workflowstacks
mcp-server

EverOS: Portable, User‑Owned Memory for Your AI Agents

EverOS gives founders a local‑first, Markdown‑native memory layer that evolves with any app or workflow, keeping data under full user control. Ideal for teams building agentic AI products. 13k+ GitHub stars.

From the repo · “One portable memory layer for every AI agent: local-first, Markdown-native, user-owned, and self-evolving across apps, tools, and workflows.”

13,560 stars+121★ this week944 forksPythonGuide quality 9/10 · reviewed Oct 8Updated 10/22/2026100% free · open source
What it does

EverOS provides a portable, local-first memory layer for AI agents, allowing them to store and evolve knowledge across different apps and workflows in a Markdown-native format

When to use it
  • •When developing AI-powered tools that require a unified knowledge base across multiple applications
  • •When needing to integrate AI agents with various workflows and tools while maintaining user-owned data
  • •When building self-evolving AI systems that require a flexible and portable memory layer
Ready-to-paste prompt
everos query 'What are the key concepts related to artificial intelligence?' --format markdown
Heads up: Ensure you have Python 3.8 or later installed, as EverOS relies on specific Python features and libraries that may not be compatible with earlier versions
Saves to your device
Use with Claude
New

Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.

🛠️ Technical setup

Expect 20–40 minutes in a terminal — or let your AI agent drive it.

Try it instantly — no install
Claude Code
mkdir -p ~/.claude/skills/everos && curl -fsSL https://workflowstacks.com/api/skills/everos/claude-skill -o ~/.claude/skills/everos/SKILL.md
Open in another AI app

Opens the app with this repo with the prompt ready to go — no copy-paste needed.

Connect the whole catalog (MCP)
claude mcp add --transport http workflowstacks https://workflowstacks.com/api/mcp

Adds a WorkflowStacks connector to Claude Code: search and load any skill here by chatting.

How EverOS: Portable, User‑Owned Memory for Your AI Agents works
Codeflow
Free to inspect

EverOS: Portable, User‑Owned Memory for Your AI Agents is a large Python project (~57k lines across 385 code files, plus 340 test files). It is a full software project: use it through its install path rather than reading it end to end. Last commit this month, Apache-2.0 license, has a test suite.

Size
Large codebase
~57k lines · 385 code files · ~8 h to skim
Setup
Developer setup
A real software project. Use it via its install path; don't expect to read it all.
Runs on
Python
Needs API keys (.env)
Python 92%TypeScript 3%JavaScript 2%HTML 2%CSS 1%
Where to start reading
  1. 1
    README.md
    Start here — what it does and how to install it
  2. 2
    CLAUDE.md
    The instructions the AI actually follows
  3. 3
    pyproject.toml
    Dependencies and the commands it exposes
  4. 4
    .env.example
    The API keys and settings you must provide
  5. 5
    src/everos/__init__.py
    Inside src/ — the main logic begins here
What's in each folder
src/Core code — the actual logic324 files
docs/Documentation21 files
examples/Examples you can copy6 files
use-cases/Folder89 files
scripts/Helper scripts15 files
benchmarks/Evaluations & benchmarks11 files
deploy/Deployment / infrastructure6 files
data/Data files4 files
READMEHas testsDocumentedCI checksExamples includedApache-2.0 licenseUpdated this month
Quick Actions
Details
Creator
EverMind-AI
Language
Python
Category
mcp-server
Published
10/28/2025

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Featured on WorkflowStacks
[![Featured on WorkflowStacks](https://workflowstacks.com/api/badge/everos.svg)](https://workflowstacks.com/skills/everos?utm_source=github&utm_medium=badge)
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Do this with a working workflow

Pages we wrote and tested that put a skill like this to work — importable n8n templates, outcome guides and Claude Desktop configs.

MCP config

Memory (knowledge graph) MCP config

Persistent memory across chats via a local knowledge graph.

Outcome guide

Score your leads by ideal-customer fit

You describe your ideal customer once and keep leads in a Google Sheet. The workflow scores each 1–5 for fit with a one-line reason, so you sort and work the best first.

Outcome guide

Prep for sales objections before every call

List your upcoming deals with a little context, and the workflow builds a prep card per deal: the 3 objections you’re most likely to hear, an honest response to each, and one sharp question to ask.

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