Serves AI agents a shared persistent memory that ingests Obsidian vaults, code, transcripts, bookmarks, and session or bug-fix captures, then classifies, promotes, and synthesizes them into tiered, retrievable knowledge. Exposes token-optimized context briefings and hybrid keyword/semantic search over MCP so multiple agents can read and write the same brain and improve across sessions.
Turns any folder of PDFs, markdown, and text files into a local, queryable knowledge base exposed as an MCP server. Enables MCP-compatible agents to semantically search indexed documents, retrieve relevant passages with source and relevance scores, list or reindex documents, and inspect cache and token usage — instead of reading whole files into context.
Enables AI agents and MCP hosts to query a local second brain built from scattered notes, docs, PDFs, and chat logs, with hybrid vector/BM25 retrieval and section-level citations.
Zero-config knowledge base for AI coding agents. Loads your markdown docs into a searchable database and exposes them as MCP tools — search, read, and manage documentation without leaving your editor. Works instantly with SQLite (no setup), upgrades to PostgreSQL + pgvector for hybrid semantic search. 6 MCP tools, 3 resources, FTS5 keyword search, 176 tests.