Memory Lite FAQs

Question 1

How does Memory Lite ensure token-efficient retrieval?

Accepted Answer

It employs a three-stage retrieval strategy: agents first request summaries (global, project, task), then perform a light search if summaries are insufficient, and finally retrieve full details for only 1-3 of the most relevant candidates. This prevents unnecessary context dumping and saves tokens.

Question 2

What kind of information can I store in Memory Lite?

Accepted Answer

You can store various types of durable knowledge, including technical decisions and their reasoning, project architecture, commands, environment notes, links, gotchas, and task states. It also organizes memory hierarchically with optional graph-lite relationships for better context.

Question 3

What is Memory Lite and what problem does it solve?

Accepted Answer

Memory Lite is a local-first, token-efficient memory server for AI coding assistants. It solves the problem of transient AI memory by providing durable storage on disk for long-term knowledge such as architectural decisions, project conventions, commands, and task states, making them accessible across sessions.

Question 4

Does Memory Lite rely on cloud services, embeddings, or vector search?

Accepted Answer

No. Memory Lite is designed to be local-first, storing data on your disk using SQLite, libSQL, or Turso. It explicitly does not store raw chat transcripts, run embeddings, use vector search, or require a server or cloud connection for its core functionality.

Question 5

How does Memory Lite integrate with AI coding assistants?

Accepted Answer

Memory Lite offers seamless integration with MCP-compatible AI coding clients like Windsurf, Cursor, and Claude Desktop. You can easily install and configure it via 'npx' directly within your client's settings, pointing to a local file, libSQL, or Turso database.

Memory Lite

Memory Lite

Características Principales

Casos de Uso

Características Principales

Casos de Uso