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Build and explore AI-enriched knowledge graphs from documents and code.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Factograph MCP" yet — see the docs or source repo.
Build a knowledge graph from my uploaded PDF papers and DOCX notes. Extract key concepts, authors, methods, and conclusions, link their relationships, and present an explorable structure organized by topic, method, and findings.
An explorable knowledge graph with entity nodes, relationship links, and topic-based navigation paths.
Read the code files in this project and build a knowledge graph of modules, classes, functions, and external dependencies. Use AI to enrich each entity with responsibility summaries and identify call relationships and potential coupling points.
A knowledge graph showing code structure and dependencies, with key module descriptions and coupling analysis.
Pin my selected core documents in the graph, create links around people, projects, terms, and dates, and then traverse from a chosen entity to related documents and upstream or downstream relationships for quick background discovery.
A linked graph anchored on key documents, plus relationship traversal results from any chosen entity.
Ingest documents into Neo4j to build and query a knowledge graph.
Turn codebases into structural graphs for efficient AI-assisted code exploration.
Search enterprise documents in natural language across PDF, PPT, and Word files.
Generate, search, and reason over knowledge graphs from data sources without API keys.
Build and explore a neuroscience-inspired knowledge graph for search and reasoning.
Analyze local paper PDFs rigorously and generate editable maps and research insights.