mem
AI Agent 持久化记忆存储系统,支持全文搜索、本地存储和快速检索,让 AI 跨会话记住用户偏好和关键信息
npx skills add runablehq/memory --skill memBefore / After 效果对比
1 组AI 每次对话都需要重新提供上下文,用户偏好和项目决策无法跨会话保留,重复解释同样的问题
AI 自动存储和检索关键信息,跨会话记住用户偏好、项目决策和代码片段,大幅减少重复沟通
description SKILL.md
mem
mem — Agent Memory Store
A CLI tool for storing and retrieving memories with full-text search. Data is stored locally in ~/.mem/mem.db.
When to Use
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Remember user preferences, project decisions, important facts
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Store code snippets, commands, configurations for later recall
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Search your knowledge base before asking the user for information you may have stored
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Attach images (screenshots, diagrams) to memories
Commands
Three operators: (none) = recall, + = remember, - = forget.
Recall (search, list, get)
mem # list recent memories
mem "deploy" # full-text search
mem "database" --tag db # search filtered by tag
mem 7sjtNVyZrNIa # get full content by ID
mem --tag prefs # list filtered by tag
mem "api" --limit 5 --json # limit results, JSON output
mem --full # show full content for all
Remember
mem + "user prefers dark mode" --tag prefs
mem + "deploy: bun build --compile" --tag deploy
mem + "chose SQLite for simplicity" --tag architecture
mem + --image ./screenshot.png --title "Current UI" --tag ui
echo "long content" | mem + --tag notes
Forget
mem - <id> # delete one memory
mem - id1 id2 id3 # delete multiple
Piping
mem "old" --json | jq -r '.[].id' | xargs -I{} mem - {}
echo "long content" | mem + --tag notes
Best Practices
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Tag consistently — Use lowercase, descriptive tags like
prefs,api,deploy,db -
Search before asking — Check if you've stored relevant information before asking the user
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Store decisions — When making architectural or design decisions, store the reasoning
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Keep memories atomic — One concept per memory for better searchability
Output Formats
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Default: One-line summary per result
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--full: Complete content inline -
--json: Structured JSON for parsing
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