@omarsar0
AFlow: Automating Agentic Workflow Generation AFlow is a novel framework for automating the generation of agentic workflows. It reformulates workflow optimization as a search problem over code-represented workflows, where LLM-invoking nodes are connected by edges. AFlow efficiently explores the search space using a variant of MCTS, iteratively refining workflows through code modification, tree-structured experience, and execution feedback. It introduces operators that encapsulate common agentic operations (like Ensemble, Review & Revise) to enhance search efficiency. Experiments across six benchmark datasets demonstrate AFlow’s effectiveness, showing a 5.7% improvement over manually designed methods and a 19.5% improvement over existing automated approaches. AFlow also enables smaller models to outperform GPT-4o on specific tasks at just 4.55% of its inference cost. The framework maintains strong performance even without predefined operators, demonstrating its ability to discover effective workflow structures. It's a compelling approach especially because it seems to work on different kinds of tasks and can potentially discover more optimal ways to optimize costs for agentic workflows. Not sure about latency but it's also interesting that they can efficiently get smaller models to outperform larger ones.