EverMind-AI

EverOS

One portable memory layer for every agent: conversations, files and trajectories kept as canonical Markdown, indexed locally by SQLite and LanceDB, with offline reflection that refines them.

12,342 901 Python Apache-2.0updated 6 days ago

memory skills

View on GitHubDispute this mapping →

Curator's take

Pick it if you want memory you can open in an editor: `.md` files are the source of truth, edited directly and picked up by a cascade watcher, with SQLite plus LanceDB as disposable indexes and no MongoDB, Elasticsearch or Redis in the stack. User episodes and profile sit apart from agent cases and skills as first-class tracks, retrieval scopes orthogonally by user, agent, app, project and session, and reflection consolidates episode clusters between sessions instead of only recalling. Trade-offs: it runs as a server with its own TOML config, full capability needs embedding and rerank models configured beyond the one-key start, Markdown-as-truth means large vaults get large, and `/api/v1` is already a legacy alias.

Mapped by ShipWithAI editors · links verified

README.md

EverOS banner

Website · Documentation · Blog · 中文

Table of Contents

Why Ever OS
Quick Start
Use Cases
Documentation
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

One OpenRouter API key is enough to start EverOS, write durable memories, and retrieve them with keyword search.

Prerequisites

Python 3.12+
One OpenRouter API key

1. Install

uv pip install everos
# or: pip install everos

Continue your stack

What teams reach for next — and why each earns a place beside EverOS. Ranked by curator confidence.

Alternative to See all + compare →

OpenViking

Volcengine's context database: memories, resources and skills as one `viking://` filesystem agents ls, tree and grep — L0/L1/L2 tiers, traceable retrieval, sessions distilled into memory.

Why it fitsBoth unify memory, files and skills into one human-readable store rather than an opaque index. OpenViking runs a `viking://` filesystem with L0/L1/L2 tiers and traceable retrieval; EverOS keeps plain Markdown as truth with local indexes and offline reflection. Filesystem metaphor versus editable files.

Acontext

Skill memory layer for agents: auto-captures learnings from runs into plain Markdown skill files you can read, edit, git and share across frameworks — memory without an opaque store.

Why it fitsShared bet on Markdown-as-memory. acontext stays minimal — learnings become skill files you git and share; EverOS is a runtime with episodes, profiles, a knowledge wiki, vector indexes and background consolidation. acontext when git is enough, EverOS when you want recall infrastructure.

MemMachine

Long-term memory layer for AI agents — episodic (graph), profile (SQL) and working memory behind Python/TS SDKs, REST and MCP; ships LangChain, LangGraph, CrewAI and LlamaIndex integrations.

Why it fitsSame job, opposite substrate: MemMachine puts episodic memory in a graph and profiles in SQL behind SDKs and REST; EverOS puts everything in Markdown you can edit by hand. Choose by whether memory should be a service or a folder.