turbo_quant_memory — Cursor AI Agent
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    turbo_quant_memory

    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.

    Turbo Quant Memory for AI Agents

    Turbo Quant Memory hero

    Latest release Python 3.11+ MCP server Local-first

    Other languages: Russian | Ukrainian

    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 workflowWith Turbo Quant Memory
    Agents forget context between sessionsAgents can continue from saved project knowledge
    Decisions stay buried in old threadsDecisions become reusable notes
    Team knowledge stays inside one person's headKnowledge becomes shared, searchable, and portable
    Token budget is wasted on repeated readingContext 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

    Tags

    agent-memoryai-agentsclaude-codecodexcursorlocal-firstmcpmcp-servermodel-context-protocolpython

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