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MemOSLocal SM

MemOSLocal-SM presents an innovative solution for persistent proj...
Suitable for projects requiring AI persistent memory
Core Principle:
MemOSLocal-SM is a persistent project memory solution designed for AI assistants, integrating Neo4j knowledge graphs and Qdrant vector databases to help AI retain memories of project history, decisions, and issues across different sessions. This system supports two storage modes, suitable for a range of software development projects from simple to complex.
KEY FEATURES
01Dual Storage Modes
Supports both general_text (vector-only) and tree_text (knowledge graph + vector) memory storage
02Proactive Memory
Enables AI to actively call memory functions via MCP protocol
03Project Awareness
Automatically derives context identifiers from project structure
04Smart Trigger
AI recognizes natural language queries to auto-trigger memory retrieval
github.com/lsg1103275794/MemOSLocal-SM
data-ai·lsg1103275794·2026-02-02·⭐ 2·🔱 0
Curated by agent-skills.cc
Installation
Download
HTTPS
git clone https://github.com/lsg1103275794/MemOSLocal-SM.git
SSH
git clone [email protected]:lsg1103275794/MemOSLocal-SM.git
GitHub CLI
gh repo clone lsg1103275794/MemOSLocal-SM
FAQ
Q: What are the installation steps for MemOSLocal SM Agent Skills?
1.Environment Setup: Deploy Neo4j & Qdrant services, install Claude Code skill
2.Memory Initialization: AI auto-creates project memory space with initial context
3.Interaction Recording: AI actively/passively records key decisions during development
4.Memory Retrieval: Natural language queries trigger relevant memory recall
Q: What are the highlights of MemOSLocal SM Agent Skills?
  • {"title":"Zero-config Integration","description":"Seamless Claude Code integration without complex setup"}
  • {"title":"Hybrid Retrieval","description":"Combines structured knowledge graphs with semantic search"}
  • {"title":"Context Awareness","description":"Auto-detects project context for precise memory targeting"}
  • {"title":"Proactive Recording","description":"Breaks passive query paradigm with AI-initiated memorization"}
Q: What are the use cases for MemOSLocal SM Agent Skills?
  • {"title":"Long-term Development","description":"Multi-day software projects requiring continuity in design decisions"}
  • {"title":"Team Collaboration","description":"Maintaining unified project history awareness across team members"}
  • {"title":"Tech Debt Management","description":"Tracking historical solutions for technical debts"}
  • {"title":"Onboarding","description":"Accelerating new member ramp-up through project memory"}
Q: What are the limitations of MemOSLocal SM Agent Skills?
  • {"title":"Deployment Complexity","description":"Requires maintaining both Neo4j and Qdrant services"}
  • {"title":"Learning Curve","description":"Requires understanding MCP protocol and mode selection"}
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