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2720480371

Multi-Agent Memory Optimizer

作者 2720480371 · GitHub ↗ · v0.2.0 · MIT-0
cross-platform ⚠ suspicious
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当前安装
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版本数
在 OpenClaw 中安装
/install memory-optimizer-base
功能描述
多Agent记忆管理系统 - 开放协作的知识库解决方案 支持私有+公共双层记忆空间,自动生成每日总结,跨Agent知识检索
安全使用建议
This package is mostly coherent with a local memory manager, but pay close attention to configuration before running. The shipped config can enable automatic summarize/publish behavior contrary to the README's safety claims — which could cause private memory files (MEMORY.md or memory/YYYY-MM-DD.md) to be uploaded to the 'public' folder without an interactive confirmation. Before installing or running: 1) Inspect config/default.json in the package and set upload.require_upload_confirm = true and upload.auto_publish = false (and keep upload.backup_private = true). 2) Run summarize and upload manually at first to verify output, do not enable crontab auto jobs until you are confident. 3) Run the tool in a safe test workspace (copy ~/.openclaw/workspace to a sandbox) to observe behavior. 4) If you rely on strict privacy, audit any generated public files before exposing them and consider disabling auto_summarize and any automatic cron tasks. If you want higher assurance, review the full source locally (it is included) and search for any network calls — none were found in the provided code, but changing config or extensions could add them later.
能力评估
Purpose & Capability
The name/description (multi-agent memory manager with private+public tiers) matches the included code: all modules operate on local filesystem paths (workspace/memory, private/public) and implement summarization, searching, uploading (to a local public directory), tiering, etc. No cloud credentials, remote endpoints, or unrelated binaries are requested. Minor discrepancy: README/SKILL.md mention optional environment variables (e.g., CHROME_PATH, MX_APIKEY) that are not required or referenced by the code — this is an unneeded note but not evidence of exfiltration.
Instruction Scope
Runtime instructions and the code read local OpenClaw memory files (MEMORY.md, memory/YYYY-MM-DD.md) and then generate and can publish summaries to memory/public. That behavior is expected for this purpose. However the documentation repeatedly claims 'upload requires manual confirmation' and 'privacy protection', while the actual included config/default.json sets upload.require_upload_confirm (and related upload defaults) in a way that can enable automatic publishing (the file shipped in the package sets auto_publish/require_upload_confirm values that contradict the safety claims). If deployed with the shipped defaults (or if config is changed), private content could be auto-published without the user noticing. The summarizer also supports automatic operation (crontab guidance) which increases the risk if confirmation is disabled.
Install Mechanism
There is no external install script or remote download — the skill is delivered as local Python scripts and JSON config. No brew/npm/remote archive fetches are present. This lowers supply-chain risk; nothing in the package retrieves or executes external code at runtime.
Credentials
The skill declares no required environment variables and the code does not read cloud credentials. That's proportional to its stated local filesystem purpose. The README/SKILL.md mention optional variables (CHROME_PATH, MX_APIKEY) as examples for other integrations, but those are not used in the shipped code. The main concern is configuration (config/default.json) controlling publish behavior — these are local config flags, not secrets, but they materially affect privacy.
Persistence & Privilege
The skill does write files: it creates agent config files under config/agents, writes summaries into the workspace memory directories, and cmd_config persists changes to config/default.json. It does not set always: true, does not modify other skills, and does not require elevated system privileges. Writing global default.json is expected for a CLI tool but is something to be aware of (it changes global behavior for all agents using that workspace).
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install memory-optimizer-base
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /memory-optimizer-base 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v0.2.0
Initial release: 双层记忆管理系统,智能总结,公共空间共享
元数据
Slug memory-optimizer-base
版本 0.2.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Multi-Agent Memory Optimizer 是什么?

多Agent记忆管理系统 - 开放协作的知识库解决方案 支持私有+公共双层记忆空间,自动生成每日总结,跨Agent知识检索. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 81 次。

如何安装 Multi-Agent Memory Optimizer?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install memory-optimizer-base」即可一键安装,无需额外配置。

Multi-Agent Memory Optimizer 是免费的吗?

是的,Multi-Agent Memory Optimizer 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Multi-Agent Memory Optimizer 支持哪些平台?

Multi-Agent Memory Optimizer 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Multi-Agent Memory Optimizer?

由 2720480371(@2720480371)开发并维护,当前版本 v0.2.0。

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