EverOS
EverMind-AI/EverOS
A Python library providing a portable memory layer for AI agents
Overview
EverOS offers a local-first, Markdown-based memory system that stores conversations and files in readable format, enabling fast retrieval with SQLite and LanceDB indexing. It's designed to work across various applications and workflows, allowing agents and makers to reuse data efficiently.
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Install
pip install EverOSREADME
Website · Documentation · Blog · 中文
Table of Contents
- Why Ever OS
- Quick Start
- Use Cases
- Documentation
- Star Us
- EverMind Ecosystems
- Contributing
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.
| Title | EverOS | Other Agent Memory Libraries |
|---|---|---|
| Markdown source of truth | ✅ Canonical .md files that are readable, editable, diffable, and Git-versioned | ❌ Usually API, vector, graph, dashboard, or database state |
| Direct file editing | ✅ Edit .md files; cascade watcher syncs | ❌ Usually SDK, API, dashboard, or backend update paths |
| Local three-part stack | ✅ Markdown + SQLite + LanceDB; no MongoDB, Elasticsearch, or Redis required | ❌ Often depends on managed services, vector DBs, graph DBs, or server stacks |
| User + agent tracks | ✅ User episodes/profile and agent cases/skills are separate first-class surfaces | ❌ Usually centered on chat history, profiles, entities, facts, or retrieval records |
| Orthogonal retrieval | ✅ Search by user_id, agent_id, app_id, project_id, and session_id | ❌ Usually app, namespace, tenant, thread, or graph scoped |
| Knowledge Wiki | ✅ Editable, source-backed Markdown knowledge pages with taxonomy, CRUD APIs, and topic search | ❌ Usually separate from memory, trapped in a dashboard, or not tied back to source files |
| Reflection | ✅ Offline memory evolution that merges episode clusters and refines profiles and skills between sessions | ❌ Usually retrieval-only memory with little background consolidation or long-horizon improvement |
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 |
| --- |