EverOS

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[Website](https://evermind.ai) · [Documentation](https://docs.evermind.ai) · [Blog](https://evermind.ai/blogs) · [中文](README.zh-CN.md) <!-- cjk-allow: language switcher -->

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<details>
  <summary><kbd>Table of Contents</kbd></summary>

<br>

- [Why EverOS](#why-ever-os)
- [Ecosystem Integrations](#ecosystem-integrations)
- [Quick Start](#quick-start)
- [Use Cases](#use-cases)
- [Documentation](#documentation)
- [EverMind Ecosystem](#evermind-ecosystem)
- [Contributing](#contributing)

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</details>


## Why EverOS

EverOS is a Python library and local-first memory runtime for agents and
makers. It gives one portable memory layer across coding assistants, apps,
devices, and workflows from day one. It stores conversations, files, and agent
trajectories as readable Markdown, then syncs local SQLite and LanceDB indexes
for fast retrieval and self-evolving reuse.

<table>
<tr>
<th width="28%">Title</th>
<th width="36%">EverOS</th>
<th width="36%">Other Agent Memory Libraries</th>
</tr>
<tr>
<td><strong>Markdown source of truth</strong></td>
<td>✅ Canonical <code>.md</code> files that are readable, editable, diffable, and Git-versioned</td>
<td>❌ Usually API, vector, graph, dashboard, or database state</td>
</tr>
<tr>
<td><strong>Direct file editing</strong></td>
<td>✅ Edit <code>.md</code> files; cascade watcher syncs</td>
<td>❌ Usually SDK, API, dashboard, or backend update paths</td>
</tr>
<tr>
<td><strong>Local three-part stack</strong></td>
<td>✅ Markdown + SQLite + LanceDB; no MongoDB, Elasticsearch, or Redis required</td>
<td>❌ Often depends on managed services, vector DBs, graph DBs, or server stacks</td>
</tr>
<tr>
<td><strong>User + agent tracks</strong></td>
<td>✅ User <code>episodes/profile</code> and agent <code>cases/skills</code> are separate first-class surfaces</td>
<td>❌ Usually centered on chat history, profiles, entities, facts, or retrieval r