An MCP server that gives AI assistants persistent memory across conversations, storing and retrieving user preferences and behaviors using vector search.
An MCP server that provides persistent semantic memory for LLMs by building a concept graph with vector search. It enables storing, linking, and retrieving concepts across conversations using Turso vector search and 256-dimensional embeddings.
A self-hosted MCP memory server that gives AI assistants persistent, semantic memory by storing facts as vector embeddings locally, supporting semantic search and swappable embedding models.
A self-hosted MCP server that provides a personal semantic memory layer for AI tools. It enables storing, searching, and managing memories using hybrid vector and keyword search, allowing AI assistants to recall information by meaning.
An MCP server that provides AI assistants with long-term semantic memory using a Markdown-based knowledge base with hybrid search (vector + FTS5) and syntax-aware chunking. It supports idempotent ingestion, project isolation, and integrity linting.