---
title: "EverOS"
type: "tool"
slug: "evermind-ai-everos"
canonical_url: "https://www.graphcanon.com/tools/evermind-ai-everos"
github_url: "https://github.com/EverMind-AI/EverOS"
homepage_url: "https://evermind.ai/everos"
stars: 10479
forks: 841
primary_language: "Python"
license: "Apache-2.0"
categories: ["data-retrieval", "ai-agents"]
tags: ["llm", "memory-management", "agentic-ai", "long-term-memory", "agent-memory"]
updated_at: "2026-07-07T19:45:00.697059+00:00"
---

# EverOS

> One portable memory layer for every AI agent

EverOS provides a local-first, user-owned, and self-evolving memory system for AI agents that uses Markdown as the source of truth. It synchronizes SQLite and LanceDB indexes for efficient data retrieval.

## Facts

- Repository: https://github.com/EverMind-AI/EverOS
- Homepage: https://evermind.ai/everos
- Stars: 10,479 · Forks: 841 · Open issues: 44 · Watchers: 108
- Primary language: Python
- License: Apache-2.0
- Last pushed: 2026-07-07T10:33:58+00:00

## Categories

- [Data & Retrieval](/categories/data-retrieval.md)
- [AI Agents](/categories/ai-agents.md)

## Tags

llm, memory-management, agentic-ai, long-term-memory, agent-memory

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## README (excerpt)

```text
<div align="center" id="readme-top">



<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>
  <a href="https://huggingface.co/EverMind-AI"><img src="https://img.shields.io/badge/🤗_HuggingFace-EverMind-F5C842?labelColor=gray&style=for-the-badge" alt="HuggingFace"></a>
  <a href="https://discord.gg/gYep5nQRZJ"><img src="https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fdiscord.com%2Fapi%2Fv10%2Finvites%2FgYep5nQRZJ%3Fwith_counts%3Dtrue&query=%24.approximate_presence_count&suffix=%20online&label=Discord&color=404EED&labelColor=gray&style=for-the-badge&logo=discord&logoColor=white" alt="Discord"></a>
  <a href="https://github.com/EverMind-AI/EverOS/discussions/67"><img src="https://img.shields.io/badge/WeCom-EverMind_社区-07C160?labelColor=gray&style=for-the-badge&logo=wechat&logoColor=white" alt="WeChat"></a>
</p>

[Website](https://evermind.ai) · [Documentation](https://docs.evermind.ai) · [Blog](https://evermind.ai/blogs) · [中文](README.zh-CN.md)

</div>


<br>

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

<br>

- [Why Ever OS](#why-ever-os)
- [Quick Start](#quick-start)
- [Use Cases](#use-cases)
- [Documentation](#documentation)
- [Star Us](#star-us)
- [EverMind Ecosystems](#evermind-ecosystems)
- [Contributing](#contributing)

<br>

</details>


## Why Ever OS

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 records</td>
</tr>
<tr>
<td><strong>Orthogonal retrieval</strong></td>
<td>✅ Search by <code>user_id</code>, <code>agent_id</code>, <code>app_id</code>, <code>project_id</code>, and <code>session_id</code></td>
<td>❌ Usually app, namespace, tenant, thread, or graph scoped</td>
</tr>
<tr>
<td><strong>Knowledge Wiki</strong></td>
<td>✅ Editable, source-backed Markdown knowledge pages with taxonomy, CRUD APIs, and topic search</td>
<td>❌ Usually separate from memory, trapped in a dashboard, or not tied back to source files</td>
</tr>
<tr>
<td><strong>Reflection</strong></td>
<td>✅ Offline memory evolution that merges episode clusters and refines profiles and skills between sessions</td>
<td>❌ Usually retrieval-only memory with little background consolidation or long-horizon improvement</td>
</tr>
</table>

<br>

## Quick Start

> Goal: play with the memory visualizer first, then start EverOS, write one
> real memory, and search it back.

### 0. Prerequisites

- Python 3.12+
- No API keys are needed for `everos demo`.
- To run the real server-backed memory flow, create two provider keys before
  `everos init`:

| Capability | Provider | Used for | Fill these `.env` slots |
| --- |
```

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/tools/evermind-ai-everos`](/api/graphcanon/tools/evermind-ai-everos)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_