Persistent memory for agentic AI
Give AI systems memory that lasts.
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.
Why memory
Most agents still behave like stateless interfaces with better wording.
HumemAI focuses on what should persist beyond a prompt: what happened, what matters now, and how structured knowledge should stay available over time. That means treating memory as a real system layer instead of a side effect hidden in context windows.
The result is a stack that can hold documents, tables, graphs, and traces in forms that remain inspectable, replayable, and useful to both people and agents.
Product
Built for real agent workflows.
See how HumemAI handles conversational history, structured knowledge, and hybrid retrieval in one system.
Pricing
Open source or hosted.
Self-host from GitHub when you want full control, or use a managed deployment when you want outcomes faster.
Open source projects
Open source projects shape the work.
Explore the main open source threads behind HumemAI, then move into dedicated project pages for the systems, papers, and implementations inside each one.
Pioneer project · 2026
Audit-Ready Memory
A public-interest project building local-first, inspectable memory infrastructure for AI systems that need audit trails, deterministic replay, and explicit deletion.
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Open-source multi-model database project
Multi-Model Databases
A systems project exploring how tables (SQL), graphs (Cypher), and semantic vector search (ANN search) can coexist in one developer experience without pretending one engine should do every job.
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PhD research project
Machines With Human-Like Memory
A PhD research project on explicit memory architectures for AI, spanning benchmarks, handcrafted agents, learned policies, and temporal knowledge-graph memory.
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