Product
A memory layer for agentic AI.
Users interact through natural language, upload documents and data, and let HumemAI organize knowledge into the right memory structures across text, tables, graphs, and vectors.
What it does
HumemAI helps agents remember in structures that stay useful over time.
Instead of forcing everything into one rolling chat log, HumemAI separates what happened, what is known, and how that knowledge should be retrieved later. That makes the system easier to inspect, easier to update, and more useful across sessions.
The goal is not to simulate memory abstractly. It is to make memory a real product layer that can support ongoing agent workflows across conversations, documents, tables, graphs, and connected data.
Episodic memory
Capture conversations, actions, and interaction history with enough structure to replay what happened over time.
Semantic memory
Keep documents, tables, entities, and relationships in the format that best preserves meaning and retrieval quality.
Hybrid retrieval
Let agents query vectors, relationships, and structured knowledge together instead of choosing one memory style for everything.
Workspace
A workspace shaped around ingestion, retrieval, and long-term memory.
Teams can use HumemAI as an integrated environment for getting information into memory, exploring it in the right structure, and retrieving it later in ways that remain legible to both people and agents.
If you want the operational breakdown between self-hosted, hosted, and custom options, the next stop is Pricing.
Next step
Choose how you want to use HumemAI.
Some teams start with the open source components and self-host. Others want a hosted setup or a deployment shaped around their workflow.