Neo4j Knowledge Graph Memory
Store and retrieve user-specific memories across sessions using Neo4j graph database. This MCP memory infrastructure enables AI assistants to maintain context, recall past interactions, and manage memories with semantic search capabilities. Transform your agent's conversations into a searchable memory bank with entities and relationships. ### Key capabilities: - **Store memories** persistently across multiple sessions - **Retrieve context** with hybrid semantic and exact search - **Manage memory banks** with multi-database project isolation - **Recall information** through vector embeddings and graph traversal - **Memory extension** for AI agents with temporal tracking - **Knowledge graph** format with intelligent relationships Perfect for building AI assistants with long-term memory, maintaining user context, and creating memory systems that remember preferences and past interactions. Self-hosted memory infrastructure built on Neo4j for reliability and performance.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately. Last scanned 2026-09-19.
Scan details: Circle-IR · 2026-09-19 · Appeal
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- container
- Category
- database
- Source
- Smithery
- Author type
- human
- Last scanned
- 2026-09-19
- Updated
- 2026-09-19
Use via MCP
Resolve Neo4j Knowledge Graph Memory from your agent
Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.
One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp--scope user for --scope project to commit it to .mcp.json.