Your papers, one graph. Ask, and get cited answers.
Ingest PDFs or pull papers from academic databases into a knowledge graph of papers, authors, topics, and citations, then chat with your corpus: answers come only from the graph, with citations, or the tool says it does not know.
GRAPHRETRIEVEANSWER
corpus-only, by construction
Answers from verified context. Not guesswork.
Each of these exists in the codebase. We name the mechanism so you can hold us to it.
Corpus-only answers
The model is constrained to the retrieved graph context: if the answer is not in your papers, it says so instead of improvising. Every answer returns its citations and the exact subgraph it used.
A real knowledge graph
Papers, authors, topics, and keywords as nodes; CITES edges between papers; topics linked when they co-occur. Indexed and queryable, not a bag of embeddings.
The discovery engine
Search across Semantic Scholar’s 200M+ paper corpus and OpenAlex, follow citation chains, and score candidates with five explainable signals: field overlap, co-citation, citation count, recency, title similarity.
Insights, automatically
Research gaps, topic clusters, field timelines, and collaboration patterns computed from the graph. Structure you would not spot reading paper by paper.
Drag-and-drop ingestion
PDF upload with live progress; metadata extracted automatically.
Collections
Scope uploads, discovery, and the graph view to a collection: one corpus per project.
3D graph exploration
Force-directed graph with type filters, search fly-to, node detail, and export.
Multi-provider dedup
Results from multiple academic databases are deduplicated before they enter your graph.
Where it standsBeta, self-hostable with Docker, with 92 backend tests behind it. There is no hosted deployment yet. The graph covers what you ingest; search reaches Semantic Scholar’s corpus, we do not index 200M papers ourselves.
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