Living Brain 将记忆解耦为独立层，支持跨模型共享与低成本检索
Living Brain 提出将 AI 记忆从推理模型中解耦，构建独立的持久化存储层。该方案允许 ChatGPT、Claude 及自定义 Agent 通过 MCP 或 API 共享同一份记忆上下文，并通过精准检索相关片段而非全量历史，实现最高 95% 的任务成本降低和 22.6 倍的 Token 节省。
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Rohan Paul (@rohanpaul_ai) · 2026-11-04 14
Memory is becoming its own layer in the AI stack, separate from whichever model does the reasoning.
@livingbrain treats AI models as swappable engines and your accumulated context as the part that stays. Agents plug in through MCP or an API, so changing the model or agent framework leaves the memory intact.
Up to 95% lower cost per task. 3.7s median end-to-end answer.
Instead of sending the model your entire history every time, Living Brain picks only the few stored memories relevant to the current question and sends those, so the model processes far fewer tokens (up to 22.6x fewer), which is what cuts the cost per task by up to 95%.
ChatGPT and Claude each keep their own private memory of you, and neither can read the other’s. Living Brain moves that memory into a separate store that both tools, plus your own agents, can read and write.
↳ 引用 @ladyxtel (Christel Buchanan): We decoupled memory from the model.
Living Brain is one persistent brain for ChatGPT, Claude, Cursor, Codex and your own agents. Write in one, retrieve in any.
92.2% on LoCoMo. 464ms median retrieval. Up to 22.6x fewer tokens per question.
LLMs have parameters. Living Brain has plasticity.