EverMind-AI/EverOS

• 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.

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:

You can use other OpenAI-compatible providers by changing the matching *__BASE_URL fields in .env.

1. Install

uv pip install everos
# or: pip install everos

2. Play With The Demo

Run this before configuring API keys or starting the server:

everos demo

The command asks for one memory and one recall question, then opens a full-screen terminal UI. This is an educational visualizer: it is hardcoded, local to the CLI, and does not connect to the EverOS server. Its job is to make the memory lifecycle visible: conversation -> memory sphere -> recall -> source proof -> confetti. See docs/everos-demo.md for the demo scope and TUI source layout.

The sphere moves through ingest, extraction, indexing, recall, source reveal, and a confetti burst after the first memory lands. Press r to replay and q to quit.

For the looping showroom view used in README media, run:

everos demo --cinematic

If your shell is not interactive, or you want a copyable preview, use:

everos demo --plain

3. Configure

Generate a starter .env file, then fill the four API key slots shown in the generated comments. With the default setup, paste your OpenRouter key into the LLM / MULTIMODAL slots and your DeepInfra key into the EMBEDDING / RERANK slots.

everos init
# or, from a source checkout:
cp .env.example .env

everos init writes ./.env by default. Use everos init --xdg to write ${XDG_CONFIG_HOME:-~/.config}/everos/.env instead.

4. Start EverOS

everos server start

Keep the server running, then open a second terminal and check it:

curl http://127.0.0.1:8000/health

Expected response:

{"status":"ok"}

everos server start searches for .env in this order: --env-file <path> → ./.env (cwd) → ${XDG_CONFIG_HOME:-~/.config}/everos/.env → ~/.everos/.env. The endpoint stack is OpenAI-protocol compatible (OpenAI / OpenRouter / vLLM / Ollama / DeepInfra) - override *__BASE_URL in the generated .env to point at any of them.

Now make the demo real. In the second terminal, run:

everos demo --live

Live demo mode connects to the running server and performs the real /health -> /api/v1/memory/add -> /api/v1/memory/flush -> /api/v1/memory/search flow before opening the same memory sphere UI. Use --server-url <url> if your server is not on http://127.0.0.1:8000.

5. Try Your First Memory

Add a tiny conversation:

TS=$(($(date +%s)*1000))

curl -X POST http://127.0.0.1:8000/api/v1/memory/add \
  -H 'Content-Type: application/json' \
  -d "{
    \"session_id\": \"demo-001\",
    \"app_id\": \"default\",
    \"project_id\": \"default\",
    \"messages\": [
      {\"sender_id\": \"alice\", \"role\": \"user\", \"timestamp\": $TS, \"content\": \"I love climbing in Yosemite every spring.\"},
      {\"sender_id\": \"alice\", \"role\": \"user\", \"timestamp\": $((TS+10000)), \"content\": \"My favorite coffee shop is Blue Bottle in SOMA.\"}
    ]
  }"

Force extraction for the local demo:

curl -X POST http://127.0.0.1:8000/api/v1/memory/flush \
  -H 'Content-Type: application/json' \
  -d '{"session_id":"demo-001","app_id":"default","project_id":"default"}'

Search it back:

curl -X POST http://127.0.0.1:8000/api/v1/memory/search \
  -H 'Content-Type: application/json' \
  -d '{
    "user_id": "alice",
    "app_id": "default",
    "project_id": "default",
    "query": "Where do I like to climb?",
    "top_k": 5
  }'

You should see the Yosemite memory in the response. If the result is empty on the first try, wait a moment and retry; Markdown is written synchronously, while the local index catches up in the background.

[!TIP] First memory unlocked. You just gave EverOS a fact, flushed it into durable Markdown-backed memory, and searched it back through the local index. That is the core loop. Want to see the source of truth? Open ~/.everos and inspect the generated Markdown files.

For annotated responses and the Markdown files EverOS creates, see QUICKSTART.md.

Optional: Ingest Multimodal Files

To ingest non-text content (image / pdf / audio / office documents) through /api/v1/memory/add content items, install the optional extra:

uv pip install 'everos[multimodal]'   # or: pip install 'everos[multimodal]'

This pulls in everalgo-parser (with the [svg] bundle for SVG support via cairosvg) and wires up the multimodal LLM client (EVEROS_MULTIMODAL__* fields in .env, defaults to google/gemini-3-flash-preview via OpenRouter).

Office document support requires LibreOffice as a system dependency. The parser shells out to soffice (LibreOffice's headless renderer) to convert .doc / .docx / .ppt / .pptx / .xls / .xlsx to PDF before feeding the result into the multimodal LLM. Without LibreOffice, office uploads return HTTP 415 with a clear error message; PDF / image / audio / HTML / email parsing is unaffected.

Install on the host before serving office documents:

brew install --cask libreoffice              # macOS
sudo apt-get install -y libreoffice          # Debian / Ubuntu

For Contributors

git clone https://github.com/EverMind-AI/EverOS.git
cd EverOS
uv sync                              # creates ./.venv and installs deps
source .venv/bin/activate            # or prefix commands with `uv run`
everos demo --plain                  # try the local educational demo; no API keys needed
everos init                          # paste OpenRouter + DeepInfra keys into .env

everos --help
make test

Use Cases

Now that you have had your first successful EverOS moment, explore what people are building with persistent memory across agents, apps, and community integrations.

Use cases show what persistent memory makes possible in real products and workflows. Some examples are packaged in this repository; others point to external demos or integrations you can study and adapt.

Reunite - Find With EverOS Parents describe what they remember. Children describe what they recall. Reunite uses semantic memory to surface the connections. Learn more	Hive Orchestrator Browser-native hive-mind for CLI coding agents - Claude Code, Codex, Gemini, and OpenCode collaborate as real PTY processes via a team protocol. Code
AI Coding Assistants With EverOS Universal long-term memory layer for AI coding assistants, powered by EverOS. Code	AI Data Technician An agentic AI system that learns from scientist interaction to inspect, analyze, and classify high-dimensional time series data - with persistent memory that improves across sessions. Code
Rokid AI Assistant With EverOS Connect to EverOS within Rokid Glasses enabling long-term memory for all of your smart activities. Coming soon	Creative Assistant With Memory Creative assistant with long-term memory, so your creative context stays available across sessions. Coming soon
Earth Online Memory Game Earth Online is a memory-aware productivity game that turns everyday planning into a living quest log. Code	Multi-Agent Orchestration Platform Golutra presents a multi-agent workforce for engineering teams, extending the IDE model from a single assistant to coordinated agents. Code
Your Personal Tasting Universe Record, visualize, and explore your tasting journey through an immersive 3D star map. Code	EverOS Open Her Build AI that feels. Open-source persona engine - personality emerges from neural drives, not prompts. Inspired by Her. Code
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