盛大集团创始人陈天桥近年来在 AI 领域（尤其是“AI长期记忆”和“脑科学与 AI 交叉”方向）进行了深度布局。

他主张通过模拟人类大脑的记忆与认知机制，解决大语言模型（LLM）的“遗忘”和上下文受限问题，使 AI Agents 具备连贯、可进化且个性化的长期记忆。

旗舰开源项目：EverMemOS（智能记忆操作系统）

EverMemOS 是由陈天桥旗下的 EverMind 团队推出的一款旗舰级开源、企业级智能记忆系统，被定位为未来 AI Agents 的“数据与记忆基础设施”。

1. 类脑级记忆生命周期（Engram-Inspired） EverMemOS 的设计灵感来源于人类大脑的记忆编码、巩固与提取机制，包含三个核心阶段：

情境痕迹形成（Episodic Trace Formation）：将实时的对话流转化为 MemCells。MemCells 是记忆的原子单元，用于捕获情境线索、事实以及有时效性的预测信号。

语义整合（Semantic Consolidation）：将分散的 MemCells 提炼并组织成更高维度的、具有主题性的语义结构 —— MemScenes。在这个过程中，系统会持续自动演化并更新用户画像（User Profile）。

重构式回忆（Reconstructive Recollection）：在 AI 需要决策时，通过 MemScene 引导的 Agent 检索机制重构上下文，帮助下游模型完成长周期的连贯推理。

2. 五层技术架构 EverMemOS 将长期记忆的读写流程分层管理：

代理层（Agentic Layer）：处理主动的记忆过程，如记忆提取、向量化、检索和重排序。

记忆层（Memory Layer）：管理核心存储和情境记忆，专注于 MemCells 的提取与生存周期管理。

检索层（Retrieval Layer）：负责多模态检索和对检索结果进行重排序，包括 BM25 + 向量检索的混合搜索以及 RRF（倒数排序融合）等。

业务层（Business Layer）：处理上层的业务逻辑与数据操作。

基础设施层（Infrastructure Layer）：兼容 MongoDB、Elasticsearch、Milvus、Redis 等主流数据库与缓存组件。

3. 性能与生态集成

Token 优化：充当智能注意力过滤器，在部分场景中可将上下文窗口的 Token 消耗降低多达 70%，同时在 LoCoMo 等长期记忆基准测试中达到了 SOTA 性能。

开源生态与插件：MCP（Model Context Protocol）支持：提供了 evermemos-mcp 插件，支持将 AI 编码助手（如 Claude Code、Cursor、Cline 等）接入 EverMemOS 记忆库。

Agent 框架集成：支持 OpenClaw 插件，使智能体能够自动捕获对话、调取历史记忆；同时支持 Live2D 虚拟角色记忆交互等应用。

GitHub 地址：

EverMind-AI/EverOS

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

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. 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
• docs/engineering.md — Contributor engineering reference: build, test, CI, conventions
• docs/migration-to-1.0.0.md — Legacy API migration notes
• CHANGELOG.md — Release notes
• CONTRIBUTING.md — How to contribute

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

EverMind Ecosystems

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

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: memory 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.

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.