// summary
MemPalace provides a local-first solution for storing and retrieving conversation history as verbatim text without the need for summarization or external API calls. The system organizes data into a structured hierarchy of wings, rooms, and drawers to enable precise, scoped semantic searches. It features a pluggable backend architecture and includes a temporal knowledge graph to manage entity relationships locally.
// technical analysis
MemPalace is a local-first AI memory system designed to store conversation history and project data as verbatim text, avoiding lossy summarization or extraction techniques. Its architecture utilizes a structured storage model where content is organized into 'wings', 'rooms', and 'drawers' to enable scoped semantic search rather than flat corpus scanning. By employing a pluggable backend interface, the project allows users to swap vector databases like ChromaDB while maintaining a privacy-focused, offline-capable retrieval layer that achieves high recall without requiring external API calls.
// key highlights
// use cases
// getting started
To begin, install the package via pip and initialize your project directory using the 'mempalace init' command. You can then populate your memory store by running 'mempalace mine' on your project files or conversation exports. Finally, use the 'mempalace search' command to query your data or 'mempalace wake-up' to load context for new sessions.