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Table of Contents

• EverOS 1.0.0
• EverOS: One Memory For All
• How EverOS Is Different
• Quick Start
• Use Cases
• Architecture At A Glance
• Storage Layout
• Features
• Project Structure
• Documentation
• Watch EverOS
• EverMind Ecosystems
• Contributing

EverOS 1.0.0

[!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.

Coming next: Knowledge Wiki will turn memory into editable, source-backed Markdown knowledge pages. Reflection, also called Dreaming, will run when the system is idle or offline to connect signals, compress history, and improve profiles and skills between sessions.

EverOS: One Memory For All

EverOS is the local memory operating system for agents and makers. It gives one portable memory layer across coding assistants, apps, devices, and workflows. Today it stores conversations, files, and agent trajectories as readable Markdown, then syncs local SQLite and LanceDB indexes for fast retrieval and self-evolving reuse.

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.

How EverOS Is Different

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	✅ Coming next: editable, source-backed Markdown knowledge pages built from memory	❌ Usually retrieval, graph, dashboards, or generated summaries instead of editable source-backed pages
Dreaming / Reflection	✅ Coming next: Reflection that runs when the system is idle or offline to connect signals, compress history, and improve profiles and skills between sessions	❌ Usually online read/write APIs, retrieval records, or summaries rather than idle-time memory consolidation

Quick Start

Goal: start EverOS, write one memory, and search it back.

0. Prerequisites

• Python 3.12+
• API keys for the default providers: OpenRouter for chat / multimodal, and DeepInfra for embedding / rerank. 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. Configure

Generate a starter .env file, then fill the four API key slots shown in the generated comments. Only two distinct keys are needed with the defaults: OpenRouter for LLM / MULTIMODAL, and DeepInfra for EMBEDDING / RERANK.

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.

3. 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.

4. 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 init                          # fill the four API key slots in .env (two distinct keys)

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 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

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)

Watch EverOS

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

Knowledge Wiki Turns scattered episodes, files, facts, and agent traces into source-backed Markdown pages for people, projects, topics, decisions, and workflows. Memory becomes something users can read, correct, link, version, and open in their existing Markdown tools.

Dreaming / Reflection Runs when the system is idle or offline to revisit stored memory, connect weak signals, compress noisy history into durable patterns, and improve profiles and skills. The agent gets better between active sessions, not only while you prompt it.

Most memory systems stop at chat history, opaque profiles, or vector recall. EverOS keeps memory local, Markdown-native, auditable, and self-evolving: raw memory stays readable, derived knowledge becomes a wiki, and Reflection turns repeated experience into more useful long-term behavior.

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.