面向智能体的记忆管理工具包, Remember Me, Refine Me.
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。
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。
智能体的记忆可以被视为：
Agent Memory = Long-Term Memory + Short-Term Memory
= (Personal + Task + Tool) Memory + (Working Memory)
- 个人记忆（Personal Memory）：理解用户偏好并适应上下文
- 任务记忆（Task Memory）：从经验中学习并在类似任务中表现更好
- 工具记忆（Tool Memory）：基于历史表现优化工具选择和参数使用
- 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
- [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。
- 快速开始
- 输入
/horse触发马年彩蛋——烟花、奔马动画和随机马年祝福。
|
马 上 有 钱 |
remecli_zh_1080p_3x.mp4 |
马 到 成 功 |
- [2025-12] 📄 我们的程序性（任务）记忆论文已在 arXiv 发布
- [2025-11] 🧠 基于工作记忆的 react-agent demo（介绍、Quick Start、代码）
- [2025-10] 🚀 直接 Python 导入：支持
from reme_ai import ReMeApp，无需 HTTP/MCP 服务 - [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）
- [2025-09] 🎉 支持异步操作，并已集成至 agentscope-runtime
- [2025-09] 🎉 集成任务记忆与个人记忆
- [2025-09] 🧪 在 appworld、bfcl(v3)、frozenlake 等环境中验证有效性（实验文档）
- [2025-08] 🚀 支持 MCP 协议（快速开始）
- [2025-06] 🚀 支持多种向量存储后端（Elasticsearch & ChromaDB）（向量库指南）
- [2024-09] 🧠 支持个性化与时间敏感的记忆存储
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：
可在不同智能体之间复用的程序性知识：
- 成功模式识别：识别有效策略并理解其背后的原理
- 失败分析学习：从错误中学习，避免重复踩坑
- 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
- 验证模式：通过验证模块确认提炼出的经验是否有效
了解如何使用任务记忆可参考：任务记忆文档
面向特定用户的情境化长期记忆：
- 个体偏好：记录用户的习惯、偏好与交互风格
- 情境自适应：基于时间与上下文动态管理记忆
- 渐进式学习：在长期多轮交互中不断加深对用户的理解
- 时间敏感：在记忆检索与整合中考虑时间因素
了解如何使用个人记忆可参考：个人记忆文档
基于真实调用数据的工具选择与使用优化：
- 历史表现追踪：记录成功率、调用耗时与 Token 成本
- LLM-as-Judge 评估：提供工具成功 / 失败原因的定性洞察
- 参数优化：从历史成功调用中学习最优参数配置
- 动态指南：将静态工具描述演化为可持续更新的「活文档」
了解如何使用工具记忆可参考：工具记忆文档
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：
- 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
- 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
📖 概念与 API：
- 消息卸载概览：Message Offload
- 卸载 / 重载算子：Message Offload Ops、Message Reload Ops
💻 端到端 Demo：
- 工作记忆快速上手：Working Memory Quick Start
- 带工作记忆的 ReAct 智能体：react_agent_with_working_memory.py
- 可运行 Demo：work_memory_demo.py
pip install reme-aigit clone https://github.com/agentscope-ai/ReMe.git
cd ReMe
pip install .复制 example.env 为 .env 并按需修改：
FLOW_LLM_API_KEY=sk-xxxx
FLOW_LLM_BASE_URL=https://xxxx/v1
FLOW_EMBEDDING_API_KEY=sk-xxxx
FLOW_EMBEDDING_BASE_URL=https://xxxx/v1reme \
backend=http \
http.port=8002 \
llm.default.model_name=qwen3-30b-a3b-thinking-2507 \
embedding_model.default.model_name=text-embedding-v4 \
vector_store.default.backend=localreme \
backend=mcp \
mcp.transport=stdio \
llm.default.model_name=qwen3-30b-a3b-thinking-2507 \
embedding_model.default.model_name=text-embedding-v4 \
vector_store.default.backend=localimport requests
# 经验总结：从执行轨迹中学习
response = requests.post("http://localhost:8002/summary_task_memory", json={
"workspace_id": "task_workspace",
"trajectories": [
{"messages": [{"role": "user", "content": "Help me create a project plan"}], "score": 1.0}
]
})
# 记忆检索：获取相关经验
response = requests.post("http://localhost:8002/retrieve_task_memory", json={
"workspace_id": "task_workspace",
"query": "How to efficiently manage project progress?",
"top_k": 1
})Python 导入版本
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:
# 经验总结：从执行轨迹中学习
result = await app.async_execute(
name="summary_task_memory",
workspace_id="task_workspace",
trajectories=[
{
"messages": [
{"role": "user", "content": "Help me create a project plan"}
],
"score": 1.0
}
]
)
print(result)
# 记忆检索：获取相关经验
result = await app.async_execute(
name="retrieve_task_memory",
workspace_id="task_workspace",
query="How to efficiently manage project progress?",
top_k=1
)
print(result)
if __name__ == "__main__":
asyncio.run(main())curl 版本
# 经验总结：从执行轨迹中学习
curl -X POST http://localhost:8002/summary_task_memory \
-H "Content-Type: application/json" \
-d '{
"workspace_id": "task_workspace",
"trajectories": [
{"messages": [{"role": "user", "content": "Help me create a project plan"}], "score": 1.0}
]
}'
# 记忆检索：获取相关经验
curl -X POST http://localhost:8002/retrieve_task_memory \
-H "Content-Type: application/json" \
-d '{
"workspace_id": "task_workspace",
"query": "How to efficiently manage project progress?",
"top_k": 1
}'# 记忆整合：从用户交互中学习
response = requests.post("http://localhost:8002/summary_personal_memory", json={
"workspace_id": "task_workspace",
"trajectories": [
{"messages":
[
{"role": "user", "content": "I like to drink coffee while working in the morning"},
{"role": "assistant",
"content": "I understand, you prefer to start your workday with coffee to stay energized"}
]
}
]
})
# 记忆检索：获取个人记忆片段
response = requests.post("http://localhost:8002/retrieve_personal_memory", json={
"workspace_id": "task_workspace",
"query": "What are the user's work habits?",
"top_k": 5
})Python 导入版本
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:
# 记忆整合：从用户交互中学习
result = await app.async_execute(
name="summary_personal_memory",
workspace_id="task_workspace",
trajectories=[
{
"messages": [
{"role": "user", "content": "I like to drink coffee while working in the morning"},
{"role": "assistant",
"content": "I understand, you prefer to start your workday with coffee to stay energized"}
]
}
]
)
print(result)
# 记忆检索：获取个人记忆片段
result = await app.async_execute(
name="retrieve_personal_memory",
workspace_id="task_workspace",
query="What are the user's work habits?",
top_k=5
)
print(result)
if __name__ == "__main__":
asyncio.run(main())curl 版本
# 记忆整合：从用户交互中学习
curl -X POST http://localhost:8002/summary_personal_memory \
-H "Content-Type: application/json" \
-d '{
"workspace_id": "task_workspace",
"trajectories": [
{"messages": [
{"role": "user", "content": "I like to drink coffee while working in the morning"},
{"role": "assistant", "content": "I understand, you prefer to start your workday with coffee to stay energized"}
]}
]
}'
# 记忆检索：获取个人记忆片段
curl -X POST http://localhost:8002/retrieve_personal_memory \
-H "Content-Type: application/json" \
-d '{
"workspace_id": "task_workspace",
"query": "What are the user'\''s work habits?",
"top_k": 5
}'import requests
# 记录工具调用结果
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
}
]
})
# 从历史生成使用指南
response = requests.post("http://localhost:8002/summary_tool_memory", json={
"workspace_id": "tool_workspace",
"tool_names": "web_search"
})
# 在使用前检索工具指南
response = requests.post("http://localhost:8002/retrieve_tool_memory", json={
"workspace_id": "tool_workspace",
"tool_names": "web_search"
})Python 导入版本
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:
# 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())curl 版本
# 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"
}'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"
})Python 导入版本
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())curl 版本
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"
}'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：
| 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
})Python 导入版本
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())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。
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%) |
| 无 ReMe | 使用 ReMe |
|---|---|
|
|
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。
🛠️ 工具记忆基准
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 与实现代码 run_reme_tool_bench.py。
- Quick Start：实用示例，可立即使用
- 直接 Python 导入：将 ReMe 直接嵌入到你的智能体代码中
- HTTP 服务 API：用于多智能体系统的 RESTful API
- MCP 协议：与 Claude Desktop 和 MCP 兼容客户端集成
- 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。
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：
- 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
- 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
@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},
}本项目基于 Apache License 2.0 开源，详情参见 LICENSE 文件。