Agent KB: Leveraging Cross-Domain Experience for Agentic Problem Solving
Authors: name: Xiangru Tang; name: Tianrui Qin; name: Tianhao Peng; name: Ziyang Zhou; name: Daniel Shao; name: Tingting Du; name: Xinming Wei; name: Peng Xia; name: Fang Wu; name: He Zhu; name: Ge Zhang; name: Jiaheng Liu; name: Xingyao Wang; name: Sirui Hong; name: Chenglin Wu; name: Hao Cheng; name: Chi Wang; name: Wangchunshu Zhou
Published: 2025-07-08 17:59:22

Abstract: As language agents tackle increasingly complex tasks, they struggle with
effective error correction and experience reuse across domains. We introduce
Agent KB, a hierarchical experience framework that enables complex agentic
problem solving via a novel Reason-Retrieve-Refine pipeline. Agent KB addresses
a core limitation: agents traditionally cannot learn from each other's
experiences. By capturing both high-level strategies and detailed execution
logs, Agent KB creates a shared knowledge base that enables cross-agent
knowledge transfer. Evaluated on the GAIA benchmark, Agent KB improves success
rates by up to 16.28 percentage points. On the most challenging tasks, Claude-3
improves from 38.46% to 57.69%, while GPT-4 improves from 53.49% to 73.26% on
intermediate tasks. On SWE-bench code repair, Agent KB enables Claude-3 to
improve from 41.33% to 53.33%. Our results suggest that Agent KB provides a
modular, framework-agnostic infrastructure for enabling agents to learn from
past experiences and generalize successful strategies to new tasks.