FlowSearcher: Synthesizing Memory-Guided Agentic Workflows for Web Information Seeking

Web search is a cornerstone for deep research agents, enabling them to acquire and reason over knowledge beyond static corpora. Yet most existing systems rely on ReAct-style tool chains with rigid, linear workflows, hindering their ability to adapt to diverse query types and tool-use strategies. We introduce FlowSearcher, a novel deep search framework that formulates web information seeking as memory-guided agentic workflow synthesis. FlowSearcher decomposes a query into subgoals and synthesizes a tailored workflow graph for each subgoal, dynamically adapting the depth, ordering, and composition of tool use. Complementing this, a hierarchical memory consolidates past workflows into reusable structural experience, which is retrieved to guide both workflow orchestration and execution on new queries. By shifting from reactive tool calls to experience-conditioned workflow design, FlowSearcher enables flexible multi-path exploration and reuse without any supervised training or RLHF. Experiments on GAIA, BrowseComp, and GPQA show that FlowSearcher consistently matches or exceeds the performance of RLHF-trained web agents under the same model backbone. Our code is released at github.com/XiangKeYiNTU/flowsearcher.