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Memory Manager
作者
Pupper0601
· GitHub ↗
· vv3.5.5
· MIT-0
84
总下载
0
收藏
0
当前安装
4
版本数
在 OpenClaw 中安装
/install pupper0601-memory-manager
功能描述
OpenClaw专用三层AI记忆管理系统。管理临时记忆(L1)/长期记忆(L2)/永久记忆(L3),支持向量语义搜索、自动压缩、OpenClaw用户身份识别和跨设备同步。
安全使用建议
Key things to consider before installing or enabling this skill:
- Source verification: The registry lists 'source: unknown' yet project files reference a GitHub repo. Confirm the repo origin and maintainer trustworthiness (inspect the upstream GitHub repository, commit history, and open issues). Do not install from an untrusted raw URL.
- Review install.sh and SKILL.md: Do not run curl | bash blindly. Download the installer and review its contents (install.sh) locally. Prefer manual installation (git clone + inspect + pip install -r requirements.txt) and use the --no-shell-rc option to prevent automatic modification of your shell files.
- Protect API keys and tokens: The installer may write API keys into shell RC files or plaintext config files (~/.memory-manager/config.json). Prefer setting EMBED_BACKEND and API keys as environment variables in a controlled way (or use a secrets manager). Avoid providing a GITHUB_TOKEN with broad scopes; if you must, create a token limited to the repository and actions needed.
- Restrict data scope and run in isolation first: The skill reads users/*/profile.md and other users' memory files and will perform git operations. If this device contains other users or sensitive files, consider running the skill in an isolated account, VM, or container and set MM_BASE_DIR to a directory you control.
- Disable auto-run / auto-enable if possible: If the platform allows, avoid enabling automatic session-start reads until you confirm behavior. If SKILL.md's 'auto_enable' or 'read_when' behavior is configurable, turn off auto-sync and run operations manually at first.
- Audit runtime behavior: After installation, inspect created config files (~/.memory-manager, ~/.openclaw/memory), check what environment variables were added to your shell rc, and inspect any cron/systemd jobs or background processes the installer creates (none obvious in the provided files, but verify).
- Run tests and dry-run: Use the included tests and the tool's --dry-run options (or run in a sandbox) to verify embedding/sync behavior without pushing data to remote services.
- If in doubt, decline GITHUB_TOKEN and avoid entering API keys interactively during install; set EMBED_BACKEND to a 'keyword-only' fallback or use provider keys you can revoke quickly.
Given the code matches the advertised functionality but contains privacy-sensitive behavior and metadata inconsistencies, proceed only after the manual reviews above or run in an isolated environment.
功能分析
Type: OpenClaw Skill
Name: pupper0601-memory-manager
Version: v3.5.5
The memory-manager skill bundle implements a sophisticated multi-tier AI memory system but performs several high-risk operations. Most notably, the `install.sh` and `memory_onboard.py` scripts modify shell configuration files (`.bashrc`, `.zshrc`) to persist API keys and aliases, and `memory_init.py` stores GitHub tokens in plain text via the `git credential-store`. While these behaviors are explicitly disclosed in the `SKILL.md` security note and are functional for the tool's purpose, they represent significant security risks regarding persistence and credential handling. Additionally, the system requires broad tool permissions (`Bash`, `Write`) and uses an unusual code-like string (`from typing import...`) as an internal data delimiter in `memory_embed.py` and `memory_index.py`, which could potentially lead to unexpected behavior in the AI agent.
能力标签
能力评估
Purpose & Capability
The codebase (Python scripts, embedding backends, sync, install.sh) matches the described memory-manager functionality (L1/L2/L3 memories, embeddings, GitHub sync). Requested binaries (python, git) and optional env vars (OPENAI_API_KEY, SILICONFLOW_API_KEY, ZHIPU_API_KEY, GITHUB_TOKEN) are reasonable for this purpose. However the registry summary/metadata claims 'required env vars: none' and 'No install spec — instruction-only skill' while the package includes many code files and SKILL.md declares EMBED_BACKEND as required and even contains an install script. This mismatch in metadata vs. actual files is a coherence issue.
Instruction Scope
Runtime instructions and code explicitly read multi-user data (users/{uid}/profile.md and entire users/ tree), run git pull/push, and by default the installer will write API keys into shell RC files unless --no-shell-rc is used. The skill's 'read_when' indicates it will read the memory repo at session start. Reading other users' profile.md and automatically persisting credentials are privacy-sensitive actions and broaden the skill's scope beyond just answering queries.
Install Mechanism
An install.sh is included and SKILL.md shows curl | bash installation examples (raw.githubusercontent.com). While GitHub raw URLs are common, piping remote scripts to bash is inherently risky. install.sh installs Python packages (openai, numpy, optional lancedb), modifies shell RC files by default to persist API keys, and creates filesystem layout under ~/.openclaw or ~/.memory-manager — these are expected for this project but represent an elevated installation risk that requires manual review before running.
Credentials
Requested environment variables (EMBED_BACKEND required; optional API keys and GITHUB_TOKEN) are proportional to a memory manager that calls external embedding services and can sync to GitHub. Concerns: (1) SKILL.md and code will persist API keys into shell RC or config files (e.g., ~/.memory-manager/config.json), which stores keys unencrypted; (2) registry metadata stated 'required env vars: none' while SKILL.md requires EMBED_BACKEND (inconsistency). Avoid providing GITHUB_TOKEN unless you trust the repo.
Persistence & Privilege
The catalog flags show always:false (good). SKILL.md metadata includes an 'install' script and 'auto_enable': true (in-SKILL metadata), and 'read_when' indicates the skill will be used at session start — combined with default platform behavior (agent can invoke skills autonomously), this means the skill may be called automatically to read memory on session start. This is not an outright privilege escalation, but the combination of auto-read + file access + ability to modify shell RCs makes it more impactful; you should confirm whether auto-enable/auto-run behavior is desired.
如何使用
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install pupper0601-memory-manager - 安装完成后,直接呼叫该 Skill 的名称或使用
/pupper0601-memory-manager触发 - 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
vv3.5.5
OpenClaw专用三层AI记忆管理系统 vv3.5.5
vv3.5.4
OpenClaw专用三层AI记忆管理系统 vv3.5.4
vv3.5.3
OpenClaw专用三层AI记忆管理系统 vv3.5.3
vv3.5.2
OpenClaw专用三层AI记忆管理系统 vv3.5.2
元数据
常见问题
Memory Manager 是什么?
OpenClaw专用三层AI记忆管理系统。管理临时记忆(L1)/长期记忆(L2)/永久记忆(L3),支持向量语义搜索、自动压缩、OpenClaw用户身份识别和跨设备同步。 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 84 次。
如何安装 Memory Manager?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install pupper0601-memory-manager」即可一键安装,无需额外配置。
Memory Manager 是免费的吗?
是的,Memory Manager 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
Memory Manager 支持哪些平台?
Memory Manager 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 Memory Manager?
由 Pupper0601(@pupper0601)开发并维护,当前版本 vv3.5.5。
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