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

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.