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docs(readme): polish launch highlights and banner (#261)

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* docs: simplify README launch highlights

* docs(readme): use six launch highlights

* docs(readme): use optimized banner asset

* ci: lint pull request titles
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Elliot Chen
2026-06-06 19:49:59 +08:00
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parent 0a99922f24
commit 79b3df4de2
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README.md
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<div align="center" id="readme-top">
![EverOS banner](https://github.com/EverMind-AI/EverOS/releases/download/v1.0.0/everos-readme-banner.jpg)
![EverOS banner](https://github.com/EverMind-AI/EverOS/releases/download/v1.0.0/everos-readme-banner-optimized.jpg)
<p align="center">
<a href="https://x.com/evermind"><img src="https://img.shields.io/badge/EverMind-000000?labelColor=gray&style=for-the-badge&logo=x&logoColor=white" alt="X"></a>
@ -54,53 +54,36 @@
<table>
<tr>
<td width="33%" valign="top">
<strong>Markdown-First Memory</strong><br>
Memory is persisted as plain Markdown: visible, auditable, hand-editable,
Git-friendly, and owned by the user.
<strong>Markdown As Source Of Truth</strong><br>
All memory is persisted as <code>.md</code> files: readable, editable,
grep-able, Git-versioned, and openable directly in Obsidian.
</td>
<td width="33%" valign="top">
<strong>Lightweight Local Stack</strong><br>
Install with Python. SQLite tracks runtime state; LanceDB powers vector,
BM25, and scalar-filter retrieval locally.
<strong>Local Three-Part Stack</strong><br>
Markdown + SQLite + LanceDB keep vectors, BM25, and scalar filters
local. No MongoDB, Elasticsearch, or Redis required.
</td>
<td width="33%" valign="top">
<strong>Layered Memory Model</strong><br>
User memory and agent memory are first-class today. Wiki-style knowledge
is the next layer in the roadmap.
<strong>Dual-Track Memory</strong><br>
Agent memory (<code>cases</code> / <code>skills</code>) and user memory
(<code>episodes</code> / <code>profile</code>) are extracted independently.
</td>
</tr>
<tr>
<td width="33%" valign="top">
<strong>Self-Evolving Agents</strong><br>
Agent memory can extract reusable cases and skills from repeated
experience, so workflows become smarter over time.
</td>
<td width="33%" valign="top">
<strong>Multimodal Ingestion</strong><br>
Text, image, audio, documents, PDF, HTML, and email can be parsed into
memory through the optional multimodal pipeline.
Text, images, audio, documents, PDFs, HTML, and email are unified into
searchable memory.
</td>
<td width="33%" valign="top">
<strong>Online And Offline Strategy Control</strong><br>
Online extraction and offline evolution stay separate, with configurable
prompts and models at each step. Dreaming is coming next.
</td>
</tr>
<tr>
<td width="33%" valign="top">
<strong>Orthogonal Memory Scope</strong><br>
Owner, memory type, and scope are independent: search by user, agent,
app, project, session, and structured filters.
<strong>Self-Evolution</strong><br>
Common skills are extracted from real usage; repeated patterns become
reusable workflows, no retraining required.
</td>
<td width="33%" valign="top">
<strong>Progressive Disclosure</strong><br>
Readable memory surfaces stay simple while deeper facts, cases, and
skills remain available.
</td>
<td width="33%" valign="top">
<strong>Modular By Design</strong><br>
EverAlgo owns algorithms; EverOS owns runtime, persistence, online flows,
and offline evolution.
<strong>Orthogonal Retrieval</strong><br>
Search independently by <code>user_id</code>, <code>agent_id</code>,
<code>app_id</code>, <code>project_id</code>, and <code>session_id</code>.
</td>
</tr>
</table>