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by Francis1998 · Agent Tool · ★ 145
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Scholar RAG Agent Scholar RAG Agent is a production-grade, local-first Agentic RAG system for scientific literature. It ingests papers from PDFs, arXiv, and Semantic Scholar; builds hybrid dense, sparse, and entity-relationship retrieval indexes; and answers research questions with multi-hop reasoning and citation-backed evidence. The project is designed for the scientific knowledge synthesis narrative behind NIW-style research impact: researchers can accelerate literature review, hypothesis validation, and grounded comparison across large corpora without losing provenance. Why Researchers Need This Most literature workflows break down when the corpus grows beyond a few papers: Issue: keyword search misses papers that use different terminology. Scholar RAG Agent combines dense semantic retrieval, BM25 sparse search, HyDE expansion, and RRF fusion so a query can match both exact terms and related scientific phrasing. Issue: single-hop RAG retrieves isolated snippets but misses evidence chains. The GraphRAG layer extracts entities and relationships, then follows bo
| Stars | 145 |
| Forks | 19 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 70.4197119915497/100 |
| Open Issues | 3 |
| Last Updated | 2026-08-16 |
| Created | 2026-06-21 |
| Platforms | python |
| Est. Tokens | ~15k |
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scholar-rag-agent is Production-grade Agentic RAG for scientific literature — multi-hop reasoning, GraphRAG, and multi-LLM routing. It is categorized as a Agent Tool with 145 GitHub stars.
scholar-rag-agent is primarily written in Python. It covers topics such as agentic-rag, anthropic, graphrag.
You can find installation instructions and usage details in the scholar-rag-agent GitHub repository at github.com/Francis1998/scholar-rag-agent. The project has 145 stars and 19 forks, indicating an active community.
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