Turbo Quant Memory

Turbo Quant Memory serves as a crucial local-first memory layer designed for AI coding agents such as Claude Code, Codex, and Cursor. It empowers agents to remember project-specific knowledge, efficiently search for smaller context snippets first, and expand on details only when genuinely required by the task. This system dramatically reduces redundant reading, transforms important decisions into searchable notes, and facilitates the promotion of valuable patterns for reuse across different projects, ultimately making AI agents more efficient and effective.

Key Features

Use Cases