A research team from Zhejiang University and Alibaba Group has introduced Memp, a framework that gives large language model (LLM) agents a form of procedural memory designed to make them more efficient at complex, multi-step tasks.
Instead of relearning workflows from scratch, Memp enables agents to store, retrieve, and update past experiences in real time.
For developers and architects, this means fewer wasted tokens, faster task completion, and the possibility of running smaller, cheaper models without sacrificing performance. This advance could influence how AI pipelines and agent architectures are built.
A research team from Zhejiang University and Alibaba Group has introduced Memp, a framework that gives large language model (LLM) agents a form of procedural memory designed to make them more efficient at complex, multi-step tasks.
Instead of relearning workflows from scratch, Memp enables agents to store, retrieve, and update past experiences in real time.
For developers and architects, this means fewer wasted tokens, faster task completion, and the possibility of running smaller, cheaper models without sacrificing performance.
This advance could influence how AI pipelines and agent architectures are built.
A research team from Zhejiang University and Alibaba Group has introduced Memp, a framework that gives large language model (LLM) agents a form of procedural memory designed to make them more efficient at complex, multi-step tasks.
Instead of relearning workflows from scratch, Memp enables agents to store, retrieve, and update past experiences in real time.
For developers and architects, this means fewer wasted tokens, faster task completion, and the possibility of running smaller, cheaper models without sacrificing performance. This advance could influence how AI pipelines and agent architectures are built.
