diff --git a/ai_agents/agents/examples/voice-assistant-with-PowerMem/README.md b/ai_agents/agents/examples/voice-assistant-with-PowerMem/README.md
index 8049f46c2c..d92cd9467c 100644
--- a/ai_agents/agents/examples/voice-assistant-with-PowerMem/README.md
+++ b/ai_agents/agents/examples/voice-assistant-with-PowerMem/README.md
@@ -1,10 +1,144 @@
 # Voice Assistant (with PowerMem)
 
-A voice assistant enhanced with [PowerMem](https://github.com/oceanbase/powermem/) memory management capabilities for persistent conversation context.
+中文版本: [README.zh-CN.md](README.zh-CN.md)
 
-## PowerMem Configuration
+A memory-enabled voice assistant built on the TEN Framework. PowerMem adds durable, semantic memory so the agent can recall user preferences, summarize prior context, and keep behavior consistent across sessions.
+
+This doc focuses on the "why" and "how" of memory in the agent, not API reference details.
+
+## Why memory for voice agents?
+
+Voice conversations are ephemeral. Without memory, every session resets to zero and the agent repeats basic questions (name, preferences, goals). Memory lets the agent:
+
+- Maintain continuity across sessions ("last time you said you prefer concise answers")
+- Personalize behavior without re-asking the same questions
+- Resolve ambiguous follow-ups by recalling prior context
+- Improve experience over time with accumulated preferences and facts
+
+## Architecture overview
+
+The TEN graph wires speech, LLM, and TTS. PowerMem plugs into the control layer to retrieve and save memory.
+
+```mermaid
+flowchart LR
+  mic((User Voice)) --> agora[agora_rtc]
+  agora --> stream[streamid_adapter]
+  stream --> stt[deepgram_asr_python]
+  stt --> main[main_python (main_control)]
+  main --> llm[openai_llm2_python]
+  llm --> main
+  main --> tts[elevenlabs_tts2_python]
+  tts --> agora
+  main --> collector[message_collector2]
+
+  main -. "search / add" .-> powermem[PowerMem\n(UserMemory/Memory)]
+```
+
+Key idea: `main_python` is the orchestrator. It decides when to retrieve memory, how to inject it into the LLM context, and when to save new memory.
+
+## How PowerMem fits into the TEN workflow
+
+PowerMem is wired through the `main_control` extension:
+
+- On **user join**, `main_python` requests a user profile from PowerMem to generate a personalized greeting.
+- On **final ASR results**, it runs a semantic search in PowerMem and injects related memory into the LLM prompt.
+- On **final LLM responses**, it saves new conversation turns to PowerMem on a configurable schedule.
+- On **idle timeout** or **shutdown**, it saves any unsaved conversation turns.
+
+All of this is driven by `powermem_config` and `enable_memorization` in `tenapp/property.json`.
+
+## What data is stored, retrieved, and updated?
+
+Stored (written to PowerMem):
+- `messages`: Only user + assistant messages from the LLM context (system messages are filtered out)
+- `user_id` and `agent_id`: Used to scope memories per user and per agent
+
+Retrieved (read from PowerMem):
+- **Related memories** for the current user query (semantic search results)
+- **User profile** summary for greeting personalization (from `UserMemory.profile()` when enabled)
+
+Updated (over time):
+- The agent only adds new conversation turns; PowerMem handles indexing, embeddings, and ranking
+
+## Step-by-step walkthrough (one conversation turn)
+
+1. **Audio in**: User speaks into the Agora channel.
+2. **ASR**: STT produces a final transcript.
+3. **Memory search**: `main_python` queries PowerMem with the transcript.
+4. **Prompt augmentation**: Related memories are injected into the LLM prompt alongside the user query.
+5. **LLM response**: The LLM responds with memory-aware context.
+6. **TTS output**: Assistant audio is streamed back via Agora.
+7. **Memory save**: The turn is saved to PowerMem by interval or idle timeout.
+
+## Memory lifecycle and save rules
+
+The agent uses two coordinated save triggers:
+
+- **Turn-based save**: every `memory_save_interval_turns` (default 5)
+- **Idle save**: after `memory_idle_timeout_seconds` of inactivity (default 30s)
+
+Pseudo-flow (simplified):
+
+```text
+on_final_llm_response:
+  if turns_since_last_save >= interval:
+    save_to_powermem()
+    cancel_idle_timer()
+  else:
+    start_or_reset_idle_timer()
+
+on_idle_timeout:
+  if unsaved_turns:
+    save_to_powermem()
+```
+
+On shutdown, the agent saves any remaining unsaved turns.
+
+## How memory changes behavior over time
+
+Over multiple sessions, the agent can:
+
+- Greet the user with known preferences ("Welcome back. Still prefer short answers?")
+- Use past tasks to infer intent ("You asked about hiking last week, here are new trails")
+- Maintain consistent tone and tool usage by recalling user feedback
+
+This happens because related memories are inserted into the LLM context before each response.
+
+## Annotated configuration (main_control)
+
+```json
+{
+  "name": "main_control",
+  "addon": "main_python",
+  "property": {
+    "agent_id": "voice_assistant_agent",
+    "user_id": "user",
+    "enable_memorization": true,
+    "enable_user_memory": true,
+    "memory_save_interval_turns": 5,
+    "memory_idle_timeout_seconds": 30.0,
+    "powermem_config": {
+      "vector_store": { "...": "OceanBase/SeekDB settings" },
+      "llm": { "...": "LLM for memory processing" },
+      "embedder": { "...": "Embedding model for search" }
+    }
+  }
+}
+```
+
+Tip: Use unique `user_id` values to isolate memory per person, and unique `agent_id` values to isolate memory per assistant persona.
+
+## Key files to explore
+
+- `ai_agents/agents/examples/voice-assistant-with-PowerMem/tenapp/property.json` - Graph wiring + PowerMem config
+- `ai_agents/agents/examples/voice-assistant-with-PowerMem/tenapp/ten_packages/extension/main_python/extension.py` - Retrieval + save logic
+- `ai_agents/agents/examples/voice-assistant-with-PowerMem/tenapp/ten_packages/extension/main_python/memory.py` - PowerMem store adapters
+- `ai_agents/agents/examples/voice-assistant-with-PowerMem/tenapp/ten_packages/extension/main_python/prompt.py` - Memory injection templates
+
+## PowerMem configuration
+
+Set the environment variables in `.env`:
 
-Set the environment variable into `.env` file:
 ```bash
 # Database
 DATABASE_PROVIDER=oceanbase
@@ -27,9 +161,9 @@ EMBEDDING_MODEL=text-embedding-v4
 EMBEDDING_DIMS=1536
 ```
 
-## Quick Start
+## Quick start
 
-1. **Start seekdb server**
+1. **Start SeekDB server**
    ```bash
    docker run -d \
       --name seekdb \
@@ -41,17 +175,17 @@ EMBEDDING_DIMS=1536
       oceanbase/seekdb:latest
    ```
 
-2. **Install dependencies:**
+2. **Install dependencies**
    ```bash
    task install
    ```
 
-3. **Run the voice assistant with MemU:**
+3. **Run the voice assistant with PowerMem**
    ```bash
    task run
    ```
 
-4. **Access the application:**
+4. **Access the application**
    - Frontend: http://localhost:3000
    - API Server: http://localhost:8080
    - TMAN Designer: http://localhost:49483
diff --git a/ai_agents/agents/examples/voice-assistant-with-PowerMem/README.zh-CN.md b/ai_agents/agents/examples/voice-assistant-with-PowerMem/README.zh-CN.md
new file mode 100644
index 0000000000..2e2e11780c
--- /dev/null
+++ b/ai_agents/agents/examples/voice-assistant-with-PowerMem/README.zh-CN.md
@@ -0,0 +1,189 @@
+# 语音助手（集成 PowerMem）
+
+基于 TEN Framework 的记忆型语音助手示例。PowerMem 提供持久的语义记忆，使助手能够记住用户偏好、总结过往上下文，并在多次会话之间保持一致的行为。
+
+本文档聚焦“为什么要有记忆”和“记忆如何工作”，而非 API 参考细节。
+
+## 为什么语音助手需要记忆？
+
+语音对话是瞬时的。没有记忆，每次会话都从零开始，助手会反复询问基础信息（姓名、偏好、目标）。有了记忆，助手可以：
+
+- 跨会话保持连续性（“上次你说更喜欢简短回答”）
+- 不再重复提问，提供更个性化的体验
+- 通过回忆上下文解决含糊追问
+- 随时间积累偏好与事实，体验越来越好
+
+## 架构概览
+
+TEN 图负责把语音、LLM 和 TTS 串联起来。PowerMem 插在控制层，负责记忆的检索与保存。
+
+```mermaid
+flowchart LR
+  mic((User Voice)) --> agora[agora_rtc]
+  agora --> stream[streamid_adapter]
+  stream --> stt[deepgram_asr_python]
+  stt --> main[main_python (main_control)]
+  main --> llm[openai_llm2_python]
+  llm --> main
+  main --> tts[elevenlabs_tts2_python]
+  tts --> agora
+  main --> collector[message_collector2]
+
+  main -. "search / add" .-> powermem[PowerMem\n(UserMemory/Memory)]
+```
+
+关键点：`main_python` 是编排者，决定何时检索记忆、如何注入 LLM 上下文，以及何时保存新记忆。
+
+## PowerMem 如何融入 TEN 工作流
+
+PowerMem 通过 `main_control` 扩展接入：
+
+- **用户加入**：`main_python` 从 PowerMem 获取用户画像，用于生成个性化欢迎语
+- **ASR 最终结果**：对用户话语做语义检索，把相关记忆注入 LLM 提示词
+- **LLM 最终响应**：按配置规则把新对话写入 PowerMem
+- **空闲超时 / 关闭**：保存尚未落库的对话内容
+
+上述逻辑由 `tenapp/property.json` 中的 `powermem_config` 与 `enable_memorization` 驱动。
+
+## 存储、检索与更新的数据是什么？
+
+写入（存储到 PowerMem）：
+- `messages`：只保存 LLM 上下文中的 user/assistant 消息（系统消息会被过滤）
+- `user_id` / `agent_id`：按用户与助手隔离记忆
+
+读取（从 PowerMem 检索）：
+- **相关记忆**：根据当前用户问题进行语义检索
+- **用户画像**：用于欢迎语个性化（`enable_user_memory` 时来自 `UserMemory.profile()`）
+
+更新（随时间变化）：
+- 助手只追加新对话；索引、向量化与排序由 PowerMem 负责
+
+## 单轮对话流程（Step-by-step）
+
+1. **音频输入**：用户通过 Agora 说话
+2. **ASR**：语音转文本生成最终转写
+3. **记忆检索**：`main_python` 用转写内容查询 PowerMem
+4. **提示词增强**：相关记忆与用户问题一起注入 LLM
+5. **LLM 响应**：生成带记忆上下文的答案
+6. **TTS 输出**：通过 Agora 播放语音
+7. **记忆保存**：按轮次或空闲超时写入 PowerMem
+
+## 记忆生命周期与保存规则
+
+保存触发有两种，并会协同避免重复保存：
+
+- **按轮次保存**：每 `memory_save_interval_turns` 轮（默认 5）
+- **空闲保存**：空闲 `memory_idle_timeout_seconds` 秒后（默认 30 秒）
+
+伪流程（简化）：
+
+```text
+on_final_llm_response:
+  if turns_since_last_save >= interval:
+    save_to_powermem()
+    cancel_idle_timer()
+  else:
+    start_or_reset_idle_timer()
+
+on_idle_timeout:
+  if unsaved_turns:
+    save_to_powermem()
+```
+
+应用关闭时，会保存最后未落库的对话。
+
+## 记忆如何改变行为
+
+跨多次会话后，助手可以：
+
+- 个性化欢迎用户（“欢迎回来，你仍然喜欢简短回答吗？”）
+- 根据历史任务推断意图（“你上周问过徒步路线，这里有新的推荐”）
+- 记住用户反馈，保持一致的语气和工具使用习惯
+
+这是因为相关记忆在每次响应前被注入到 LLM 上下文中。
+
+## 注释配置（main_control）
+
+```json
+{
+  "name": "main_control",
+  "addon": "main_python",
+  "property": {
+    "agent_id": "voice_assistant_agent",
+    "user_id": "user",
+    "enable_memorization": true,
+    "enable_user_memory": true,
+    "memory_save_interval_turns": 5,
+    "memory_idle_timeout_seconds": 30.0,
+    "powermem_config": {
+      "vector_store": { "...": "OceanBase/SeekDB settings" },
+      "llm": { "...": "LLM for memory processing" },
+      "embedder": { "...": "Embedding model for search" }
+    }
+  }
+}
+```
+
+提示：为每位真实用户设置不同的 `user_id`，为不同助手人格设置不同的 `agent_id`，可实现隔离记忆。
+
+## 关键文件索引
+
+- `ai_agents/agents/examples/voice-assistant-with-PowerMem/tenapp/property.json` - 图配置与 PowerMem 入口
+- `ai_agents/agents/examples/voice-assistant-with-PowerMem/tenapp/ten_packages/extension/main_python/extension.py` - 记忆检索与保存逻辑
+- `ai_agents/agents/examples/voice-assistant-with-PowerMem/tenapp/ten_packages/extension/main_python/memory.py` - PowerMem 存储适配
+- `ai_agents/agents/examples/voice-assistant-with-PowerMem/tenapp/ten_packages/extension/main_python/prompt.py` - 记忆注入模板
+
+## PowerMem 配置
+
+在 `.env` 中设置环境变量：
+
+```bash
+# Database
+DATABASE_PROVIDER=oceanbase
+OCEANBASE_HOST=127.0.0.1
+OCEANBASE_PORT=2881
+OCEANBASE_USER=root
+OCEANBASE_PASSWORD=password
+OCEANBASE_DATABASE=oceanbase
+OCEANBASE_COLLECTION=memories
+
+# LLM Provider (for PowerMem)
+LLM_PROVIDER=qwen
+LLM_API_KEY=your_qwen_api_key
+LLM_MODEL=qwen-plus
+
+# Embedding Provider (for PowerMem)
+EMBEDDING_PROVIDER=qwen
+EMBEDDING_API_KEY=your_qwen_api_key
+EMBEDDING_MODEL=text-embedding-v4
+EMBEDDING_DIMS=1536
+```
+
+## 快速开始
+
+1. **启动 SeekDB 服务**
+   ```bash
+   docker run -d \
+      --name seekdb \
+      -p 2881:2881 \
+      -p 2886:2886 \
+      -v ./data:/var/lib/oceanbase \
+      -e SEEKDB_DATABASE=powermem \
+      -e ROOT_PASSWORD=password \
+      oceanbase/seekdb:latest
+   ```
+
+2. **安装依赖**
+   ```bash
+   task install
+   ```
+
+3. **运行集成 PowerMem 的语音助手**
+   ```bash
+   task run
+   ```
+
+4. **访问应用**
+   - Frontend: http://localhost:3000
+   - API Server: http://localhost:8080
+   - TMAN Designer: http://localhost:49483