An MCP server that lets LLM agents accumulate organizational knowledge across sessions, scoped by domain, backed by Neo4j with enforced structural invariants.
Enables AI agents to store, retrieve, and connect information in a Neo4j graph database as persistent memory, with semantic relationships, natural language search, and temporal tracking across conversations.
Enables AI agents to maintain persistent, inspectable understanding through typed, revisable updates, and to coordinate multi-agent work via shared graph-based stigmergy.
Audit-grade memory backbone for agent teams. Bi-temporal facts (event time + transaction time, with recall(as_of=...) replay), 6-step deterministic retrieval (no LLM in the critical path), conversation ingest with speaker-locked dual-pass extraction, per-tenant Postgres row-level security, and Ed25519-signed provenance. Postgres + pgvector + Neo4j defaults.
Provides persistent knowledge graph memory for AI agents, enabling them to store, recall, and query facts about people, projects, and relationships across sessions.
Enables storage and retrieval of knowledge in a graph database format, allowing users to create, update, search, and delete entities and relationships in a Neo4j-powered knowledge graph through natural language.