<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Hypergraph on SoloSoft</title><link>https://www.solosoft.dev/tags/hypergraph/</link><description>Recent content in Hypergraph on SoloSoft</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Fri, 01 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://www.solosoft.dev/tags/hypergraph/index.xml" rel="self" type="application/rss+xml"/><item><title>EverOS: Open-Source Long-Term Memory Operating System for Self-Evolving AI Agents</title><link>https://www.solosoft.dev/post/everos-agent-memory-2026/</link><pubDate>Fri, 01 May 2026 00:00:00 +0000</pubDate><guid>https://www.solosoft.dev/post/everos-agent-memory-2026/</guid><description>&lt;p&gt;&lt;a href="https://github.com/EverMind-AI/EverOS"&gt;EverOS&lt;/a&gt; is an open-source long-term memory operating system for AI agents developed by &lt;strong&gt;EverMind&lt;/strong&gt;, the AI research lab backed by &lt;strong&gt;Shanda Group&lt;/strong&gt;. In an era where most AI agents operate with short-term, session-bound memory, EverOS introduces a persistent, self-organizing memory infrastructure that lets agents remember, reason, and evolve across sessions indefinitely.&lt;/p&gt;
&lt;p&gt;The project has garnered over 4,200 GitHub stars and is backed by multiple peer-reviewed papers accepted at ACL 2026. Its monorepo architecture unifies four core components: &lt;strong&gt;EverCore&lt;/strong&gt; (the self-organizing memory OS), &lt;strong&gt;HyperMem&lt;/strong&gt; (the hypergraph memory engine), &lt;strong&gt;EverMemBench&lt;/strong&gt; (a three-layer memory evaluation framework), and &lt;strong&gt;EvoAgentBench&lt;/strong&gt; (an agent self-evolution benchmark).&lt;/p&gt;

&lt;figure class="mermaid-wrapper not-prose" role="img" aria-label="Mermaid diagram"&gt;
 &lt;div class="mermaid-container"&gt;
 &lt;pre class="mermaid"&gt;graph TD
 A[Agent/LLM] --&amp;gt; B[EverCore]
 B --&amp;gt; C[HyperMem Hypergraph]
 B --&amp;gt; D[mRAG Multimodal Retriever]
 C --&amp;gt; E[Topic Hyperedges]
 C --&amp;gt; F[Event Hyperedges]
 C --&amp;gt; G[Fact Hyperedges]
 D --&amp;gt; H[Dense Vectors]
 D --&amp;gt; I[Sparse Keywords]
 D --&amp;gt; J[Multimodal Signals]
 B --&amp;gt; K[Evolved Skills]
 K --&amp;gt; A&lt;/pre&gt;
 &lt;script type="application/mermaid"&gt;graph TD
 A[Agent/LLM] --&gt; B[EverCore]
 B --&gt; C[HyperMem Hypergraph]
 B --&gt; D[mRAG Multimodal Retriever]
 C --&gt; E[Topic Hyperedges]
 C --&gt; F[Event Hyperedges]
 C --&gt; G[Fact Hyperedges]
 D --&gt; H[Dense Vectors]
 D --&gt; I[Sparse Keywords]
 D --&gt; J[Multimodal Signals]
 B --&gt; K[Evolved Skills]
 K --&gt; A&lt;/script&gt;
 &lt;/div&gt;
&lt;/figure&gt;&lt;p&gt;What makes EverOS truly groundbreaking is its &lt;strong&gt;self-evolving capability&lt;/strong&gt;. Agents can automatically distill skills and patterns from their task execution history, leading to a measured &lt;strong&gt;234.8% relative improvement&lt;/strong&gt; in complex task success rates over the baseline. This is not merely a caching layer &amp;ndash; it is an active memory that grows smarter the more it is used.&lt;/p&gt;</description></item></channel></rss>