All 19 Dependencies
Every package MemPalace depends on, ranked by repo health score.
MemPalace is a local-first AI memory system that stores conversation history and project content as verbatim text and retrieves it with semantic search. Unlike summarization-based memory tools, MemPalace never paraphrases or extracts — every drawer holds the original content exactly as written, which is the key to its benchmark-leading retrieval accuracy.
The palace uses a spatial metaphor to organize knowledge: people and projects become wings, topics become rooms, and the original verbatim content lives in drawers. Searches can be scoped to a specific wing or room rather than run against a flat corpus, dramatically improving precision. The hybrid search pipeline combines BM25 keyword matching with vector semantic similarity, with closet pointers providing an additional ranking signal.
The retrieval layer is pluggable through a well-defined backend contract (RFC 001). ChromaDB is the default, but alternative backends — SQLite exact-vector, Qdrant (REST), and pgvector (Postgres) — can be swapped in without touching the rest of the system. Embeddings are generated locally using an ONNX-based model (embeddinggemma-300m for multilingual support, or MiniLM for English-only), with hardware acceleration available via CUDA, CoreML, and DirectML.
Beyond file mining, MemPalace includes a temporal entity-relationship knowledge graph backed by SQLite, 33 MCP server tools for integration with Claude Code and other AI tools, auto-save hooks for Claude Code/Codex CLI/Cursor IDE, and multi-agent support where each specialist agent gets its own wing and diary in the palace.
