Google Research· Research

ReasoningBank: Enabling agents to learn from experience

Jun Yan and Chen-Yu Lee, Research Scientists, Google Cloud ReasoningBank is a novel agent memory framework that uses successful and failed experiences to distill generalizable reasoning strategies, enabling an agent to continuously learn from experience after deployment. Quick links Paper ReasoningBank code Share Copy link × Agents are becoming increasingly crucial in tackling complex real-world tasks, ranging from general web navigation to assisting with extensive software engineering codebases. However, as these agents transition into persistent, long-running roles in the real world, they face a critical limitation: they struggle to analyze and learn from successful and failed experiences after deployment. Agents approaching each new task without a memory mechanism will repeatedly make the same strategic errors and discard valuable insights. To address this, various forms of agent memory have been introduced to store information about past interactions for reuse. However, existing methods generally focus on saving exhaustive records of every action taken — such as the trajectory memory used in …

Announced

21 Apr 2026 16:42 UTC