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    <title>Memory on Leafw&#39;s Blog</title>
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    <description>Recent content in Memory on Leafw&#39;s Blog</description>
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    <managingEditor>wyr95626@gmail.com (Leafw)</managingEditor>
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      <title>Anthropic Taught Agents to &#39;Dream&#39;: Rethinking Long-Term Memory for AI</title>
      <link>https://leafw.net/en/anthropic-agent-memory/</link>
      <pubDate>Sun, 08 Nov 2026 00:00:00 +0000</pubDate><author>wyr95626@gmail.com (Leafw)</author>
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      <description>&lt;p&gt;&lt;img&#xA;        class=&#34;lazyload&#34;&#xA;        src=&#34;https://leafw.net/svg/loading.min.svg&#34;&#xA;        data-src=&#34;https://leafw-blog-pic.oss-cn-hangzhou.aliyuncs.com/anthropic-agent-memory-cover.png&#34;&#xA;        data-srcset=&#34;https://leafw-blog-pic.oss-cn-hangzhou.aliyuncs.com/anthropic-agent-memory-cover.png, https://leafw-blog-pic.oss-cn-hangzhou.aliyuncs.com/anthropic-agent-memory-cover.png 1.5x, https://leafw-blog-pic.oss-cn-hangzhou.aliyuncs.com/anthropic-agent-memory-cover.png 2x&#34;&#xA;        data-sizes=&#34;auto&#34;&#xA;        alt=&#34;https://leafw-blog-pic.oss-cn-hangzhou.aliyuncs.com/anthropic-agent-memory-cover.png&#34;&#xA;        title=&#34;https://leafw-blog-pic.oss-cn-hangzhou.aliyuncs.com/anthropic-agent-memory-cover.png&#34; /&gt;&lt;/p&gt;&#xA;&lt;p&gt;On November 6, 2026, Anthropic published a technical article titled &lt;a href=&#34;https://www.anthropic.com/research/components-of-agent-memory&#34; target=&#34;_blank&#34; rel=&#34;noopener noreffer &#34;&gt;&lt;em&gt;The components of agent memory&lt;/em&gt;&lt;/a&gt;. It tackles a question that is becoming increasingly important in agent development: &lt;strong&gt;how do you give an AI agent long-term memory that is actually valuable?&lt;/strong&gt;&lt;/p&gt;&#xA;&lt;p&gt;When people hear &amp;ldquo;agent memory,&amp;rdquo; the first thing that comes to mind is usually saving conversation history to a database, then using embeddings and vector search to pull relevant pieces back into the context when needed.&lt;/p&gt;</description>
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