An MCP server that transforms codebases into knowledge graphs using Neo4J, enabling AI assistants to understand code structure, relationships, and metrics for more context-aware assistance.
A local-first MCP server that gives AI coding assistants persistent, structured, human-readable memory for a software project by storing project knowledge as Markdown files in the project's .dev-context-memory/ folder.
A local-first MCP server that enables AI coding assistants to map codebases, transcribe onboarding videos with keyframe screenshots, and maintain structured Obsidian knowledge vaults directly from IDE chat.
An Obsidian-compatible memory graph and context management MCP server that lets AI agents navigate project architecture and relationships via wikilinks before scanning raw code, reducing token waste.
A universal MCP server providing persistent, structured memory through a knowledge graph with graph storage, semantic vector search, and multi-hop traversal for AI agents and IDEs.