docs: make Chinese README the default

README.md

@@ -1,3 +1,9 @@
+<!-- WEHUB_ZH_README -->
+> [!NOTE]
+> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。  
+> [English](./README.en.md) · [原始项目](https://github.com/EverMind-AI/EverOS) · [上游 README](https://github.com/EverMind-AI/EverOS/blob/HEAD/README.md)  
+> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。
+
 <div align="center" id="readme-top">
 
 ![EverOS banner](https://github.com/user-attachments/assets/8e217d39-5d15-4c6c-9b54-3e83add4e0f2)
@@ -9,7 +15,7 @@
   <a href="https://github.com/EverMind-AI/EverOS/discussions/67"><img src="https://img.shields.io/badge/WeCom-EverMind_社区-07C160?labelColor=gray&style=for-the-badge&logo=wechat&logoColor=white" alt="WeChat"></a>
 </p>
 
-[Website](https://evermind.ai) · [Documentation](https://docs.evermind.ai) · [Blog](https://evermind.ai/blogs) · [中文](README.zh-CN.md)
+[官网](https://evermind.ai) · [文档](https://docs.evermind.ai) · [博客](https://evermind.ai/blogs) · [English](README.md)
 
 </div>
 
@@ -17,30 +23,30 @@
 <br>
 
 <details>
-  <summary><kbd>Table of Contents</kbd></summary>
+  <summary><kbd>目录</kbd></summary>
 
 <br>
 
-- [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)
+- [为什么选择 EverOS](#为什么选择-everos)
+- [快速开始](#快速开始)
+- [使用场景](#使用场景)
+- [文档](#文档)
+- [Star 支持](#star-支持)
+- [EverMind 生态](#evermind-生态)
+- [参与贡献](#参与贡献)
 
 <br>
 
 </details>
 
 
-## Why Ever OS
+## 为什么选择 EverOS
 
-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.
+EverOS 是面向 agents 和 makers 的 Python library 与 local-first memory
+runtime。它从 day one 开始就提供一层可携带的记忆层，让记忆穿过 coding
+assistants、apps、devices 和 workflows。它会把 conversations、files 和
+agent trajectories 保存为可读 Markdown，并同步本地 SQLite 与 LanceDB
+索引，用于快速检索和自进化复用。
 
 <table>
 <tr>
@@ -50,111 +56,112 @@ for fast retrieval and self-evolving reuse.
 </tr>
 <tr>
 <td><strong>Markdown source of truth</strong></td>
-<td>✅ Canonical <code>.md</code> files that are readable, editable, diffable, and Git-versioned</td>
-<td>❌ Usually API, vector, graph, dashboard, or database state</td>
+<td>✅ 标准 <code>.md</code> 文件：可读、可编辑、可 diff、可 Git 版本化</td>
+<td>❌ 通常是 API、vector、graph、dashboard 或 database state</td>
 </tr>
 <tr>
-<td><strong>Direct file editing</strong></td>
-<td>✅ Edit <code>.md</code> files; cascade watcher syncs</td>
-<td>❌ Usually SDK, API, dashboard, or backend update paths</td>
+<td><strong>直接文件编辑</strong></td>
+<td>✅ 编辑 <code>.md</code>；cascade watcher 同步</td>
+<td>❌ 通常需要 SDK、API、dashboard 或 backend update path</td>
 </tr>
 <tr>
-<td><strong>Local three-part stack</strong></td>
-<td>✅ Markdown + SQLite + LanceDB; no MongoDB, Elasticsearch, or Redis required</td>
-<td>❌ Often depends on managed services, vector DBs, graph DBs, or server stacks</td>
+<td><strong>本地三件套</strong></td>
+<td>✅ Markdown + SQLite + LanceDB；不需要 MongoDB、Elasticsearch 或 Redis</td>
+<td>❌ 常依赖 managed service、vector DB、graph DB 或 server stack</td>
 </tr>
 <tr>
-<td><strong>User + agent tracks</strong></td>
-<td>✅ User <code>episodes/profile</code> and agent <code>cases/skills</code> are separate first-class surfaces</td>
-<td>❌ Usually centered on chat history, profiles, entities, facts, or retrieval records</td>
+<td><strong>用户 + Agent 双轨</strong></td>
+<td>✅ 用户 <code>episodes/profile</code> 与 Agent <code>cases/skills</code> 是分离的一等记忆表面</td>
+<td>❌ 通常围绕 chat history、profiles、entities、facts 或 retrieval records</td>
 </tr>
 <tr>
-<td><strong>Orthogonal retrieval</strong></td>
-<td>✅ Search by <code>user_id</code>, <code>agent_id</code>, <code>app_id</code>, <code>project_id</code>, and <code>session_id</code></td>
-<td>❌ Usually app, namespace, tenant, thread, or graph scoped</td>
+<td><strong>正交检索作用域</strong></td>
+<td>✅ 按 <code>user_id</code>、<code>agent_id</code>、<code>app_id</code>、<code>project_id</code> 和 <code>session_id</code> 检索</td>
+<td>❌ 通常按 app、namespace、tenant、thread 或 graph 来组织</td>
 </tr>
 <tr>
 <td><strong>Knowledge Wiki</strong></td>
-<td>✅ Editable, source-backed Markdown knowledge pages with taxonomy, CRUD APIs, and topic search</td>
-<td>❌ Usually separate from memory, trapped in a dashboard, or not tied back to source files</td>
+<td>✅ 可编辑、可溯源的 Markdown 知识页，支持 taxonomy、CRUD APIs 和 topic search</td>
+<td>❌ 通常和 memory 分离，被锁在 dashboard 里，或者无法回溯到源文件</td>
 </tr>
 <tr>
 <td><strong>Reflection</strong></td>
-<td>✅ Offline memory evolution that merges episode clusters and refines profiles and skills between sessions</td>
-<td>❌ Usually retrieval-only memory with little background consolidation or long-horizon improvement</td>
+<td>✅ 离线记忆进化：在 session 之间合并 episode clusters，并持续改进 profiles 和 skills</td>
+<td>❌ 通常只是 retrieval-only memory，缺少后台 consolidation 和长周期改进</td>
 </tr>
 </table>
 
 <br>
 
-## Quick Start
+## 快速开始
 
-> Goal: play with the memory visualizer first, then start EverOS, write one
-> real memory, and search it back.
+> 目标：先体验 memory visualizer，然后启动 EverOS，写入一条真实记忆，
+> 再把它搜索回来。
 
-### 0. Prerequisites
+### 0. 前置条件
 
 - 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`:
+- `everos demo` 不需要 API keys。
+- 如果要运行真正的 server-backed memory flow，中文默认推荐先在
+  [阿里云百炼控制台](https://bailian.console.aliyun.com/) 创建一个
+  DashScope API Key：
 
-| Capability | Provider | Used for | Fill these `.env` slots |
+| 能力 | 默认 Provider | 用途 | 填入这些 `.env` 字段 |
 | --- | --- | --- | --- |
-| Chat + multimodal | [OpenRouter](https://openrouter.ai/) | `LLM` / `MULTIMODAL` | `EVEROS_LLM__API_KEY`, `EVEROS_MULTIMODAL__API_KEY` |
-| Embedding + rerank | [DeepInfra](https://deepinfra.com/) | `EMBEDDING` / `RERANK` | `EVEROS_EMBEDDING__API_KEY`, `EVEROS_RERANK__API_KEY` |
+| Chat / extraction | [阿里云百炼 / DashScope](https://bailian.console.aliyun.com/) | `LLM` | `EVEROS_LLM__API_KEY` |
+| Embedding | [阿里云百炼 / DashScope](https://bailian.console.aliyun.com/) | `EMBEDDING` | `EVEROS_EMBEDDING__API_KEY` |
+| Re-rank | [阿里云百炼 / DashScope](https://bailian.console.aliyun.com/) | `RERANK` | `EVEROS_RERANK__API_KEY` |
 
-You can use other OpenAI-compatible providers by changing the matching
-`*__BASE_URL` fields in `.env`.
+同一个 DashScope API Key 可以填到这三个 slot。多模态文件摄取仍通过
+`EVEROS_MULTIMODAL__*` 单独配置；如果只跑下面的文本记忆闭环，不需要先配置它。
 
-### 1. Install
+### 1. 安装
 
 ```bash
 uv pip install everos
 # or: pip install everos
 ```
 
-### 2. Play With The Demo
+### 2. 体验 Demo
 
-Run this before configuring API keys or starting the server:
+在配置 API keys 或启动 server 之前，先运行：
 
 ```bash
 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.
+这个命令会询问一条记忆和一个召回问题，然后打开一个全屏 terminal UI。
+这是一个 educational visualizer：它是 hardcoded 的，只在 CLI 本地运行，
+不会连接 EverOS server。它的作用是把 memory lifecycle 变成可感知的过程：
+conversation -> memory sphere -> recall -> source proof -> confetti。Demo
+范围和 TUI 代码结构见 [docs/everos-demo.md](docs/everos-demo.md)。
 
-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.
+Sphere 会经历 ingest、extraction、indexing、recall、source reveal，
+并在第一条记忆落地后进入 confetti successful moment。按 `r` 可以 replay，
+按 `q` 可以退出。
 
 <p align="center">
   <img src="https://gist.githubusercontent.com/cyfyifanchen/afa2cf40bf138a3ec96d917e8f2791a2/raw/d4ce82a6ddd7b3ebaf221e4825af993aeca5a7ce/everos-demo-tui-animation.svg" alt="Animated EverOS demo preview showing the memory sphere moving through recall and confetti states" width="720">
 </p>
 
-For the looping showroom view used in README media, run:
+README 媒体使用的循环 showroom view 可以这样运行：
 
 ```bash
 everos demo --cinematic
 ```
 
-If your shell is not interactive, or you want a copyable preview, use:
+如果 shell 不是 interactive，或者你只想看一个可复制的静态预览：
 
 ```bash
 everos demo --plain
 ```
 
-### 3. Configure
+### 3. 配置
 
-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.
+生成一个 starter `.env` 文件，然后根据生成的注释填入对应的 API key 字段。
+中文 quick start 默认推荐使用
+[阿里云百炼控制台](https://bailian.console.aliyun.com/) 的 DashScope API Key
+配置 `LLM` / `EMBEDDING` / `RERANK` 三个核心能力。
 
 ```bash
 everos init
@@ -162,47 +169,63 @@ everos init
 cp .env.example .env
 ```
 
-`everos init` writes `./.env` by default. Use `everos init --xdg` to
-write `${XDG_CONFIG_HOME:-~/.config}/everos/.env` instead.
+`everos init` 默认写入 `./.env`。也可以使用 `everos init --xdg`
+写入 `${XDG_CONFIG_HOME:-~/.config}/everos/.env`。
 
-### 4. Start EverOS
+百炼三件套示例：
+
+```env
+EVEROS_LLM__MODEL=qwen-plus
+EVEROS_LLM__API_KEY=<DASHSCOPE_API_KEY>
+EVEROS_LLM__BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
+
+EVEROS_EMBEDDING__MODEL=text-embedding-v4
+EVEROS_EMBEDDING__API_KEY=<DASHSCOPE_API_KEY>
+EVEROS_EMBEDDING__BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
+
+EVEROS_RERANK__MODEL=gte-rerank-v2
+EVEROS_RERANK__API_KEY=<DASHSCOPE_API_KEY>
+EVEROS_RERANK__BASE_URL=https://dashscope.aliyuncs.com
+```
+
+### 4. 启动 EverOS
 
 ```bash
 everos server start
 ```
 
-Keep the server running, then open a second terminal and check it:
+保持服务运行，然后打开第二个 terminal 检查：
 
 ```bash
 curl http://127.0.0.1:8000/health
 ```
 
-Expected response:
+预期响应：
 
 ```json
 {"status":"ok"}
 ```
 
-`everos server start` searches for `.env` in this order: `--env-file <path>` →
-`./.env` (cwd) → `${XDG_CONFIG_HOME:-~/.config}/everos/.env` → `~/.everos/.env`.
-The endpoint stack is OpenAI-protocol compatible (OpenAI / OpenRouter / vLLM /
-Ollama / DeepInfra) - override `*__BASE_URL` in the generated `.env` to point
-at any of them.
+`everos server start` 会按以下顺序查找 `.env`：`--env-file <path>` →
+`./.env`（当前目录）→ `${XDG_CONFIG_HOME:-~/.config}/everos/.env` →
+`~/.everos/.env`。端点栈兼容 OpenAI protocol（OpenAI / OpenRouter /
+vLLM / Ollama / DeepInfra）。你可以覆盖生成的 `.env` 中的 `*__BASE_URL`
+来指向任意这些模型服务。
 
-Now make the demo real. In the second terminal, run:
+现在可以把 demo 跑成真实 server flow。在第二个 terminal 里运行：
 
 ```bash
 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`.
+Live demo mode 会连接正在运行的 server，并在打开同一个 memory sphere UI
+之前真实执行 `/health` -> `/api/v1/memory/add` -> `/api/v1/memory/flush` ->
+`/api/v1/memory/search`。如果 server 不在 `http://127.0.0.1:8000`，可以使用
+`--server-url <url>`。
 
-### 5. Try Your First Memory
+### 5. 试写第一条记忆
 
-Add a tiny conversation:
+添加一个很小的 conversation：
 
 ```bash
 TS=$(($(date +%s)*1000))
@@ -220,7 +243,7 @@ curl -X POST http://127.0.0.1:8000/api/v1/memory/add \
   }"
 ```
 
-Force extraction for the local demo:
+为了本地 demo，手动触发一次 extraction：
 
 ```bash
 curl -X POST http://127.0.0.1:8000/api/v1/memory/flush \
@@ -228,7 +251,7 @@ curl -X POST http://127.0.0.1:8000/api/v1/memory/flush \
   -d '{"session_id":"demo-001","app_id":"default","project_id":"default"}'
 ```
 
-Search it back:
+再把这条记忆搜索回来：
 
 ```bash
 curl -X POST http://127.0.0.1:8000/api/v1/memory/search \
@@ -242,58 +265,52 @@ curl -X POST http://127.0.0.1:8000/api/v1/memory/search \
   }'
 ```
 
-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.
+响应里应该能看到 Yosemite 相关记忆。如果第一次搜索为空，稍等片刻再试；
+Markdown 会同步写入，本地索引会在后台追上。
 
 > [!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.
+> **第一条记忆已经写入。**
+> 你刚刚把一个事实交给 EverOS，把它整理进可持久化的 Markdown-backed memory，
+> 并通过本地索引把它搜索回来。这就是 EverOS 的核心闭环。
+> 想看看 source of truth？打开 `~/.everos`，直接检查生成的 Markdown 文件。
 
-For annotated responses and the Markdown files EverOS creates, see
-[QUICKSTART.md](QUICKSTART.md).
+带完整响应和 Markdown 文件说明的 walkthrough 见 [QUICKSTART.md](QUICKSTART.md)。
 
-### Optional: Ingest Multimodal Files
+### 可选：摄取多模态文件
 
-To ingest non-text content (image / pdf / audio / office documents)
-through `/api/v1/memory/add` `content` items, install the optional
-extra:
+如果要通过 `/api/v1/memory/add` 的 `content` items 摄取非文本内容
+（image / pdf / audio / office documents），安装可选 extra：
 
 ```bash
 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).
+这会引入 `everalgo-parser`（包含用于 SVG 支持的 `[svg]` bundle，通过
+cairosvg）并接入多模态 LLM client（`.env` 中的 `EVEROS_MULTIMODAL__*`
+字段，默认通过 OpenRouter 使用 `google/gemini-3-flash-preview`）。
 
-**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.
+**Office 文档支持需要 LibreOffice 作为系统依赖。** parser 会调用
+`soffice`（LibreOffice 的 headless renderer），先把 `.doc` / `.docx` /
+`.ppt` / `.pptx` / `.xls` / `.xlsx` 转换为 PDF，再交给多模态 LLM。
+如果没有 LibreOffice，office 上传会返回 HTTP 415，并带有明确错误信息；
+PDF / image / audio / HTML / email 解析不受影响。
 
-Install on the host before serving office documents:
+在提供 office 文档服务前，请先在宿主机安装：
 
 ```bash
 brew install --cask libreoffice              # macOS
 sudo apt-get install -y libreoffice          # Debian / Ubuntu
 ```
 
-### For Contributors
+### 贡献者开发
 
 ```bash
 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 demo --plain                  # 先体验本地 educational demo；不需要 API keys
+everos init                          # 把百炼 DashScope API Key 填进 .env
 
 everos --help
 make test
@@ -306,15 +323,13 @@ make test
 
 </div>
 
-## Use Cases
+## 使用场景
 
-Now that you have had your first successful EverOS moment, explore what people
-are building with persistent memory across agents, apps, and community
-integrations.
+现在你已经完成了第一个成功的 EverOS moment，可以继续看看大家如何把持久记忆
+用在 agents、apps 和社区集成里。
 
-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.
+这些使用场景展示了持久记忆可以在真实产品和工作流中带来什么能力。
+有些示例已经打包在本仓库中，另一些则指向外部 demo 或集成，你可以研究并复用。
 
 <table>
 <tr>
@@ -322,11 +337,11 @@ external demos or integrations you can study and adapt.
 
 [![banner-gif](https://github.com/user-attachments/assets/840470d7-a838-4c05-8685-dd797d4e9cdf)](https://evermind.ai/usecase_reunite)
 
-#### Reunite - Find With EverOS
+#### Reunite - 用 EverOS 找回连接
 
-Parents describe what they remember. Children describe what they recall. Reunite uses semantic memory to surface the connections.
+父母描述他们记得的线索，孩子描述他们残留的回忆。Reunite 使用语义记忆来浮现这些连接。
 
-[Learn more](https://evermind.ai/usecase_reunite)
+[了解更多](https://evermind.ai/usecase_reunite)
 
 </td>
 <td width="50%" valign="top">
@@ -335,9 +350,9 @@ Parents describe what they remember. Children describe what they recall. Reunite
 
 #### 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.
+面向 CLI coding agents 的 browser-native hive-mind。Claude Code、Codex、Gemini 和 OpenCode 作为真实 PTY 进程，通过团队协议协作。
 
-[Code](https://github.com/tt-a1i/hive)
+[代码](https://github.com/tt-a1i/hive)
 
 </td>
 </tr>
@@ -347,11 +362,11 @@ Browser-native hive-mind for CLI coding agents - Claude Code, Codex, Gemini, and
 
 [![banner-gif](https://github.com/user-attachments/assets/867d9329-ce9a-496f-ab1e-15c77974e5fa)](https://github.com/tt-a1i/evermemos-mcp)
 
-#### AI Coding Assistants With EverOS
+#### 接入 EverOS 的 AI 编程助手
 
-Universal long-term memory layer for AI coding assistants, powered by EverOS.
+由 EverOS 驱动的通用长期记忆层，面向 AI coding assistants。
 
-[Code](https://github.com/tt-a1i/evermemos-mcp)
+[代码](https://github.com/tt-a1i/evermemos-mcp)
 
 </td>
 <td width="50%" valign="top">
@@ -360,9 +375,9 @@ Universal long-term memory layer for AI coding assistants, powered by EverOS.
 
 #### 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.
+一个 agentic AI 系统，可以从科学家的交互中学习，用于检查、分析和分类高维时间序列数据，并通过跨 session 改进的持久记忆持续变强。
 
-[Code](https://github.com/yuansui123/AI-Data-Technician-EverMemOS)
+[代码](https://github.com/yuansui123/AI-Data-Technician-EverMemOS)
 
 </td>
 </tr>
@@ -372,22 +387,22 @@ An agentic AI system that learns from scientist interaction to inspect, analyze,
 
 ![banner-gif](https://github.com/user-attachments/assets/650b901b-c9ba-4001-bac7-626b009df830)
 
-#### Rokid AI Assistant With EverOS
+#### 接入 EverOS 的 Rokid AI 助手
 
-Connect to EverOS within Rokid Glasses enabling long-term memory for all of your smart activities.
+在 Rokid Glasses 中连接 EverOS，为你的智能活动启用长期记忆。
 
-Coming soon
+即将推出
 
 </td>
 <td width="50%" valign="top">
 
 ![banner-gif](https://github.com/user-attachments/assets/85b338b2-e48e-4a65-9f30-0bc6998df872)
 
-#### Creative Assistant With Memory
+#### 带长期记忆的创意助手
 
-Creative assistant with long-term memory, so your creative context stays available across sessions.
+拥有长期记忆的创意助手，让你的创作上下文可以跨 session 持续可用。
 
-Coming soon
+即将推出
 
 </td>
 </tr>
@@ -403,22 +418,22 @@ Coming soon
 
 [![banner-gif](https://github.com/user-attachments/assets/f30617a1-adc0-4271-bc0e-c3a0b28cb903)](https://github.com/xunyud/Earth-Online)
 
-#### Earth Online Memory Game
+#### Earth Online 记忆游戏
 
-Earth Online is a memory-aware productivity game that turns everyday planning into a living quest log.
+Earth Online 是一款 memory-aware productivity game，把日常计划变成一个持续生长的 quest log。
 
-[Code](https://github.com/xunyud/Earth-Online)
+[代码](https://github.com/xunyud/Earth-Online)
 
 </td>
 <td width="50%" valign="top">
 
 [![banner-gif](https://github.com/user-attachments/assets/57d8cda7-35a5-4561-b794-5520dffc917b)](https://github.com/golutra/golutra)
 
-#### Multi-Agent Orchestration Platform
+#### 多 Agent 编排平台
 
-Golutra presents a multi-agent workforce for engineering teams, extending the IDE model from a single assistant to coordinated agents.
+Golutra 为工程团队提供 multi-agent workforce，把 IDE 从单一 assistant 扩展为协同 agents。
 
-[Code](https://github.com/golutra/golutra)
+[代码](https://github.com/golutra/golutra)
 
 </td>
 </tr>
@@ -427,11 +442,11 @@ Golutra presents a multi-agent workforce for engineering teams, extending the ID
 
 [![banner-gif](https://github.com/user-attachments/assets/75f19db5-30f6-4eed-9b1e-c9c6a0e6b7de)](https://github.com/Yangtze-Seventh/taste-verse)
 
-#### Your Personal Tasting Universe
+#### 你的个人品鉴宇宙
 
-Record, visualize, and explore your tasting journey through an immersive 3D star map.
+通过沉浸式 3D 星图记录、可视化并探索你的 tasting journey。
 
-[Code](https://github.com/Yangtze-Seventh/taste-verse)
+[代码](https://github.com/Yangtze-Seventh/taste-verse)
 
 </td>
 <td width="50%" valign="top">
@@ -440,9 +455,9 @@ Record, visualize, and explore your tasting journey through an immersive 3D star
 
 #### EverOS Open Her
 
-Build AI that feels. Open-source persona engine - personality emerges from neural drives, not prompts. Inspired by Her.
+构建有感受的 AI。开源 persona engine，让 personality 从 neural drives 中涌现，而不是来自 prompts。灵感来自 Her。
 
-[Code](https://github.com/kellyvv/OpenHer)
+[代码](https://github.com/kellyvv/OpenHer)
 
 </td>
 </tr>
@@ -452,22 +467,22 @@ Build AI that feels. Open-source persona engine - personality emerges from neura
 
 [![banner-gif](https://github.com/user-attachments/assets/550071c1-dc39-4964-9f67-ffdfad792345)](https://chromewebstore.google.com/detail/ruminer-browser-agent/lbccjohfpdpimbhpckljimgolndfmfif)
 
-#### Browser Agent For Personal Memory
+#### 面向个人记忆的浏览器 Agent
 
-Ruminer brings persistent memory to a browser agent so it can carry personal context across web tasks.
+Ruminer 为 browser agent 带来持久记忆，让它能在不同网页任务之间携带个人上下文。
 
-[Plugin](https://chromewebstore.google.com/detail/ruminer-browser-agent/lbccjohfpdpimbhpckljimgolndfmfif)
+[插件](https://chromewebstore.google.com/detail/ruminer-browser-agent/lbccjohfpdpimbhpckljimgolndfmfif)
 
 </td>
 <td width="50%" valign="top">
 
 [![banner-gif](https://github.com/user-attachments/assets/c258a6c4-fe70-497a-98d1-3dade4a932f6)](https://github.com/nanxingw/EverMem)
 
-#### EverMem Sync With EverOS
+#### EverMem 与 EverOS 同步
 
-One command to connect any AI coding CLI to EverMemOS long-term memory.
+一条命令，把任意 AI coding CLI 连接到 EverMemOS 长期记忆。
 
-[Code](https://github.com/nanxingw/EverMem)
+[代码](https://github.com/nanxingw/EverMem)
 
 </td>
 </tr>
@@ -483,22 +498,22 @@ One command to connect any AI coding CLI to EverMemOS long-term memory.
 
 [![banner-gif](https://github.com/user-attachments/assets/39274473-ceb3-48fb-a031-e22230decbe2)](https://github.com/mco-org/mco)
 
-#### MCO - Orchestrate AI Coding Agents
+#### MCO - 编排 AI Coding Agents
 
-MCO equips your primary agent with an agent team that can work together to solve complex tasks.
+MCO 为你的主 Agent 配备一个 agent team，让它们可以一起处理复杂任务。
 
-[Code](https://github.com/mco-org/mco)
+[代码](https://github.com/mco-org/mco)
 
 </td>
 <td width="50%" valign="top">
 
 [![banner-gif](https://github.com/user-attachments/assets/314c9126-8e08-4688-bbbb-8555ad58cf67)](https://github.com/onenewborn/StudyBuddy-public)
 
-#### Study Buddy With Self-Evolving Memory
+#### 带自进化记忆的 Study Buddy
 
-Study proactively with an agent that has self-evolving memory.
+使用拥有 self-evolving memory 的 Agent，主动辅助学习。
 
-[Code](https://github.com/onenewborn/StudyBuddy-public)
+[代码](https://github.com/onenewborn/StudyBuddy-public)
 
 </td>
 </tr>
@@ -508,22 +523,22 @@ Study proactively with an agent that has self-evolving memory.
 
 [![banner-gif](https://github.com/user-attachments/assets/21da76aa-9a8b-48e0-9134-42429d7390e7)](https://github.com/TonyLiangDesign/MemoCare)
 
-#### Alzheimer's Memory Assistant
+#### 阿尔茨海默症记忆助手
 
-Empowering individuals with advanced memory support and daily assistance.
+通过高级记忆支持和日常辅助，帮助有需要的人更好地生活。
 
-[Code](https://github.com/TonyLiangDesign/MemoCare)
+[代码](https://github.com/TonyLiangDesign/MemoCare)
 
 </td>
 <td width="50%" valign="top">
 
 [![banner-gif](https://github.com/user-attachments/assets/e2428df3-ea11-4e88-8f9c-dad437dd8998)](https://github.com/AlexL1024/NeuralConnect)
 
-#### Memory-Driven Multi-Agent NPC Experience
+#### 记忆驱动的 Multi-Agent NPC 体验
 
-An iOS sci-fi mystery game where players explore and uncover the truth.
+一款 iOS 科幻悬疑游戏，玩家可以探索世界并揭开真相。
 
-[Code](https://github.com/AlexL1024/NeuralConnect)
+[代码](https://github.com/AlexL1024/NeuralConnect)
 
 </td>
 </tr>
@@ -535,20 +550,20 @@ An iOS sci-fi mystery game where players explore and uncover the truth.
 
 #### Mobi Companion
 
-An iOS app where users create, nurture, and live with a personalized AI companion called Mobi.
+一款 iOS app，用户可以创建、养成并与名为 Mobi 的个性化 AI companion 一起生活。
 
-[Code](https://github.com/elontusk5219-prog/Mobi)
+[代码](https://github.com/elontusk5219-prog/Mobi)
 
 </td>
 <td width="50%" valign="top">
 
 [![banner-gif](https://github.com/user-attachments/assets/9aabcaa9-f97a-49d2-9109-0b5bb696ed41)](https://github.com/JaMesLiMers/EvermemCompetition-Spiro)
 
-#### AI Wearable With Memory
+#### 带记忆的 AI 可穿戴设备
 
-A context-native AI wearable that listens to everyday life and converts conversations into memory.
+一个 context-native AI wearable，聆听日常生活，并把对话转换为记忆。
 
-[Code](https://github.com/JaMesLiMers/EvermemCompetition-Spiro)
+[代码](https://github.com/JaMesLiMers/EvermemCompetition-Spiro)
 
 </td>
 </tr>
@@ -563,22 +578,22 @@ A context-native AI wearable that listens to everyday life and converts conversa
 
 [![banner-gif](https://github.com/user-attachments/assets/df9677ec-386f-4c56-a428-08bca25c54dc)](docs/migration-to-1.0.0.md)
 
-#### Legacy OpenClaw Agent Memory
+#### Legacy OpenClaw Agent 记忆
 
-Archived pre-1.0.0 plugin reference. New integrations should use the current EverOS API.
+已归档的 pre-1.0.0 plugin reference。新的集成应使用当前 EverOS API。
 
-[Learn more](docs/migration-to-1.0.0.md)
+[了解更多](docs/migration-to-1.0.0.md)
 
 </td>
 <td width="50%" valign="top">
 
 [![banner-gif](https://github.com/user-attachments/assets/3a2357a1-c0c3-464a-8979-0d1cdfc9b0d4)](https://github.com/TEN-framework/ten-framework/tree/04cb80601374fa9e35b4e544b2dbd23286ca7763/ai_agents/agents/examples/voice-assistant-with-EverMemOS)
 
-#### Live2D Character With Memory
+#### 带记忆的 Live2D 角色
 
-Add long-term memory to a real-time Live2D character, powered by [TEN Framework](https://github.com/TEN-framework/ten-framework).
+为实时 Live2D character 添加长期记忆，由 [TEN Framework](https://github.com/TEN-framework/ten-framework) 驱动。
 
-[Code](https://github.com/TEN-framework/ten-framework/tree/04cb80601374fa9e35b4e544b2dbd23286ca7763/ai_agents/agents/examples/voice-assistant-with-EverMemOS)
+[代码](https://github.com/TEN-framework/ten-framework/tree/04cb80601374fa9e35b4e544b2dbd23286ca7763/ai_agents/agents/examples/voice-assistant-with-EverMemOS)
 
 </td>
 </tr>
@@ -587,11 +602,11 @@ Add long-term memory to a real-time Live2D character, powered by [TEN Framework]
 
 [![banner-gif](https://github.com/user-attachments/assets/c36bdc04-97d3-4fe9-97d9-4b93b475595a)](https://screenshot-analysis-vercel.vercel.app/)
 
-#### Computer-Use With Memory
+#### 带记忆的 Computer-Use
 
-Run screenshot-based analysis with computer-use and store the results in memory.
+运行基于截图的分析任务，并把结果存入记忆。
 
-[Live Demo](https://screenshot-analysis-vercel.vercel.app/)
+[在线演示](https://screenshot-analysis-vercel.vercel.app/)
 
 </td>
 <td width="50%" valign="top">
@@ -600,9 +615,9 @@ Run screenshot-based analysis with computer-use and store the results in memory.
 
 #### Game Of Thrones Memories
 
-A demonstration of AI memory infrastructure through an interactive Q&A experience with *A Game of Thrones*.
+通过与 *A Game of Thrones* 互动问答体验，展示 AI 记忆基础设施。
 
-[Code](use-cases/game-of-throne-demo)
+[代码](use-cases/game-of-throne-demo)
 
 </td>
 </tr>
@@ -613,20 +628,20 @@ A demonstration of AI memory infrastructure through an interactive Q&A experienc
 
 #### Claude Code Plugin
 
-Persistent memory for Claude Code. Automatically saves and recalls context from past coding sessions.
+Claude Code 的持久记忆插件。自动保存并回忆过去 coding sessions 的上下文。
 
-[Code](use-cases/claude-code-plugin)
+[代码](use-cases/claude-code-plugin)
 
 </td>
 <td width="50%" valign="top">
 
 [![banner-gif](https://github.com/user-attachments/assets/d521d28c-0ccd-44ff-aecc-828245e2f973)](https://main.d2j21qxnymu6wl.amplifyapp.com/graph.html)
 
-#### Memory Graph Visualization
+#### 记忆图谱可视化
 
-Explore stored entities and relationships in a graph interface. Frontend demo; backend integration is in progress.
+在图界面中探索已存储的 entities 和 relationships。前端 demo 已可用；后端集成仍在进行中。
 
-[Live Demo](https://main.d2j21qxnymu6wl.amplifyapp.com/graph.html)
+[在线演示](https://main.d2j21qxnymu6wl.amplifyapp.com/graph.html)
 
 </td>
 </tr>
@@ -639,15 +654,15 @@ Explore stored entities and relationships in a graph interface. Frontend demo; b
 
 </div>
 
-## Documentation
+## 文档
 
-- [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
+- [docs/everos-demo.md](docs/everos-demo.md) - Demo 范围与 TUI 源码布局
+- [docs/how-memory-works.md](docs/how-memory-works.md) - Markdown、SQLite、LanceDB 与 recall flow
+- [docs/use-cases.md](docs/use-cases.md) - 完整使用场景 gallery 和集成示例
+- [docs/engineering.md](docs/engineering.md) - 贡献者工程参考:构建、测试、CI 与规范
+- [docs/migration-to-1.0.0.md](docs/migration-to-1.0.0.md) - Legacy API 迁移说明
+- [CHANGELOG.md](CHANGELOG.md) - 发布记录
+- [CONTRIBUTING.md](CONTRIBUTING.md) - 如何贡献
 
 <br>
 <div align="right">
@@ -656,15 +671,14 @@ Explore stored entities and relationships in a graph interface. Frontend demo; b
 
 </div>
 
-## Star Us
+## Star 支持
 
-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.
+如果 EverOS 对你的 Agent stack 有帮助，请 Star 这个仓库。它会帮助更多
+builders 发现这个项目，也会给 memory ecosystem 一个更强的信号，让它持续改进。
 
-### Star History
+### Star 趋势
 
-[![Star History Chart](https://api.star-history.com/svg?repos=EverMind-AI/EverOS&type=Date)](https://www.star-history.com/#EverMind-AI/EverOS&Date)
+[![Star 趋势图](https://api.star-history.com/svg?repos=EverMind-AI/EverOS&type=Date)](https://www.star-history.com/#EverMind-AI/EverOS&Date)
 
 <br>
 <div align="right">
@@ -673,50 +687,50 @@ to keep improving.
 
 </div>
 
-## EverMind Ecosystems
+## EverMind 生态
 
-EverMind is an open-source ecosystem for long-term memory, self-evolving
-agents, AI-native interfaces, and memory evaluation.
+EverMind 是一个面向长期记忆、自进化 Agent、AI-native interfaces 和记忆评测的开源生态。
 
 <table>
 <tr>
-<th colspan="2">EverMind Open-Source Ecosystem</th>
+<th colspan="2">EverMind 开源生态</th>
 </tr>
 <tr>
 <td><strong>Memory Runtime</strong></td>
-<td><a href="https://github.com/EverMind-AI/EverOS">EverOS</a> - the local memory operating system and research-backed runtime for agent and user memory.</td>
+<td><a href="https://github.com/EverMind-AI/EverOS">EverOS</a> - 本地记忆操作系统，以及有研究支撑的 Agent 和用户记忆 runtime。</td>
 </tr>
 <tr>
 <td><strong>Self-Improving Agent Harness</strong></td>
-<td><a href="https://github.com/EverMind-AI/Raven">Raven</a> - the self-improving agent harness that brings memory, proactivity, context control, and skill evolution into terminal-native agents.</td>
+<td><a href="https://github.com/EverMind-AI/Raven">Raven</a> - The Self-Improving Agent Harness，把记忆、主动性、上下文控制和 skill evolution 带进终端原生 Agent。</td>
 </tr>
 <tr>
-<td><strong>Algorithm Engine</strong></td>
-<td><a href="https://github.com/EverMind-AI/EverAlgo">EverAlgo</a> - stateless extraction, ranking, parsing, and memory operators that power EverOS.</td>
+<td><strong>算法引擎</strong></td>
+<td><a href="https://github.com/EverMind-AI/EverAlgo">EverAlgo</a> - stateless extraction、ranking、parsing 和 memory operators，为 EverOS 提供算法能力。</td>
 </tr>
 <tr>
 <td><strong>Hypergraph Memory</strong></td>
-<td><a href="https://github.com/EverMind-AI/HyperMem">HyperMem</a> - hypergraph memory for long-term conversations, with its own benchmark-backed topic -> episode -> fact retrieval method.</td>
+<td><a href="https://github.com/EverMind-AI/HyperMem">HyperMem</a> - 面向长期对话的 hypergraph memory，拥有独立的 benchmark-backed topic -> episode -> fact 检索方法。</td>
 </tr>
 <tr>
 <td><strong>Benchmarks</strong></td>
-<td><a href="https://github.com/EverMind-AI/EverMemBench">EverMemBench</a> · <a href="https://github.com/EverMind-AI/EvoAgentBench">EvoAgentBench</a> - evaluation suites for conversational memory and agent self-evolution.</td>
+<td><a href="https://github.com/EverMind-AI/EverMemBench">EverMemBench</a> · <a href="https://github.com/EverMind-AI/EvoAgentBench">EvoAgentBench</a> - conversational memory 和 Agent self-evolution 的评测套件。</td>
 </tr>
 <tr>
 <td><strong>Long-Context Research</strong></td>
-<td><a href="https://github.com/EverMind-AI/MSA">MSA</a> - Memory Sparse Attention for scalable latent memory and 100M-token contexts.</td>
+<td><a href="https://github.com/EverMind-AI/MSA">MSA</a> - Memory Sparse Attention，用于可扩展 latent memory 和 100M-token contexts。</td>
 </tr>
 <tr>
-<td><strong>Personal Memory Layer</strong></td>
-<td><a href="https://github.com/EverMind-AI/EverMe">EverMe</a> - CLI and agent plugin suite for cross-device, cross-agent personal memory.</td>
+<td><strong>个人记忆层</strong></td>
+<td><a href="https://github.com/EverMind-AI/EverMe">EverMe</a> - CLI 和 Agent plugin suite，用于跨设备、跨 Agent 的个人记忆。</td>
 </tr>
 <tr>
-<td><strong>Developer Integrations</strong></td>
-<td><a href="https://github.com/EverMind-AI/evermem-claude-code">evermem-claude-code</a> · <a href="https://github.com/EverMind-AI/everos-plugins">everos-plugins</a> - plugins, skills, and migration tooling for AI coding agents.</td>
+<td><strong>开发者集成</strong></td>
+<td><a href="https://github.com/EverMind-AI/evermem-claude-code">evermem-claude-code</a> · <a href="https://github.com/EverMind-AI/everos-plugins">everos-plugins</a> - AI coding agents 的 plugins、skills 和 migration tooling。</td>
 </tr>
 </table>
 
-Together, these repositories form EverMind's research-to-runtime stack: new memory methods, reusable algorithms, benchmark evidence, and practical agent integrations.
+这些仓库共同构成 EverMind 的 research-to-runtime stack：新的记忆方法、
+可复用算法、benchmark evidence，以及可落地的 Agent 集成。
 
 <br>
 <div align="right">
@@ -727,37 +741,42 @@ Together, these repositories form EverMind's research-to-runtime stack: new memo
 
 <br>
 
-## Contributing
+## 参与贡献
 
-Contributions are welcome across the whole repository: memory methods, benchmark coverage, use-case examples, documentation, and bug fixes. Browse [Issues](https://github.com/EverMind-AI/EverOS/issues) to find a good entry point, then open a PR when you are ready.
+欢迎为整个仓库贡献：架构方法、benchmark coverage、use-case examples、文档和 bug fixes。
+浏览 [Issues](https://github.com/EverMind-AI/EverOS/issues) 找到适合的切入点，
+准备好后即可提交 PR。
 
 <br>
 
 > [!TIP]
 >
-> **Welcome all kinds of contributions** 🎉
+> **欢迎各种形式的贡献** 🎉
 >
-> 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.
+> 一起让 EverOS 变得更好。代码、文档、benchmark reports、use-case write-ups
+> 和 integration examples 都很有价值。也欢迎在社交媒体上分享你的项目，启发更多人。
 >
-> Connect with one of the EverOS maintainers [@elliotchen200](https://x.com/elliotchen200) on 𝕏 or [@cyfyifanchen](https://github.com/cyfyifanchen) on GitHub for project updates, discussions, and collaboration opportunities.
+> 你可以在 𝕏 上联系 EverOS maintainer [@elliotchen200](https://x.com/elliotchen200)，
+> 或在 GitHub 上联系 [@cyfyifanchen](https://github.com/cyfyifanchen)，获取项目更新、
+> 讨论和协作机会。
 
 ![divider](https://github.com/user-attachments/assets/2e2bbcc6-e6d8-4227-83c6-0620fc96f761#gh-light-mode-only)
 ![divider](https://github.com/user-attachments/assets/d57fad08-4f49-4a1c-bdfc-f659a5d86150#gh-dark-mode-only)
 
-### Code Contributors
+### 代码贡献者
 
 [![EverOS Contributors](https://contrib.rocks/image?repo=EverMind-AI/EverOS)](https://github.com/EverMind-AI/EverOS/graphs/contributors)
 
 ![divider](https://github.com/user-attachments/assets/2e2bbcc6-e6d8-4227-83c6-0620fc96f761#gh-light-mode-only)
 ![divider](https://github.com/user-attachments/assets/d57fad08-4f49-4a1c-bdfc-f659a5d86150#gh-dark-mode-only)
 
-### License
+### 许可证
 
-[Apache License 2.0](LICENSE) — see [NOTICE](NOTICE) for third-party attributions.
+[Apache License 2.0](LICENSE) - 第三方归属说明请见 [NOTICE](NOTICE)。
 
-### Citation
+### 引用
 
-If you use EverOS in research, see [CITATION.md](CITATION.md).
+如果你在研究中使用 EverOS，请参考 [CITATION.md](CITATION.md)。
 
 <br>