Persistent Memory
Your AI agents retain context across every session. Knowledge graphs persist indefinitely — no more cold starts, no more forgotten conversations. Memories are durable, queryable, and always available.
Built for production memory workloads
Not a demo. Not a toy. Neuroloom is infrastructure designed to handle persistent agent memory at scale — from solo developers to enterprise teams.
Semantic Search
Retrieve the right memory at the right moment. Vector similarity search powered by pgvector surfaces relevant context even when the exact words differ. Tunable recall with HNSW indexing.
MCP-Native Integration
Drop-in integration with any MCP-compatible host. Connect Claude Desktop, Cursor, or your own AI agent in minutes — no SDK installation, no custom middleware. Just a server URL and an API key.
Workspace Isolation
Every workspace is a hermetically sealed knowledge graph. Multi-tenant by design — team memories never cross workspace boundaries. Compliance-ready with per-workspace access control.
Structured Knowledge Graphs
Memories are more than key-value pairs. Neuroloom stores entities, relations, and observations as first-class graph nodes, enabling reasoning over connected knowledge — not just retrieval.
Low-Latency Retrieval
Purpose-built for real-time agent workloads. Approximate nearest-neighbor search returns relevant memories in milliseconds, keeping your agent's response time fast even with millions of stored facts.
See it in action
The fastest way to understand Neuroloom is to connect it to an agent. Takes under five minutes.