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Table of Contents

• EverOS 1.0.0 Highlights
• Why EverOS
• Quick Start
• Architecture At A Glance
• Storage Layout
• Features
• Project Structure
• Documentation
• Use Cases
• Watch EverOS
• EverMind Ecosystems
• Contributing

EverOS 1.0.0 Highlights

Important

EverOS 1.0.0 is a major release for self-evolving memory. It brings a local-first runtime, Markdown as the source of truth, hybrid retrieval, multimodal ingestion, user and agent memory scopes, and modular algorithms through EverAlgo.

Watch this repository for the next wave of memory-system work, including Wiki-style knowledge layers and Dreaming for deeper offline evolution.

Markdown As Source Of Truth All memory is persisted as .md files: readable, editable, grep-able, Git-versioned, and openable directly in Obsidian.	Local Three-Part Stack Markdown + SQLite + LanceDB keep vectors, BM25, and scalar filters local. No MongoDB, Elasticsearch, or Redis required.	Dual-Track Memory Agent memory (cases / skills) and user memory (episodes / profile) are extracted independently.
Multimodal Ingestion Text, images, audio, documents, PDFs, HTML, and email are unified into searchable memory.	Self-Evolution Common skills are extracted from real usage; repeated patterns become reusable workflows, no retraining required.	Orthogonal Retrieval Search independently by user_id, agent_id, app_id, project_id, and session_id.

Why EverOS

EverOS is an open-source Python framework for self-evolving long-term memory across agents and platforms. It gives makers one portable memory layer for every agent they use - Claude Code, Codex, OpenClaw, Hermes, and more - so context, decisions, files, and trajectories can follow the work instead of staying trapped in one tool.

EverOS stores conversations, agent trajectories, and files as readable Markdown, then syncs local SQLite and LanceDB indexes for fast retrieval. Agents can reuse past cases and skills, improve from repeated workflows, and become more proactive over time.

The system is built around three boundaries:

1. Memory content stays readable - Markdown is the durable source of truth.
2. Runtime state stays local - SQLite tracks state and LanceDB handles vector, BM25, and scalar-filter search.
3. Algorithms stay modular - EverAlgo owns memory algorithms; EverOS owns runtime, persistence, online flows, and offline evolution.

Quick Start

1. Install EverOS

uv pip install everos
# or: pip install everos

2. Initialize Configuration

Generate a starter .env file, then fill the API key fields shown in the generated comments.

everos init

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

3. Start The Server

everos --help
everos server start

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.

For a step-by-step walkthrough (add a conversation, flush, search, then read the markdown), 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 init                          # fill the four API key slots in .env (two distinct keys)

everos --help
make test

Architecture At A Glance

┌───────────────────────────────────────────────┐
│  entrypoints/  (CLI + HTTP API)                │  presentation
├───────────────────────────────────────────────┤
│  service/      (use cases: memorize/retrieve)  │  application
├───────────────────────────────────────────────┤
│  memory/       (extract + search + cascade)    │  domain
├───────────────────────────────────────────────┤
│  infra/        (markdown / sqlite / lancedb)   │  infrastructure
└───────────────────────────────────────────────┘
        ↑                    ↑
   component/            core/
   (LLM/Embedding)       (observability/lifespan)

DDD 5 layers, single-direction dependency. See docs/architecture.md.

Storage Layout

~/.everos/
├── default_app/                  # app_id  ("default" → "default_app" on disk)
│   └── default_project/          # project_id ("default" → "default_project")
│       ├── users/<user_id>/
│       │   ├── user.md           # profile
│       │   ├── episodes/         # daily-log episodes (visible)
│       │   ├── .atomic_facts/    # nested facts (dotfile-hidden)
│       │   └── .foresights/      # predictive memory (dotfile-hidden)
│       └── agents/<agent_id>/
│           ├── agent.md
│           ├── .cases/           # one task case per entry
│           └── skills/           # named procedural memories
├── .index/                       # derived indexes (rebuildable from md)
│   ├── sqlite/system.db          # state + queue + audit
│   └── lancedb/*.lance/          # vector + BM25 + scalar
└── .tmp/                         # transient working files

Open any <app>/<project>/users/<user_id>/ folder in Obsidian — your agent's brain is just files. The dotfile directories (.atomic_facts/, .foresights/, .cases/) stay hidden by default so the visible folder is the user-facing memory surface, while extracted derivatives sit quietly alongside.

Features

• Hybrid retrieval: BM25 + cosine vector ANN + scalar filters, backed by LanceDB
• Cascade index sync: edit a .md → file watcher → entry-level diff → LanceDB sync, sub-second
• Multi-source extraction: conversations / agent trajectories / file knowledge
• Dual-track memory: user-track (Episodes / Profiles) + agent-track (Cases / Skills)
• Async-first: full asyncio, single event loop
• Multi-modal: text + small image / audio inline; large media via S3/OSS reference

Project Structure

everos/                        # repo root
├── src/everos/                # main package (src layout)
│   ├── entrypoints/           # cli + api
│   ├── service/               # use case orchestration
│   ├── memory/                # domain: extract + search + cascade + prompt_slots
│   ├── infra/                 # storage: markdown + lancedb + sqlite
│   ├── component/             # cross-cutting: llm / embedding / config / utils
│   ├── core/                  # runtime: observability / lifespan / context
│   └── config/                # configuration data + Settings schema
├── tests/                     # unit / integration / golden / fixtures
├── docs/                      # design docs
└── .claude/                   # team-shared rules + skills (auto-loaded by Claude Code)

Documentation

• docs/overview.md — Project overview & vision
• docs/architecture.md — DDD layered architecture & dependency rules
• docs/engineering.md — Engineering & dev-efficiency infrastructure (CI / tooling / Claude Code)
• docs/use-cases.md — Full use-case gallery and integration examples
• docs/migration-to-1.0.0.md — Legacy API and infrastructure migration notes
• CHANGELOG.md — Release notes
• CONTRIBUTING.md — How to contribute
• .claude/rules/ — Detailed coding conventions (auto-loaded by Claude Code)

Use Cases

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 EverOS 1.0.0 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

Watch EverOS

EverOS 1.0.0 is the first release of a larger memory-system roadmap. Watch this repository for upcoming work on Wiki-style memory, Dreaming, deeper offline evolution, benchmark releases, and more real-world agent integrations.

If EverOS is useful to your agent stack, starring the repo helps more builders discover it.

Star History

EverMind Ecosystems

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

EverMind Open-Source Ecosystem
Core Memory Architecture	EverOS - the local memory operating system and research-backed runtime for agent and user memory.
Algorithm Engine	EverAlgo - stateless extraction, ranking, parsing, and memory operators that power EverOS.
Alternative Architecture	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: architecture 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.