MemoryLoom FAQs

Question 1

What is MemoryLoom?

Accepted Answer

MemoryLoom is an MCP-compatible persistent memory server designed for AI clients, editors, and IDEs. It provides a clean interface for managing and retrieving structured memory, enhancing AI context and tool interaction.

Question 2

How does MemoryLoom retrieve memories?

Accepted Answer

It utilizes advanced metadata-aware retrieval with a hybrid ranking system that combines token overlap, lightweight embedding similarity (character n-gram hashing), and customizable metadata weighting for precise and relevant results.

Question 3

What storage options does MemoryLoom offer?

Accepted Answer

MemoryLoom features pluggable storage backends, supporting both file-backed JSON for local, atomic writes with rolling backups, and scalable PostgreSQL for robust, transactional, and database-backed solutions.

Question 4

Can MemoryLoom handle advanced semantic understanding for AI?

Accepted Answer

MemoryLoom includes a lightweight character n-gram hashing for embedding similarity, suitable for small to medium memory sets. For production use with large datasets or complex semantic understanding, integrating a dedicated, production-grade vector embedding service is recommended.

Question 5

Is MemoryLoom easy to deploy and integrate?

Accepted Answer

Yes, MemoryLoom offers one-click deployment options for numerous cloud platforms (e.g., Railway, Fly.io) and supports both local stdio and remote HTTP connection modes. It integrates with popular MCP-compatible editors like Claude Desktop, Cursor, VS Code, and Zed.

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