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A paper presents a framework where LLM agents are represented as computational graphs with nodes as operations and edges as information flow, enabling automatic optimization of both node prompts and edge connectivity via RL, turning scattered agent swarms into a single optimizable graph.
This article explains how to build a swarm of LLM agents with different expert roles that argue over a decision to reach a better conclusion, providing full Python code for the orchestrator, experts, and merge components.