Memory FAQs

Question 1

How does Memory ensure secure access for AI agents?

Accepted Answer

Memory uses API key authentication, where keys are hashed at rest. Each memory is scoped to an individual agent (private) or a team (shared), preventing unauthorized access and ensuring data isolation between agents.

Question 2

What are the primary benefits of using Memory for AI agents?

Accepted Answer

Memory enables AI agents to retain knowledge persistently across sessions, retrieve only semantically relevant information through advanced search, and operate with secure, scoped data storage. This leads to more context-aware, intelligent, and efficient agent behavior.

Question 3

What is Memory (MCP Memory Server)?

Accepted Answer

Memory is a local server that provides AI agents with persistent, semantically-retrieved memory. It stores information in a local SQLite database, generating vector embeddings via Ollama to ensure agents only access the most relevant data across sessions.

Question 4

Can I integrate a different embedding model with Memory?

Accepted Answer

Yes, Memory's embedding backend is highly flexible. You can easily swap out the default Ollama integration by modifying a single Python function (`get_embedding`) to use any other embedding model or service.

Memory

主要功能

使用案例

Memory

主要功能

使用案例