Long-Term Agent Memory
Stores conversations as episodic memories and recalls them through a graph search.
Install
npx skills add FlowElement-xinliuyuansu/m_flow --skill mflow-memory -g -a claude-code -y- Repository
- FlowElement-xinliuyuansu/m_flow4.5k
- Category
- AI and agents
- Updated
- 2mo ago
- License
- Apache-2.0
- Context cost
- Lightabout 498 tokens
- Runs scripts
- Yes, review before use
What it does
Long-term memory engine for OpenClaw agents using M-flow knowledge graphs. Stores conversations as structured episodic memories and retrieves via graph-routed search. Use when the agent needs to remember past conversations, recall user preferences, or maintain context across sessions. Requires Docker.
M-flow Memory
Gives your agent persistent long-term memory powered by M-flow's cognitive memory engine.
Setup
Run once:
bash ~/.openclaw/skills/mflow-memory/scripts/setup.sh
This starts the M-flow MCP server and registers it with OpenClaw.
Memory Strategy
Follow these rules for every conversation:
-
Before answering, silently call
searchwith the user's question to check for relevant memories. If memories are found, use them as context. -
At the end of a conversation that contained meaningful information (decisions, facts, preferences, plans, not greetings or small talk), call
save_interactionwith the conversation content. -
When the user explicitly asks you to remember something ("remember that I'm allergic to peanuts", "note that the deadline is Friday"), call
save_interactionimmediately, do not wait until the end of the conversation. -
When the user says "remember", "last time", "before", "previously", or references past events, always call
searchfirst. -
Do not store trivial exchanges, repeated information, or content the user asks you to forget.
Available Tools (via MCP)
After setup, these tools are automatically available:
save_interaction, Store a conversation as memory (preferred for dialogue)search, Search memories by natural language queryquery, Ask a question and get an answer grounded in memoriesmemorize, Build knowledge graph from previously added dataingest, One-step store + memorize (for documents)list_data, List stored datasetsdelete, Remove specific memoriesmemorize_status, Check if memorization is still processingprune, Reset all memory
Troubleshooting
# Check if M-flow is running
bash ~/.openclaw/skills/mflow-memory/scripts/status.sh
# Restart
docker restart mflow-memory
# View logs
docker logs mflow-memory --tail 20
# Complete removal
bash ~/.openclaw/skills/mflow-memory/scripts/teardown.sh

