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turbo_quant_memory
Lexus2016 March 25, 2026
1 copies 0 downloads
Local-first MCP memory server for AI coding agents with compact retrieval and project/global scopes.
Agent Definition
# Turbo Quant Memory for AI Agents  [](https://github.com/Lexus2016/turbo_quant_memory/releases) [](https://www.python.org/downloads/) [](https://modelcontextprotocol.io/) [](https://github.com/Lexus2016/turbo_quant_memory) Other languages: [Russian](README.ru.md) | [Ukrainian](README.uk.md) Turbo Quant Memory is the memory layer that makes AI agents feel like long-term teammates instead of short-term chat sessions. If you use Claude Code, Codex, Cursor, OpenCode, Cursor CLI, or any MCP client, this is how you keep your institutional knowledge alive between tasks. ## Why It Matters Most agent workflows fail in the same place: memory. - Great insights disappear in chat history. - Every new task restarts from zero. - Teams re-explain the same architecture again and again. Turbo Quant Memory fixes this by making your project knowledge persistent, searchable, and reusable. ## Why Teams Choose Turbo Quant Memory | Typical AI workflow | With Turbo Quant Memory | |---|---| | Agents forget context between sessions | Agents can continue from saved project knowledge | | Decisions stay buried in old threads | Decisions become reusable notes | | Team knowledge stays inside one person's head | Knowledge becomes shared, searchable, and portable | | Token budget is wasted on repeated reading | Context is loaded smarter, so more budget goes to reasoning | ## The Core Promise Your agents stop behaving like temporary assistants and start behaving like members of the team. ## What Makes It Different - Local-first by design: your memory stays under your control. - One memory layer for many client
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