📄 中文摘要
随着 AI 代理在工作流程中的日益整合，内存问题成为一项持续挑战。如何赋予这些代理回忆过去互动的能力、在会话间维持上下文以及在先前知识的基础上进行构建是关键。提出了一种实用的四层文件基础内存架构，适用于任何 AI 代理，无论是 ChatGPT、Claude、Agent Zero 还是本地 LLMs。这种架构旨在解决大多数 AI 代理默认无状态的问题，使其能够在交互中保持记忆，从而提升用户体验和工作效率。
为 AI 代理构建持久内存：一种四层文件架构
📄 English Summary
Building Persistent Memory for AI Agents: A 4-Layer File-Based Architecture
As AI agents become more integrated into workflows, the challenge of memory persists. The ability for these agents to recall past interactions, maintain context across sessions, and build upon previous knowledge is crucial. A practical 4-layer file-based memory architecture is proposed, which works with any AI agent, whether it is ChatGPT, Claude, Agent Zero, or local LLMs. This architecture aims to address the default stateless nature of most AI agents, enabling them to retain memory during interactions, thereby enhancing user experience and productivity.