Various human-designed prompt engineering techniques have been proposed to improve problem solvers based on Large Language Models (LLMs), yielding many disparate code bases. We unify these approaches by describing LLM-based agents as computational graphs. The nodes implement functions to process multimodal data or query LLMs, and the edges describe the information flow between operations. Graphs can be recursively combined into larger composite graphs representing hierarchies of inter-agent collaboration (where edges connect operations of different agents). Our novel automatic graph optimizers (1) refine node-level LLM prompts (node optimization) and (2) improve agent orchestration by changing graph connectivity (edge optimization). Experiments demonstrate that our framework can be used to efficiently develop, integrate, and automatically improve various LLM agents. The code can be found at https://github.com/metauto-ai/gptswarm.

人工设计的提示工程技术已经被提出来改善基于大型语言模型的问题解决方法，通过以计算图的形式来描述基于LLM的代理人，实现多模态数据处理或查询LLMs，优化节点级别的LLM提示（节点优化）和改进代理人编排（边优化），该框架能够高效地开发、整合和自动改进各种LLM代理人。

可优化的图形语言代理