diff --git a/README.md b/README.md
index e69de29b..686f3f27 100644
--- a/README.md
+++ b/README.md
@@ -0,0 +1,430 @@
+
+ 
+
+
+
+ 
+ 
+ 
+ 
+
+
+
+ ReMe (formerly MemoryScope): Memory Management Framework for Agents
+ Remember Me, Refine Me
+
+
+---
+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 Management
+```
+
+Personal memory helps "**understand user needs**", while task memory helps agents "**perform better**".
+
+---
+
+## 📰 Latest Updates
+
+- **[2025-09]** 🎉 ReMe v0.1.x
+ 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 -> [Quick Start Guide](./doc/mcp_quick_start.md).
+- **[2025-06]** 🚀 Multiple backend vector storage support (Elasticsearch &
+ ChromaDB) -> [Quick Start Guide](./doc/vector_store_api_guide.md).
+- **[2024-09]** 🧠 [MemoryScope](https://github.com/modelscope/Reme/tree/memoryscope_branch) v0.1.x released,
+ personalized and time-aware memory storage and usage.
+
+---
+
+## ✨ Architecture Design
+
+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](./doc/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](./doc/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
+})
+```
+
+
+curl version
+
+```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
+ }'
+```
+
+
+
+
+Node.js version
+
+```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));
+```
+
+
+
+#### 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
+})
+```
+
+
+curl version
+
+```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
+ }'
+```
+
+
+
+
+Node.js version
+
+```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));
+```
+
+
+
+---
+
+## 📦 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": "./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
+
+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** | **0.175** | **0.281** |
+
+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
+
+| Without memory | With memory |
+|:-------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------:|
+| 
| 
|
+
+We tested on 100 random frozenlake maps using qwen3-8b:
+
+| Method | pass rate |
+|--------------|------------------|
+| without Reme | 0.66 |
+| with Reme | 0.72 **(+9.1%)** |
+
+You can find more details on reproducing the experiment in [quickstart.md](cookbook/frozenlake/quickstart.md).
+
+### 🔧 BFCL-V3 Experiment
+
+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](./doc/vector_store_api_guide.md)**: Configure local/vector databases and usage
+- **[MCP Guide](./doc/mcp_quick_start.md)**: Create MCP services
+- **Link Description**: Operators used in personal memory and task memory and their meanings can be found
+ in [personal memory](./doc/personal_memory) and [task memory](./doc/task_memory) respectively. You can modify the
+ config to customize the links
+- **[Example Collection](./cookbook)**: Real use cases and best practices
+
+---
+
+## 🤝 Contribution
+
+We believe the best memory systems come from collective wisdom. Contributions welcome:
+
+### 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
+
+[Guide](./doc/contribution.md)
+---
+
+## 📄 Citation
+
+```bibtex
+@software{ReMe2025,
+ title = {ReMe: Memory Framework for AI Agent},
+ 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.
+
+---
diff --git a/README_ZH.md b/README_ZH.md
index 6345951a..d12052b3 100644
--- a/README_ZH.md
+++ b/README_ZH.md
@@ -1,7 +1,7 @@
-
+
@@ -34,7 +34,7 @@ ReMe为AI智能体提供了统一的记忆与经验系统——在跨用户、
中找到。
- **[2025-09]** 🧪 我们在appworld, bfcl(v3)
以及frozenlake环境验证了任务记忆抽取与复用在Agent中的效果，更多信息请查看 [appworld exp](./cookbook/appworld/quickstart.md), [bfcl exp](./cookbook/bfcl/quickstart.md)
- and [frozenlake exp](./cookbook/frozenlake/quickstart.md)。
+ 和 [frozenlake exp](./cookbook/frozenlake/quickstart.md)。
- **[2025-08]** 🚀 MCP协议支持已上线-> [快速开始指南](./doc/mcp_quick_start.md)。
- **[2025-06]** 🚀 多后端向量存储支持 (Elasticsearch & ChromaDB) -> [快速开始指南](./doc/vector_store_api_guide.md)。
- **[2024-09]** 🧠 [MemoryScope](https://github.com/modelscope/Reme/tree/memoryscope_branch) v0.1.x 发布，个性化和时间感知的记忆存储与使用。
@@ -61,7 +61,7 @@ ReMe整合两种互补的记忆能力：
- **渐进学习**：通过长期交互逐步建立深度理解
- **时间感知**：检索和整合时都具备时间敏感性
-- 你可以从[personal memory](./doc/personal_memory/personal_memory.md)了解更多如何使用personal memory的方法
+你可以从[personal memory](./doc/personal_memory/personal_memory.md)了解更多如何使用personal memory的方法
---
@@ -101,8 +101,8 @@ FLOW_EMBEDDING_BASE_URL=https://xxxx/v1
### HTTP服务启动
```bash
reme \
- backend=http \
- http.port=8001 \
+ 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
@@ -385,6 +385,8 @@ Pass@K 衡量的是在生成的 K 个样本中，至少有一个成功完成任
- 使用示例和教程
- 最佳实践指南
+[指南](./doc/contribution.md)
+
---
## 📄 引用
@@ -392,7 +394,7 @@ Pass@K 衡量的是在生成的 K 个样本中，至少有一个成功完成任
```bibtex
@software{ReMe2025,
title = {ReMe: Memory Framework for AI Agent},
- author = {jinli.yl, dengjiaji.djj, caozouying.czy},
+ author = {Li Yu, Jiaji Deng, Zouying Cao},
url = {https://github.com/modelscope/ReMe},
year = {2025}
}
diff --git a/doc/mcp_quick_start.md b/doc/mcp_quick_start.md
index aebef04d..30f6b754 100644
--- a/doc/mcp_quick_start.md
+++ b/doc/mcp_quick_start.md
@@ -8,7 +8,7 @@ integration with MCP-compatible clients.
- How to set up and configure ReMe MCP server
- How to connect to the server using Python MCP clients
- How to use task memory operations through MCP
-- How to build experience-enhanced agents with MCP integration
+- How to build memory-enhanced agents with MCP integration
## 📋 Prerequisites
diff --git a/doc/task_memory/task_memory.md b/doc/task_memory/task_memory.md
index 79afaa5e..5e232225 100644
--- a/doc/task_memory/task_memory.md
+++ b/doc/task_memory/task_memory.md
@@ -11,7 +11,7 @@ Task Memory represents knowledge extracted from previous task executions, includ
Each task memory contains:
- `when_to_use`: Conditions that indicate when this memory is relevant
-- `content`: The actual knowledge or experience to be applied
+- `content`: The actual knowledge or memory to be applied
- Metadata about the memory's source and utility
## Configuration Logic
@@ -43,7 +43,7 @@ The `retrieve_task_memory` flow fetches relevant memories based on a query:
```yaml
retrieve_task_memory:
flow_content: build_query_op >> recall_vector_store_op >> rerank_memory_op >> rewrite_memory_op
- description: "Retrieves the most relevant top-k memory experiences from historical data based on the current query to enhance task-solving capabilities"
+ description: "Retrieves the most relevant top-k memory from historical data based on the current query to enhance task-solving capabilities"
```
This flow:
diff --git a/doc/task_memory/task_summary_ops.md b/doc/task_memory/task_summary_ops.md
index c4de0bc2..138f5812 100644
--- a/doc/task_memory/task_summary_ops.md
+++ b/doc/task_memory/task_summary_ops.md
@@ -44,7 +44,7 @@ Extracts task memories from successful trajectories.
### Functionality
-- Processes successful trajectories to identify valuable experiences
+- Processes successful trajectories to identify valuable memories
- Can work with both entire trajectories and segmented step sequences
- Uses LLM to extract structured task memories with when-to-use conditions
diff --git a/reme_ai/constants/common_constants.py b/reme_ai/constants/common_constants.py
index 74645416..99ce3887 100644
--- a/reme_ai/constants/common_constants.py
+++ b/reme_ai/constants/common_constants.py
@@ -5,8 +5,6 @@
WORKFLOW_NAME = "workflow_name"
-MEMORYSCOPE_CONTEXT = "memoryscope_context"
-
RESULT = "result"
MEMORIES = "memories"
diff --git a/reme_ai/summary/task/__init__.py b/reme_ai/summary/task/__init__.py
index 169f8958..30e6193b 100644
--- a/reme_ai/summary/task/__init__.py
+++ b/reme_ai/summary/task/__init__.py
@@ -2,7 +2,6 @@ from .comparative_extraction_op import ComparativeExtractionOp
from .failure_extraction_op import FailureExtractionOp
from .memory_deduplication_op import MemoryDeduplicationOp
from .memory_validation_op import MemoryValidationOp
-from .pdf_preprocess_op_wrapper import PDFPreprocessOp
from .simple_comparative_summary_op import SimpleComparativeSummaryOp
from .simple_summary_op import SimpleSummaryOp
from .success_extraction_op import SuccessExtractionOp