Comparison
paper-qa vs graphrag
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 graphrag if graphRAG is a Python-based tool designed for integrating retrieval and generation processes in large language models using graph structures.
Markdown twin · paper-qa alternatives · graphrag alternatives
GraphCanon updated 2d
vs
Trust & integrity
| Signal | paper-qa | graphrag |
|---|---|---|
| Maintenance | Very active (5d since push) As of 2d · github_public_v1 | Very active (1d since push) As of 4d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2d · github_public_v1 | Not a fork · Organization account As of 4d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- paper-qa
- High accuracy RAG for answering questions from scientific documents with citations
- graphrag
- A modular graph-based Retrieval-Augmented Generation (RAG) system
Stars
- paper-qa
- 9.0k
- graphrag
- 36k
Forks
- paper-qa
- 907
- graphrag
- 3.7k
Open issues
- paper-qa
- 141
- graphrag
- 46
Language
- paper-qa
- Python
- graphrag
- Python
Adopt for
- paper-qa
- PaperQA2 version 5 is a retrieval-augmented generation (RAG) system optimized for extracting information from scientific documents, enhancing user queries with citations.
- graphrag
- GraphRAG is a Python-based tool designed for integrating retrieval and generation processes in large language models using graph structures.
Persona
- paper-qa
- -
- graphrag
- -
Runtime
- paper-qa
- -
- graphrag
- -
License
- paper-qa
- 'Apache-2.0' - Permissive free software license that allows for both non-commercial use and commercial exploitation of the package.
- graphrag
- MIT
Last pushed
- paper-qa
- Aug 12, 2026
- graphrag
- Aug 14, 2026
Categories
- paper-qa
- Data & Retrieval, Model Training
- graphrag
- Data & Retrieval, LLM Frameworks
Trust and health
Days since push
- paper-qa
- 5d
- graphrag
- 1d
Open issues (now)
- paper-qa
- 141
- graphrag
- 46
Stars delta
- paper-qa
- +154 (30d)
- graphrag
- +1.0k (30d)
Open issues delta
- paper-qa
- 0 (30d)
- graphrag
- -15 (30d)
Full report
- paper-qa
- Trust report
- graphrag
- Trust report
Typed relationship
paper-qa alternative graphragPaperQA2 and GraphRAG both serve as modular systems for RAG, focusing on retrieval from structured sources to enhance machine learning models.
Choose paper-qa if…
- License: paper-qa is Apache-2.0, graphrag is MIT.
- Requirements: Min 4 GB RAM.
- 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: ai, science, search.
- Also covers Model Training.
- Your project specifically requires processing and querying scientific documents, as PaperQA2 offers specialized capabilities tuned for this domain.
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.
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: gpt, gpt-4, graph, llm.
- Also covers LLM Frameworks.
- When you need to leverage graph structures to enhance the efficiency of information retrieval within a Retrieval-Augmented Generation setup.
When NOT to use graphrag
- If your application does not require or benefit from the specific graph-based approach GraphRAG employs; traditional RAG systems might be sufficient without the added layer of complexity introduced by
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (Future-House/paper-qa) · observed Aug 18, 2026
- GitHub forks (Future-House/paper-qa) · observed Aug 18, 2026
- Last push (Future-House/paper-qa) · observed Aug 12, 2026
- License file (Apache-2.0) · observed Aug 18, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (microsoft/graphrag) · observed Aug 16, 2026
- GitHub forks (microsoft/graphrag) · observed Aug 16, 2026
- Last push (microsoft/graphrag) · observed Aug 14, 2026
- License file (MIT) · observed Aug 16, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: paper-qa 9.0k · graphrag 36k (synced Aug 18, 2026).
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; 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: ai, science, search; Also covers Model Training; Your project specifically requires processing and querying scientific documents, as PaperQA2 offers specialized capabilities tuned for this domain.
- 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: gpt, gpt-4, graph, llm; Also covers LLM Frameworks; When you need to leverage graph structures to enhance the efficiency of information retrieval within a Retrieval-Augmented Generation setup.
- 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 graphrag?
- If your application does not require or benefit from the specific graph-based approach GraphRAG employs; traditional RAG systems might be sufficient without the added layer of complexity introduced by
- Is paper-qa or graphrag more popular on GitHub?
- graphrag has more GitHub stars (35,519 vs 9,048). 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 paper-qa alternatives and graphrag alternatives (paper-qa markdown twin, graphrag markdown twin), 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 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: Very active. graphrag: 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 graphrag?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: paper-qa trust report; graphrag trust report.