Built to give AI agents real memory

Hypermemory is a foundational memory layer for long-running AI agents. We built it because every serious AI deployment we encountered hit the same wall: agents that reason well but forget everything, sessions that reset, context that evaporates.

The result is a hybrid retrieval system that combines semantic search, BM25 keyword matching, temporal scoring, fact extraction, and multi-hop reasoning — everything needed for an agent to remember what happened, understand what changed, and reason across time.

Hypermemory achieves state-of-the-art performance on the LoCoMo benchmark across all five domains: Temporal Reasoning, Open Domain, Inferential, Single Hop, and Multi Hop questions.

We are an open-source project. Every line of the memory layer is public, auditable, and MIT licensed. You can self-host on your own infrastructure, or use our managed API.