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 follow rigid ReAct-style tool chains locked into fixed workflow structures, which hinders their ability to flexibly handle diverse query types and tool-use strategies. We introduce $\textbf{FlowSearcher}$, a novel web search framework built on agentic workflow synthesis. FlowSearcher decomposes queries into sub-goals, each orchestrated by a tailored workflow graph that adapts the depth and order of tool use, giving the system structural flexibility to handle diverse sub-goals ranging from simple lookups and focused navigation to multi-hop information synthesis. Complementing this, a hierarchical memory distills past workflows into structured experience, providing reusable context that improves orchestration and guides tool use on new queries. This shift from reactive tool calls to memory-driven workflow design and execution marks a principled step toward deliberative web research. Empirical results on GAIA, BrowseComp, and GPQA show that our memory-driven, training-free workflow synthesis consistently matches or exceeds the performance of RLHF-trained systems, pointing toward a new direction of agent design grounded in memory-enhanced structural planning rather than parameter fine-tuning.