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+# ReMe Long-Term Memory in AgentScope
+
+This example demonstrates how to:
+
+- Use ReMe (Reflection Memory) to provide three specialized types of persistent memory storage for AgentScope agents
+- Record and retrieve personal information, task execution trajectories, and tool usage patterns across sessions
+- Integrate long-term memory with ReActAgent for context-aware conversations and continuous learning
+- Configure DashScope embedding models and vector stores for efficient memory management
+
+## Overview
+
+ReMe (Reflection Memory) provides three types of long-term memory for intelligent agents:
+
+1. **Personal Memory** (`ReMePersonalLongTermMemory`) - Records and retrieves persistent personal information, preferences, and facts about users
+2. **Task Memory** (`ReMeTaskLongTermMemory`) - Learns from task execution trajectories and retrieves relevant past experiences for similar tasks
+3. **Tool Memory** (`ReMeToolLongTermMemory`) - Records tool execution results and generates usage guidelines to improve tool calling
+
+## Prerequisites
+
+- Python 3.12 or higher
+- DashScope API key from Alibaba Cloud (for the examples)
+
+## QuickStart
+
+### Installation
+
+```bash
+# Install agentscope from source
+cd {PATH_TO_AGENTSCOPE}
+pip install -e .
+
+# Install required dependencies
+pip install reme-ai python-dotenv
+```
+
+### Setup
+
+Set up your API key:
+
+```bash
+export DASHSCOPE_API_KEY='YOUR_API_KEY'
+```
+
+Or create a `.env` file:
+
+```bash
+DASHSCOPE_API_KEY=YOUR_API_KEY
+```
+
+### Run Examples
+
+```bash
+# Personal Memory Example - 5 core interfaces
+python personal_memory_example.py
+
+# Task Memory Example - 5 core interfaces
+python task_memory_example.py
+
+# Tool Memory Example - Complete workflow with ReActAgent
+python tool_memory_example.py
+```
+
+> **Note**: The examples use DashScope models by default. To use OpenAI or other models, modify the model initialization in the example code accordingly.
+
+## Key Features
+
+- **Three Specialized Memory Types**: Personal, Task, and Tool memory for different use cases
+- **Dual Interface Design**: Both tool functions (for agent calling) and direct methods (for programmatic use)
+- **Vector-based Retrieval**: Efficient semantic search using embedding models and vector stores
+- **Async-first Architecture**: Full async/await support for non-blocking operations
+- **ReActAgent Integration**: Seamless integration with AgentScope's ReActAgent and Toolkit
+- **Automatic Context Management**: Uses async context managers for proper resource handling
+
+## Core Concepts
+
+### Memory Types and Their Use Cases
+
+| Memory Type | Purpose | When to Use |
+|------------|---------|-------------|
+| **Personal Memory** | Store user preferences, habits, and personal facts | User profiles, personalized assistants, long-term user context |
+| **Task Memory** | Learn from task execution trajectories | Problem-solving, debugging, repeated workflows, learning from past successes |
+| **Tool Memory** | Record tool usage patterns and generate guidelines | Tool-using agents, improving tool call accuracy, avoiding past errors |
+
+### Interface Design
+
+**Personal Memory** and **Task Memory** provide **5 core interfaces**:
+
+1. **`record_to_memory()`** - Tool function for agents to record memories (returns `ToolResponse`)
+2. **`retrieve_from_memory()`** - Tool function for agents to retrieve memories (returns `ToolResponse`)
+3. **`record()`** - Direct method for programmatic recording (returns `None`)
+4. **`retrieve()`** - Direct method for programmatic retrieval (returns `str`)
+5. **ReActAgent Integration** - Use memory with `long_term_memory` and `long_term_memory_mode` parameters
+
+**Tool Memory** provides **2 core interfaces** (no tool functions):
+
+1. **`record()`** - Direct method for recording tool execution results (returns `None`)
+2. **`retrieve()`** - Direct method for retrieving tool usage guidelines (returns `str`)
+
+## Usage Examples
+
+### 1. Personal Memory
+
+**Use Case**: Record and retrieve user preferences, habits, and personal information.
+
+```python
+import asyncio
+import os
+from agentscope.memory import ReMePersonalLongTermMemory
+from agentscope.embedding import DashScopeTextEmbedding
+from agentscope.message import Msg
+from agentscope.model import DashScopeChatModel
+
+
+async def main():
+    # Initialize personal memory
+    personal_memory = ReMePersonalLongTermMemory(
+        agent_name="Friday",
+        user_name="user_123",
+        model=DashScopeChatModel(
+            model_name="qwen3-max",
+            api_key=os.environ.get("DASHSCOPE_API_KEY"),
+            stream=False,
+        ),
+        embedding_model=DashScopeTextEmbedding(
+            model_name="text-embedding-v4",
+            api_key=os.environ.get("DASHSCOPE_API_KEY"),
+            dimensions=1024,
+        ),
+    )
+
+    # Use async context manager (required!)
+    async with personal_memory:
+        # Interface 1: record_to_memory (tool function)
+        result = await personal_memory.record_to_memory(
+            thinking="User sharing travel preferences",
+            content=[
+                "I prefer to stay in homestays when traveling to Hangzhou",
+                "I like to visit the West Lake in the morning",
+                "I enjoy drinking Longjing tea",
+            ],
+        )
+
+        # Interface 2: retrieve_from_memory (tool function)
+        result = await personal_memory.retrieve_from_memory(
+            keywords=["Hangzhou travel", "tea preference"],
+        )
+
+        # Interface 3: record (direct method)
+        await personal_memory.record(
+            msgs=[
+                Msg(role="user", content="I work as a software engineer", name="user"),
+                Msg(role="assistant", content="Got it!", name="assistant"),
+            ],
+        )
+
+        # Interface 4: retrieve (direct method)
+        memories = await personal_memory.retrieve(
+            msg=Msg(role="user", content="What do you know about my work?", name="user"),
+        )
+        print(memories)
+
+
+asyncio.run(main())
+```
+
+**Integration with ReActAgent** (Interface 5):
+
+```python
+from agentscope.agent import ReActAgent
+from agentscope.formatter import DashScopeChatFormatter
+from agentscope.memory import InMemoryMemory
+from agentscope.tool import Toolkit
+
+async def use_with_agent():
+    personal_memory = ReMePersonalLongTermMemory(...)
+
+    async with personal_memory:
+        agent = ReActAgent(
+            name="Friday",
+            sys_prompt="You are Friday with long-term memory. Always record user information and retrieve memories when needed.",
+            model=DashScopeChatModel(...),
+            formatter=DashScopeChatFormatter(),
+            toolkit=Toolkit(),
+            memory=InMemoryMemory(),
+            long_term_memory=personal_memory,  # Attach personal memory
+            long_term_memory_mode="both",  # Enable both record and retrieve tools
+        )
+
+        # Agent can now use record_to_memory and retrieve_from_memory as tools
+        msg = Msg(role="user", content="I prefer staying in homestays", name="user")
+        response = await agent(msg)
+```
+
+### 2. Task Memory
+
+**Use Case**: Learn from task execution trajectories and retrieve relevant experiences.
+
+```python
+from agentscope.memory import ReMeTaskLongTermMemory
+
+
+async def main():
+    # Initialize task memory
+    task_memory = ReMeTaskLongTermMemory(
+        agent_name="TaskAssistant",
+        user_name="task_workspace_123",  # Acts as workspace_id
+        model=DashScopeChatModel(...),
+        embedding_model=DashScopeTextEmbedding(...),
+    )
+
+    async with task_memory:
+        # Interface 1: record_to_memory with score
+        result = await task_memory.record_to_memory(
+            thinking="Recording successful debugging approach",
+            content=[
+                "For API 404 errors: Check route definition, verify URL path, ensure correct port",
+                "Always use linter to catch typos in route paths",
+            ],
+            score=0.95,  # High score for successful trajectory
+        )
+
+        # Interface 2: retrieve_from_memory
+        result = await task_memory.retrieve_from_memory(
+            keywords=["debugging", "API errors"],
+        )
+
+        # Interface 3: record with score in direct method
+        await task_memory.record(
+            msgs=[
+                Msg(role="user", content="I'm getting a 404 error", name="user"),
+                Msg(role="assistant", content="Let's check the route path...", name="assistant"),
+                Msg(role="user", content="Found the typo!", name="user"),
+            ],
+            score=0.95,  # Optional score for this trajectory
+        )
+
+        # Interface 4: retrieve (direct method)
+        experiences = await task_memory.retrieve(
+            msg=Msg(role="user", content="How to debug API errors?", name="user"),
+        )
+        print(experiences)
+
+
+asyncio.run(main())
+```
+
+**Integration with ReActAgent** (Interface 5):
+
+```python
+async def use_with_agent():
+    task_memory = ReMeTaskLongTermMemory(...)
+
+    async with task_memory:
+        agent = ReActAgent(
+            name="TaskAssistant",
+            sys_prompt="You are a task assistant. Record solutions and retrieve past experiences before solving problems.",
+            model=DashScopeChatModel(...),
+            formatter=DashScopeChatFormatter(),
+            toolkit=Toolkit(),
+            memory=InMemoryMemory(),
+            long_term_memory=task_memory,
+            long_term_memory_mode="both",
+        )
+
+        # Agent learns from task executions over time
+        msg = Msg(role="user", content="How should I optimize database queries?", name="user")
+        response = await agent(msg)
+```
+
+### 3. Tool Memory
+
+**Use Case**: Record tool execution results and generate usage guidelines for better tool calling.
+
+**Complete Workflow**:
+
+```python
+import json
+from datetime import datetime
+from agentscope.memory import ReMeToolLongTermMemory
+from agentscope.tool import Toolkit, ToolResponse
+from agentscope.message import Msg, TextBlock
+
+
+# Step 1: Define tools
+async def web_search(query: str, max_results: int = 5) -> ToolResponse:
+    """Search the web for information."""
+    result = f"Found {max_results} results for query: '{query}'"
+    return ToolResponse(content=[TextBlock(type="text", text=result)])
+
+
+async def main():
+    # Initialize tool memory
+    tool_memory = ReMeToolLongTermMemory(
+        agent_name="ToolBot",
+        user_name="tool_workspace_demo",
+        model=DashScopeChatModel(...),
+        embedding_model=DashScopeTextEmbedding(...),
+    )
+
+    async with tool_memory:
+        # Step 2: Record tool execution history (accepts JSON strings in msgs)
+        tool_result = {
+            "create_time": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+            "tool_name": "web_search",
+            "input": {"query": "Python asyncio tutorial", "max_results": 10},
+            "output": "Found 10 results for query: 'Python asyncio tutorial'",
+            "token_cost": 150,
+            "success": True,
+            "time_cost": 2.3
+        }
+
+        # Interface 1: record (accepts JSON strings in message content)
+        await tool_memory.record(
+            msgs=[Msg(role="assistant", content=json.dumps(tool_result), name="assistant")],
+        )
+
+        # Step 3: Retrieve tool guidelines
+        # Interface 2: retrieve returns summarized guidelines
+        guidelines = await tool_memory.retrieve(
+            msg=Msg(role="user", content="web_search", name="user"),
+        )
+
+        # Step 4: Inject guidelines into agent system prompt
+        toolkit = Toolkit()
+        toolkit.register_tool_function(web_search)
+
+        base_prompt = "You are ToolBot, a helpful AI assistant."
+        enhanced_prompt = f"{base_prompt}\n\n# Tool Guidelines:\n{guidelines}"
+
+        agent = ReActAgent(
+            name="ToolBot",
+            sys_prompt=enhanced_prompt,  # Guidelines enhance tool usage
+            model=DashScopeChatModel(...),
+            formatter=DashScopeChatFormatter(),
+            toolkit=toolkit,
+            memory=InMemoryMemory(),
+        )
+
+        # Agent now uses tools with learned guidelines
+        msg = Msg(role="user", content="Search for Python design patterns", name="user")
+        response = await agent(msg)
+
+
+asyncio.run(main())
+```
+
+> **Note**: Tool Memory does NOT provide `record_to_memory()` and `retrieve_from_memory()` tool functions. It only provides direct `record()` and `retrieve()` methods. Tool Memory is designed to be used programmatically to enhance agent system prompts, not as agent-callable tools.
+
+## API Reference
+
+### Common Parameters
+
+All memory types share these initialization parameters:
+
+```python
+ReMePersonalLongTermMemory(
+    agent_name: str,           # Name of the agent using this memory
+    user_name: str,            # User identifier (acts as workspace_id in ReMe)
+    model: ModelWrapper,       # LLM for summarization and processing
+    embedding_model: EmbeddingWrapper,  # Embedding model for vector retrieval
+    vector_store_dir: str = "./memory_vector_store",  # Storage location
+)
+```
+
+### Interface Specifications
+
+#### Personal Memory
+
+| Interface | Type | Signature | Returns | Description |
+|-----------|------|-----------|---------|-------------|
+| `record_to_memory` | Tool Function | `(thinking: str, content: list[str])` | `ToolResponse` | Record personal information with reasoning |
+| `retrieve_from_memory` | Tool Function | `(keywords: list[str], limit: int = 3)` | `ToolResponse` | Retrieve memories by keywords |
+| `record` | Direct Method | `(msgs: list[Msg])` | `None` | Record message conversations |
+| `retrieve` | Direct Method | `(msg: Msg, top_k: int = 3)` | `str` | Query-based retrieval |
+
+**Parameters**:
+- `thinking`: Reasoning about what to record
+- `content`: List of strings to remember
+- `keywords`: Search keywords
+- `limit`: Results per keyword (tool function, default: 3)
+- `top_k`: Total results to retrieve (direct method, default: 3)
+
+#### Task Memory
+
+| Interface | Type | Signature | Returns | Description |
+|-----------|------|-----------|---------|-------------|
+| `record_to_memory` | Tool Function | `(thinking: str, content: list[str], score: float = 1.0)` | `ToolResponse` | Record task trajectory with score |
+| `retrieve_from_memory` | Tool Function | `(keywords: list[str], top_k: int = 5)` | `ToolResponse` | Retrieve experiences by keywords |
+| `record` | Direct Method | `(msgs: list[Msg], score: float = 1.0)` | `None` | Record message conversations with score |
+| `retrieve` | Direct Method | `(msg: Msg, top_k: int = 5)` | `str` | Query-based experience retrieval |
+
+**Parameters**:
+- `thinking`: Reasoning about the task execution
+- `content`: Task execution information and insights
+- `score`: Success score for the trajectory (0.0-1.0, default: 1.0)
+- `keywords`: Search keywords (e.g., task type, domain)
+- `top_k`: Number of results to retrieve (default: 5)
+
+#### Tool Memory
+
+| Interface | Type | Signature | Returns | Description |
+|-----------|------|-----------|---------|-------------|
+| `record` | Direct Method | `(msgs: list[Msg])` | `None` | Record tool results as messages (JSON format) |
+| `retrieve` | Direct Method | `(msg: Msg)` | `str` | Retrieve guidelines for tools |
+
+**Parameters**:
+- `msgs`: List of messages where `content` contains JSON strings with tool execution metadata:
+  - `create_time`: Timestamp (`"%Y-%m-%d %H:%M:%S"`)
+  - `tool_name`: Tool identifier
+  - `input`: Parameters used (dict)
+  - `output`: Execution result (str)
+  - `token_cost`: Token usage (int)
+  - `success`: Execution status (bool)
+  - `time_cost`: Duration in seconds (float)
+- `msg`: Message containing tool name to retrieve guidelines for
+- **Note**: Tool Memory does NOT provide tool functions (`record_to_memory` and `retrieve_from_memory`). It only provides direct methods for programmatic use.
+
+### ReActAgent Integration Modes
+
+When attaching **Personal Memory** or **Task Memory** to ReActAgent, use the `long_term_memory_mode` parameter:
+
+```python
+agent = ReActAgent(
+    name="Assistant",
+    long_term_memory=memory,  # ReMePersonalLongTermMemory or ReMeTaskLongTermMemory
+    long_term_memory_mode="both",  # Options: "record", "retrieve", "both"
+    # ... other parameters
+)
+```
+
+**Modes**:
+- `"record"`: Only adds `record_to_memory` tool to agent
+- `"retrieve"`: Only adds `retrieve_from_memory` tool to agent
+- `"both"`: Adds both tools (recommended for most use cases)
+
+> **Note**: Tool Memory does NOT support ReActAgent integration with tool functions. Use Tool Memory programmatically to enhance system prompts as shown in the Tool Memory example.
+
+### Async Context Manager (Required!)
+
+All ReMe memory types **must** be used with async context managers:
+
+```python
+async with long_term_memory:
+    # All memory operations must be within this context
+    await long_term_memory.record(msgs=[...])
+    result = await long_term_memory.retrieve(msg=...)
+```
+
+This ensures:
+- Proper initialization of the ReMe backend
+- Resource cleanup after operations
+- Vector store connection management
+
+### Custom Configuration
+
+```python
+from agentscope.memory import ReMePersonalLongTermMemory
+
+# Custom storage location and models
+memory = ReMePersonalLongTermMemory(
+    agent_name="Friday",
+    user_name="user_123",
+    model=your_custom_model,  # Any AgentScope-compatible LLM
+    embedding_model=your_embedding,  # Any AgentScope-compatible embedding model
+    vector_store_dir="./custom_path",  # Custom storage directory
+)
+```
+
+## Example Files Overview
+
+### `personal_memory_example.py`
+
+Demonstrates **5 core interfaces** for personal memory:
+
+1. **`record_to_memory()`** - Record user preferences using tool function
+2. **`retrieve_from_memory()`** - Search memories by keywords using tool function
+3. **`record()`** - Direct recording of message conversations
+4. **`retrieve()`** - Direct query-based retrieval
+5. **ReActAgent Integration** - Agent autonomously uses memory tools
+
+**Key Features**:
+- Recording travel preferences, work habits, and personal information
+- Keyword-based and query-based retrieval
+- System prompt guidelines for agent memory usage
+- Automatic memory tool calling by ReActAgent
+
+### `task_memory_example.py`
+
+Demonstrates **5 core interfaces** for task memory:
+
+1. **`record_to_memory()`** - Record task experiences with scores
+2. **`retrieve_from_memory()`** - Retrieve relevant experiences by keywords
+3. **`record()`** - Direct recording with trajectory scores
+4. **`retrieve()`** - Direct experience retrieval
+5. **ReActAgent Integration** - Agent learns from past task executions
+
+**Key Features**:
+- Recording project planning, debugging, and development experiences
+- Score-based trajectory evaluation (0.0-1.0)
+- Learning from successful and failed attempts
+- Continuous improvement through experience retrieval
+
+### `tool_memory_example.py`
+
+Demonstrates the **complete workflow** for tool memory:
+
+1. **Mock tools** - Define and register tools to Toolkit
+2. **Record tool history** - Store execution results with metadata using `record()`
+3. **Retrieve guidelines** - Get summarized usage guidelines using `retrieve()`
+4. **Enhance agent prompt** - Inject guidelines into system prompt
+5. **Use ReActAgent** - Agent uses tools with learned guidelines
+
+**Key Features**:
+- JSON-formatted tool execution recording via direct `record()` method
+- Automatic guideline generation through summarization
+- Multi-tool guideline retrieval via direct `retrieve()` method
+- System prompt enhancement for better tool usage
+- **Note**: Tool Memory does NOT provide agent-callable tool functions
+
+## Architecture
+
+### Inheritance Hierarchy
+
+```
+ReMeLongTermMemoryBase (abstract base)
+├── ReMePersonalLongTermMemory
+├── ReMeTaskLongTermMemory
+└── ReMeToolLongTermMemory
+```
+
+**`ReMeLongTermMemoryBase`** provides:
+- Integration with ReMe library's `ReMeApp`
+- Async context manager implementation
+- Common interface definitions
+- Vector store and embedding management
+
+### Memory Storage
+
+- **Location**: `./memory_vector_store/` (configurable)
+- **Isolation**: Each `user_name` maintains separate storage
+- **Persistence**: Memories persist across sessions
+- **Format**: Vector embeddings with metadata
+
+## Best Practices
+
+### 1. System Prompt Design
+
+For agents with long-term memory, clearly specify when to record and retrieve:
+
+```python
+sys_prompt = """
+You are an assistant with long-term memory.
+
+Recording Guidelines:
+- Record when users share personal information, preferences, or important facts
+- Record successful task execution approaches and solutions
+- Record tool execution results with detailed metadata
+
+Retrieval Guidelines:
+- ALWAYS retrieve before answering questions about past information
+- Retrieve when dealing with similar tasks to past executions
+- Check tool guidelines before using tools
+"""
+```
+
+### 2. Score Assignment (Task Memory)
+
+Use meaningful scores to prioritize experiences:
+
+```python
+# Successful trajectory
+await task_memory.record_to_memory(..., score=0.95)
+
+# Partially successful
+await task_memory.record_to_memory(..., score=0.6)
+
+# Failed trajectory (still useful to learn from)
+await task_memory.record_to_memory(..., score=0.2)
+```
+
+### 3. Tool Memory Workflow
+
+Follow this pattern for tool memory:
+
+```
+1. Execute tool → 2. Record result → 3. Trigger summarization → 4. Retrieve guidelines → 5. Use in agent
+```
+
+## Troubleshooting
+
+### Common Issues
+
+**Issue**: `RuntimeError: Memory not initialized`
+- **Solution**: Always use `async with memory:` context manager
+
+**Issue**: No memories retrieved
+- **Solution**: Ensure you've recorded memories first and check `user_name` matches
+
+**Issue**: Tool memory not generating guidelines
+- **Solution**: Record multiple tool executions to trigger summarization
+
+**Issue**: Agent not using memory tools
+- **Solution**: Check `long_term_memory_mode="both"` and verify system prompt encourages memory usage
+
+## References
+
+- [ReMe Library](https://github.com/modelscope/ReMe) - Core memory implementation
+- [AgentScope Documentation](https://github.com/modelscope/agentscope) - Framework documentation
+- [DashScope API](https://dashscope.aliyun.com/) - Model API for examples