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What is EverOS

EverOS is an open-source Python framework that turns conversations, agent trajectories, and files into structured, retrievable, evolving long-term memory for AI agents and user chats. Designed for lightweight local deployments (small teams, individual developers), with three core principles:

  1. Markdown as Source of Truth — All memory persists as plain .md files. Open, edit, grep, version with Git, view in Obsidian. No black-box database lock-in.
  2. Lightweight three-piece storageMarkdown files (truth) + SQLite (state/queue) + LanceDB (vector + BM25 + scalar). No MongoDB / Elasticsearch / Milvus / Redis / Kafka required.
  3. EverAlgo as pure algorithm library — Memory extraction algorithms are decoupled into a separate library; this project orchestrates and persists.

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.


Quick start

Install as a package

uv pip install everos # or: pip install everos# Generate a starter .env (OpenRouter + DeepInfra defaults; bundled inside the wheel)
everos init # writes ./.env (use --xdg for ~/.config/everos/.env)# Edit .env and fill the API key fields (see comments inside).
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.

Multi-modal (optional)

To ingest non-text content (image / pdf / audio / office documents) through /api/v1/memory/addcontent 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 a step-by-step walkthrough (add a conversation → flush → search → read the markdown), see QUICKSTART.md.

Develop locally

git clone https://github.com/EverMind-AI/EverOS.git
cd EverOS
uv sync # creates ./.venv and installs depssource .venv/bin/activate # — or skip activation and prefix every command with `uv run`
everos init # fill the four API key slots in .env (two distinct keys)
everos --help
make test

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 + vector (HNSW/IVF-PQ) + scalar filter, single-query in 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


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.

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Reunite - Find with EverOS

Parents describe what they remember. Children describe what they recall. Reunite uses semantic memory to surface the connections.

Learn more

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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

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AI Coding Assistants with EverOS

Universal long-term memory layer for AI coding assistants, powered by EverOS.

Code

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AI Data Techician

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

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Rokid AI Assistant with EverOS

Connect to EverOS within Rokid Glasses enabling long-term memory for all of your smart activities.

Coming soon

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Creative Assistant with Memory

Creative assistant with long-term memory, never forget your crativites anymore.

Coming soon

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Earth Online Memory Game

Earth Online is a memory-aware productivity game that turns everyday planning into a living quest log.

Code

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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

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Your Personal Tasting Universe

Record, visualize, and explore your tasting journey through an immersive 3D star map.

Code

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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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Browser Agent for Personal Memory

Ruminer brings persistent memory to a browser agent so it can carry personal context across web tasks.

Plugin

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EverMem Sync with EverOS

One command to connect any AI coding CLI to EverMemOS long-term memory.

Code

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MCO - Orchestrate AI Coding Agents

MCO equips your primary agent with an agent team that can work together to solve complex tasks.

Code

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Study Buddy with Self-Evolving Memory

Study proactively with an agent that has self-evolving memory.

Code

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Alzheimer’s Memory Assistant

Empowering individuals with advanced memory support and daily assistance.

Code

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Memory-Driven Multi-Agent NPC Experience

An iOS sci-fi mystery game where players explore and uncover the truth.

Code

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Mobi Companion

An iOS app where users create, nurture, and live with a personalized AI companion called Mobi.

Code

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AI Wearable with Memory

A context-native AI wearable that listens to everyday life and converts conversations into memory.

Code

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Legacy OpenClaw Agent Memory

Archived pre-1.0.0 plugin reference. New integrations should use the EverOS 1.0.0 API.

Learn more

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Live2D Character with Memory

Add long-term memory to a real-time Live2D character, powered by TEN Framework.

Code

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Computer-Use with Memory

Run screenshot-based analysis with computer-use and store the results in memory.

Live Demo

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Game of Thrones Memories

A demonstration of AI memory infrastructure through an interactive Q&A experience with A Game of Thrones.

Code

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Claude Code Plugin

Persistent memory for Claude Code. Automatically saves and recalls context from past coding sessions.

Code

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Memory Graph Visualization

Explore stored entities and relationships in a graph interface. Frontend demo; backend integration is in progress.

Live Demo


Stay Tuned

Star the repo or join the community links above to follow new architecture methods, benchmark releases, and memory-enabled use cases.

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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.

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Code Contributors

EverOS Contributors

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License

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

Citation

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


About

EverMemOS is an open-source, enterprise-grade intelligent memory system. Our mission is to build AI memory that never forgets, making every conversation built on previous understanding.

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