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Give LLMs persistent knowledge graph memory with semantic retrieval and contextual recall.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Neo4j Knowledge Graph MCP Server" yet — see the docs or source repo.
Store the following user preferences, conversation history, and project context in the Neo4j knowledge graph, and use them for semantic retrieval and contextual recall in future responses: the user prefers concise answers, is preparing an AI product launch, and is focused on enterprise customers.
The system stores key information in structured form and uses prior context to produce more coherent future responses.
Retrieve nodes, relationships, and recently added information related to an enterprise AI product launch from the Neo4j knowledge graph, summarize the key points, and highlight the items most relevant to market positioning.
Returns a summary of relevant entities and relationships, highlighting recent updates and the most useful business insights.
Using temporal data in the Neo4j knowledge graph, compare how this project's requirements, risks, and decisions changed over the last three months, and summarize the key turning points.
Outputs a time-based summary of changes to help the user understand project evolution.
Provide persistent graph memory, semantic search, and traversal for AI agents.
Give AI assistants persistent knowledge graph memory across sessions and workflows.
Ingest documents into Neo4j to build and query a knowledge graph.
Provide persistent graph-memory storage and retrieval for LLM agents via MCP.
Persistent knowledge-graph memory for MCP with semantic search and version tracking.
Turn unstructured text into a searchable knowledge graph memory system.