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memory-manager

三级记忆管理系统 (Three-Tier Memory Management)。用于管理 AI 代理的短期、中期、长期记忆。包括:(1) 滑动窗口式短期记忆,(2) 自动摘要生成中期记忆,(3) 向量检索长期记忆 (RAG)。当需要管理对话历史、优化上下文、构建个人知识库、或实现记忆持久化时使用此 Skill。

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Installation

Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.

SkillHub CLI

skillhub install forvendettaw/monica-three-tier-memory

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Skill instructions

Memory Manager Skill

管理 AI 代理的三级记忆系统:短期(滑动窗口)、中期(自动摘要)、长期(向量检索)。

快速开始

# 初始化记忆系统
python3 scripts/memory_manager.py init

# 添加短期记忆
python3 scripts/memory_manager.py add --type short --content "用户喜欢黑色"

# 查询记忆
python3 scripts/memory_manager.py search "用户的偏好"

架构概览

层级 存储位置 触发条件 用途
短期 memory/sliding-window.json 实时 保持当前对话连贯
中期 memory/summaries/ Token 阈值 压缩历史,保留大意
长期 memory/vector-store/ 语义检索 永久记忆,RAG

核心功能

1. 短期记忆:滑动窗口
  • 配置:config/window_size(默认 10 条)
  • 逻辑:FIFO 队列,超出则丢弃最旧消息
  • 文件:memory/sliding-window.json
2. 中期记忆:自动摘要
  • 触发:当前 token > config/summary_threshold(默认 4000)
  • 模型:使用廉价模型(如 GPT-3.5-Haiku)
  • 输出:memory/summaries/YYYY-MM-DD.json
3. 长期记忆:向量检索
  • 后端:ChromaDB(本地向量库)
  • 存:对话结束/摘要生成后自动向量化存储
  • 取:每次查询前先检索相关记忆

配置文件

创建 memory/config.yaml:

memory:
  short_term:
    enabled: true
    window_size: 10
    max_tokens: 2000

  medium_term:
    enabled: true
    summary_threshold: 4000
    summary_model: "glm-4-flash"  # 或 gpt-3.5-turbo

  long_term:
    enabled: true
    backend: "chromadb"
    top_k: 3
    min_relevance: 0.7

使用场景

  • 新对话开始:先 search 长期记忆,注入相关上下文
  • 对话中:自动管理短期/中期记忆,超阈值自动摘要
  • 对话结束:将重要信息存入长期记忆

详细用法

See REFERENCES.md for complete command reference.

Source information

Source
github
Repository
openclaw/skills
Path
skills/forvendettaw/monica-three-tier-memory
Version
—
Commit
72bb06066a6e9ca1107d900c79fb034c43be9b9c
Classification source
rules
Status
active
Visibility
public

These signals come from indexed source analysis and may lag behind changes. Check the current source files.

Extracted content

SKILL.md
# Memory Manager Skill

管理 AI 代理的三级记忆系统:短期(滑动窗口)、中期(自动摘要)、长期(向量检索)。

## 快速开始

```bash
# 初始化记忆系统
python3 scripts/memory_manager.py init

# 添加短期记忆
python3 scripts/memory_manager.py add --type short --content "用户喜欢黑色"

# 查询记忆
python3 scripts/memory_manager.py search "用户的偏好"
```

## 架构概览

| 层级 | 存储位置 | 触发条件 | 用途 |
|------|----------|----------|------|
| 短期 | `memory/sliding-window.json` | 实时 | 保持当前对话连贯 |
| 中期 | `memory/summaries/` | Token 阈值 | 压缩历史,保留大意 |
| 长期 | `memory/vector-store/` | 语义检索 | 永久记忆,RAG |

## 核心功能

### 1. 短期记忆:滑动窗口

- 配置:`config/window_size`(默认 10 条)
- 逻辑:FIFO 队列,超出则丢弃最旧消息
- 文件:`memory/sliding-window.json`

### 2. 中期记忆:自动摘要

- 触发:当前 token > `config/summary_threshold`(默认 4000)
- 模型:使用廉价模型(如 GPT-3.5-Haiku)
- 输出:`memory/summaries/YYYY-MM-DD.json`

### 3. 长期记忆:向量检索

- 后端:ChromaDB(本地向量库)
- 存:对话结束/摘要生成后自动向量化存储
- 取:每次查询前先检索相关记忆

## 配置文件

创建 `memory/config.yaml`:

```yaml
memory:
  short_term:
    enabled: true
    window_size: 10
    max_tokens: 2000

  medium_term:
    enabled: true
    summary_threshold: 4000
    summary_model: "glm-4-flash"  # 或 gpt-3.5-turbo

  long_term:
    enabled: true
    backend: "chromadb"
    top_k: 3
    min_relevance: 0.7
```

## 使用场景

- **新对话开始**:先 `search` 长期记忆,注入相关上下文
- **对话中**:自动管理短期/中期记忆,超阈值自动摘要
- **对话结束**:将重要信息存入长期记忆

## 详细用法

See [REFERENCES.md](references/references.md) for complete command reference.

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