements: Min 4 GB RAM. - Tags unique to paper-qa: rag, science, search. - Your project specifically requires processing and querying scientific documents, as PaperQA2 offers specialized capabilities tuned for this domain. ### Choose txtai if… - Tags unique to txtai: agents, ai-agents, embeddings, information retrieval. - Also covers AI Agents, Evaluation & Observability, Inference & Serving, Vector Databases. - When you need a comprehensive framework that integrates semantic search, LLM orchestration, and language model workflows in a single package. ## 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 txtai - If your project strictly requires a framework that is not Python-based, as txtai is specifically designed for Python environments. - When you need a tool that focuses solely on a specific aspect of AI, such as only semantic search or only LLM orchestration, as txtai's all-in-one approach might introduce unnecessary complexity. - If your project cannot accommodate the Apache-2.0 license, as txtai is distributed under this license and may not be suitable for projects with different licensing requirements. ## Common questions ### What is the difference between paper-qa and txtai? paper-qa: High accuracy RAG for answering questions from scientific documents with citations. txtai: All-in-one AI framework for semantic search, LLM orchestration and language model workflows. See the comparison table for live GitHub stats and shared categories. ### When should I choose paper-qa over txtai? Choose paper-qa over txtai when Requirements: Min 4 GB RAM; Tags unique to paper-qa: 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 txtai over paper-qa? Choose txtai over paper-qa when Tags unique to txtai: agents, ai-agents, embeddings, information retrieval; Also covers AI Agents, Evaluation & Observability, Inference & Serving, Vector Databases; When you need a comprehensive framework that integrates semantic search, LLM orchestration, and language model workflows in a single package. ### 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 txtai? If your project strictly requires a framework that is not Python-based, as txtai is specifically designed for Python environments. When you need a tool that focuses solely on a specific aspect of AI, such as only semantic search or only LLM orchestration, as txtai's all-in-one approach might introduce unnecessary complexity. If your project cannot accommodate the Apache-2.0 license, as txtai is distributed under this license and may not be suitable for projects with different licensing requirements. ### Is paper-qa or txtai more popular on GitHub? txtai has more GitHub stars (12,959 vs 9,048). Stars measure visibility, not whether either tool fits your constraints. ### Are paper-qa and txtai open source? Yes - both are open-source projects on GitHub (paper-qa: Apache-2.0, txtai: Apache-2.0). ### Where can I find alternatives to paper-qa or txtai? GraphCanon lists graph-backed alternatives at [paper-qa alternatives](/tools/future-house-paper-qa/alternatives) and [txtai alternatives](/tools/neuml-txtai/alternatives) ([paper-qa markdown twin](/tools/future-house-paper-qa/alternatives.md), [txtai markdown twin](/tools/neuml-txtai/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-neuml-txtai.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, paper-qa or txtai? paper-qa: Very active. txtai: Very 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 txtai? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [paper-qa trust report](/tools/future-house-paper-qa/trust); [txtai trust report](/tools/neuml-txtai/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/_