Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
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。
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。
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
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)。
# 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"
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
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
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
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
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
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
See REFERENCES.md for complete command reference.
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Largest current source with strong distribution and engagement signals.