Chen Tianqiao, founder of Shanda Group, has made deep inroads into AI in recent years, particularly in the areas of “AI long-term memory” and the intersection of “brain science and AI.” He advocates simulating the memory and cognitive mechanisms of the human brain to solve the “forgetting” and limited context window problems of large language models (LLMs), enabling AI agents to possess coherent, evolvable, and personalized long-term memory.

Flagship Open-Source Project: EverMemOS (Intelligent Memory Operating System)
EverMemOS is a flagship open-source, enterprise-grade intelligent memory system launched by Chen Tianqiao’s EverMind team, positioned as the “data and memory infrastructure” for future AI agents.

1. Brain-Inspired Memory Lifecycle (Engram-Inspired)
EverMemOS’s design is inspired by the encoding, consolidation, and retrieval mechanisms of the human brain, consisting of three core phases:

• Episodic Trace Formation: Converting real-time conversation streams into MemCells. MemCells are the atomic units of memory that capture situational cues, facts, and time-sensitive predictive signals.
• Semantic Consolidation: Refining and organizing scattered MemCells into higher-dimensional, thematic semantic structures — MemScenes. During this process, the system continuously evolves and updates the user profile.
• Reconstructive Recollection: When the AI needs to make decisions, it reconstructs context via a MemScene-guided agent retrieval mechanism, helping downstream models achieve long-cycle coherent reasoning.

2. Five-Layer Technical Architecture
EverMemOS manages the read/write flow of long-term memory in layers:

• Agentic Layer: Handles active memory processes such as memory extraction, vectorization, retrieval, and re-ranking.
• Memory Layer: Manages core storage and episodic memory, focusing on MemCell extraction and lifecycle management.
• Retrieval Layer: Responsible for multimodal retrieval and re-ranking of results, including hybrid search (BM25 + vector retrieval) and RRF (Reciprocal Rank Fusion).
• Business Layer: Handles upper-level business logic and data operations.
• Infrastructure Layer: Compatible with mainstream databases and caching components such as MongoDB, Elasticsearch, Milvus, and Redis.

3. Performance and Ecosystem Integration

• Token Optimization: Acts as an intelligent attention filter, reducing token consumption of context windows by up to 70% in some scenarios, while achieving SOTA performance on long-term memory benchmarks like LoCoMo.
• Open-Source Ecosystem & Plugins:
  • MCP (Model Context Protocol) Support: Provides the evermemos-mcp plugin, allowing AI coding assistants (e.g., Claude Code, Cursor, Cline) to connect to the EverMemOS memory bank.
  • Agent Framework Integration: Supports the OpenClaw plugin, enabling agents to automatically capture conversations and retrieve historical memories; also supports Live2D virtual character memory interactions and other applications.
• GitHub URL:

EverMind-AI/EverOS

Source: https://github.com/EverMind-AI/EverOS

EverOS banner

Website (https://evermind.ai) · Documentation (https://docs.evermind.ai) · Blog (https://evermind.ai/blogs) · 中文

Table of Contents

• Why Ever OS
• Quick Start
• Use Cases
• Documentation
• Star Us
• 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.

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:

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 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 <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.

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 <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.

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 demo --plain # try the local educational demo; no API keys needed
everos init # paste OpenRouter + DeepInfra keys into .env
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 (https://github.com/TEN-framework/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