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docs(README): add comprehensive documentation for ReMe.ai framework
- Added detailed information about the project's architecture, installation, and usage - Included sections on task memory and personal memory management - Provided examples and use cases for the framework - Updated project metadata and version information
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rag_nodes_index.jsonl
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alfworld_data
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beyondagent/dataset/appworld/data
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beyond*
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cookbook/appworld/exp_result/*
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cookbook/appworld/file_vector_store/*
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experiencemaker/tool/web_search_cach/*
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README.md
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README.md
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# ReMe.ai
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<p align="center">
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<img src="doc/figure/logo.jpg" alt="ReMe.ai Logo" width="100%">
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</p>
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<p align="center">
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<a href="https://pypi.org/project/reme-ai/"><img src="https://img.shields.io/badge/python-3.12+-blue" alt="Python Version"></a>
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<a href="https://pypi.org/project/reme-ai/"><img src="https://img.shields.io/badge/pypi-v1.0.0-blue?logo=pypi" alt="PyPI Version"></a>
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<a href="./LICENSE"><img src="https://img.shields.io/badge/license-Apache--2.0-black" alt="License"></a>
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<a href="https://github.com/modelscope/ReMe.ai"><img src="https://img.shields.io/github/stars/modelscope/ReMe.ai?style=social" alt="GitHub Stars"></a>
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</p>
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<p align="center">
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<strong>记忆驱动的AI智能体框架</strong><br>
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<em>"如果说我比别人看得更远些，那是因为我站在了巨人的肩膀上。" —— 牛顿</em>
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</p>
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---
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Remember Everyone, Recreate Everything
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Remember Me, Reshape Me
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Remember Me, Refine Me
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Remember Me, Reinvent Me
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今天的每个AI智能体都在从零开始。每当智能体处理任务时，它都在重新发明无数其他智能体已经发现的解决方案。这就像要求每个人都从头发现火、农业和数学一样。
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ReMe.ai希望改变这一点。我们为AI智能体提供了统一的记忆与经验系统——在跨用户、跨任务、跨智能体下抽取、复用和分享记忆的能力。
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```
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任务经验 (Task Memory) + 个人记忆 (Personal Memory) = agent的记忆管理
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```
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个人记忆回答"**如何理解用户需要**"，任务记忆回答"**如何做得更好**"，
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---
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## 📰 最新动态
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- **[2025-09]** 🎉 ReMe.ai v1.0.0 正式发布，整合任务经验与个人记忆
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- **[2025-08]** 🚀 MCP协议支持已上线！→ [快速开始指南](./doc/mcp_quick_start.md)
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- **[2025-07]** 📚 完整文档和快速开始指南发布
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- **[2025-06]** 🚀 多后端向量存储支持 (Elasticsearch & ChromaDB)
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---
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## ✨ 架构设计
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### 🎯 双模记忆系统
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ReMe.ai整合两种互补的记忆能力：
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#### 🧠 **任务经验 (Task Memory/Experience)**
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跨智能体复用的程序性知识
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- **成功模式识别**：识别有效策略并理解其根本原理
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- **失败分析学习**：从错误中学习，避免重复同样的问题
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- **规划策略**：不同问题类型的规划策略
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- **工具使用模式**：经过验证的有效工具使用方法
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- **标准操作流程**：经过验证的方法论和流程
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你可以从[快速开始指南](./doc/task_memory_readme.md)了解更多如何使用task memory的方法
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#### 👤 **个人记忆 (personal memory)**
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特定用户的情境化记忆
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- **个体偏好**：用户的习惯、偏好和交互风格
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- **情境适应**：基于时间和上下文的智能记忆管理
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- **渐进学习**：通过长期交互逐步建立深度理解
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- **时间感知**：检索和整合时都具备时间敏感性
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- 你可以从[快速开始指南](./doc/personal_memory_readme.md)了解更多如何使用personal memory的方法
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---
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## 🛠️ 安装
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### 从PyPI安装（推荐）
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```bash
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pip install reme-ai
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```
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### 从源码安装
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```bash
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git clone https://github.com/modelscope/ReMe.ai.git
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cd ReMe.ai
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pip install .
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```
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### 环境配置
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创建`.env`文件：
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```bash
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# 必需：LLM API配置
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LLM_API_KEY="sk-xxx"
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LLM_BASE_URL="https://xxx.com/v1"
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# 必需：嵌入模型配置
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EMBEDDING_MODEL_API_KEY="sk-xxx"
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EMBEDDING_MODEL_BASE_URL="https://xxx.com/v1"
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```
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---
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## 🚀 快速开始
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### HTTP服务启动
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```bash
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reme \
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backend=http \
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http.port=8001 \
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llm.default.model_name=qwen3-30b-a3b-thinking-2507 \
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embedding_model.default.model_name=text-embedding-v4 \
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vector_store.default.backend=local
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```
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### MCP服务器支持
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```bash
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reme \
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backend=mcp \
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mcp.transport=stdio \
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llm.default.model_name=qwen3-30b-a3b-thinking-2507 \
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embedding_model.default.model_name=text-embedding-v4 \
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vector_store.default.backend=local
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```
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### 核心API使用
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#### 任务经验管理
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```python
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import requests
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# 经验总结器：从执行轨迹学习
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response = requests.post("http://localhost:8002/summary_task_memory", json={
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"workspace_id": "task_workspace",
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"trajectories": [
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{"messages": [{"role": "user", "content": "帮我制定项目计划"}], "score": 1.0}
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]
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})
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# 经验检索器：获取相关经验
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response = requests.post("http://localhost:8002/retrieve_task_memory", json={
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"workspace_id": "task_workspace",
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"query": "如何高效管理项目进度？",
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"top_k": 1
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})
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```
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#### 个人记忆管理
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```python
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# 记忆整合：从用户交互中学习
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response = requests.post("http://localhost:8002/summary_personal_memory", json={
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"workspace_id": "task_workspace",
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"trajectories": [
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{"messages":
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[
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{"role": "user", "content": "我喜欢早上喝咖啡工作"},
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{"role": "assistant", "content": "了解，您习惯早上用咖啡提神来开始工作"}
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]
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}
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]
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})
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# 记忆检索：获取个人记忆片段
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response = requests.post("http://localhost:8002/retrieve_personal_memory", json={
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"workspace_id": "task_workspace",
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"query": "用户的工作习惯是什么？",
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"top_k": 5
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})
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```
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---
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## 🧪 实验结果
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### Appworld基准测试
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使用qwen3-8b在Appworld上的测试结果：
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| 方法 | pass@1 | pass@2 | pass@4 |
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|----------------------------|-----------|-------------|-----------|
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| 无记忆（基线） | 0.083 | 0.140 | 0.228 |
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| **使用任务经验** | **0.109** | **0.175** | **0.281** |
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详见：[quickstart.md](cookbook/appworld/quickstart.md)
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### FrozenLake实验
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使用qwen3-8b在100个随机FrozenLake地图上测试：
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| 方法 | 通过率 |
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|---------------------------|-----------------|
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| 无记忆（基线） | 0.66 |
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| **使用任务经验** | 0.72 **(+9.1%)** |
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| 无经验 | 有经验 |
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|:----------------------------------------------------------:|:---------------------------------------:|
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| <p align="center"><img src="doc/figure/frozenlake_failure.gif" alt="失败案例" width="30%"></p> | <p align="center"><img src="doc/figure/frozenlake_success.gif" alt="成功案例" width="30%"></p>
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详见：[quickstart.md](cookbook/frozenlake/quickstart.md)
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---
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## 📦 即用型经验库
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ReMe.ai提供预构建的经验库，智能体可以立即使用经过验证的最佳实践：
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### 可用经验库
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- **`appworld_v1.jsonl`**：Appworld智能体交互的记忆库，涵盖复杂任务规划和执行模式
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- **`bfcl_v1.jsonl`**：BFCL工具调用的工作记忆库
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### 快速使用
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```python
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# 加载预构建经验
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response = requests.post("http://localhost:8002/vector_store", json={
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"workspace_id": "appworld_v1",
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"action": "load",
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"path": "./library/"
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})
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# 查询相关经验
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response = requests.post("http://localhost:8002/retrieve_task_memory", json={
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"workspace_id": "appworld_v1",
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"query": "如何导航到设置并更新用户资料？",
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"top_k": 1
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})
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```
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## 📚 相关资源
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- **[快速开始](./cookbook/simple_demo/quick_start.md)**：通过实际示例快速上手
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- **[向量存储设置](./doc/vector_store_setup.md)**：生产部署指南
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- **[配置指南](./doc/configuration_guide.md)**：详细配置参考
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- **[操作文档](./doc/operations_documentation.md)**：操作配置说明
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- **[示例集合](./cookbook)**：实际用例和最佳实践
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---
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## 🤝 贡献
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我们相信最好的记忆系统来自集体智慧。欢迎贡献：
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### 代码贡献
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- 新操作和工具开发
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- 后端实现和优化
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- API增强和新端点
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### 文档改进
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- 使用示例和教程
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- 最佳实践指南
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- 翻译和本地化
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---
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## 📄 引用
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```bibtex
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@software{ReMe2025,
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title = {ReMe.ai: Memory-Driven AI Agent Framework},
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author = {The ReMe.ai Team},
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url = {https://github.com/modelscope/ReMe.ai},
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year = {2025}
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}
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```
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---
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## ⚖️ 许可证
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本项目采用Apache License 2.0许可证 - 详情请参阅[LICENSE](./LICENSE)文件。
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---
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@ -4,9 +4,9 @@ build-backend = "setuptools.build_meta"
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[project]
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name = "reme_ai"
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version = "0.1.0"
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description = "Remember me of memory and experience"
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authors = [{ name = "reme_ai_team", email = "reme_ai_team@alibaba-inc.com" }]
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version = "0.1.3"
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description = "Remember me"
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authors = [{ name = "reme_team", email = "reme_team@alibaba-inc.com" }]
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license = { file = "LICENSE" }
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readme = "README.md"
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requires-python = ">=3.12"
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[tool.setuptools.packages.find]
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where = ["."]
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include = ["reme_ai*"]
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exclude = ["memoryscope*"]
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exclude = ["memoryscope*", "test*", "cookbook*", "doc*", "library*", "dist*"]
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[tool.setuptools.package-data]
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experiencemaker = [
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"config/*.yaml",
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reme_ai = [
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"**/*.yaml",
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"**/*.py",
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"**/*.json",
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]
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[project.scripts]
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@ -3,4 +3,4 @@ from reme_ai import retrieve
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from reme_ai import summary
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from reme_ai import vector_store
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__version__ = "0.1.0"
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__version__ = "0.1.3"
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