Turn arXiv and LaTeX mathematical papers into theorem dependency graphs for AI agents through MCP.
PaperGraph turns local or arXiv LaTeX papers into theorem dependency graphs that AI agents can query through MCP. PaperGraph v0.4.2 hardens first-use analysis with clearer theorem-kind metadata, dependency diagnostics, and guardrails against overreading sparse graphs.
PaperGraph v0.4.0 introduced the persistent, cross-paper SQLite workspace: retain a small literature collection, search theorem text, follow theorem dependencies, and inspect citation evidence without asking an agent to re-read every source paper.
Single-paper tools expose theorem-like environments, labels, and \ref relationships. A workspace keeps many independently imported papers together. It is deliberately evidence-first: every citation result identifies the source paper, bibliography key, LaTeX command, source file, and resolution status. This is useful for grounded reading and review; it is not semantic theorem matching or a claim that two similarly worded results are equivalent.
- Load local single-file or multi-file LaTeX projects, or safely prepare arXiv source projects. - Keep theorem, reference, and citation records in a local SQLite workspace. - Search theorem titles and bodies across papers, with stable global IDs. - Traverse direct or recursive theorem dependencies and inspect incoming or outgoing citation evidence, including unresolved citations. - Preserve the active single-paper graph and active workspace independently.
From the project README.
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