Live-Evo: Online Evolution of Agentic Memory from Continuous Feedback

Large language model (LLM) agents are increasingly equipped with memory, which are stored experience and reusable guidance that can improve task-solving performance. Recent \emph{self-evolving} systems update memory based on interaction outcomes, but most existing evolution pipelines are developed for static train/test splits and only approximate online learning by folding static benchmarks, making them brittle under true distribution shift and continuous feedback. We introduce $\text{textsc}Live-Evo$, an online self-evolving memory system that learns from a stream of incoming data over time. $\text{textsc}Live-Evo$ decouples \emph{what happened} from \emph{how to use it} via an Experience Bank and a Meta-Guideline Bank, compiling task-adaptive guidelines from retrieved experiences for each task. To manage memory online, $\text{textsc}Live-Evo$ maintains experience weights and updates them from feedback: experiences that consistently help are reinforced and retrieved more often, while misleading or stale experiences are down-weighted and gradually forgotten, analogous to reinforcement and decay in human memory. On the live $\mathit{\text{Prophet}}\mathit{\text{Arena}}$ benchmark over a 10-week horizon, $\text{textsc}Live-Evo$ improves Brier score by 20.8% and increases market returns by 12.9%, while also transferring to deep-research benchmarks with consistent gains over strong baselines. Our code is available at https://github.com/ag2ai/Live-Evo.