add mkdocs for homepage

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+---
+title: Welcome to ReMe
+summary: Memory Management Framework for Agents
+order: 1
+show_datetime: true
+---
+
+<p align="center">
+ <img src="figure/reme_logo.png" alt="ReMe Logo" width="50%">
+</p>
+
+<p align="center">
+  <a href="https://pypi.org/project/reme-ai/"><img src="https://img.shields.io/badge/python-3.12+-blue" alt="Python Version"></a>
+  <a href="https://pypi.org/project/reme-ai/"><img src="https://img.shields.io/badge/pypi-v0.1-blue?logo=pypi" alt="PyPI Version"></a>
+  <a href="./LICENSE"><img src="https://img.shields.io/badge/license-Apache--2.0-black" alt="License"></a>
+  <a href="https://github.com/modelscope/ReMe"><img src="https://img.shields.io/github/stars/modelscope/ReMe?style=social" alt="GitHub Stars"></a>
+</p>
+
+<p align="center">
+  <strong>ReMe (formerly MemoryScope): Memory Management Framework for Agents</strong><br>
+  <em>Remember Me, Refine Me.</em>
+</p>
+
+---
+ReMe provides AI agents with a unified memory system—enabling the ability to extract, reuse, and share memories across
+users, tasks, and agents.
+
+```
+Personal Memory + Task Memory = Agent Memory
+```
+
+Personal memory helps "**understand user preferences**", while task memory helps agents "**perform better**".
+
+---
+
+## 📰 Latest Updates
+
+- **[2025-09]** 🎉 ReMe v0.1.8 has been officially released, adding support for asynchronous operations. It has also been
+  integrated into the memory service of agentscope-runtime.
+- **[2025-09]** 🎉 ReMe v0.1 officially released, integrating task memory and personal memory. If you want to use the
+  original memoryscope project, you can find it
+  in [MemoryScope](https://github.com/modelscope/Reme/tree/memoryscope_branch).
+- **[2025-09]** 🧪 We validated the effectiveness of task memory extraction and reuse in agents in appworld, bfcl(v3),
+  and frozenlake environments. For more information,
+  check [appworld exp](./cookbook/appworld/quickstart.md), [bfcl exp](./cookbook/bfcl/quickstart.md),
+  and [frozenlake exp](./cookbook/frozenlake/quickstart.md).
+- **[2025-08]** 🚀 MCP protocol support is now available -> [MCP Quick Start](mcp_quick_start.md).
+- **[2025-06]** 🚀 Multiple backend vector storage support (Elasticsearch &
+  ChromaDB) -> [Vector DB quick start](docs/vector_store_api_guide.md).
+- **[2024-09]** 🧠 [MemoryScope](https://github.com/modelscope/Reme/tree/memoryscope_branch) v0.1 released,
+  personalized and time-aware memory storage and usage.
+
+---
+
+## ✨ Architecture Design
+
+<p align="center">
+ <img src="figure/reme_structure.jpg" alt="ReMe Logo" width="100%">
+</p>
+
+ReMe integrates two complementary memory capabilities:
+
+#### 🧠 **Task Memory/Experience**
+
+Procedural knowledge reused across agents
+
+- **Success Pattern Recognition**: Identify effective strategies and understand their underlying principles
+- **Failure Analysis Learning**: Learn from mistakes and avoid repeating the same issues
+- **Comparative Patterns**: Different sampling trajectories provide more valuable memories through comparison
+- **Validation Patterns**: Confirm the effectiveness of extracted memories through validation modules
+
+Learn more about how to use task memory from [task memory](task_memory/task_memory.md)
+
+#### 👤 **Personal Memory**
+
+Contextualized memory for specific users
+
+- **Individual Preferences**: User habits, preferences, and interaction styles
+- **Contextual Adaptation**: Intelligent memory management based on time and context
+- **Progressive Learning**: Gradually build deep understanding through long-term interaction
+- **Time Awareness**: Time sensitivity in both retrieval and integration
+
+Learn more about how to use personal memory from [personal memory](personal_memory/personal_memory.md)
+
+---
+
+## 🛠️ Installation
+
+### Install from PyPI (Recommended)
+
+```bash
+pip install reme-ai
+```
+
+### Install from Source
+
+```bash
+git clone https://github.com/modelscope/ReMe.git
+cd ReMe
+pip install .
+```
+
+### Environment Configuration
+
+Copy `example.env` to .env and modify the corresponding parameters:
+
+```bash
+# Required: LLM API Configuration
+FLOW_LLM_API_KEY=sk-xxxx
+FLOW_LLM_BASE_URL=https://xxxx/v1
+
+# Required: Embedding Model Configuration  
+FLOW_EMBEDDING_API_KEY=sk-xxxx
+FLOW_EMBEDDING_BASE_URL=https://xxxx/v1
+```
+
+---
+
+## 🚀 Quick Start
+
+### HTTP Service Startup
+
+```bash
+reme \
+  backend=http \
+  http.port=8002 \
+  llm.default.model_name=qwen3-30b-a3b-thinking-2507 \
+  embedding_model.default.model_name=text-embedding-v4 \
+  vector_store.default.backend=local
+```
+
+### MCP Server Support
+
+```bash
+reme \
+  backend=mcp \
+  mcp.transport=stdio \
+  llm.default.model_name=qwen3-30b-a3b-thinking-2507 \
+  embedding_model.default.model_name=text-embedding-v4 \
+  vector_store.default.backend=local
+```
+
+### Core API Usage
+
+#### Task Memory Management
+
+```python
+import requests
+
+# Experience Summarizer: Learn from execution trajectories
+response = requests.post("http://localhost:8002/summary_task_memory", json={
+    "workspace_id": "task_workspace",
+    "trajectories": [
+        {"messages": [{"role": "user", "content": "Help me create a project plan"}], "score": 1.0}
+    ]
+})
+
+# Retriever: Get relevant memories
+response = requests.post("http://localhost:8002/retrieve_task_memory", json={
+    "workspace_id": "task_workspace",
+    "query": "How to efficiently manage project progress?",
+    "top_k": 1
+})
+```
+
+<details>
+<summary>curl version</summary>
+
+```bash
+# Experience Summarizer: Learn from execution trajectories
+curl -X POST http://localhost:8002/summary_task_memory \
+  -H "Content-Type: application/json" \
+  -d '{
+    "workspace_id": "task_workspace",
+    "trajectories": [
+      {"messages": [{"role": "user", "content": "Help me create a project plan"}], "score": 1.0}
+    ]
+  }'
+
+# Retriever: Get relevant memories
+curl -X POST http://localhost:8002/retrieve_task_memory \
+  -H "Content-Type: application/json" \
+  -d '{
+    "workspace_id": "task_workspace",
+    "query": "How to efficiently manage project progress?",
+    "top_k": 1
+  }'
+```
+
+</details>
+
+<details>
+<summary>Node.js version</summary>
+
+```javascript
+// Experience Summarizer: Learn from execution trajectories
+fetch("http://localhost:8002/summary_task_memory", {
+  method: "POST",
+  headers: {
+    "Content-Type": "application/json",
+  },
+  body: JSON.stringify({
+    workspace_id: "task_workspace",
+    trajectories: [
+      {messages: [{role: "user", content: "Help me create a project plan"}], score: 1.0}
+    ]
+  })
+})
+.then(response => response.json())
+.then(data => console.log(data));
+
+// Retriever: Get relevant memories
+fetch("http://localhost:8002/retrieve_task_memory", {
+  method: "POST",
+  headers: {
+    "Content-Type": "application/json",
+  },
+  body: JSON.stringify({
+    workspace_id: "task_workspace",
+    query: "How to efficiently manage project progress?",
+    top_k: 1
+  })
+})
+.then(response => response.json())
+.then(data => console.log(data));
+```
+
+</details>
+
+#### Personal Memory Management
+
+```python
+# Memory Integration: Learn from user interactions
+response = requests.post("http://localhost:8002/summary_personal_memory", json={
+    "workspace_id": "task_workspace",
+    "trajectories": [
+        {"messages":
+            [
+                {"role": "user", "content": "I like to drink coffee while working in the morning"},
+                {"role": "assistant",
+                 "content": "I understand, you prefer to start your workday with coffee to stay energized"}
+            ]
+        }
+    ]
+})
+
+# Memory Retrieval: Get personal memory fragments
+response = requests.post("http://localhost:8002/retrieve_personal_memory", json={
+    "workspace_id": "task_workspace",
+    "query": "What are the user's work habits?",
+    "top_k": 5
+})
+```
+
+<details>
+<summary>curl version</summary>
+
+```bash
+# Memory Integration: Learn from user interactions
+curl -X POST http://localhost:8002/summary_personal_memory \
+  -H "Content-Type: application/json" \
+  -d '{
+    "workspace_id": "task_workspace",
+    "trajectories": [
+      {"messages": [
+        {"role": "user", "content": "I like to drink coffee while working in the morning"},
+        {"role": "assistant", "content": "I understand, you prefer to start your workday with coffee to stay energized"}
+      ]}
+    ]
+  }'
+
+# Memory Retrieval: Get personal memory fragments
+curl -X POST http://localhost:8002/retrieve_personal_memory \
+  -H "Content-Type: application/json" \
+  -d '{
+    "workspace_id": "task_workspace",
+    "query": "What are the user's work habits?",
+    "top_k": 5
+  }'
+```
+
+</details>
+
+<details>
+<summary>Node.js version</summary>
+
+```javascript
+// Memory Integration: Learn from user interactions
+fetch("http://localhost:8002/summary_personal_memory", {
+  method: "POST",
+  headers: {
+    "Content-Type": "application/json",
+  },
+  body: JSON.stringify({
+    workspace_id: "task_workspace",
+    trajectories: [
+      {messages: [
+        {role: "user", content: "I like to drink coffee while working in the morning"},
+        {role: "assistant", content: "I understand, you prefer to start your workday with coffee to stay energized"}
+      ]}
+    ]
+  })
+})
+.then(response => response.json())
+.then(data => console.log(data));
+
+// Memory Retrieval: Get personal memory fragments
+fetch("http://localhost:8002/retrieve_personal_memory", {
+  method: "POST",
+  headers: {
+    "Content-Type": "application/json",
+  },
+  body: JSON.stringify({
+    workspace_id: "task_workspace",
+    query: "What are the user's work habits?",
+    top_k: 5
+  })
+})
+.then(response => response.json())
+.then(data => console.log(data));
+```
+
+</details>
+
+---
+
+## 📦 Ready-to-Use Libraries
+
+ReMe provides pre-built memory libraries that agents can immediately use with verified best practices:
+
+### Available Libraries
+
+- **`appworld.jsonl`**: Memory library for Appworld agent interactions, covering complex task planning and execution
+  patterns
+- **`bfcl_v3.jsonl`**: Working memory library for BFCL tool calls
+
+### Quick Usage
+
+```python
+# Load pre-built memories
+response = requests.post("http://localhost:8002/vector_store", json={
+    "workspace_id": "appworld",
+    "action": "load",
+    "path": "./docs/library/"
+})
+
+# Query relevant memories
+response = requests.post("http://localhost:8002/retrieve_task_memory", json={
+    "workspace_id": "appworld",
+    "query": "How to navigate to settings and update user profile?",
+    "top_k": 1
+})
+```
+
+## 🧪 Experiments
+
+### 🌍 [Appworld Experiment](./cookbook/appworld/quickstart.md)
+
+We tested ReMe on Appworld using qwen3-8b:
+
+| Method       | pass@1            | pass@2            | pass@4            |
+|--------------|-------------------|-------------------|-------------------|
+| without ReMe | 0.083             | 0.140             | 0.228             |
+| with ReMe    | 0.109 **(+2.6%)** | 0.175 **(+3.5%)** | 0.281 **(+5.3%)** |
+
+Pass@K measures the probability that at least one of the K generated samples successfully completes the task (
+score=1).  
+The current experiment uses an internal AppWorld environment, which may have slight differences.
+
+You can find more details on reproducing the experiment in [quickstart.md](cookbook/appworld/quickstart.md).
+
+### 🧊 [Frozenlake Experiment](./cookbook/frozenlake/quickstart.md)
+
+|                                        without ReMe                                         |                                          with ReMe                                          |
+|:-------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------:|
+|   <p align="center"><img src="figure/frozenlake_failure.gif" alt="GIF 1" width="30%"></p>   |   <p align="center"><img src="figure/frozenlake_success.gif" alt="GIF 2" width="30%"></p>   |
+
+We tested on 100 random frozenlake maps using qwen3-8b:
+
+| Method       | pass rate        |
+|--------------|------------------|
+| without ReMe | 0.66             |
+| with ReMe    | 0.72 **(+6.0%)** |
+
+You can find more details on reproducing the experiment in [quickstart.md](cookbook/frozenlake/quickstart.md).
+
+### 🔧 [BFCL-V3 Experiment](./cookbook/bfcl/quickstart.md)
+
+We tested ReMe on BFCL-V3 multi-turn-base (randomly split 50train/150val) using qwen3-8b:
+
+| Method       | pass@1              | pass@2              | pass@4              |
+|--------------|---------------------|---------------------|---------------------|
+| without ReMe | 0.2472              | 0.2733              | 0.2922              |
+| with ReMe    | 0.3061 **(+5.89%)** | 0.3500 **(+7.67%)** | 0.3888 **(+9.66%)** |
+
+## 📚 Resources
+
+- **[Quick Start](./cookbook/simple_demo)**: Get started quickly with practical examples
+- **[Vector Storage Setup](vector_store_api_guide.md)**: Configure local/vector databases and usage
+- **[MCP Guide](mcp_quick_start.md)**: Create MCP services
+- **[personal memory](personal_memory)** & **[task memory](task_memory)** : Operators used in personal memory and task memory, You can modify the config to customize the pipelines.
+- **[Example Collection](./cookbook)**: Real use cases and best practices
+
+---
+
+## 🤝 Contribution
+
+We believe the best memory systems come from collective wisdom. Contributions welcome 👉[Guide](contribution.md):
+
+### Code Contributions
+
+- New operation and tool development
+- Backend implementation and optimization
+- API enhancements and new endpoints
+
+### Documentation Improvements
+
+- Usage examples and tutorials
+- Best practice guides
+
+
+---
+
+## 📄 Citation
+
+```bibtex
+@software{ReMe2025,
+  title = {ReMe: Memory Management Framework for Agents},
+  author = {Li Yu, Jiaji Deng, Zouying Cao},
+  url = {https://github.com/modelscope/ReMe},
+  year = {2025}
+}
+```
+
+---
+
+## ⚖️ License
+
+This project is licensed under the Apache License 2.0 - see the [LICENSE](./LICENSE) file for details.
+
+---
+
+## Star History
+
+[![Star History Chart](https://api.star-history.com/svg?repos=modelscope/ReMe&type=Date)](https://www.star-history.com/#modelscope/ReMe&Date)
+
--- a/mkdocs.yml
+++ b/mkdocs.yml
@ -0,0 +1,56 @@
+site_name: ReMe
+site_url: https://github.com/modelscope/ReMe/
+site_description: "Memory Management Framework for Agents"
+theme:
+  name: shadcn
+  image: figure/reme_logo.png
+  show_stargazers: true
+
+
+nav:
+  - Welcome: index.md
+
+  - Library:
+      - Library Home: library/library.md
+
+  - Personal Memory:
+      - Overview: personal_memory/personal_memory.md
+      - Retrieve Ops: personal_memory/personal_retrieve_ops.md
+      - Summary Ops: personal_memory/personal_summary_ops.md
+
+  - Task Memory:
+      - Overview: task_memory/task_memory.md
+      - Retrieve Ops: task_memory/task_retrieve_ops.md
+      - Summary Ops: task_memory/task_summary_ops.md
+
+  - SOP Memory:
+      - Making SOP Memories: sop_memory/making_sop_memories.md
+
+  - Extensions:
+      - Mcp: mcp_quick_start.md
+      - Vector Store: vector_store_api_guide.md
+
+  - Contributions: contribution.md
+
+plugins:
+  - search
+  - excalidraw
+
+markdown_extensions:
+  admonition:
+  codehilite:
+  fenced_code:
+  footnotes:
+  extra:
+  pymdownx.blocks.details:
+  pymdownx.tabbed:
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