Local Memory FAQs

Question 1

What operations can AI agents perform with Local Memory?

Accepted Answer

AI agents can interact with Local Memory through several tools: `memory.search` for semantic retrieval, `memory.save` to store new insights, `memory.supersede` to update or mark old entries as outdated, and `memory.delete` to permanently remove memories.

Question 2

How does Local Memory store and retrieve information?

Accepted Answer

It utilizes an embedded Zvec vector database for storage and performs semantic search over memories. Embeddings are generated locally using Ollama (e.g., embeddinggemma) to facilitate intelligent retrieval.

Question 3

What is Local Memory and its primary purpose?

Accepted Answer

Local Memory is a local Model Context Protocol (MCP) server designed for AI agents. Its primary purpose is to manage and persist an agent's operational memory and context, allowing for durable coding context across sessions and projects.

Question 4

Does Local Memory require an external database or cloud service?

Accepted Answer

No, Local Memory runs entirely locally. It uses Zvec as an embedded, in-process vector database, eliminating the need for any external vector databases or cloud services.

Question 5

Can Local Memory handle context for multiple AI projects?

Accepted Answer

Yes, Local Memory supports workspace-scoped memory management using a `WORKSPACE_KEY`. This allows you to keep memories separated and relevant to specific projects while using a single shared local database.

Local Memory

Local Memory

Key Features

Use Cases

Key Features

Use Cases