AGENT KB: A Hierarchical Memory Framework for Cross-Domain Agentic Problem Solving

As language agents tackle increasingly complex tasks, they struggle with effective error correction and knowledge reuse across different domains. We present Agent KB, a hierarchical memory framework that enables cross-domain agent learning through a novel \textcolor{cyan}{\texttt{Reason}}\textbf{-}\textcolor{purple}{\texttt{Retrieve}}\textbf{-}\textcolor{blue}{\texttt{Refine}} pipeline. Our dual-phase approach combines workflow-level knowledge retrieval with targeted execution pattern refinement, allowing agents to break free from limited reasoning pathways by incorporating diverse problem-solving strategies. Evaluations on GAIA benchmark demonstrate substantial performance gains, with Agent KB improving success rates by up to \textbf{16.28} percentage points overall. Most notably, on challenging tasks, Claude-3.7 with Agent KB increased performance from \texttt{38.46\%} to \texttt{57.69\%}, while GPT-4.1 showed similar improvements on intermediate tasks (\texttt{53.49\%} to \texttt{73.26\%}). For SWE-bench code repair tasks, our system significantly improved resolution rates, with Claude-3.7 achieving a \textbf{12.0} percentage point gain (\texttt{41.33\%} to \texttt{53.33\%}). Agent KB provides a modular, agent-agnostic infrastructure that facilitates continuous improvement through knowledge sharing across task boundaries and agent architectures.