Episodic memory
Capture what happened, when it happened, and why it matters across interactions.
Persistent memory for agentic AI
Turn conversations, documents, tables, and graphs into usable long-term memory.
HumemAI gives AI systems a memory layer that stays persistent across sessions, adapts to mixed data types, and remains inspectable instead of turning into a black box.
How HumemAI worksGUI + hosted + API-ready
Semantic memory
Keep documents, tables, graphs, and connected systems in the most useful structure.
Hybrid retrieval
Let agents query relationships, vectors, and structured knowledge in one memory layer.
Product
A memory layer designed for real agent workflows
See how HumemAI handles conversational history, structured knowledge, and hybrid retrieval in one system.
Learn morePricing
Free open source and paid hosted options
Developers can self-host from GitHub. Teams that want outcomes can use a managed deployment.
Learn moreProjects
Research, prototypes, and funded work in public
Follow the projects that shaped HumemAI so far, from audit-ready memory systems to embedded database work.
Learn moreWhy now
Agents can act, but they still forget.
The world is getting more agentic, but most systems remain stateless chat wrappers. HumemAI focuses on the missing layer: persistent, explainable memory that agents can use over time.
- Persistent memory across sessions instead of stateless chat
- Support for documents, tables, graphs, and connected data
- Open-source foundations with a hosted product path
- Explainable memory structures that teams can inspect and control