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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

2. Try the standalone demo — no key required

Before configuring a provider or starting the server, run:

everos demo

The command asks for one memory and one recall question, then opens a full-screen terminal visualizer. It is hardcoded and local to the CLI: it does not need an API key, start or call the EverOS server, or change anything in the real memory workflow below.

Press r to replay and q to quit. For a non-interactive preview, use everos demo --plain; for the looping showroom view, use everos demo --cinematic. See docs/everos-demo.md for the visualizer's scope.

3. Initialize and add your OpenRouter key

everos init

This creates ~/.everos/everos.toml and ~/.everos/ome.toml. Open ~/.everos/everos.toml; the generated model and OpenRouter URL are already correct, so replace only the empty api_key:

[llm]
model = "openai/gpt-4.1-mini"
api_key = "<OPENROUTER_API_KEY>"
base_url = "https://openrouter.ai/api/v1"

This is the smallest Tier 1 setup: memory add, flush, Markdown persistence, cascade indexing, and keyword search.

Use everos init --root <path> if you want a different memory root. Pass the same --root <path> to subsequent commands.

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

Look for "status":"ok". With this one-key setup, capabilities.llm is true; embedding and rerank remain false until you configure them.

5. Add and retrieve your first memory

Note

Business endpoints live under /api/v2. The older /api/v1 prefix still resolves to the same handlers so existing integrations keep working, but it is a legacy alias that may be removed in a future major release — write new code against /api/v2.

Add a tiny conversation:

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

curl -X POST http://127.0.0.1:8000/api/v2/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.\"}
    ]
  }"

Flush the memory at the end of the session:

curl -X POST http://127.0.0.1:8000/api/v2/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/v2/memory/search \
  -H 'Content-Type: application/json' \
  -d '{
    "user_id": "alice",
    "app_id": "default",
    "project_id": "default",
    "query": "Where do I like to climb?",
    "method": "keyword",
    "top_k": 5
  }'

You should see the Yosemite memory in the response. Keep "method": "keyword" in this one-key setup because the API defaults to hybrid search, which requires an embedding provider.

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.

What works with one key?

The OpenRouter one-key setup is EverOS Tier 1. It supports server startup, memory add and flush, durable Markdown storage, cascade indexing, and keyword search. Add optional providers only when you need the features below:

Configuration	Adds
[llm] only	Core memory flow and keyword search
Add [embedding]	Vector/user hybrid search, reflection, and skill extraction
Add [rerank] too	Agentic search, default agent hybrid search, and Knowledge Wiki
Add [multimodal] and parser extra	Image, PDF, audio, and office-file ingestion

Missing optional capabilities are reported by /health and return a clear HTTP 422 if you request a feature that needs them.

Note

everos demo --live is different from the standalone demo in step 2: it connects to a running server and uses the real add/flush/search flow. It uses hybrid search, so add an embedding provider before you run it.

Optional: Ingest Multimodal Files

To ingest non-text content (image / pdf / audio / office documents) through /api/v2/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). Configure the [multimodal] section in everos.toml; its default model is 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                          # add one OpenRouter key to ~/.everos/everos.toml

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
Browser Agent For Personal Memory Ruminer brings persistent memory to a browser agent so it can carry personal context across web tasks. Plugin	EverMem Sync With EverOS One command to connect any AI coding CLI to EverMemOS long-term memory. Code
MCO - Orchestrate AI Coding Agents MCO equips your primary agent with an agent team that can work together to solve complex tasks. Code	Study Buddy With Self-Evolving Memory Study proactively with an agent that has self-evolving memory. Code
Alzheimer's Memory Assistant Empowering individuals with advanced memory support and daily assistance. Code	Memory-Driven Multi-Agent NPC Experience An iOS sci-fi mystery game where players explore and uncover the truth. Code
Mobi Companion An iOS app where users create, nurture, and live with a personalized AI companion called Mobi. Code	AI Wearable With Memory A context-native AI wearable that listens to everyday life and converts conversations into memory. Code
Legacy OpenClaw Agent Memory Archived pre-1.0.0 plugin reference. New integrations should use the current EverOS API. Learn more	Live2D Character With Memory Add long-term memory to a real-time Live2D character, powered by TEN Framework. Code
Computer-Use With Memory Run screenshot-based analysis with computer-use and store the results in memory. Live Demo	Game Of Thrones Memories A demonstration of AI memory infrastructure through an interactive Q&A experience with A Game of Thrones. Code
Claude Code Plugin Persistent memory for Claude Code. Automatically saves and recalls context from past coding sessions. Code	Memory Graph Visualization Explore stored entities and relationships in a graph interface. Frontend demo; backend integration is in progress. Live Demo

Documentation

• docs/everos-demo.md — Demo scope and TUI source layout
• docs/how-memory-works.md — Markdown, SQLite, LanceDB, and recall flow
• docs/use-cases.md — Full use-case gallery and integration examples
• docs/engineering.md — Contributor engineering reference: build, test, CI, conventions
• docs/migration-to-1.0.0.md — Legacy API migration notes
• CHANGELOG.md — Release notes
• CONTRIBUTING.md — How to contribute

EverMind Ecosystems

EverMind is an open-source ecosystem for long-term memory, self-evolving agents, AI-native interfaces, and memory evaluation.

EverMind Open-Source Ecosystem
Memory Runtime	EverOS - the local memory operating system and research-backed runtime for agent and user memory.
Self-Improving Agent Harness	Raven - the self-improving agent harness that brings memory, proactivity, context control, and skill evolution into terminal-native agents.
Algorithm Engine	EverAlgo - stateless extraction, ranking, parsing, and memory operators that power EverOS.
Hypergraph Memory	HyperMem - hypergraph memory for long-term conversations, with its own benchmark-backed topic -> episode -> fact retrieval method.
Benchmarks	EverMemBench · EvoAgentBench - evaluation suites for conversational memory and agent self-evolution.
Long-Context Research	MSA - Memory Sparse Attention for scalable latent memory and 100M-token contexts.
Personal Memory Layer	EverMe - CLI and agent plugin suite for cross-device, cross-agent personal memory.
Developer Integrations	evermem-claude-code · everos-plugins - plugins, skills, and migration tooling for AI coding agents.

Together, these repositories form EverMind's research-to-runtime stack: new memory methods, reusable algorithms, benchmark evidence, and practical agent integrations.

Contributing

Contributions are welcome across the whole repository: memory methods, benchmark coverage, use-case examples, documentation, and bug fixes. Browse Issues to find a good entry point, then open a PR when you are ready.

Tip

Welcome all kinds of contributions 🎉

Help make EverOS better. Code, documentation, benchmark reports, use-case write-ups, and integration examples are all valuable. Share your projects on social media to inspire others.

Connect with one of the EverOS maintainers @elliotchen200 on 𝕏 or @cyfyifanchen on GitHub for project updates, discussions, and collaboration opportunities.

Code Contributors

License

Apache License 2.0 — see NOTICE for third-party attributions.

Citation

If you use EverOS in research, see CITATION.md.