ReMe是一个新兴的AI Agent的记忆管理框架，它创新性地将 Agent 的记忆系统划分为Personal Memory （个人记忆）、Task Memory（任务记忆）+Tool Memory（工具记忆）三大模块，旨在帮助 Agent 在与用户和任务的交互中，智能地抽取、复用和共享记忆。本教程将引导您完成基于 ReMe + AnalyticDB for MySQL 的 AI Agent 记忆管理实践，内容涵盖环境准备、服务启动，以及三大核心记忆模块的详细操作演示。
前提条件
在开始之前，请确保您已满足以下条件：
已创建AnalyticDB for MySQL 版集群，并确保其版本支持向量检索功能（如余弦相似度）。具体操作请参见：企业版、基础版及湖仓版快速入门。
已获取支持 AnalyticDB for MySQL 的 ReMe 框架版本。
说明请通过钉钉联系阿里云服务支持（钉钉账号：
x5v_rm8wqzuqf），获取对应安装包。已获取模型服务 API Key，本教程使用阿里云百炼大模型服务平台，请开通服务并获取 API Key，详情请参考文档：获取API Key。
操作指南
环境准备
准备 AnalyticDB for MySQL。
在您的 AnalyticDB for MySQL 集群中创建一个用于本教程的数据库。
CREATE DATABASE reme;如果您计划通过公网连接，请在 AnalyticDB for MySQL 控制台为集群开启公网访问地址，并将您运行 Demo 环境的公网 IP 地址添加到白名单中。
安装 ReMe 及依赖。
检查 Python 环境：确保您的运行环境中已安装 Python。本教程在 Python 3.12.2 版本下验证通过。
# 使用Mac, 检查Python版本, 如无请至官网安装 https://www.python.org/ python3 --version # 使用Linux, 检查Python版本, 如无使用 yum 进行安装, sudo yum install python python --version安装 ReMe 框架：解压您获取的 Mem0 框架文件，并安装必要的依赖库。
# 解压并安装 flowllm unzip flowllm.zip cd flowllm pip install . # 解压并安装 ReMe unzip ReMe.zip cd Reme pip install .
配置并启动
配置环境变量：
为了安全起见，建议将大模型的 API Key 配置为环境变量。
echo "export DASHSCOPE_API_KEY='<YOUR_DASHSCOPE_API_KEY>'" >> ~/.bashrc # 可选,默认为新加坡 echo "export DASHSCOPE_BASE_URL='https://dashscope-intl.aliyuncs.com/compatible-mode/v1'" >> ~/.bashrc source ~/.bashrc #检查是否生效 echo $DASHSCOPE_API_KEY echo $DASHSCOPE_BASE_URL将AnalyticDB for MySQL的连接信息配置至环境变量：
cd ReMe mv example.env .env编辑
.env文件，将以下内容替换为您的实际配置信息：FLOW_EMBEDDING_API_KEY=<YOUR_DASHSCOPE_API_KEY> FLOW_EMBEDDING_BASE_URL=<YOUR_DASHSCOPE_BASE_URL> FLOW_LLM_API_KEY=<YOUR_DASHSCOPE_API_KEY> FLOW_LLM_BASE_URL=<YOUR_DASHSCOPE_BASE_URL> ADB_HOST=<YOUR_ADB_HOST> ADB_PORT=3306 ADB_USER=<YOUR_ADB_USER> ADB_PASSWORD=<YOUR_ADB_PASSWORD> ADB_DATABASE=reme启动 ReMe 服务：
执行以下命令启动 ReMe 服务。
cd ReMe reme \ backend=http \ http.port=8002 \ llm.default.model_name=qwen3-max \ embedding_model.default.model_name=text-embedding-v4 \ vector_store.default.backend=aliyun_adb_mysql服务启动成功后，控制台将显示服务运行状态。
启动参数说明：
参数
说明
LLM
使用阿里云百炼平台的 qwen3-max 模型（可替换为其他支持的模型）
Embedder
使用阿里云百炼平台的 text-embedding-v4 模型，向量维度为 1536
vector_store
使用 AnalyticDB for MySQL 作为向量存储后端
核心记忆操作演示
个人记忆
个人记忆用于存储和检索与特定用户相关的静态信息，如姓名、职业、兴趣爱好等。
运行 Demo：
cd ReMe python3 ./cookbook/simple_demo/use_personal_memory_demo.py运行效果分析：
创建对话并提取信息：Demo 首先通过多轮对话输入用户的个人信息（姓名、职业、兴趣等）。ReMe 会自动从对话中抽取这些信息并存入个人记忆。
记忆摘要与存储：系统将提取的信息归纳为结构化的记忆条目，包含关键词、来源消息和观察类型等元数据，并持久化到AnalyticDB for MySQL中。
语义检索验证：最后，Demo 通过一系列自然语言提问来验证记忆的检索效果。
Demo 执行的检索示例及预期结果：
lhj@B-F21WMGFD-2205 ReMe % python3 ./cookbook/simple_demo/use_personal_memory_demo.py Clearing existing memories... {"answer": "", "messages": [], "success": true, "metadata": {"action_result": ""}} Creating conversation with personal information... Summarizing personal memories... { "answer": "", "messages": [], "success": true, "metadata": { "memory_list": [ { "workspace_id": "personal_memory_demo", "memory_id": "7f253fb5ab75402eaa84e23cee76ef52", "memory_type": "personal", "when_to_use": "John Smith, 28 years old, tech company, San Francisco", "content": "user's name is John Smith, age 28, works at a tech company in San Francisco", "score": 0.0, "time_created": "2025-11-04 17:22:50", "time_modified": "2025-11-04 17:22:50", "author": "qwen3-max", "metadata": { "keywords": "John Smith, 28 years old, tech company, San Francisco", "source_message": "My name is John Smith, I'm 28 years old, and I work at a tech company in San Francisco", "observation_type": "personal_info" }, "target": "user", "reflection_subject": "" }, { "workspace_id": "personal_memory_demo", "memory_id": "d423370cfa5e47e2b53f5c77f4e2353e", "memory_type": "personal", "when_to_use": "software engineer, backend development, Python, Go", "content": "user is a software engineer specializing in backend development with Python and Go", "score": 0.0, "time_created": "2025-11-04 17:22:50", "time_modified": "2025-11-04 17:22:50", "author": "qwen3-max", "metadata": { "keywords": "software engineer, backend development, Python, Go", "source_message": "I'm a software engineer, mainly doing backend development using Python and Go", "observation_type": "personal_info" }, "target": "user", "reflection_subject": "" }, { "workspace_id": "personal_memory_demo", "memory_id": "88474b71f7f24ac3961a349914407250", "memory_type": "personal", "when_to_use": "basketball, sci-fi movies, Dune Part 2", "content": "user enjoys playing basketball and watching sci-fi movies, recently watched Dune Part 2", "score": 0.0, "time_created": "2025-11-04 17:22:50", "time_modified": "2025-11-04 17:22:50", "author": "qwen3-max", "metadata": { "keywords": "basketball, sci-fi movies, Dune Part 2", "source_message": "I enjoy playing basketball and watching sci-fi movies. I recently watched Dune Part 2", "observation_type": "personal_info" }, "target": "user", "reflection_subject": "" }, { "workspace_id": "personal_memory_demo", "memory_id": "9a8b4f81c66b4068b2e53146374ebc7a", "memory_type": "personal", "when_to_use": "cat, Shadow, pet", "content": "user has a 3-year-old cat named Shadow", "score": 0.0, "time_created": "2025-11-04 17:22:50", "time_modified": "2025-11-04 17:22:50", "author": "qwen3-max", "metadata": { "keywords": "cat, Shadow, pet", "source_message": "I have a cat named Shadow who is 3 years old", "observation_type": "personal_info" }, "target": "user", "reflection_subject": "" }, { "workspace_id": "personal_memory_demo", "memory_id": "b72a6d0927e04ea7a2de9e28b4c008ec", "memory_type": "personal", "when_to_use": "Japanese cuisine, sushi, ramen", "content": "user is interested in Japanese cuisine, especially sushi and ramen", "score": 0.0, "time_created": "2025-11-04 17:22:50", "time_modified": "2025-11-04 17:22:50", "author": "qwen3-max", "metadata": { "keywords": "Japanese cuisine, sushi, ramen", "source_message": "I'm really interested in Japanese cuisine, especially sushi and ramen", "observation_type": "personal_info" }, "target": "user", "reflection_subject": "" }, { "workspace_id": "personal_memory_demo", "memory_id": "f900b79060804a1587d59032c63f1e3c", "memory_type": "personal", "when_to_use": "Japan, Tokyo, Kyoto, trip", "content": "user is planning a trip to Tokyo and Kyoto in December 2025", "score": 0.0, "time_created": "2025-11-04 17:22:46", "time_modified": "2025-11-04 17:22:46", "author": "qwen3-max", "metadata": { "keywords": "Japan, Tokyo, Kyoto, trip", "time_info": "December 2025", "source_message": "I'm planning a trip to Japan next month, mainly to Tokyo and Kyoto", "observation_type": "personal_info_with_time" }, "target": "user", "reflection_subject": "" } ], "deleted_memory_ids": [], "update_result": { "deleted_count": 0, "inserted_count": 6 } } } Waiting for memories to be processed... Retrieving personal memories... Query: What's my name and age? { "answer": "user's name is John Smith, age 28, works at a tech company in San Francisco\nuser has a 3-year-old cat named Shadow\nuser is planning a trip to Tokyo and Kyoto in December 2025", "messages": [], "success": true, "metadata": { "memory_list": [ { "workspace_id": "personal_memory_demo", "memory_id": "7f253fb5ab75402eaa84e23cee76ef52", "memory_type": "personal", "when_to_use": "John Smith, 28 years old, tech company, San Francisco", "content": "user's name is John Smith, age 28, works at a tech company in San Francisco", "score": 0.038895229, "time_created": "2025-11-04 17:22:50", "time_modified": "2025-11-04 17:22:50", "author": "qwen3-max", "metadata": { "keywords": "John Smith, 28 years old, tech company, San Francisco", "source_message": "My name is John Smith, I'm 28 years old, and I work at a tech company in San Francisco", "observation_type": "personal_info", "match_event_flag": "0", "match_msg_flag": "0" }, "target": "user", "reflection_subject": "" }, { "workspace_id": "personal_memory_demo", "memory_id": "9a8b4f81c66b4068b2e53146374ebc7a", "memory_type": "personal", "when_to_use": "cat, Shadow, pet", "content": "user has a 3-year-old cat named Shadow", "score": 0.027221944999999997, "time_created": "2025-11-04 17:22:50", "time_modified": "2025-11-04 17:22:50", "author": "qwen3-max", "metadata": { "keywords": "cat, Shadow, pet", "source_message": "I have a cat named Shadow who is 3 years old", "observation_type": "personal_info", "match_event_flag": "0", "match_msg_flag": "0" }, "target": "user", "reflection_subject": "" }, { "workspace_id": "personal_memory_demo", "memory_id": "f900b79060804a1587d59032c63f1e3c", "memory_type": "personal", "when_to_use": "Japan, Tokyo, Kyoto, trip", "content": "user is planning a trip to Tokyo and Kyoto in December 2025", "score": 0.024106982, "time_created": "2025-11-04 17:22:46", "time_modified": "2025-11-04 17:22:46", "author": "qwen3-max", "metadata": { "keywords": "Japan, Tokyo, Kyoto, trip", "time_info": "December 2025", "source_message": "I'm planning a trip to Japan next month, mainly to Tokyo and Kyoto", "observation_type": "personal_info_with_time", "match_event_flag": "0", "match_msg_flag": "0" }, "target": "user", "reflection_subject": "" } ] } } Query: What do I do for work? { "answer": "user's name is John Smith, age 28, works at a tech company in San Francisco\nuser is a software engineer specializing in backend development with Python and Go\nuser is planning a trip to Tokyo and Kyoto in December 2025", "messages": [], "success": true, "metadata": { "memory_list": [ { "workspace_id": "personal_memory_demo", "memory_id": "7f253fb5ab75402eaa84e23cee76ef52", "memory_type": "personal", "when_to_use": "John Smith, 28 years old, tech company, San Francisco", "content": "user's name is John Smith, age 28, works at a tech company in San Francisco", "score": 0.029179364000000003, "time_created": "2025-11-04 17:22:50", "time_modified": "2025-11-04 17:22:50", "author": "qwen3-max", "metadata": { "keywords": "John Smith, 28 years old, tech company, San Francisco", "source_message": "My name is John Smith, I'm 28 years old, and I work at a tech company in San Francisco", "observation_type": "personal_info", "match_event_flag": "0", "match_msg_flag": "0" }, "target": "user", "reflection_subject": "" }, { "workspace_id": "personal_memory_demo", "memory_id": "d423370cfa5e47e2b53f5c77f4e2353e", "memory_type": "personal", "when_to_use": "software engineer, backend development, Python, Go", "content": "user is a software engineer specializing in backend development with Python and Go", "score": 0.024785686, "time_created": "2025-11-04 17:22:50", "time_modified": "2025-11-04 17:22:50", "author": "qwen3-max", "metadata": { "keywords": "software engineer, backend development, Python, Go", "source_message": "I'm a software engineer, mainly doing backend development using Python and Go", "observation_type": "personal_info", "match_event_flag": "0", "match_msg_flag": "0" }, "target": "user", "reflection_subject": "" }, { "workspace_id": "personal_memory_demo", "memory_id": "f900b79060804a1587d59032c63f1e3c", "memory_type": "personal", "when_to_use": "Japan, Tokyo, Kyoto, trip", "content": "user is planning a trip to Tokyo and Kyoto in December 2025", "score": 0.017342727, "time_created": "2025-11-04 17:22:46", "time_modified": "2025-11-04 17:22:46", "author": "qwen3-max", "metadata": { "keywords": "Japan, Tokyo, Kyoto, trip", "time_info": "December 2025", "source_message": "I'm planning a trip to Japan next month, mainly to Tokyo and Kyoto", "observation_type": "personal_info_with_time", "match_event_flag": "0", "match_msg_flag": "0" }, "target": "user", "reflection_subject": "" } ] } } Query: What are my hobbies? { "answer": "user is planning a trip to Tokyo and Kyoto in December 2025\nuser is a software engineer specializing in backend development with Python and Go\nuser is interested in Japanese cuisine, especially sushi and ramen", "messages": [], "success": true, "metadata": { "memory_list": [ { "workspace_id": "personal_memory_demo", "memory_id": "f900b79060804a1587d59032c63f1e3c", "memory_type": "personal", "when_to_use": "Japan, Tokyo, Kyoto, trip", "content": "user is planning a trip to Tokyo and Kyoto in December 2025", "score": 0.025223058, "time_created": "2025-11-04 17:22:46", "time_modified": "2025-11-04 17:22:46", "author": "qwen3-max", "metadata": { "keywords": "Japan, Tokyo, Kyoto, trip", "time_info": "December 2025", "source_message": "I'm planning a trip to Japan next month, mainly to Tokyo and Kyoto", "observation_type": "personal_info_with_time", "match_event_flag": "0", "match_msg_flag": "0" }, "target": "user", "reflection_subject": "" }, { "workspace_id": "personal_memory_demo", "memory_id": "d423370cfa5e47e2b53f5c77f4e2353e", "memory_type": "personal", "when_to_use": "software engineer, backend development, Python, Go", "content": "user is a software engineer specializing in backend development with Python and Go", "score": 0.024959816000000003, "time_created": "2025-11-04 17:22:50", "time_modified": "2025-11-04 17:22:50", "author": "qwen3-max", "metadata": { "keywords": "software engineer, backend development, Python, Go", "source_message": "I'm a software engineer, mainly doing backend development using Python and Go", "observation_type": "personal_info", "match_event_flag": "0", "match_msg_flag": "0" }, "target": "user", "reflection_subject": "" }, { "workspace_id": "personal_memory_demo", "memory_id": "b72a6d0927e04ea7a2de9e28b4c008ec", "memory_type": "personal", "when_to_use": "Japanese cuisine, sushi, ramen", "content": "user is interested in Japanese cuisine, especially sushi and ramen", "score": 0.020527664, "time_created": "2025-11-04 17:22:50", "time_modified": "2025-11-04 17:22:50", "author": "qwen3-max", "metadata": { "keywords": "Japanese cuisine, sushi, ramen", "source_message": "I'm really interested in Japanese cuisine, especially sushi and ramen", "observation_type": "personal_info", "match_event_flag": "0", "match_msg_flag": "0" }, "target": "user", "reflection_subject": "" } ] } } Query: Do I have any pets? { "answer": "user has a 3-year-old cat named Shadow\nuser's name is John Smith, age 28, works at a tech company in San Francisco\nuser is planning a trip to Tokyo and Kyoto in December 2025", "messages": [], "success": true, "metadata": { "memory_list": [ { "workspace_id": "personal_memory_demo", "memory_id": "9a8b4f81c66b4068b2e53146374ebc7a", "memory_type": "personal", "when_to_use": "cat, Shadow, pet", "content": "user has a 3-year-old cat named Shadow", "score": 0.044214043, "time_created": "2025-11-04 17:22:50", "time_modified": "2025-11-04 17:22:50", "author": "qwen3-max", "metadata": { "keywords": "cat, Shadow, pet", "source_message": "I have a cat named Shadow who is 3 years old", "observation_type": "personal_info", "match_event_flag": "0", "match_msg_flag": "0" }, "target": "user", "reflection_subject": "" }, { "workspace_id": "personal_memory_demo", "memory_id": "7f253fb5ab75402eaa84e23cee76ef52", "memory_type": "personal", "when_to_use": "John Smith, 28 years old, tech company, San Francisco", "content": "user's name is John Smith, age 28, works at a tech company in San Francisco", "score": 0.026690182000000003, "time_created": "2025-11-04 17:22:50", "time_modified": "2025-11-04 17:22:50", "author": "qwen3-max", "metadata": { "keywords": "John Smith, 28 years old, tech company, San Francisco", "source_message": "My name is John Smith, I'm 28 years old, and I work at a tech company in San Francisco", "observation_type": "personal_info", "match_event_flag": "0", "match_msg_flag": "0" }, "target": "user", "reflection_subject": "" }, { "workspace_id": "personal_memory_demo", "memory_id": "f900b79060804a1587d59032c63f1e3c", "memory_type": "personal", "when_to_use": "Japan, Tokyo, Kyoto, trip", "content": "user is planning a trip to Tokyo and Kyoto in December 2025", "score": 0.023277275, "time_created": "2025-11-04 17:22:46", "time_modified": "2025-11-04 17:22:46", "author": "qwen3-max", "metadata": { "keywords": "Japan, Tokyo, Kyoto, trip", "time_info": "December 2025", "source_message": "I'm planning a trip to Japan next month, mainly to Tokyo and Kyoto", "observation_type": "personal_info_with_time", "match_event_flag": "0", "match_msg_flag": "0" }, "target": "user", "reflection_subject": "" } ] } } Query: What are my travel plans? { "answer": "user is planning a trip to Tokyo and Kyoto in December 2025\nuser's name is John Smith, age 28, works at a tech company in San Francisco\nuser is a software engineer specializing in backend development with Python and Go", "messages": [], "success": true, "metadata": { "memory_list": [ { "workspace_id": "personal_memory_demo", "memory_id": "f900b79060804a1587d59032c63f1e3c", "memory_type": "personal", "when_to_use": "Japan, Tokyo, Kyoto, trip", "content": "user is planning a trip to Tokyo and Kyoto in December 2025", "score": 0.04186047600000001, "time_created": "2025-11-04 17:22:46", "time_modified": "2025-11-04 17:22:46", "author": "qwen3-max", "metadata": { "keywords": "Japan, Tokyo, Kyoto, trip", "time_info": "December 2025", "source_message": "I'm planning a trip to Japan next month, mainly to Tokyo and Kyoto", "observation_type": "personal_info_with_time", "match_event_flag": "0", "match_msg_flag": "0" }, "target": "user", "reflection_subject": "" }, { "workspace_id": "personal_memory_demo", "memory_id": "7f253fb5ab75402eaa84e23cee76ef52", "memory_type": "personal", "when_to_use": "John Smith, 28 years old, tech company, San Francisco", "content": "user's name is John Smith, age 28, works at a tech company in San Francisco", "score": 0.025156727000000004, "time_created": "2025-11-04 17:22:50", "time_modified": "2025-11-04 17:22:50", "author": "qwen3-max", "metadata": { "keywords": "John Smith, 28 years old, tech company, San Francisco", "source_message": "My name is John Smith, I'm 28 years old, and I work at a tech company in San Francisco", "observation_type": "personal_info", "match_event_flag": "0", "match_msg_flag": "0" }, "target": "user", "reflection_subject": "" }, { "workspace_id": "personal_memory_demo", "memory_id": "d423370cfa5e47e2b53f5c77f4e2353e", "memory_type": "personal", "when_to_use": "software engineer, backend development, Python, Go", "content": "user is a software engineer specializing in backend development with Python and Go", "score": 0.019123243, "time_created": "2025-11-04 17:22:50", "time_modified": "2025-11-04 17:22:50", "author": "qwen3-max", "metadata": { "keywords": "software engineer, backend development, Python, Go", "source_message": "I'm a software engineer, mainly doing backend development using Python and Go", "observation_type": "personal_info", "match_event_flag": "0", "match_msg_flag": "0" }, "target": "user", "reflection_subject": "" } ] } } Query: What foods do I like? { "answer": "user is interested in Japanese cuisine, especially sushi and ramen\nuser is planning a trip to Tokyo and Kyoto in December 2025\nuser is a software engineer specializing in backend development with Python and Go", "messages": [], "success": true, "metadata": { "memory_list": [ { "workspace_id": "personal_memory_demo", "memory_id": "b72a6d0927e04ea7a2de9e28b4c008ec", "memory_type": "personal", "when_to_use": "Japanese cuisine, sushi, ramen", "content": "user is interested in Japanese cuisine, especially sushi and ramen", "score": 0.0316972, "time_created": "2025-11-04 17:22:50", "time_modified": "2025-11-04 17:22:50", "author": "qwen3-max", "metadata": { "keywords": "Japanese cuisine, sushi, ramen", "source_message": "I'm really interested in Japanese cuisine, especially sushi and ramen", "observation_type": "personal_info", "match_event_flag": "0", "match_msg_flag": "0" }, "target": "user", "reflection_subject": "" }, { "workspace_id": "personal_memory_demo", "memory_id": "f900b79060804a1587d59032c63f1e3c", "memory_type": "personal", "when_to_use": "Japan, Tokyo, Kyoto, trip", "content": "user is planning a trip to Tokyo and Kyoto in December 2025", "score": 0.022572586000000002, "time_created": "2025-11-04 17:22:46", "time_modified": "2025-11-04 17:22:46", "author": "qwen3-max", "metadata": { "keywords": "Japan, Tokyo, Kyoto, trip", "time_info": "December 2025", "source_message": "I'm planning a trip to Japan next month, mainly to Tokyo and Kyoto", "observation_type": "personal_info_with_time", "match_event_flag": "0", "match_msg_flag": "0" }, "target": "user", "reflection_subject": "" }, { "workspace_id": "personal_memory_demo", "memory_id": "d423370cfa5e47e2b53f5c77f4e2353e", "memory_type": "personal", "when_to_use": "software engineer, backend development, Python, Go", "content": "user is a software engineer specializing in backend development with Python and Go", "score": 0.021622963000000002, "time_created": "2025-11-04 17:22:50", "time_modified": "2025-11-04 17:22:50", "author": "qwen3-max", "metadata": { "keywords": "software engineer, backend development, Python, Go", "source_message": "I'm a software engineer, mainly doing backend development using Python and Go", "observation_type": "personal_info", "match_event_flag": "0", "match_msg_flag": "0" }, "target": "user", "reflection_subject": "" } ] } }查询问题
预期检索结果
"What do I do for work?"
返回与工作相关的记忆（软件工程师、Python/Go后端开发）。
"What are my hobbies?"
返回兴趣爱好相关的记忆（日本料理、旅行计划）。
"Do I have any pets?"
返回宠物相关的记忆（3岁的猫 Shadow）。
任务记忆
任务记忆用于记录 Agent 在完成特定任务过程中积累的经验和方法论，使其在处理未来相似任务时能够复用历史经验，提升效率和质量。
运行 Demo：
cd ReMe python3 ./cookbook/simple_demo/use_task_memory_demo.py运行效果分析：
首次任务执行：Agent 接收“分析特斯拉公司”的任务，并将其拆解为公司概况、财务表现、技术创新等六个维度，然后通过搜索工具收集信息。
方法论提炼：任务完成后，系统从执行过程中提炼出可复用的方法论层面的经验。例如：
如何将一个复杂的分析任务拆解为多个子任务。
如何通过两阶段检索策略来填补信息空白。
如何组织输出结构以提升报告的可读性。
记忆复用：当 Agent 接收到类似的新任务（如“分析小米公司”）时，系统会自动检索并应用此前积累的任务经验，生成一个增强后的、更优的查询策略，从而产出更全面、结构更合理的分析结果。
Demo 执行的检索示例及预期结果：
=== Task Memory Demo === 1. Deleting workspace... Workspace 'test_workspace' deleted successfully 2. Running agent with first query... Agent response: I have gathered comprehensive information about Xiaomi Corporation through multiple targeted searches. Here's a summary of the analysis: 1. **Company Overview**: Xiaomi Corporation is a Chinese multinational technology company founded in 2010, known for its smartphones, smart home devices, and IoT ecosystem. It operates globally with a strong presence in Asia, Europe, and Latin America. 2. **Financial Performance (2024)**: Xiaomi reported robust financial results in 2024, with significant revenue growth driven by smartphone sales and expansion in its Internet of Things (IoT) product lines. The company maintained profitability despite global economic challenges. 3. **Product Portfolio & Innovations**: Xiaomi continues to innovate in areas such as high-performance smartphones (e.g., Mi series), foldable phones, electric vehicles (Xiaomi SU7), and an expanding range of smart home products integrated into its MIUI ecosystem. 4. **Market Position**: Xiaomi remains one of the top five global smartphone manufacturers, particularly strong in emerging markets. Its competitive pricing strategy and focus on value-for-money products have helped it gain market share. 5. **Challenges & Risks (2025)**: Key challenges include increasing competition from brands like Samsung, Apple, and Oppo, supply chain volatility, geopolitical tensions affecting international operations, and the need to sustain innovation in the EV sector. 6. **Recent Developments (2025)**: In early 2025, Xiaomi launched its new electric vehicle model, the SU7 Pro, which received positive reviews. The company also expanded its AI-powered smart home platform and strengthened partnerships in Southeast Asia and Europe. This analysis provides a holistic view of Xiaomi Corporation’s current status, strategic direction, and future outlook. Messages saved to messages.jsonl 3. Running memory-augmented agent workflow... --- Building memories with query: 'Analyze the company Tesla.' --- Agent response: I have gathered comprehensive information about Tesla through multiple search queries. Here is a summary of the key aspects: 1. **Company Overview**: Tesla, Inc. is an American electric vehicle and clean energy company founded in 2003 by Martin Eberhard and Marc Tarpenning, with Elon Musk playing a pivotal role since 2004. The company designs, manufactures, and sells electric vehicles (EVs), solar panels, and energy storage systems. 2. **Financial Performance (2024)**: Tesla reported strong financial results in 2024, with increased revenue driven by higher vehicle deliveries and growth in its energy products segment. However, the company faced challenges related to production scaling and global economic conditions affecting demand. 3. **Recent Innovations and Technology Developments**: Tesla continues to lead in EV technology, including advancements in battery efficiency, autonomous driving (Full Self-Driving or FSD), and the development of next-generation platforms like the "Tesla Model 2" (rumored). The company also expanded its AI capabilities through projects like Optimus (humanoid robot). 4. **CEO Elon Musk's Recent Activities**: Elon Musk has been actively involved in public discussions about AI ethics, infrastructure projects (e.g., Hyperloop), and the future of transportation. He has also engaged in high-profile social media interactions and strategic decisions impacting Tesla’s direction. 5. **Market Position and Competition**: Tesla remains a dominant player in the global EV market, though it faces increasing competition from traditional automakers (e.g., Ford, Volkswagen) and new entrants (e.g., BYD, Rivian). Despite this, Tesla maintains a strong brand presence and technological edge. 6. **Environmental Impact and Sustainability Initiatives**: Tesla is committed to reducing carbon emissions through its products and operations. The company emphasizes sustainable manufacturing practices, renewable energy integration, and recycling programs for batteries. This analysis provides a well-rounded view of Tesla’s current status, challenges, and future outlook. --- Summarizing conversation to create memories --- Memory list: [{'workspace_id': 'test_workspace', 'memory_id': '4a0f9408ee2b4cdda721d6bfed8a0a24', 'memory_type': 'task', 'when_to_use': 'When analyzing a complex, multi-faceted entity like a major corporation that requires coverage across business, financial, technological, leadership, competitive, and sustainability dimensions.', 'content': "The agent decomposed the broad request 'Analyze the company Tesla' into six distinct thematic subtasks (overview, financials, innovation, leadership, competition, sustainability) and executed parallel and sequential tool calls to gather targeted information. This structured decomposition ensured comprehensive coverage without redundancy, while iterative querying allowed refinement based on emerging context.", 'score': 0.85, 'time_created': '2025-11-04 19:35:23', 'time_modified': '2025-11-04 19:35:23', 'author': 'qwen3-max', 'metadata': {'when_to_use': 'When analyzing a complex, multi-faceted entity like a major corporation that requires coverage across business, financial, technological, leadership, competitive, and sustainability dimensions.', 'experience': "The agent decomposed the broad request 'Analyze the company Tesla' into six distinct thematic subtasks (overview, financials, innovation, leadership, competition, sustainability) and executed parallel and sequential tool calls to gather targeted information. This structured decomposition ensured comprehensive coverage without redundancy, while iterative querying allowed refinement based on emerging context.", 'tags': ['problem decomposition', 'multi-perspective analysis', 'corporate analysis', 'iterative search'], 'confidence': 0.95, 'step_type': 'reasoning', 'tools_used': ['dashscope_search']}}, {'workspace_id': 'test_workspace', 'memory_id': '51ddcd1b679e45fc86aacd5982a1cc54', 'memory_type': 'task', 'when_to_use': 'When initial information gathering is insufficient to form a holistic answer, and additional contextual layers (e.g., leadership impact or market dynamics) are needed to enrich understanding.', 'content': 'After an initial round of three searches, the agent proactively identified gaps—specifically around leadership influence and competitive landscape—and issued a second wave of targeted queries. This adaptive two-phase retrieval strategy demonstrates effective metacognition: assessing coverage completeness and filling critical knowledge gaps before synthesis.', 'score': 0.85, 'time_created': '2025-11-04 19:35:23', 'time_modified': '2025-11-04 19:35:23', 'author': 'qwen3-max', 'metadata': {'when_to_use': 'When initial information gathering is insufficient to form a holistic answer, and additional contextual layers (e.g., leadership impact or market dynamics) are needed to enrich understanding.', 'experience': 'After an initial round of three searches, the agent proactively identified gaps—specifically around leadership influence and competitive landscape—and issued a second wave of targeted queries. This adaptive two-phase retrieval strategy demonstrates effective metacognition: assessing coverage completeness and filling critical knowledge gaps before synthesis.', 'tags': ['adaptive querying', 'gap analysis', 'context enrichment', 'sequential tool use'], 'confidence': 0.9, 'step_type': 'decision', 'tools_used': ['dashscope_search']}}, {'workspace_id': 'test_workspace', 'memory_id': '79e7bb2e055640f3975c12c719e10092', 'memory_type': 'task', 'when_to_use': 'When synthesizing information from multiple heterogeneous sources into a coherent, structured summary for user consumption.', 'content': 'The final response organized findings into six clearly labeled thematic sections that directly mirrored the query decomposition strategy. This alignment between information-gathering structure and output format enhanced clarity, reduced cognitive load, and ensured all subtasks were visibly addressed—demonstrating strong traceability from intent to delivery.', 'score': 0.85, 'time_created': '2025-11-04 19:35:23', 'time_modified': '2025-11-04 19:35:23', 'author': 'qwen3-max', 'metadata': {'when_to_use': 'When synthesizing information from multiple heterogeneous sources into a coherent, structured summary for user consumption.', 'experience': 'The final response organized findings into six clearly labeled thematic sections that directly mirrored the query decomposition strategy. This alignment between information-gathering structure and output format enhanced clarity, reduced cognitive load, and ensured all subtasks were visibly addressed—demonstrating strong traceability from intent to delivery.', 'tags': ['structured synthesis', 'thematic organization', 'answer coherence', 'user-centric summarization'], 'confidence': 0.85, 'step_type': 'action', 'tools_used': []}}] Memory saved to memory.jsonl --- Retrieving memories for query: 'Analyze Xiaomi Corporation' --- Retrieved memory: To effectively analyze Xiaomi Corporation, adopt a structured, multi-dimensional approach that systematically examines its core business segments, financial performance, technological innovation, leadership strategy, competitive positioning, and sustainability initiatives. Begin by decomposing the analysis into these six thematic pillars to ensure comprehensive coverage without redundancy. Use parallel and sequential information-gathering methods to collect targeted data on each dimension—starting with Xiaomi’s product ecosystem (smartphones, IoT, EVs), revenue streams, and geographic footprint. If initial findings lack depth in critical areas such as leadership vision or competitive dynamics in key markets like China, India, or Europe, proactively issue follow-up queries to fill those gaps. This adaptive two-phase retrieval ensures a holistic understanding before synthesis. Finally, organize the output into clearly labeled sections that mirror the original analytical framework, creating a coherent, traceable narrative that directly addresses the user’s request while minimizing cognitive load. This method—proven effective for complex corporate analyses like Tesla—ensures actionable, well-rounded insights tailored to Xiaomi’s unique position at the intersection of consumer electronics, smart hardware, and emerging mobility. --- Running agent with memory-augmented query --- Augmented query: To effectively analyze Xiaomi Corporation, adopt a structured, multi-dimensional approach that systematically examines its core business segments, financial performance, technological innovation, leadership strategy, competitive positioning, and sustainability initiatives. Begin by decomposing the analysis into these six thematic pillars to ensure comprehensive coverage without redundancy. Use parallel and sequential information-gathering methods to collect targeted data on each dimension—starting with Xiaomi’s product ecosystem (smartphones, IoT, EVs), revenue streams, and geographic footprint. If initial findings lack depth in critical areas such as leadership vision or competitive dynamics in key markets like China, India, or Europe, proactively issue follow-up queries to fill those gaps. This adaptive two-phase retrieval ensures a holistic understanding before synthesis. Finally, organize the output into clearly labeled sections that mirror the original analytical framework, creating a coherent, traceable narrative that directly addresses the user’s request while minimizing cognitive load. This method—proven effective for complex corporate analyses like Tesla—ensures actionable, well-rounded insights tailored to Xiaomi’s unique position at the intersection of consumer electronics, smart hardware, and emerging mobility. User Question: Analyze Xiaomi Corporation Agent response: Based on the comprehensive analysis of Xiaomi Corporation, here is a structured overview across key dimensions: ### 1. **Business Segments & Product Ecosystem** Xiaomi operates through three core segments: - **Smartphones**: A cornerstone of its business, with a strong global presence. - **IoT and Smart Home Devices**: A rapidly growing segment including wearables, home appliances, and smart lighting. Xiaomi’s ecosystem approach integrates hardware, software, and services. - **Electric Vehicles (EVs)**: A strategic new venture launched in 2021, with the Mi EV model entering production. This marks a significant pivot into mobility. ### 2. **Financial Performance & Revenue Streams** In 2024, Xiaomi reported robust financial performance: - Revenue growth driven by smartphone sales and IoT expansion. - Diversified revenue streams: smartphones (~50%), IoT (~30%), and EVs (~20% projected). - Strong profitability in key markets like China and India, with increasing margins from high-margin IoT products. ### 3. **Technological Innovation** Xiaomi is investing heavily in R&D: - Development of proprietary AI chips (e.g., Surge series) to enhance performance in smartphones and IoT devices. - Focus on AI integration across its ecosystem, including voice assistants and smart home automation. - Advancements in battery technology and energy efficiency for both mobile and EV platforms. ### 4. **Leadership Strategy & Vision** Under CEO Lei Jun, Xiaomi’s strategy emphasizes: - "Internet + Hardware + Software" integration to create seamless user experiences. - Long-term vision of building a global smart lifestyle ecosystem. - Strategic focus on innovation, cost-efficiency, and rapid market entry. ### 5. **Competitive Positioning** Xiaomi holds strong positions in key markets: - **China**: One of the top 3 smartphone vendors, leveraging brand loyalty and affordable premium offerings. - **India**: Market leader in smartphones, with aggressive pricing and extensive retail networks. - **Europe**: Rapidly gaining share, particularly in mid-to-high-end segments, due to design quality and value-for-money propositions. ### 6. **Sustainability & Future Initiatives** Xiaomi is advancing sustainability efforts: - Commitment to carbon neutrality goals by 2030. - Eco-friendly materials in packaging and product design. - EV development aligns with global green mobility trends, with zero-emission vehicles under development. ### Additional Insights - **Global Expansion**: Aggressive international rollout via online platforms and local partnerships, especially in Southeast Asia, Latin America, and Europe. - **Supply Chain**: Collaborates with leading manufacturers (e.g., Foxconn, BYD) while maintaining control over core R&D and design. - **Consumer Loyalty**: High customer retention due to ecosystem lock-in and consistent product quality. Xiaomi’s future success hinges on its ability to scale EV production, deepen AI integration, and maintain its competitive edge in emerging markets. 4. Dumping memory to disk... Memory dumped to ./ 5. Loading memory from disk... Memory loaded from ./ === Demo Complete ===
工具记忆
工具记忆用于记录 Agent 使用各类工具（API、函数等）的经验，包括工具的核心功能、成功模式、常见问题和最佳实践。
运行 Demo：
cd ReMe python3 ./cookbook/simple_demo/use_tool_memory_demo.py运行效果分析：
模拟工具调用：Demo 首先模拟了 70 条工具调用记录，涵盖
database_query、file_processor、web_search三种工具。生成经验摘要：系统自动分析这些调用记录，为每种工具生成一份详细的使用经验摘要。包含以下关键维度：
维度
说明
核心功能
工具的主要用途和适用场景。
成功模式
工具调用成功的典型条件和参数组合。
常见问题
工具调用失败的常见原因分析。
最佳实践
使用建议和注意事项。
统计信息
成功率、平均耗时、平均 Token 消耗等量化指标。
Demo 执行的检索示例及预期结果：
2025-11-04 19:39:10.101 | INFO | flowllm.utils.common_utils:load_env:64 - load env_path=.env set FLOW_APP_NAME=ReMe [1] Cleaning workspace... ✓ Workspace 'test_tool_memory_workspace' deleted [2] Adding mock tool call results... [ADD] 70 results for 3 tools: database_query, file_processor, web_search ✓ Added successfully, created/updated 3 tool memories [3] Summarizing tool memories... [SUMMARIZE] web_search,database_query,file_processor ✓ Summarized 3 tool memories ============================================================ Tool: web_search ============================================================ 1. **Core Function**: The web_search tool retrieves authoritative technical documentation, tutorials, and best practices for specific developer-focused queries (e.g., "GraphQL advantages", "OAuth2 flow"). Use it when seeking reliable, in-depth technical information. 2. **Success Patterns**: High-quality results occur with precise, domain-specific queries (e.g., "JWT authentication", "MongoDB schema design") combined with valid parameters like language="en" and filter="technical_docs". These consistently yield 14–19 relevant, authoritative sources. 3. **Common Issues**: Queries fail or return minimal results when: (a) the query is overly generic (e.g., "test query 1"), (b) an unsupported language (e.g., "unknown") is specified, or (c) the request times out (10s cap). Nonsensical queries correctly return zero results but are handled gracefully. 4. **Best Practices**: - Always use specific, technical keywords that reflect real developer tasks or concepts. - Validate language parameters (use "en" or other supported codes) and avoid placeholder or invalid values. ## Statistics - **Success Rate**: 75.00% - **Average Score**: 0.350 - **Average Time Cost**: 4.300s - **Average Token Cost**: 96.0 ============================================================ Tool: database_query ============================================================ 1. **Core Function**: Executes SQL-like queries on specified tables, returning matching rows up to a user-defined limit. Use when retrieving filtered, ordered, or pattern-matched data from known tables. 2. **Success Patterns**: Queries succeed when using valid table/column names, clear WHERE conditions (e.g., id > N), and reasonable limits. Results are reliable when row counts respect the limit or truthfully reflect available data (even if fewer rows exist). 3. **Common Issues**: Frequent failures occur when: (a) returned rows exceed the specified limit—indicating improper limit enforcement; (b) invalid table/column names cause errors; or (c) results omit actual data rows, returning only metadata. 4. **Best Practices**: - Always verify table and column names match the schema to avoid execution errors. - Treat returned row counts skeptically if they exceed your limit—reformulate or validate the query. - For large datasets with LIKE or sorting, expect performance notes; consider indexing suggestions provided in successful responses. ## Statistics - **Success Rate**: 80.00% - **Average Score**: 0.650 - **Average Time Cost**: 2.157s - **Average Token Cost**: 55.8 ============================================================ Tool: file_processor ============================================================ 1. **Core Function**: The file_processor reads structured and unstructured files (CSV, JSON, XML, TXT, PDF) and returns metadata—file size and record count—without exposing raw content. Use it to quickly assess file contents before deeper processing. 2. **Success Patterns**: Works reliably on files under ~500KB across all supported formats when valid paths and encodings (e.g., UTF-8) are provided. Consistently returns useful metadata for small-to-medium files in under 3 seconds. 3. **Common Issues**: Fails silently on large files (>500KB–1MB), returning only a size warning without metadata or content—despite marking the call as "successful." Also fails if the file path is invalid, though it correctly reports this error. 4. **Best Practices**: - Pre-check file size; avoid using file_processor on files likely exceeding 500KB. - Always validate file paths before calling to prevent "file not found" errors. - Use UTF-8 encoding for text-based formats unless otherwise required. ## Statistics - **Success Rate**: 83.33% - **Average Score**: 0.722 - **Average Time Cost**: 7.058s - **Average Token Cost**: 173.6 [4] Retrieving tool memories... [RETRIEVE] web_search ✓ Retrieved 1 memories Tool: web_search Calls: 30 ------------------------------------------------------------ 1. **Core Function**: The web_search tool retrieves authoritative technical documentation, tutorials, and best practices for specific developer-focused queries (e.g., "GraphQL advantages", "OAuth2 flow"). Use it when seeking reliable, in-depth technical information. 2. **Success Patterns**: High-quality results occur with precise, domain-specific queries (e.g., "JWT authentication", "MongoDB schema design") combined with valid parameters like language="en" and filter="technical_docs". These consistently yield 14–19 relevant, authoritative sources. 3. **Common Issues**: Queries fail or return minimal results when: (a) the query is overly generic (e.g., "test query 1"), (b) an unsupported language (e.g., "unknown") is specified, or (c) the request times out (10s cap). Nonsensical queries correctly return zero results but are handled gracefully. 4. **Best Practices**: - Always use specific, technical keywords that reflect real developer tasks or concepts. - Validate language parameters (use "en" or other supported codes) and avoid placeholder or invalid values. ## Statistics - **Success Rate**: 75.00% - **Average Score**: 0.350 - **Average Time Cost**: 4.300s - **Average Token Cost**: 96.0 ✓ Saved to tool_memory.json [RETRIEVE] database_query ✓ Retrieved 1 memories Tool: database_query Calls: 22 ------------------------------------------------------------ 1. **Core Function**: Executes SQL-like queries on specified tables, returning matching rows up to a user-defined limit. Use when retrieving filtered, ordered, or pattern-matched data from known tables. 2. **Success Patterns**: Queries succeed when using valid table/column names, clear WHERE conditions (e.g., id > N), and reasonable limits. Results are reliable when row counts respect the limit or truthfully reflect available data (even if fewer rows exist). 3. **Common Issues**: Frequent failures occur when: (a) returned rows exceed the specified limit—indicating improper limit enforcement; (b) invalid table/column names cause errors; or (c) results omit actual data rows, returning only metadata. 4. **Best Practices**: - Always verify table and column names match the schema to avoid execution errors. - Treat returned row counts skeptically if they exceed your limit—reformulate or validate the query. - For large datasets with LIKE or sorting, expect performance notes; consider indexing suggestions provided in successful responses. ## Statistics - **Success Rate**: 80.00% - **Average Score**: 0.650 - **Average Time Cost**: 2.157s - **Average Token Cost**: 55.8 ✓ Saved to tool_memory.json [RETRIEVE] file_processor ✓ Retrieved 1 memories Tool: file_processor Calls: 18 ------------------------------------------------------------ 1. **Core Function**: The file_processor reads structured and unstructured files (CSV, JSON, XML, TXT, PDF) and returns metadata—file size and record count—without exposing raw content. Use it to quickly assess file contents before deeper processing. 2. **Success Patterns**: Works reliably on files under ~500KB across all supported formats when valid paths and encodings (e.g., UTF-8) are provided. Consistently returns useful metadata for small-to-medium files in under 3 seconds. 3. **Common Issues**: Fails silently on large files (>500KB–1MB), returning only a size warning without metadata or content—despite marking the call as "successful." Also fails if the file path is invalid, though it correctly reports this error. 4. **Best Practices**: - Pre-check file size; avoid using file_processor on files likely exceeding 500KB. - Always validate file paths before calling to prevent "file not found" errors. - Use UTF-8 encoding for text-based formats unless otherwise required. ## Statistics - **Success Rate**: 83.33% - **Average Score**: 0.722 - **Average Time Cost**: 7.058s - **Average Token Cost**: 173.6 ✓ Saved to tool_memory.json [5] Testing memory persistence... ✓ Memory dumped to ./ ✓ Memory loaded from ./ ============================================================ DEMO COMPLETE ✓ ============================================================
高级实践：AppWorld 对比实验（可选）
ReMe 的 cookbook 目录下提供了基于 AppWorld 等基准测试集的对比实验，用于量化验证记忆增强对 Agent 性能的提升效果。
警告
该实验预计运行时间较长，且会消耗较多的大模型服务 Token，请根据实际情况决定是否运行。
步骤一：准备实验环境。
# 创建 conda 环境 conda create -p ./appworld-env python==3.12 conda activate ./appworld-env # 安装依赖 pip install -r requirements.txt # 安装 AppWorld 并下载数据集 pip install appworld appworld install appworld download data步骤二：加载记忆库。
curl -X POST "http://0.0.0.0:8002/vector_store" \ -H "Content-Type: application/json" \ -d '{ "workspace_id": "appworld", "action": "load", "path": "./docs/library" }'步骤三：运行实验脚本。
python run_appworld.py该脚本将在开发数据集上运行 AppWorld 任务，对比使用 ReMe 记忆（
use_memory=True）和不使用记忆两种模式下 Agent 的表现。主要配置参数：
参数
说明
默认值
max_workers并行工作线程数
6
num_runs每个任务的重复运行次数
4
use_memory是否启用 ReMe 记忆库
-
步骤四：统计实验结果。
python run_exp_statistic.py实验指标说明：
指标
说明
best@1单次运行的平均性能
best@k取 k 次运行中的最佳性能并求平均，k 值越高越能反映潜力上限
实验结果文件保存在
./exp_result/目录下，统计摘要导出至experiment_summary.csv。