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AI with human‑like memory

HumemAI separates conversations from data using two complementary memory systems inspired by cognitive science:

Episodic: Your conversations with HumemAI agents are stored as a temporal knowledge graph capturing the "where", "when", and "what" of each interaction. This personal, time‑aware memory can be visualized and persisted in graph databases for efficient read/write operations.

Semantic: Your databases—SQL (relational) and NoSQL (document, graph, key‑value, etc.)—connect through lightweight adapters. When needed, we apply minimal, reversible transformations so data aligns with supported engines and schemas—avoiding wholesale migrations.

Together, these memory systems enable hybrid retrieval: relationship‑aware queries over your conversation history combined with semantic search across your data, delivering contextually grounded and explainable answers.

Try the playground · About our approach

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