Hippo, biologically inspired memory for AI agents
In plain words
Hippo is a memory system for AI agents inspired by biological memory processes. It enables agents to store, retrieve, and learn from past interactions in ways that mimic how brains organize information. The system is designed for developers building AI agents that need to improve performance over time through accumulated experience. Hippo's biologically-inspired approach offers an alternative to traditional database storage methods for AI applications.
written from the facts on this page · September 2026
Does the same job
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Most RAG setups fail because they treat memory like a static filing cabinet. When every transient bug fix or abandoned rule is stored forever, the context window eventually chokes on noise, spiking token costs and degrading the agent's reasoning. This implementation experiments with a biological approach by using the Ebbinghaus forgetting curve to manage context as a living substrate. Memories are assigned a "strength" score where each recall reinforces the data and flattens its decay curve (spaced repetition), while unused data eventually hits a threshold and is pruned. To solve the…
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