AI memory with biological decay (52% recall)
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…
AI memory with biological decay (52% recall) launched on April 26, 2026 with 98 votes, #421 of 21,248 launches that month and more than 95% of that year's launches.
In plain words
AI Memory with Biological Decay is a retrieval system that manages agent memory using the Ebbinghaus forgetting curve instead of static storage. Memories receive strength scores that increase with use and decrease over time, with unused data eventually pruned to reduce context window bloat. A graph layer over the vector store helps locate semantically relevant but dissimilar information. It targets AI builders struggling with token costs and reasoning degradation from accumulated noise in traditional RAG setups.
written from the facts on this page · September 2026
From the sources
Agentic AI memory with Ebbinghaus forgetting curve decay. +16pp better recall than Mem0 on LoCoMo. - sachitrafa/YourMemory
In the maker’s words, at launch
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…
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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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