Introduction
Memory Graph creates persistent knowledge graphs that store relationships between concepts, entities, and facts. This enables AI systems to maintain context across conversations and make informed decisions based on accumulated knowledge.
Key Features
- Graph-based entity and relationship storage
- Semantic similarity matching
- Automatic entity linking and disambiguation
- Temporal reasoning for time-based facts
- Graph traversal for context retrieval
Real-World Use Cases
- Customer profile management in CRM systems
- Document knowledge extraction and linking
- Question-answering systems with context
- Recommendation engines based on relationships
- Compliance knowledge management
Getting Started
To begin using this MCP, start by configuring your API credentials and environment. Most integrations provide comprehensive documentation and quick-start guides. Connect the MCP to your workflow, test with sample data, and gradually expand to production use cases. The CSGA platform provides monitoring and logging to track usage and identify optimization opportunities.
Integration with CSGA Platform
This MCP integrates seamlessly with the CSGA Global platform. Through our unified dashboard, you can monitor usage, track performance metrics, and manage configurations. The platform provides analytics on MCP performance, cost tracking, and ROI analysis. Integration with other MCPs in the ecosystem enables powerful multi-tool workflows and automated operations across your entire technology stack.
Related Training & Resources
Explore our comprehensive MOOC (Massive Open Online Course) dedicated to this MCP. Learn from expert instructors, work through practical exercises, and earn certifications to validate your expertise. Access course materials, video tutorials, and community forums to deepen your understanding.
For detailed technical documentation, API reference, and advanced integration patterns, visit the full MCP documentation portal.