Enables AI coding agents to search, read, create, update, and delete notes in a local Obsidian vault through hybrid semantic and lexical retrieval, with all embedding and vector storage running locally.
Provides a hybrid search engine for Obsidian vaults, enabling LLM agents to query notes with BM25 keyword and vector semantic search, metadata filtering, and sibling-document retrieval.
A local-first RAG, MCP, REST, and CLI bridge for Obsidian vaults that enables AI agents to retrieve cited knowledge from notes without uploading the vault.
Provides semantic search and keyword search over Obsidian notes, along with direct note retrieval, allowing external AI agents to query and access the vault.
Enables AI agents to navigate, search, and rank notes in an Obsidian vault using graph relationships, providing context-aware access to knowledge without exposing raw file contents.
Enables retrieval-augmented question answering over Obsidian vaults and document folders, with local embeddings, vector search, and cited source paths.