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31 KiB
Markdown
902 lines
No EOL
31 KiB
Markdown
<p align="center">
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<img src="docs/_static/figure/reme_logo.png" alt="ReMe 标志" width="50%">
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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-3.10+-blue" alt="Python Version"></a>
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<a href="https://pypi.org/project/reme-ai/"><img src="https://img.shields.io/pypi/v/reme-ai.svg?logo=pypi" alt="PyPI Version"></a>
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<a href="https://pepy.tech/project/reme-ai/"><img src="https://img.shields.io/pypi/dm/reme-ai" alt="PyPI Downloads"></a>
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<a href="https://github.com/agentscope-ai/ReMe"><img src="https://img.shields.io/github/commit-activity/m/agentscope-ai/ReMe?style=flat-square" alt="GitHub commit activity"></a>
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</p>
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<p align="center">
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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="./README.md"><img src="https://img.shields.io/badge/English-Click-yellow" alt="English"></a>
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<a href="./README_ZH.md"><img src="https://img.shields.io/badge/简体中文-点击查看-orange" alt="简体中文"></a>
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<a href="https://github.com/agentscope-ai/ReMe"><img src="https://img.shields.io/github/stars/agentscope-ai/ReMe?style=social" alt="GitHub Stars"></a>
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</p>
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<p align="center">
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<strong>面向智能体的记忆管理工具包, Remember Me, Refine Me.</strong><br>
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<em><sub>如果 ReMe 对你有帮助，欢迎点一个 ⭐ Star，你的支持是我们持续改进的动力。</sub></em>
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</p>
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---
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ReMe 是一个**模块化的记忆管理工具包**，为 AI 智能体提供统一的记忆能力——支持在用户、任务与智能体之间提取、复用与共享记忆。
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智能体的记忆可以被视为：
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```text
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Agent Memory = Long-Term Memory + Short-Term Memory
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= (Personal + Task + Tool) Memory + (Working Memory)
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```
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- **个人记忆（Personal Memory）**：理解用户偏好并适应上下文
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- **任务记忆（Task Memory）**：从经验中学习并在类似任务中表现更好
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- **工具记忆（Tool Memory）**：基于历史表现优化工具选择和参数使用
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- **工作记忆（Working Memory）**：管理长运行智能体的短期上下文，避免上下文溢出
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---
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## 📰 最新进展
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- **[2026-02]** 💻 ReMeCli：终端 AI 聊天助手，内置记忆管理能力。当对话过长时自动将旧内容压缩为摘要以释放上下文空间，同时将重要信息以 Markdown 文件持久化存储，供未来会话自动检索使用。记忆设计灵感来源于 [OpenClaw](https://github.com/openclaw/openclaw)。
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- [快速开始](docs/cli/quick_start_en.md)
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- 输入 `/horse` 触发马年彩蛋——烟花、奔马动画和随机马年祝福。
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<table border="0" cellspacing="0" cellpadding="0" style="border: none;">
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<tr style="border: none;">
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<td width="10%" style="border: none; vertical-align: middle; text-align: center;">
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<strong>马<br>上<br>有<br>钱</strong>
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</td>
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<td width="80%" style="border: none;">
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<video src="https://github.com/user-attachments/assets/befa7e40-63ba-4db2-8251-516024616e00" autoplay muted loop controls></video>
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</td>
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<td width="10%" style="border: none; vertical-align: middle; text-align: center;">
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<strong>马<br>到<br>成<br>功</strong>
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</td>
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</tr>
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</table>
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- **[2025-12]** 📄 我们的程序性（任务）记忆论文已在 [arXiv](https://arxiv.org/abs/2512.10696) 发布
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- **[2025-11]** 🧠 基于工作记忆的 react-agent demo（[介绍](docs/work_memory/message_offload.md)、[Quick Start](docs/cookbook/working/quick_start.md)、[代码](cookbook/working_memory/work_memory_demo.py)）
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- **[2025-10]** 🚀 直接 Python 导入：支持 `from reme_ai import ReMeApp`，无需 HTTP/MCP 服务
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- **[2025-10]** 🔧 工具记忆：支持基于数据驱动的工具选择与参数优化（[指南](docs/tool_memory/tool_memory.md)）
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- **[2025-09]** 🎉 支持异步操作，并已集成至 agentscope-runtime
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- **[2025-09]** 🎉 集成任务记忆与个人记忆
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- **[2025-09]** 🧪 在 appworld、bfcl(v3)、frozenlake 等环境中验证有效性（[实验文档](docs/cookbook)）
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- **[2025-08]** 🚀 支持 MCP 协议（[快速开始](docs/mcp_quick_start.md)）
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- **[2025-06]** 🚀 支持多种向量存储后端（Elasticsearch & ChromaDB）（[向量库指南](docs/vector_store_api_guide.md)）
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- **[2024-09]** 🧠 支持个性化与时间敏感的记忆存储
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---
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## ✨ 架构设计
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<p align="center">
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<img src="docs/_static/figure/reme_structure.jpg" alt="ReMe 架构" width="80%">
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</p>
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ReMe 提供了一个**模块化的记忆管理工具包**，具有可插拔的组件，可以集成到任何智能体框架中。系统包括：
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#### 🧠 **任务记忆 / 经验记忆（Task Memory/Experience）**
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可在不同智能体之间复用的程序性知识：
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- **成功模式识别**：识别有效策略并理解其背后的原理
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- **失败分析学习**：从错误中学习，避免重复踩坑
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- **对比式模式**：通过多条采样轨迹的对比获取更有价值的记忆
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- **验证模式**：通过验证模块确认提炼出的经验是否有效
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了解如何使用任务记忆可参考：[任务记忆文档](docs/task_memory/task_memory.md)
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#### 👤 **个人记忆（Personal Memory）**
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面向特定用户的情境化长期记忆：
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- **个体偏好**：记录用户的习惯、偏好与交互风格
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- **情境自适应**：基于时间与上下文动态管理记忆
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- **渐进式学习**：在长期多轮交互中不断加深对用户的理解
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- **时间敏感**：在记忆检索与整合中考虑时间因素
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了解如何使用个人记忆可参考：[个人记忆文档](docs/personal_memory/personal_memory.md)
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#### 🔧 **工具记忆（Tool Memory）**
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基于真实调用数据的工具选择与使用优化：
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- **历史表现追踪**：记录成功率、调用耗时与 Token 成本
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- **LLM-as-Judge 评估**：提供工具成功 / 失败原因的定性洞察
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- **参数优化**：从历史成功调用中学习最优参数配置
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- **动态指南**：将静态工具描述演化为可持续更新的「活文档」
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了解如何使用工具记忆可参考：[工具记忆文档](docs/tool_memory/tool_memory.md)
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#### 🧠 **工作记忆（Working Memory）**
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面向长流程智能体的短期上下文记忆，通过**消息卸载与重载（message offload & reload）**实现：
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- **消息卸载（Message Offload）**：将体积巨大的工具输出压缩为外部文件或 LLM 摘要
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- **消息重载（Message Reload）**：按需搜索（`grep_working_memory`）并读取（`read_working_memory`）已卸载的内容
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📖 **概念与 API：**
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- 消息卸载概览：[Message Offload](docs/work_memory/message_offload.md)
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- 卸载 / 重载算子：[Message Offload Ops](docs/work_memory/message_offload_ops.md)、[Message Reload Ops](docs/work_memory/message_reload_ops.md)
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💻 **端到端 Demo：**
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- 工作记忆快速上手：[Working Memory Quick Start](docs/cookbook/working/quick_start.md)
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- 带工作记忆的 ReAct 智能体：[react_agent_with_working_memory.py](cookbook/working_memory/react_agent_with_working_memory.py)
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- 可运行 Demo：[work_memory_demo.py](cookbook/working_memory/work_memory_demo.py)
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---
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## 🛠️ 安装
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### 通过 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/agentscope-ai/ReMe.git
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cd ReMe
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pip install .
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```
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### 环境变量配置
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复制 `example.env` 为 `.env` 并按需修改：
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```bash
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FLOW_LLM_API_KEY=sk-xxxx
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FLOW_LLM_BASE_URL=https://xxxx/v1
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FLOW_EMBEDDING_API_KEY=sk-xxxx
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FLOW_EMBEDDING_BASE_URL=https://xxxx/v1
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```
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---
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## 🚀 快速开始
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### 启动 HTTP 服务
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```bash
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reme \
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backend=http \
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http.port=8002 \
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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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### 启动 MCP Server
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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 用法
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#### 任务记忆管理
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```python
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import requests
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# 经验总结：从执行轨迹中学习
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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": "Help me create a project plan"}], "score": 1.0}
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]
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})
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# 记忆检索：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": "How to efficiently manage project progress?",
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"top_k": 1
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})
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```
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<details>
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<summary>Python 导入版本</summary>
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```python
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import asyncio
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from reme_ai import ReMeApp
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async def main():
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async with ReMeApp(
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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=memory"
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) as app:
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# 经验总结：从执行轨迹中学习
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result = await app.async_execute(
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name="summary_task_memory",
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workspace_id="task_workspace",
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trajectories=[
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{
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"messages": [
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{"role": "user", "content": "Help me create a project plan"}
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],
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"score": 1.0
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}
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]
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)
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print(result)
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# 记忆检索：获取相关经验
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result = await app.async_execute(
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name="retrieve_task_memory",
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workspace_id="task_workspace",
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query="How to efficiently manage project progress?",
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top_k=1
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)
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print(result)
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if __name__ == "__main__":
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asyncio.run(main())
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```
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</details>
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<details>
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<summary>curl 版本</summary>
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```bash
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# 经验总结：从执行轨迹中学习
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curl -X POST http://localhost:8002/summary_task_memory \
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-H "Content-Type: application/json" \
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-d '{
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"workspace_id": "task_workspace",
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"trajectories": [
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{"messages": [{"role": "user", "content": "Help me create a project plan"}], "score": 1.0}
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]
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}'
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# 记忆检索：获取相关经验
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curl -X POST http://localhost:8002/retrieve_task_memory \
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-H "Content-Type: application/json" \
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-d '{
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"workspace_id": "task_workspace",
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"query": "How to efficiently manage project progress?",
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"top_k": 1
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}'
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```
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</details>
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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": "I like to drink coffee while working in the morning"},
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{"role": "assistant",
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"content": "I understand, you prefer to start your workday with coffee to stay energized"}
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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": "What are the user's work habits?",
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"top_k": 5
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})
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```
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<details>
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<summary>Python 导入版本</summary>
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```python
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import asyncio
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from reme_ai import ReMeApp
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async def main():
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async with ReMeApp(
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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=memory"
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) as app:
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# 记忆整合：从用户交互中学习
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result = await app.async_execute(
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name="summary_personal_memory",
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workspace_id="task_workspace",
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trajectories=[
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{
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"messages": [
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{"role": "user", "content": "I like to drink coffee while working in the morning"},
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{"role": "assistant",
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"content": "I understand, you prefer to start your workday with coffee to stay energized"}
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]
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}
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]
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)
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print(result)
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# 记忆检索：获取个人记忆片段
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result = await app.async_execute(
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name="retrieve_personal_memory",
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workspace_id="task_workspace",
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query="What are the user's work habits?",
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top_k=5
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)
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print(result)
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if __name__ == "__main__":
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asyncio.run(main())
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```
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</details>
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<details>
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<summary>curl 版本</summary>
|
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|
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```bash
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# 记忆整合：从用户交互中学习
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curl -X POST http://localhost:8002/summary_personal_memory \
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-H "Content-Type: application/json" \
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-d '{
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"workspace_id": "task_workspace",
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"trajectories": [
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{"messages": [
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{"role": "user", "content": "I like to drink coffee while working in the morning"},
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{"role": "assistant", "content": "I understand, you prefer to start your workday with coffee to stay energized"}
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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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curl -X POST http://localhost:8002/retrieve_personal_memory \
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-H "Content-Type: application/json" \
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-d '{
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"workspace_id": "task_workspace",
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"query": "What are the user'\''s work habits?",
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"top_k": 5
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}'
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```
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</details>
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#### 工具记忆管理
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```python
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import requests
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# 记录工具调用结果
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response = requests.post("http://localhost:8002/add_tool_call_result", json={
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"workspace_id": "tool_workspace",
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"tool_call_results": [
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{
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"create_time": "2025-10-21 10:30:00",
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"tool_name": "web_search",
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"input": {"query": "Python asyncio tutorial", "max_results": 10},
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"output": "Found 10 relevant results...",
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"token_cost": 150,
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"success": True,
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"time_cost": 2.3
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}
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]
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})
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# 从历史生成使用指南
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response = requests.post("http://localhost:8002/summary_tool_memory", json={
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"workspace_id": "tool_workspace",
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"tool_names": "web_search"
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})
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# 在使用前检索工具指南
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response = requests.post("http://localhost:8002/retrieve_tool_memory", json={
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"workspace_id": "tool_workspace",
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"tool_names": "web_search"
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})
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```
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|
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<details>
|
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<summary>Python 导入版本</summary>
|
||
|
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```python
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import asyncio
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from reme_ai import ReMeApp
|
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|
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async def main():
|
||
async with ReMeApp(
|
||
"llm.default.model_name=qwen3-30b-a3b-thinking-2507",
|
||
"embedding_model.default.model_name=text-embedding-v4",
|
||
"vector_store.default.backend=memory"
|
||
) as app:
|
||
# 记录工具调用结果
|
||
result = await app.async_execute(
|
||
name="add_tool_call_result",
|
||
workspace_id="tool_workspace",
|
||
tool_call_results=[
|
||
{
|
||
"create_time": "2025-10-21 10:30:00",
|
||
"tool_name": "web_search",
|
||
"input": {"query": "Python asyncio tutorial", "max_results": 10},
|
||
"output": "Found 10 relevant results...",
|
||
"token_cost": 150,
|
||
"success": True,
|
||
"time_cost": 2.3
|
||
}
|
||
]
|
||
)
|
||
print(result)
|
||
|
||
# 从历史生成使用指南
|
||
result = await app.async_execute(
|
||
name="summary_tool_memory",
|
||
workspace_id="tool_workspace",
|
||
tool_names="web_search"
|
||
)
|
||
print(result)
|
||
|
||
# 在使用前检索工具指南
|
||
result = await app.async_execute(
|
||
name="retrieve_tool_memory",
|
||
workspace_id="tool_workspace",
|
||
tool_names="web_search"
|
||
)
|
||
print(result)
|
||
|
||
if __name__ == "__main__":
|
||
asyncio.run(main())
|
||
```
|
||
|
||
</details>
|
||
|
||
<details>
|
||
<summary>curl 版本</summary>
|
||
|
||
```bash
|
||
# 记录工具调用结果
|
||
curl -X POST http://localhost:8002/add_tool_call_result \
|
||
-H "Content-Type: application/json" \
|
||
-d '{
|
||
"workspace_id": "tool_workspace",
|
||
"tool_call_results": [
|
||
{
|
||
"create_time": "2025-10-21 10:30:00",
|
||
"tool_name": "web_search",
|
||
"input": {"query": "Python asyncio tutorial", "max_results": 10},
|
||
"output": "Found 10 relevant results...",
|
||
"token_cost": 150,
|
||
"success": true,
|
||
"time_cost": 2.3
|
||
}
|
||
]
|
||
}'
|
||
|
||
# 从历史生成使用指南
|
||
curl -X POST http://localhost:8002/summary_tool_memory \
|
||
-H "Content-Type: application/json" \
|
||
-d '{
|
||
"workspace_id": "tool_workspace",
|
||
"tool_names": "web_search"
|
||
}'
|
||
|
||
# 在使用前检索工具指南
|
||
curl -X POST http://localhost:8002/retrieve_tool_memory \
|
||
-H "Content-Type: application/json" \
|
||
-d '{
|
||
"workspace_id": "tool_workspace",
|
||
"tool_names": "web_search"
|
||
}'
|
||
```
|
||
|
||
</details>
|
||
|
||
#### 工作记忆管理
|
||
|
||
```python
|
||
import requests
|
||
|
||
# 对长对话 / 长流程的工作记忆进行压缩与总结
|
||
response = requests.post("http://localhost:8002/summary_working_memory", json={
|
||
"messages": [
|
||
{
|
||
"role": "system",
|
||
"content": "You are a helpful assistant. First use `Grep` to find the line numbers that match the keywords or regular expressions, and then use `ReadFile` to read the code around those locations. If no matches are found, never give up; try different parameters, such as searching with only part of the keywords. After `Grep`, use the `ReadFile` command to view content starting from a specified `offset` and `limit`, and do not exceed 100 lines. If the current content is insufficient, you can continue trying different `offset` and `limit` values with the `ReadFile` command."
|
||
},
|
||
{
|
||
"role": "user",
|
||
"content": "搜索下reme项目的的README内容"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"tool_calls": [
|
||
{
|
||
"index": 0,
|
||
"id": "call_6596dafa2a6a46f7a217da",
|
||
"function": {
|
||
"arguments": "{\"query\": \"readme\"}",
|
||
"name": "web_search"
|
||
},
|
||
"type": "function"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "ultra large context , over 50000 tokens......"
|
||
},
|
||
{
|
||
"role": "user",
|
||
"content": "根据readme回答task memory在appworld的效果是多少，需要具体的数值"
|
||
}
|
||
],
|
||
"working_summary_mode": "auto",
|
||
"compact_ratio_threshold": 0.75,
|
||
"max_total_tokens": 20000,
|
||
"max_tool_message_tokens": 2000,
|
||
"group_token_threshold": 4000,
|
||
"keep_recent_count": 2,
|
||
"store_dir": "test_working_memory",
|
||
"chat_id": "demo_chat_id"
|
||
})
|
||
```
|
||
|
||
<details>
|
||
<summary>Python 导入版本</summary>
|
||
|
||
```python
|
||
import asyncio
|
||
from reme_ai import ReMeApp
|
||
|
||
|
||
async def main():
|
||
async with ReMeApp(
|
||
"llm.default.model_name=qwen3-30b-a3b-thinking-2507",
|
||
"embedding_model.default.model_name=text-embedding-v4",
|
||
"vector_store.default.backend=memory"
|
||
) as app:
|
||
# 对长对话 / 长流程的工作记忆进行压缩与总结
|
||
result = await app.async_execute(
|
||
name="summary_working_memory",
|
||
messages=[
|
||
{
|
||
"role": "system",
|
||
"content": "You are a helpful assistant. First use `Grep` to find the line numbers that match the keywords or regular expressions, and then use `ReadFile` to read the code around those locations. If no matches are found, never give up; try different parameters, such as searching with only part of the keywords. After `Grep`, use the `ReadFile` command to view content starting from a specified `offset` and `limit`, and do not exceed 100 lines. If the current content is insufficient, you can continue trying different `offset` and `limit` values with the `ReadFile` command."
|
||
},
|
||
{
|
||
"role": "user",
|
||
"content": "搜索下reme项目的的README内容"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"tool_calls": [
|
||
{
|
||
"index": 0,
|
||
"id": "call_6596dafa2a6a46f7a217da",
|
||
"function": {
|
||
"arguments": "{\"query\": \"readme\"}",
|
||
"name": "web_search"
|
||
},
|
||
"type": "function"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "ultra large context , over 50000 tokens......"
|
||
},
|
||
{
|
||
"role": "user",
|
||
"content": "根据readme回答task memory在appworld的效果是多少，需要具体的数值"
|
||
}
|
||
],
|
||
working_summary_mode="auto",
|
||
compact_ratio_threshold=0.75,
|
||
max_total_tokens=20000,
|
||
max_tool_message_tokens=2000,
|
||
group_token_threshold=4000,
|
||
keep_recent_count=2,
|
||
store_dir="test_working_memory",
|
||
chat_id="demo_chat_id",
|
||
)
|
||
print(result)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
asyncio.run(main())
|
||
```
|
||
|
||
</details>
|
||
|
||
<details>
|
||
<summary>curl 版本</summary>
|
||
|
||
```bash
|
||
curl -X POST http://localhost:8002/summary_working_memory \
|
||
-H "Content-Type: application/json" \
|
||
-d '{
|
||
"messages": [
|
||
{
|
||
"role": "system",
|
||
"content": "You are a helpful assistant. First use `Grep` to find the line numbers that match the keywords or regular expressions, and then use `ReadFile` to read the code around those locations. If no matches are found, never give up; try different parameters, such as searching with only part of the keywords. After `Grep`, use the `ReadFile` command to view content starting from a specified `offset` and `limit`, and do not exceed 100 lines. If the current content is insufficient, you can continue trying different `offset` and `limit` values with the `ReadFile` command."
|
||
},
|
||
{
|
||
"role": "user",
|
||
"content": "搜索下reme项目的的README内容"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"tool_calls": [
|
||
{
|
||
"index": 0,
|
||
"id": "call_6596dafa2a6a46f7a217da",
|
||
"function": {
|
||
"arguments": "{\"query\": \"readme\"}",
|
||
"name": "web_search"
|
||
},
|
||
"type": "function"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "ultra large context , over 50000 tokens......"
|
||
},
|
||
{
|
||
"role": "user",
|
||
"content": "根据readme回答task memory在appworld的效果是多少，需要具体的数值"
|
||
}
|
||
],
|
||
"working_summary_mode": "auto",
|
||
"compact_ratio_threshold": 0.75,
|
||
"max_total_tokens": 20000,
|
||
"max_tool_message_tokens": 2000,
|
||
"group_token_threshold": 4000,
|
||
"keep_recent_count": 2,
|
||
"store_dir": "test_working_memory",
|
||
"chat_id": "demo_chat_id"
|
||
}'
|
||
```
|
||
|
||
</details>
|
||
|
||
---
|
||
|
||
## 📦 开箱即用的记忆库
|
||
|
||
ReMe 提供一个**记忆库**，包含预先提取的、生产就绪的记忆，智能体可以立即加载和使用：
|
||
|
||
### 可用记忆包
|
||
|
||
| 记忆包 | 领域 | 规模 | 描述 |
|
||
|----------------------|------------|----------------|--------------------------------------------------------|
|
||
| **`appworld.jsonl`** | 任务执行 | ~100 条记忆 | 复杂任务规划模式、多步骤工作流和错误恢复策略 |
|
||
| **`bfcl_v3.jsonl`** | 工具使用 | ~150 条记忆 | 函数调用模式、参数优化和工具选择策略 |
|
||
|
||
### 加载预构建记忆
|
||
|
||
```python
|
||
# 加载内置记忆
|
||
response = requests.post("http://localhost:8002/vector_store", json={
|
||
"workspace_id": "appworld",
|
||
"action": "load",
|
||
"path": "./docs/library/"
|
||
})
|
||
|
||
# 查询相关记忆
|
||
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
|
||
})
|
||
```
|
||
|
||
<details>
|
||
<summary>Python 导入版本</summary>
|
||
|
||
```python
|
||
import asyncio
|
||
from reme_ai import ReMeApp
|
||
|
||
async def main():
|
||
async with ReMeApp(
|
||
"llm.default.model_name=qwen3-30b-a3b-thinking-2507",
|
||
"embedding_model.default.model_name=text-embedding-v4",
|
||
"vector_store.default.backend=memory"
|
||
) as app:
|
||
# 加载内置记忆
|
||
result = await app.async_execute(
|
||
name="vector_store",
|
||
workspace_id="appworld",
|
||
action="load",
|
||
path="./docs/library/"
|
||
)
|
||
print(result)
|
||
|
||
# 查询相关记忆
|
||
result = await app.async_execute(
|
||
name="retrieve_task_memory",
|
||
workspace_id="appworld",
|
||
query="How to navigate to settings and update user profile?",
|
||
top_k=1
|
||
)
|
||
print(result)
|
||
|
||
if __name__ == "__main__":
|
||
asyncio.run(main())
|
||
```
|
||
|
||
</details>
|
||
|
||
---
|
||
|
||
## 🧪 实验结果
|
||
|
||
### 🌍 [Appworld 实验](docs/cookbook/appworld/quickstart.md)
|
||
|
||
我们在 Appworld 环境上使用 Qwen3-8B（非思考模式）进行评测：
|
||
|
||
| 方法 | Avg@4 | Pass@4 |
|
||
|-----------|-------------------|-------------------|
|
||
| 无 ReMe | 0.1497 | 0.3285 |
|
||
| 使用 ReMe | 0.1706 **(+2.09%)** | 0.3631 **(+3.46%)** |
|
||
|
||
Pass@K 衡量在生成 K 个候选中，至少一个成功完成任务（score=1）的概率。
|
||
当前实验使用的是内部 AppWorld 环境，可能与对外版本存在轻微差异。
|
||
|
||
关于如何复现实验的更多细节，见 [quickstart.md](docs/cookbook/appworld/quickstart.md)。
|
||
|
||
### 🔧 [BFCL-V3 实验](docs/cookbook/bfcl/quickstart.md)
|
||
|
||
我们在 BFCL-V3 multi-turn-base 任务（随机划分 50 train / 150 val）上，使用 Qwen3-8B（思考模式）进行评测：
|
||
|
||
| 方法 | Avg@4 | Pass@4 |
|
||
|------------|-----------------|---------------------|
|
||
| 无 ReMe | 0.4033 | 0.5955 |
|
||
| 使用 ReMe | 0.4450 **(+4.17%)** | 0.6577 **(+6.22%)** |
|
||
|
||
### 🧊 [Frozenlake 实验](docs/cookbook/frozenlake/quickstart.md)
|
||
|
||
| 无 ReMe | 使用 ReMe |
|
||
|:------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------:|
|
||
| <p align="center"><img src="docs/_static/figure/frozenlake_failure.gif" alt="失败示例" width="30%"></p> | <p align="center"><img src="docs/_static/figure/frozenlake_success.gif" alt="成功示例" width="30%"></p> |
|
||
|
||
我们在 100 张随机 frozenlake 地图上，使用 qwen3-8b 进行测试：
|
||
|
||
| 方法 | 通过率 |
|
||
|------------|-----------------|
|
||
| 无 ReMe | 0.66 |
|
||
| 使用 ReMe | 0.72 **(+6.0%)** |
|
||
|
||
更多复现实验细节见 [quickstart.md](docs/cookbook/frozenlake/quickstart.md)。
|
||
|
||
### 🛠️ [工具记忆基准](docs/tool_memory/tool_bench.md)
|
||
|
||
我们在一个受控基准上，使用三个模拟搜索工具与 Qwen3-30B-Instruct 评估工具记忆的效果：
|
||
|
||
| 场景 | 平均分 | 提升 |
|
||
|-----------------------|--------|------------|
|
||
| 训练集（无记忆） | 0.650 | - |
|
||
| 测试集（无记忆） | 0.672 | 基线 |
|
||
| **测试集（使用记忆）** | **0.772** | **+14.88%** |
|
||
|
||
**关键结论：**
|
||
- 工具记忆可以基于历史表现进行数据驱动的工具选择
|
||
- 通过学习参数配置，成功率约提升 15%
|
||
|
||
更多细节见 [tool_bench.md](docs/tool_memory/tool_bench.md) 与实现代码 [run_reme_tool_bench.py](cookbook/tool_memory/run_reme_tool_bench.py)。
|
||
|
||
---
|
||
|
||
## 📚 资源
|
||
|
||
### 快速入门
|
||
- **[Quick Start](./cookbook/simple_demo)**：实用示例，可立即使用
|
||
- [工具记忆 Demo](cookbook/simple_demo/use_tool_memory_demo.py)：工具记忆的完整生命周期演示
|
||
- [工具记忆基准](cookbook/tool_memory/run_reme_tool_bench.py)：评估工具记忆效果
|
||
|
||
### 集成指南
|
||
- **[直接 Python 导入](docs/cookbook/working/quick_start.md)**：将 ReMe 直接嵌入到你的智能体代码中
|
||
- **[HTTP 服务 API](docs/vector_store_api_guide.md)**：用于多智能体系统的 RESTful API
|
||
- **[MCP 协议](docs/mcp_quick_start.md)**：与 Claude Desktop 和 MCP 兼容客户端集成
|
||
|
||
### 记忆系统配置
|
||
- **[个人记忆](docs/personal_memory)**：用户偏好学习和上下文自适应
|
||
- **[任务记忆](docs/task_memory)**：程序性知识提取和复用
|
||
- **[工具记忆](docs/tool_memory)**：数据驱动的工具选择和优化
|
||
- **[工作记忆](docs/work_memory/message_offload.md)**：长流程智能体的短期上下文管理
|
||
|
||
### 高级主题
|
||
- **[算子管道](reme_ai/config/default.yaml)**：通过修改算子链来自定义记忆处理工作流
|
||
- **[向量存储后端](docs/vector_store_api_guide.md)**：配置本地、Elasticsearch、Qdrant 或 ChromaDB 存储
|
||
- **[案例集](./cookbook)**：真实场景的用例和最佳实践
|
||
|
||
---
|
||
|
||
## ⭐ 社区与支持
|
||
|
||
- **Star & Watch**：Star 可以让更多智能体开发者发现 ReMe；Watch 能帮助你第一时间获知新版本与特性。
|
||
- **分享你的成果**：在 Issue 或 Discussion 中分享 ReMe 为你的智能体解锁了什么——我们非常乐意展示社区的优秀案例。
|
||
- **需要新功能？** 提交 Feature Request，我们将一起完善它。
|
||
|
||
---
|
||
|
||
## 🤝 参与贡献
|
||
|
||
我们相信，最好的记忆系统来自社区的集体智慧。欢迎贡献 👉[贡献指南](docs/contribution.md)：
|
||
|
||
### 代码贡献
|
||
|
||
- **新算子**：开发自定义记忆处理算子（检索、总结等）
|
||
- **后端实现**：添加对新向量存储或 LLM 提供商的支持
|
||
- **记忆服务**：扩展新的记忆类型或能力
|
||
- **API 增强**：改进现有端点或添加新端点
|
||
|
||
### 文档改进
|
||
|
||
- **集成示例**：展示如何将 ReMe 与不同智能体框架集成
|
||
- **算子教程**：记录自定义算子开发
|
||
- **最佳实践指南**：分享有效的记忆管理模式
|
||
- **用例研究**：展示 ReMe 在实际应用中的使用
|
||
|
||
---
|
||
|
||
## 📄 引用
|
||
|
||
```bibtex
|
||
@software{AgentscopeReMe2025,
|
||
title = {AgentscopeReMe: Memory Management Kit for Agents},
|
||
author = {Li Yu and
|
||
Jiaji Deng and
|
||
Zouying Cao and
|
||
Weikang Zhou and
|
||
Tiancheng Qin and
|
||
Qingxu Fu and
|
||
Sen Huang and
|
||
Xianzhe Xu and
|
||
Zhaoyang Liu and
|
||
Boyin Liu},
|
||
url = {https://reme.agentscope.io},
|
||
year = {2025}
|
||
}
|
||
|
||
@misc{AgentscopeReMe2025Paper,
|
||
title={Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution},
|
||
author={Zouying Cao and
|
||
Jiaji Deng and
|
||
Li Yu and
|
||
Weikang Zhou and
|
||
Zhaoyang Liu and
|
||
Bolin Ding and
|
||
Hai Zhao},
|
||
year={2025},
|
||
eprint={2512.10696},
|
||
archivePrefix={arXiv},
|
||
primaryClass={cs.AI},
|
||
url={https://arxiv.org/abs/2512.10696},
|
||
}
|
||
```
|
||
|
||
---
|
||
|
||
## ⚖️ 许可证
|
||
|
||
本项目基于 Apache License 2.0 开源，详情参见 [LICENSE](./LICENSE) 文件。
|
||
|
||
---
|
||
|
||
## Star 历史
|
||
|
||
[](https://www.star-history.com/#agentscope-ai/ReMe&Date) |