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Provide persistent graph-memory storage and retrieval for LLM agents via MCP.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "mcp-memory-graphdb" yet — see the docs or source repo.
Using mcp-memory-graphdb, design a long-term memory setup for my Claude agent: store user preferences, past tasks, and key facts with entities, relations, and timestamps, and provide a recommended schema plus write examples.
A graph-based memory design with node and relation definitions, storage guidance, and example write operations.
Show how to use mcp-memory-graphdb to retrieve facts and preferences from a user's last three relevant conversations and turn them into a context summary for an LLM response.
A relevance-ranked memory summary that can be injected into model context for more consistent answers.
I have multiple MCP-based LLM agents. Use mcp-memory-graphdb to design a shared memory layer so they can write, query, and relate shared knowledge, and explain considerations for concurrency, persistence, and data isolation.
A shared-memory architecture recommendation covering read/write flows, data organization, and operational considerations.
Provide persistent graph memory, semantic search, and traversal for AI agents.
Persistent knowledge-graph memory for MCP with semantic search and version tracking.
Provides persistent graph memory for LLMs with auto-linking and layered recall.
Build a semantic graph from project files for search, knowledge, and task management.
Build a project knowledge graph for code search, traversal, and Q&A.
Lightweight vector memory for AI agents to store, search, and delete memories.