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Install in OpenClaw
/install mflow-memory
Description
Long-term memory engine for OpenClaw agents using M-flow knowledge graphs. Stores conversations as structured episodic memories and retrieves via graph-route...
Usage Guidance
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.
Capability Analysis
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.
Capability Tags
Capability Assessment
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.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install mflow-memory - After installation, invoke the skill by name or use
/mflow-memory - Provide required inputs per the skill's parameter spec and get structured output
Version History
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
Metadata
Frequently Asked Questions
What is M-flow Memory?
Long-term memory engine for OpenClaw agents using M-flow knowledge graphs. Stores conversations as structured episodic memories and retrieves via graph-route... It is an AI Agent Skill for Claude Code / OpenClaw, with 211 downloads so far.
How do I install M-flow Memory?
Run "/install mflow-memory" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is M-flow Memory free?
Yes, M-flow Memory is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does M-flow Memory support?
M-flow Memory is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created M-flow Memory?
It is built and maintained by FANGZONG (@flowelement-alexunbridled); the current version is v0.3.6.
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