mirror of
https://github.com/agentscope-ai/ReMe.git
synced 2026-08-28 05:25:04 +00:00
feat(docs): add tool memory documentation and update ReMe memory model
- Introduce tool memory as a new memory type in ReMe framework - Update agent memory formula to include tool memory- Add comprehensive tool memory documentation with usage examples - Include tool memory API usage guides for Python, curl, and Node.js - Add tool memory benchmark results showing 14.88% improvement- Create new tool memory demo and benchmark resources - Update documentation structure to reflect three memory capabilities- Add TODO list with planned features including automatic tool exploration- Fix mermaid diagram numbering in tool memory documentation - Remove outdated latest updates section from main documentation - Update resource links to include tool memory references
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jinli.yl
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English | [**中文**](./README_ZH.md)
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<p align="center">
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<img src="docs/figure/reme_logo.png" alt="ReMe Logo" width="50%">
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</p>
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@ -551,6 +549,8 @@ You can find more details in [tool_bench.md](docs/tool_memory/tool_bench.md) and
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## 📚 Resources
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- **[Quick Start](./cookbook/simple_demo)**: Get started quickly with practical examples
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- [Tool Memory Demo](cookbook/simple_demo/use_tool_memory_demo.py): Complete lifecycle demonstration of tool memory
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- [Tool Memory Benchmark](cookbook/tool_memory/run_reme_tool_bench.py): Evaluate tool memory effectiveness
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- **[Vector Storage Setup](docs/vector_store_api_guide.md)**: Configure local/vector databases and usage
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- **[MCP Guide](docs/mcp_quick_start.md)**: Create MCP services
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- **[Personal Memory](docs/personal_memory)**, **[Task Memory](docs/task_memory)** & **[Tool Memory](docs/tool_memory)**: Operators used in personal memory, task memory and tool memory. You can modify the config to customize the pipelines.
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412
README_ZH.md
412
README_ZH.md
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@ -1,412 +0,0 @@
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中文 | [**English**](./README.md)
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<p align="center">
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<img src="docs/figure/reme_logo.png" alt="ReMe Logo" 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-3.12+-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-v0.1-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"><img src="https://img.shields.io/github/stars/modelscope/ReMe?style=social" alt="GitHub Stars"></a>
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</p>
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<p align="center">
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<strong>ReMe (formerly MemoryScope)：为Agent设计的记忆管理框架</strong><br>
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<em>Remember Me, Refine Me.</em>
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</p>
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---
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ReMe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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个性化记忆 (Personal Memory) + 任务经验 (Task Memory)= agent记忆
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```
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个性化记忆能够"**理解用户偏好**"，任务记忆让agent"**做得更好**"，
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---
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## 📰 最新动态
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- **[2025-09]** 🎉 ReMe v0.1
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正式发布，整合任务记忆与个人记忆。如果想使用原始的memoryscope项目，你可以在[MemoryScope](https://github.com/modelscope/Reme/tree/memoryscope_branch)
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中找到。
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- **[2025-09]** 🧪 我们在appworld, bfcl(v3)
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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](docs/cookbook/appworld/quickstart.md), [bfcl exp](docs/cookbook/bfcl/quickstart.md)
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和 [frozenlake exp](docs/cookbook/frozenlake/quickstart.md)。
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- **[2025-08]** 🚀 MCP协议支持已上线-> [MCP指南](docs/mcp_quick_start.md)。
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- **[2025-06]** 🚀 多后端向量存储支持 (Elasticsearch & ChromaDB) -> [向量数据库指南](docs/vector_store_api_guide.md)。
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- **[2024-09]** 🧠 [MemoryScope](https://github.com/modelscope/Reme/tree/memoryscope_branch) v0.1 发布，个性化和时间感知的记忆存储与使用。
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---
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## ✨ 功能设计
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<p align="center">
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<img src="docs/figure/reme_structure.jpg" alt="ReMe Logo" width="100%">
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</p>
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ReMe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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- **对比模式**：不同采样轨迹通过对比得到更有价值的经验
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- **验证模式**：经过验证模块确认抽取记忆的有效性
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你可以从[task memory](docs/task_memory/task_memory.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[personal memory](docs/personal_memory/personal_memory.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.git
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cd ReMe
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pip install .
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```
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### 环境配置
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复制 `example.env` 为 .env并修改其中对应参数：
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```bash
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FLOW_APP_NAME=ReMe
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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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### 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=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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### 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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#### 任务记忆管理
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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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<details>
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<summary>curl 版本</summary>
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```bash
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# 经验总结器：从执行轨迹学习
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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": "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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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": "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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</details>
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<details>
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<summary>Node.js 版本</summary>
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```javascript
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// 经验总结器：从执行轨迹学习
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fetch("http://localhost:8002/summary_task_memory", {
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method: "POST",
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headers: {
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"Content-Type": "application/json",
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},
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body: JSON.stringify({
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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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})
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.then(response => response.json())
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.then(data => console.log(data));
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// 经验检索器：获取相关经验
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fetch("http://localhost:8002/retrieve_task_memory", {
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method: "POST",
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headers: {
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"Content-Type": "application/json",
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},
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body: JSON.stringify({
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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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.then(response => response.json())
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.then(data => console.log(data));
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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": "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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<details>
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<summary>curl 版本</summary>
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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": "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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# 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": "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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</details>
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<details>
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<summary>Node.js 版本</summary>
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```javascript
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// 记忆整合：从用户交互中学习
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fetch("http://localhost:8002/summary_personal_memory", {
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method: "POST",
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headers: {
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"Content-Type": "application/json",
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},
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body: JSON.stringify({
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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: "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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.then(response => response.json())
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.then(data => console.log(data));
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// 记忆检索：获取个人记忆片段
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fetch("http://localhost:8002/retrieve_personal_memory", {
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method: "POST",
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headers: {
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"Content-Type": "application/json",
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},
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body: JSON.stringify({
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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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.then(response => response.json())
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.then(data => console.log(data));
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```
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</details>
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---
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## 📦 即用型经验库
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ReMe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.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_v3.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",
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"action": "load",
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"path": "./docs/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",
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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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## 🧪 实验
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### 🌍 [Appworld 实验](docs/cookbook/appworld/quickstart.md)
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|
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我们在 Appworld 上使用 qwen3-8b 测试 ReMe：
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|
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| 方法 | pass@1 | pass@2 | pass@4 |
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|--------------|-------------------|-------------------|-------------------|
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| without ReMe | 0.083 | 0.140 | 0.228 |
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| with ReMe | 0.109 **(+2.6%)** | 0.175 **(+3.5%)** | 0.281 **(+5.3%)** |
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|
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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务（score=1）的概率。
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当前实验使用的是一个内部的 AppWorld 环境，可能存在轻微差异。
|
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|
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你可以在 [quickstart.md](docs/cookbook/appworld/quickstart.md) 中找到复现实验的更多细节。
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|
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|
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### 🧊 [Frozenlake 实验](docs/cookbook/frozenlake/quickstart.md)
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|
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| 不使用ReMe | 使用ReMe |
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||||
|:--------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------:|
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||||
| <p align="center"><img src="docs/figure/frozenlake_failure.gif" alt="GIF 1" width="30%"></p> | <p align="center"><img src="docs/figure/frozenlake_success.gif" alt="GIF 2" width="30%"></p> |
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|
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我们在 100 个随机 frozenlake 地图上使用 qwen3-8b 进行测试：
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|
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| 方法 | pass rate |
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|--------------|------------------|
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| without ReMe | 0.66 |
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| with ReMe | 0.72 **(+6.0%)** |
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|
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你可以在 [quickstart.md](docs/cookbook/frozenlake/quickstart.md) 中找到复现实验的更多细节。
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### 🔧 [BFCL-V3 实验](docs/cookbook/bfcl/quickstart.md)
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|
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我们在 BFCL-V3 multi-turn-base (随机划分50train/150val) 上使用 qwen3-8b 测试 ReMe：
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|
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| 方法 | pass@1 | pass@2 | pass@4 |
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|--------------|---------------------|---------------------|---------------------|
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| without ReMe | 0.2472 | 0.2733 | 0.2922 |
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| with ReMe | 0.3061 **(+5.89%)** | 0.3500 **(+7.67%)** | 0.3888 **(+9.66%)** |
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|
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## 📚 相关资源
|
||||
|
||||
- **[快速开始](./cookbook/simple_demo)**：通过实际示例快速上手
|
||||
- **[向量存储设置](docs/vector_store_api_guide.md)**：配置本地/向量数据库以及使用
|
||||
- **[mcp指南](docs/mcp_quick_start.md)**：创建mcp服务
|
||||
- **[个性化记忆](docs/personal_memory)** 与 [任务记忆](docs/task_memory): 个性化记忆与任务记忆中分别使用的算子及其含义，你可以修改config以自定义链路
|
||||
- **[示例集合](./cookbook)**：实际用例和最佳实践
|
||||
|
||||
---
|
||||
|
||||
## 🤝 贡献
|
||||
|
||||
我们相信最好的记忆系统来自集体智慧。欢迎贡献👉[指南](docs/contribution.md)：
|
||||
|
||||
### 代码贡献
|
||||
- 新操作和工具开发
|
||||
- 后端实现和优化
|
||||
- API增强和新端点
|
||||
|
||||
### 文档改进
|
||||
- 使用示例和教程
|
||||
- 最佳实践指南
|
||||
|
||||
---
|
||||
|
||||
## 📄 引用
|
||||
|
||||
```bibtex
|
||||
@software{ReMe2025,
|
||||
title = {ReMe: Memory Management Framework for Agents},
|
||||
author = {Li Yu, Jiaji Deng, Zouying Cao},
|
||||
url = {https://github.com/modelscope/ReMe},
|
||||
year = {2025}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## ⚖️ 许可证
|
||||
|
||||
本项目采用Apache License 2.0许可证 - 详情请参阅[LICENSE](./LICENSE)文件。
|
||||
|
||||
---
|
||||
|
||||
## Star 历史
|
||||
[](https://www.star-history.com/#modelscope/ReMe&Date)
|
||||
190
docs/index.md
190
docs/index.md
|
|
@ -26,29 +26,10 @@ ReMe provides AI agents with a unified memory system—enabling the ability to e
|
|||
users, tasks, and agents.
|
||||
|
||||
```
|
||||
Personal Memory + Task Memory = Agent Memory
|
||||
Personal Memory + Task Memory + Tool Memory = Agent Memory
|
||||
```
|
||||
|
||||
Personal memory helps "**understand user preferences**", while task memory helps agents "**perform better**".
|
||||
|
||||
---
|
||||
|
||||
## 📰 Latest Updates
|
||||
|
||||
- **[2025-09]** 🎉 ReMe v0.1.8 has been officially released, adding support for asynchronous operations. It has also been
|
||||
integrated into the memory service of agentscope-runtime.
|
||||
- **[2025-09]** 🎉 ReMe v0.1 officially released, integrating task memory and personal memory. If you want to use the
|
||||
original memoryscope project, you can find it
|
||||
in [MemoryScope](https://github.com/modelscope/Reme/tree/memoryscope_branch).
|
||||
- **[2025-09]** 🧪 We validated the effectiveness of task memory extraction and reuse in agents in appworld, bfcl(v3),
|
||||
and frozenlake environments. For more information,
|
||||
check [appworld exp](./cookbook/appworld/quickstart.md), [bfcl exp](./cookbook/bfcl/quickstart.md),
|
||||
and [frozenlake exp](./cookbook/frozenlake/quickstart.md).
|
||||
- **[2025-08]** 🚀 MCP protocol support is now available -> [MCP Quick Start](mcp_quick_start.md).
|
||||
- **[2025-06]** 🚀 Multiple backend vector storage support (Elasticsearch &
|
||||
ChromaDB) -> [Vector DB quick start](vector_store_api_guide.md).
|
||||
- **[2024-09]** 🧠 [MemoryScope](https://github.com/modelscope/Reme/tree/memoryscope_branch) v0.1 released,
|
||||
personalized and time-aware memory storage and usage.
|
||||
Personal memory helps "**understand user preferences**", task memory helps agents "**perform better**", and tool memory enables "**smarter tool usage**".
|
||||
|
||||
---
|
||||
|
||||
|
|
@ -58,7 +39,7 @@ Personal memory helps "**understand user preferences**", while task memory helps
|
|||
<img src="figure/reme_structure.jpg" alt="ReMe Logo" width="100%">
|
||||
</p>
|
||||
|
||||
ReMe integrates two complementary memory capabilities:
|
||||
ReMe integrates three complementary memory capabilities:
|
||||
|
||||
#### 🧠 **Task Memory/Experience**
|
||||
|
||||
|
|
@ -82,6 +63,17 @@ Contextualized memory for specific users
|
|||
|
||||
Learn more about how to use personal memory from [personal memory](personal_memory/personal_memory.md)
|
||||
|
||||
#### 🔧 **Tool Memory**
|
||||
|
||||
Data-driven tool selection and usage optimization
|
||||
|
||||
- **Historical Performance Tracking**: Success rates, execution times, and token costs from real usage
|
||||
- **LLM-as-Judge Evaluation**: Qualitative insights on why tools succeed or fail
|
||||
- **Parameter Optimization**: Learn optimal parameter configurations from successful calls
|
||||
- **Dynamic Guidelines**: Transform static tool descriptions into living, learned manuals
|
||||
|
||||
Learn more about how to use tool memory from [tool memory](tool_memory/tool_memory.md)
|
||||
|
||||
---
|
||||
|
||||
## 🛠️ Installation
|
||||
|
|
@ -320,6 +312,140 @@ fetch("http://localhost:8002/retrieve_personal_memory", {
|
|||
|
||||
</details>
|
||||
|
||||
#### Tool Memory Management
|
||||
|
||||
```python
|
||||
import requests
|
||||
|
||||
# Record tool execution results
|
||||
response = requests.post("http://localhost:8002/add_tool_call_result", json={
|
||||
"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
|
||||
}
|
||||
]
|
||||
})
|
||||
|
||||
# Generate usage guidelines from history
|
||||
response = requests.post("http://localhost:8002/summary_tool_memory", json={
|
||||
"workspace_id": "tool_workspace",
|
||||
"tool_names": "web_search"
|
||||
})
|
||||
|
||||
# Retrieve tool guidelines before use
|
||||
response = requests.post("http://localhost:8002/retrieve_tool_memory", json={
|
||||
"workspace_id": "tool_workspace",
|
||||
"tool_names": "web_search"
|
||||
})
|
||||
```
|
||||
|
||||
<details>
|
||||
<summary>curl version</summary>
|
||||
|
||||
```bash
|
||||
# Record tool execution results
|
||||
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
|
||||
}
|
||||
]
|
||||
}'
|
||||
|
||||
# Generate usage guidelines from history
|
||||
curl -X POST http://localhost:8002/summary_tool_memory \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"workspace_id": "tool_workspace",
|
||||
"tool_names": "web_search"
|
||||
}'
|
||||
|
||||
# Retrieve tool guidelines before use
|
||||
curl -X POST http://localhost:8002/retrieve_tool_memory \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"workspace_id": "tool_workspace",
|
||||
"tool_names": "web_search"
|
||||
}'
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>Node.js version</summary>
|
||||
|
||||
```javascript
|
||||
// Record tool execution results
|
||||
fetch("http://localhost:8002/add_tool_call_result", {
|
||||
method: "POST",
|
||||
headers: {
|
||||
"Content-Type": "application/json",
|
||||
},
|
||||
body: JSON.stringify({
|
||||
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
|
||||
}
|
||||
]
|
||||
})
|
||||
})
|
||||
.then(response => response.json())
|
||||
.then(data => console.log(data));
|
||||
|
||||
// Generate usage guidelines from history
|
||||
fetch("http://localhost:8002/summary_tool_memory", {
|
||||
method: "POST",
|
||||
headers: {
|
||||
"Content-Type": "application/json",
|
||||
},
|
||||
body: JSON.stringify({
|
||||
workspace_id: "tool_workspace",
|
||||
tool_names: "web_search"
|
||||
})
|
||||
})
|
||||
.then(response => response.json())
|
||||
.then(data => console.log(data));
|
||||
|
||||
// Retrieve tool guidelines before use
|
||||
fetch("http://localhost:8002/retrieve_tool_memory", {
|
||||
method: "POST",
|
||||
headers: {
|
||||
"Content-Type": "application/json",
|
||||
},
|
||||
body: JSON.stringify({
|
||||
workspace_id: "tool_workspace",
|
||||
tool_names: "web_search"
|
||||
})
|
||||
})
|
||||
.then(response => response.json())
|
||||
.then(data => console.log(data));
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
---
|
||||
|
||||
## 📦 Ready-to-Use Libraries
|
||||
|
|
@ -391,12 +517,30 @@ We tested ReMe on BFCL-V3 multi-turn-base (randomly split 50train/150val) using
|
|||
| without ReMe | 0.2472 | 0.2733 | 0.2922 |
|
||||
| with ReMe | 0.3061 **(+5.89%)** | 0.3500 **(+7.67%)** | 0.3888 **(+9.66%)** |
|
||||
|
||||
### 🛠️ [Tool Memory Benchmark](tool_memory/tool_bench.md)
|
||||
|
||||
We evaluated Tool Memory effectiveness using a controlled benchmark with three mock search tools using Qwen3-30B-Instruct:
|
||||
|
||||
| Scenario | Avg Score | Improvement |
|
||||
|-----------------------|-----------|--------------------|
|
||||
| Train (No Memory) | 0.650 | - |
|
||||
| Test (No Memory) | 0.672 | Baseline |
|
||||
| **Test (With Memory)** | **0.772** | **+14.88%** |
|
||||
|
||||
**Key Findings:**
|
||||
- Tool Memory enables data-driven tool selection based on historical performance
|
||||
- Success rates improved by ~15% with learned parameter configurations
|
||||
|
||||
You can find more details in [tool_bench.md](tool_memory/tool_bench.md) and the implementation at [run_reme_tool_bench.py](https://github.com/modelscope/ReMe/tree/main/cookbook/tool_memory/run_reme_tool_bench.py).
|
||||
|
||||
## 📚 Resources
|
||||
|
||||
- **[Quick Start](https://github.com/modelscope/ReMe/tree/main/cookbook/simple_demo)**: Get started quickly with practical examples
|
||||
- [Tool Memory Demo](https://github.com/modelscope/ReMe/tree/main/cookbook/simple_demo/use_tool_memory_demo.py): Complete lifecycle demonstration of tool memory
|
||||
- [Tool Memory Benchmark](https://github.com/modelscope/ReMe/tree/main/cookbook/tool_memory/run_reme_tool_bench.py): Evaluate tool memory effectiveness
|
||||
- **[Vector Storage Setup](vector_store_api_guide.md)**: Configure local/vector databases and usage
|
||||
- **[MCP Guide](mcp_quick_start.md)**: Create MCP services
|
||||
- **[personal memory](personal_memory/personal_memory.md)** & **[task memory](task_memory/task_memory.md)** : Operators used in personal memory and task memory, You can modify the config to customize the pipelines.
|
||||
- **[Personal Memory](personal_memory/personal_memory.md)**, **[Task Memory](task_memory/task_memory.md)** & **[Tool Memory](tool_memory/tool_memory.md)**: Operators used in personal memory, task memory and tool memory. You can modify the config to customize the pipelines.
|
||||
- **[Example Collection](./cookbook/appworld/quickstart.md)**: Real use cases and best practices
|
||||
|
||||
---
|
||||
|
|
|
|||
25
docs/todo.md
Normal file
25
docs/todo.md
Normal file
|
|
@ -0,0 +1,25 @@
|
|||
# TODO
|
||||
|
||||
## Planned Features
|
||||
|
||||
### 1. Automatic Tool Exploration Mode
|
||||
Add an automatic tool exploration mode that generates tool memory by:
|
||||
- Automatically discovering and testing available tools
|
||||
- Learning tool usage patterns and best practices
|
||||
- Building a comprehensive tool memory database from exploration results
|
||||
|
||||
### 2. Desktop Pet Personal Assistant
|
||||
Build a desktop pet personal assistant with:
|
||||
- Interactive desktop companion interface
|
||||
- Personalized assistance capabilities
|
||||
- Integration with ReMe's memory system
|
||||
|
||||
### 3. Task Memory Research Implementation
|
||||
We are currently working on implementing features based on task memory research papers. Coming soon.
|
||||
|
||||
### 4. Mem-Agent Exploration
|
||||
We are exploring mem-agent to implement agentic memory pathways:
|
||||
- Investigating agent-driven memory management
|
||||
- Developing autonomous memory retrieval and storage mechanisms
|
||||
- Building more intelligent memory update strategies
|
||||
|
||||
|
|
@ -194,14 +194,14 @@ Tool Memory operates through three complementary operations that work together t
|
|||
|
||||
```mermaid
|
||||
graph LR
|
||||
A[Agent] -->|1. retrieve_tool_memory| B[(Vector Store)]
|
||||
A[Agent] -->|1 retrieve_tool_memory| B[(Vector Store)]
|
||||
B -->|Guidelines| A
|
||||
A -->|Execute Tool| C[Tool]
|
||||
A -->|2 Execute Tool| C[Tool]
|
||||
C -->|Result| A
|
||||
A -->|add_tool_call_result| D[LLM Evaluate]
|
||||
A -->|3 add_tool_call_result| D[LLM Evaluate]
|
||||
D -->|Store| B
|
||||
B -->|Periodic| E[summary_tool_memory]
|
||||
E -->|Update Guidelines| B
|
||||
E -->|4 Update Guidelines| B
|
||||
```
|
||||
|
||||
### Operation Flow
|
||||
|
|
|
|||
|
|
@ -43,6 +43,7 @@ nav:
|
|||
- BFCL: cookbook/bfcl/quickstart.md
|
||||
- FrozenLake: cookbook/frozenlake/quickstart.md
|
||||
|
||||
- TODO: todo.md
|
||||
|
||||
- Contributions: contribution.md
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue