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Memory Processor

作者 zfanmy · GitHub ↗ · v0.1.1 · MIT-0
cross-platform ⚠ suspicious
298
总下载
0
收藏
0
当前安装
1
版本数
在 OpenClaw 中安装
/install memprocessor
功能描述
Enables AI agents to develop independent, evolving personas through organic memory growth, self-reflection, and layered memory management.
安全使用建议
This package appears to implement the advertised persona/memory functionality, but there are multiple coherence and security concerns you should address before running it: 1) Do not run as root; change BASE_DIR from /root/.openclaw to a directory you control (or a temporary/isolated path). 2) Review and modify config.py (HOST, PORT, ALLOWED_ORIGINS, API_KEY) so the HTTP API is not publicly open or unauthenticated by default. 3) Inspect requirements.txt for any dependencies that make network calls or download models; decide whether to run in an isolated VM/container. 4) Be aware the service will persist user-provided content to disk (L3 markdown files, SQLite, FAISS index). If that content is sensitive, plan encryption or limited retention. 5) Fix documentation mismatches (example docs show port 9090 but config defaults to 8080). 6) If you need stronger assurance, ask the maintainer for an explanation of the default BASE_DIR choice, a documented authentication model, and full contents of embedding and persona service files to confirm whether they call external APIs. Running this code in a sandboxed environment (container/VM) and performing a manual code review of omitted service files (embedding/persona_service) is recommended before enabling it in any production agent.
功能分析
Type: OpenClaw Skill Name: memprocessor Version: 0.1.1 The skill bundle is classified as suspicious primarily due to the use of 'pickle.loads' in 'app/core/l1_storage.py', which is a well-known Remote Code Execution (RCE) vulnerability if the cached data is tampered with. Furthermore, 'memory-manager.sh' and 'app/config.py' contain hardcoded paths to the '/root' directory and specific Conda environments, suggesting the application expects to run with elevated privileges and lacks proper environment isolation. While the 'persona' generation and multi-tier memory logic appear consistent with the stated purpose, these high-risk technical choices and broad filesystem assumptions pose a security risk to the host system.
能力评估
Purpose & Capability
The repository implements a multi-layer memory system, persona engine, REST API, and CLI which aligns with the 'Memory Processor' description. However the registry metadata claimed 'instruction-only' (no install spec) while many code files are present; that mismatch is noteworthy. Also the app uses a fixed BASE_DIR (/root/.openclaw) for persistent storage which is a surprising design choice for a general-purpose skill and suggests it will write files as root unless reconfigured.
Instruction Scope
SKILL.md instructs installing requirements and running the bundled start-simple.py and demonstrates curl usage. The instructions omit key operational details: default config writes persistent data to BASE_DIR, the server defaults (config.py) specify PORT=8080 but README/SKILL examples use port 9090 — an inconsistency. The documentation does not call out that the service exposes an HTTP API with ALLOWED_ORIGINS ['*'] by default and an optional API_KEY that is empty by default, meaning it may run unauthenticated and accept incoming requests. The runtime instructions therefore leave broad discretion (network exposure, persistent storage) without warning.
Install Mechanism
There is no platform install spec in the registry; the SKILL.md expects the user to run 'pip install -r requirements.txt' and run the bundled Python scripts. The package contains requirements.txt and pyproject.toml and code files (no external download URLs). This is typical for a Python project but means the user will execute third-party Python dependencies locally — inspect requirements.txt before installing.
Credentials
Registry declares no required env vars, but the code reads many configuration settings (via pydantic Settings), including REDIS credentials, SQLITE path and an API_KEY. Defaults: BASE_DIR is hard-coded to '/root/.openclaw', ALLOWED_ORIGINS defaults to ['*'], API_KEY is empty. The skill will therefore create and persist files under /root/.openclaw, open a HTTP port, and allow wide CORS unless you change settings. Requesting no credentials is coherent, but the lack of required-declared config paths is inconsistent with actual disk and network access in the code.
Persistence & Privilege
The skill is not forced-always or privileged in the registry, but the code intentionally persists data across four storage layers (in-memory, SQLite, markdown files, FAISS vectors) under a fixed base directory. That persistent presence and the default to bind to all interfaces (HOST 0.0.0.0) and permissive CORS are significant privileges and should be treated as such; the SKILL.md does not warn about them.
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 memprocessor
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /memprocessor 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
v0.1.1
DreamMoon-MemProcessor 0.1.1 introduces a pioneering memory system focused on organic persona generation for AI agents. - Enables AI agents to grow personas organically through experience and reflection - Features a 10-dimensional persona model (Big Five + AI traits) - Employs a four-layer memory architecture for efficient information management - Supports automatic persistence and semantic search - Provides quick start instructions for easy setup and persona generation
元数据
Slug memprocessor
版本 0.1.1
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

Memory Processor 是什么?

Enables AI agents to develop independent, evolving personas through organic memory growth, self-reflection, and layered memory management. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 298 次。

如何安装 Memory Processor?

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

Memory Processor 是免费的吗?

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

Memory Processor 支持哪些平台?

Memory Processor 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 Processor?

由 zfanmy(@zfanmy)开发并维护,当前版本 v0.1.1。

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