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在 OpenClaw 中安装
/install mflow-memory
功能描述
Long-term memory engine for OpenClaw agents using M-flow knowledge graphs. Stores conversations as structured episodic memories and retrieves via graph-route...
安全使用建议
This package appears to implement an on‑host memory service, but before installing you should: 1) Note the inconsistency between the registry metadata and the SKILL.md (the script requires Docker and an LLM_API_KEY). 2) Inspect and verify the referenced Docker image and digest (flowelement/m_flow-mcp@sha256:...) on Docker Hub or the project's repo; only run images you trust. 3) Consider creating a dedicated LLM/OpenAI API key with limited usage/quota and monitoring usage, since the key is injected into the container and can be used to consume credits or exfiltrate data. 4) Expect conversations (including sensitive info) may be stored in the Docker volume — decide whether to run in an isolated environment or decline storing certain information. 5) If you need higher assurance, ask the publisher for source for the Docker image, or run their code in a sandbox and/or review the upstream repository and container contents before running setup.sh.
功能分析
Type: OpenClaw Skill
Name: mflow-memory
Version: 0.3.6
The mflow-memory skill provides a legitimate long-term memory engine for OpenClaw agents. The setup and teardown scripts (setup.sh, teardown.sh) follow standard practices for managing a local Docker-based MCP server, including the use of a specific image digest for integrity and safe JSON manipulation via Python. No evidence of data exfiltration, malicious execution, or harmful prompt injection was found; the instructions in skill.md are strictly aligned with the stated purpose of managing conversation context.
能力标签
能力评估
Purpose & Capability
The skill's declared purpose (long-term memory via an M-flow MCP) matches the files and runtime instructions: it pulls and runs an m_flow-mcp Docker image, exposes MCP tools, and registers the server with OpenClaw. However the registry metadata at the top of the package lists no required binaries or env vars while the SKILL.md and setup scripts clearly require Docker and an LLM_API_KEY — an inconsistency that should be resolved before trusting the package.
Instruction Scope
SKILL.md instructs the agent to always call search before answering and to save interactions at conversation end or on explicit requests to remember. That is expected for a memory skill, but it implies automatic collection and storage of conversation content (potentially sensitive data). The setup and teardown scripts also modify ~/.openclaw/openclaw.json to register/unregister the MCP — this is within scope but should be visible to the user.
Install Mechanism
No formal install spec is present; installation is done by running provided shell scripts which pull a Docker image from Docker Hub. The image is referenced with a sha256 digest (good practice). Running a third-party Docker image is an action with real risk because it executes arbitrary code on the host and will run with whatever privileges Docker grants; users should verify the image source and digest before running.
Credentials
The skill requires an LLM API key (LLM_API_KEY) used both by the local MCP and passed into the Docker container. This is a sensitive credential: the container will be able to make API calls and consume your account credits and could exfiltrate data. The fact that the registry metadata omits this required secret increases the concern (the package does not declare the sensitive requirement where the registry expects it).
Persistence & Privilege
The skill is not force-enabled (always: false). After setup it registers a local MCP server in the user's OpenClaw config so the agent gains long-term memory tools and may call them autonomously per SKILL.md rules. The setup persists data in a Docker volume and edits the user's ~/.openclaw/openclaw.json — both expected for this feature but worth awareness.
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 mflow-memory - 安装完成后，直接呼叫该 Skill 的名称或使用
/mflow-memory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
v0.3.6
v0.3.6: ChromaDB stability fixes (version pin, None metadata, test assertion)
v0.3.5
v0.3.5: Playground face recognition deployment, one-command setup script, ChromaDB fix
v0.3.4
Update to M-flow 0.3.4: fix 20+ production bugs including LLM compatibility (max_tokens), session history crash, audio/image processing, UUID serialization, and security hardening
v1.0.2
Update Docker image digest to 0.3.3 (fixes episode routing + logging)
v1.0.1
Declare Docker + LLM_API_KEY requirements in metadata; pin Docker image digest; add homepage/repository links
v1.0.0
Initial release: long-term memory for OpenClaw agents via M-flow MCP server
元数据
常见问题
M-flow Memory 是什么？
Long-term memory engine for OpenClaw agents using M-flow knowledge graphs. Stores conversations as structured episodic memories and retrieves via graph-route... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 211 次。
如何安装 M-flow Memory？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install mflow-memory」即可一键安装，无需额外配置。
M-flow Memory 是免费的吗？
是的，M-flow Memory 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
M-flow Memory 支持哪些平台？
M-flow Memory 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 M-flow Memory？
由 FANGZONG（@flowelement-alexunbridled）开发并维护，当前版本 v0.3.6。
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