EverOS

Website · Documentation · Blog · [中文](README.zh-CN.md)

Table of Contents

• [Why Ever OS](#why-ever-os)
• [Quick Start](#quick-start)
• [Use Cases](#use-cases)
• [Documentation](#documentation)
• [Star Us](#star-us)
• [EverMind Ecosystems](#evermind-ecosystems)
• [Contributing](#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 userid, agentid, appid, projectid, 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

> 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:

| Capability | Provider | Used for | Fill these .env slots | | --- | --- | --- | --- | | Chat + multimodal | OpenRouter | LLM / MULTIMODAL | EVEROS_LLM__API_KEY, EVEROS_MULTIMODAL__API_KEY | | Embedding + rerank | DeepInfra | EMBEDDING / RERANK | EVEROS_EMBEDDING__API_KEY, EVEROS_RERANK__API_KEY |

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](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 → ./.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 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](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

[](#readme-top)

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.

[](https://evermind.ai/usecase_reunite)

Reunite - Find With EverOS

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

Learn more

[](https://github.com/tt-a1i/hive)

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

[](https://github.com/tt-a1i/evermemos-mcp)

AI Coding Assistants With EverOS

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

Code

[](https://github.com/yuansui123/AI-Data-Technician-EverMemOS)

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

[](https://github.com/xunyud/Earth-Online)

Earth Online Memory Game

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

Code

[](https://github.com/golutra/golutra)

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

[](https://github.com/Yangtze-Seventh/taste-verse)

Your Personal Tasting Universe

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

Code

[](https://github.com/kellyvv/OpenHer)

EverOS Open Her

Build AI that feels. Open-source persona engine - personality emerges from neural drives, not prompts. Inspired by Her.

Code

[](https://chromewebstore.google.com/detail/ruminer-browser-agent/lbccjohfpdpimbhpckljimgolndfmfif)

Browser Agent For Personal Memory

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

Plugin

[](https://github.com/nanxingw/EverMem)

EverMem Sync With EverOS

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

Code

[](https://github.com/mco-org/mco)

MCO - Orchestrate AI Coding Agents

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

Code

[](https://github.com/onenewborn/StudyBuddy-public)

Study Buddy With Self-Evolving Memory

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

Code

[](https://github.com/TonyLiangDesign/MemoCare)

Alzheimer's Memory Assistant

Empowering individuals with advanced memory support and daily assistance.

Code

[](https://github.com/AlexL1024/NeuralConnect)

Memory-Driven Multi-Agent NPC Experience

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

Code

[](https://github.com/elontusk5219-prog/Mobi)

Mobi Companion

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

Code

[](https://github.com/JaMesLiMers/EvermemCompetition-Spiro)

AI Wearable With Memory

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

Code

[](docs/migration-to-1.0.0.md)

Legacy OpenClaw Agent Memory

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

[Learn more](docs/migration-to-1.0.0.md)

[](https://github.com/TEN-framework/ten-framework/tree/04cb80601374fa9e35b4e544b2dbd23286ca7763/ai_agents/agents/examples/voice-assistant-with-EverMemOS)

Live2D Character With Memory

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

Code

[](https://screenshot-analysis-vercel.vercel.app/)

Computer-Use With Memory

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

Live Demo

[](use-cases/game-of-throne-demo)

Game Of Thrones Memories

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

[Code](use-cases/game-of-throne-demo)

[](use-cases/claude-code-plugin)

Claude Code Plugin

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

[Code](use-cases/claude-code-plugin)

[](https://main.d2j21qxnymu6wl.amplifyapp.com/graph.html)

Memory Graph Visualization

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

Live Demo

[](#readme-top)

Documentation

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

[](#readme-top)

Star Us

If EverOS is useful to your agent stack, please star the repo. It helps more builders discover the project and gives the memory ecosystem a stronger signal to keep improving.

Star History

[](https://www.star-history.com/#EverMind-AI/EverOS&Date)

[](#readme-top)

EverMind Ecosystems

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

EverMind Open-Source Ecosystem

Memory Runtime 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.

Hypergraph Memory HyperMem - hypergraph memory for long-term conversations, with its own benchmark-backed topic -> episode -> fact retrieval method.

Benchmarks EverMemBench · EvoAgen

…

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: EverMind-AI
• Source: EverMind-AI/EverOS
• License: Apache-2.0
• Homepage: https://evermind.ai/everos

Install and usage instructions live in the source repository linked above.