[{"data":1,"prerenderedAt":193},["ShallowReactive",2],{"/open_source/modules/memories/parametric_memory":3,"surround-/open_source/modules/memories/parametric_memory":179},{"id":4,"title":5,"avatar":6,"banner":6,"body":7,"category":6,"desc":6,"description":166,"extension":173,"links":6,"meta":174,"navigation":6,"path":175,"seo":176,"stem":177,"__hash__":178},"docs/en/open_source/modules/memories/parametric_memory.md","Parametric Memory *(Coming Soon)*",null,{"type":8,"value":9,"toc":165},"minimark",[10,21,32,39,46,51,82,86,91,94,98,101,131,135,142,162],[11,12,13],"note",{},[14,15,16,20],"p",{},[17,18,19],"strong",{},"Coming Soon","\nThis feature is still under active development. Stay tuned for updates!",[14,22,23,27,28,31],{},[24,25,26],"code",{},"Parametric Memory"," is the core ",[17,29,30],{},"long-term knowledge and capability store"," inside MemOS.\nUnlike plaintext or activation memories, parametric memory is embedded directly within a model’s weights — encoding deep representations of language structure, world knowledge, and general reasoning abilities.",[14,33,34,35,38],{},"In the MemOS architecture, parametric memory does not just refer to static pre-trained weights. It also includes modular weight components such as ",[17,36,37],{},"LoRA adapters"," and plug-in expert modules. These allow you to incrementally expand or specialize your LLM’s capabilities without retraining the entire model.",[14,40,41,42,45],{},"For example, you could distill structured or stable knowledge into parametric form, save it as a ",[17,43,44],{},"capability block",", and dynamically load or unload it during inference. This makes it easy to create “expert sub-models” for tasks like legal reasoning, financial analysis, or domain-specific summarization — all managed by MemOS.",[47,48,50],"h2",{"id":49},"design-goals","Design Goals",[52,53,55],"list",{"icon":54},"ph:check-circle-duotone",[56,57,58,65,71],"ul",{},[59,60,61,64],"li",{},[17,62,63],{},"Controllability"," — Generate, load, swap, or compose parametric modules\non demand.",[59,66,67,70],{},[17,68,69],{},"Plasticity"," — Evolve alongside plaintext and activation memories; support knowledge distillation and rollback.",[59,72,73,76,77,81],{},[17,74,75],{},"Traceability"," ",[78,79,80],"em",{},"(Coming Soon)"," — Versioning and governance for parametric blocks.",[47,83,85],{"id":84},"current-status","Current Status",[14,87,88,90],{},[24,89,26],{}," is currently under design and prototyping.\nAPIs for generating, compressing, and hot-swapping parametric modules will be released in future versions — supporting multi-task, multi-role, and multi-agent architectures.",[14,92,93],{},"Stay tuned!",[47,95,97],{"id":96},"related-modules","Related Modules",[14,99,100],{},"While parametric memory is under development, try out these today:",[56,102,103,113,122],{},[59,104,105,112],{},[17,106,107],{},[108,109,111],"a",{"href":110},"/open_source/modules/memories/general_textual_memory","GeneralTextMemory",": Flexible vector-based semantic storage.",[59,114,115,121],{},[17,116,117],{},[108,118,120],{"href":119},"/open_source/modules/memories/tree_textual_memory","TreeTextMemory",": Structured, hierarchical knowledge graphs.",[59,123,124,130],{},[17,125,126],{},[108,127,129],{"href":128},"/open_source/modules/memories/kv_cache_memory","Activation Memory",": Efficient runtime state caching.",[47,132,134],{"id":133},"developer-note","Developer Note",[14,136,137,138,141],{},"Parametric Memory will complete MemOS’s vision of a unified ",[17,139,140],{},"Memory³"," architecture:",[56,143,144,150,156],{},[59,145,146,149],{},[17,147,148],{},"Parametric",": Embedded knowledge",[59,151,152,155],{},[17,153,154],{},"Activation",": Ephemeral runtime states",[59,157,158,161],{},[17,159,160],{},"Plaintext",": Structured, traceable external memories",[14,163,164],{},"Bringing all three together enables adaptable, evolvable, and explainable intelligent systems.",{"title":166,"searchDepth":167,"depth":167,"links":168},"",2,[169,170,171,172],{"id":49,"depth":167,"text":50},{"id":84,"depth":167,"text":85},{"id":96,"depth":167,"text":97},{"id":133,"depth":167,"text":134},"md",{},"/en/open_source/modules/memories/parametric_memory",{"title":5,"description":166},"en/open_source/modules/memories/parametric_memory","7z6htBP-yk6duN0UsooFvwpYbo35Rvsuh7OpZ5mS47w",[180,187],{"title":181,"path":128,"stem":182,"icon":183,"framework":6,"module":6,"class":184,"target":-1,"active":185,"defaultOpen":185,"children":-1,"description":186},"KV Cache Memory","open_source/modules/memories/kv_cache_memory","i-ri-database-2-line",[],false,"KVCacheMemory is a specialized memory module in MemOS for storing and managing key-value (KV) caches, primarily used to accelerate large language model (LLM) inference and support efficient context reuse. It is especially useful for activation memory in conversational and generative AI systems.",{"title":188,"path":189,"stem":190,"icon":191,"framework":6,"module":6,"class":192,"target":-1,"active":185,"defaultOpen":185,"children":-1,"description":-1},"Performance Tuning","/open_source/best_practice/performance_tuning","open_source/best_practice/performance_tuning","i-ri-speed-line",[],1770372088273]