Hippo, biologically inspired memory for AI agents
Biologically-inspired memory for AI agents. Decay, retrieval strengthening, consolidation. Zero dependencies. - kitfunso/hippo-memory
Hippo, biologically inspired memory for AI agents launched on April 6, 2026 with 128 votes, #271 of 21,248 launches that month and more than 95% of that year's launches.
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
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AI memory with biological decay (52% recall)Apr 2026 · github.com · ▲98Most 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…
YourMemoryApr 2026 · yourmemoryai.vercel.app · ▲93This is a project that I have been building for a while now, YourMemory is a solution to agentic memory which focuses on pruning of noise rather than hoarding of data. In the current state of agentic memory most of the context is stored in the form of a MD file or is derived through a RAG model where you store each and everything. Both of the solution leads to bloated context which does not optimize the usage of any tokens. In this system we only keep relevant data in our memory and prune all the unnecessary data. The relevance of a data is derived through multiple factors such as recall…
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