YourMemory, agentic memory is a pruning problem, not a hoarding problem
This 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…
In plain words
YourMemory is an AI memory management system designed for agents that prioritizes pruning irrelevant data over accumulating everything. Rather than storing all information in markdown files or through retrieval-augmented generation, YourMemory maintains an optimized context by keeping only relevant data based on factors like recall rate, importance, and category connections. The system supports both episodic and semantic memory types while maintaining a flat memory size, operating similarly to how human brains manage information storage and retrieval.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
This 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 rate, importance, category, to which memory chain it's connected to etc. These parameters are fine tuned so that we can cater to both episodic memory and semantic memory. Our memory layer keeps the size flat in this manner. You can draw correlation of this infrastructure with how Human brain store and prune memory. The enterprise model is something very exciting as we can extract relevant memories from each user, agent and sub agent in this layer and that can be used by any one in the org, ensuring memory optimization at an enterprise level.
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