OpWeave: Self-Expanding Agentic Workflows with Hierarchical Transferable Memory

Automated agentic workflow design typically searches a fixed operator library, limiting adaptation when required capabilities are absent and forcing repeated search across tasks. We present OpWeave, a framework that self-expands, optimizes, and reuses a verified language of agentic workflows. From a small development subset, OpWeave profiles the task, retrieves procedural evidence and prior experience, and uses this evidence to propose typed operators and reusable workflow patterns. OpWeave then optimizes an initial workflow using development-set feedback and execution traces, where hierarchical attribution localizes failures at progressively finer granularity to keep each repair targeted. Verified elements and compact experience summaries are retained for reuse across tasks. Across six benchmarks spanning three task categories, OpWeave outperforms the strongest automated workflow design baseline on five of six tasks, with an average gain of 3.1 points. In a search-free setting, OpWeave uses memory priors to guide workflow generation and matches the strongest baseline with only 0.02% of the tokens of a full run. These results show that OpWeave enables adaptive and reusable workflow design by expanding and retaining its workflow language across tasks.