Landscape architecture, a discipline that crafts a creative and integrated vision of a site while navigating underlying complexities of the environment, is rapidly evolving with the surge of AI tools and large language models (LLMs). This leads to a pertinent question: Can AI grasp the nuanced intricacies in portions of landscape architecture or entire design concepts to enhance environmental comprehension and invigorate the creative process? In this instance, we focus on the metabolic understandings of site, systems, and large sale city planning around ‘urban metabolism’, and if AI can assist in innovating at the systems performance cycles level of a project, and in the graphics which illustrate those processes. Building upon designers’ existing practices in platforms like Midjourney and ChatGPT to generate ideas in text and images through prompts, this research developed a new tool to decode environment metabolism: EcoCircuit. EcoCircuit, empowered by LLMs, features a Text2Flow workflow that allows users to input an environmental description and generate complex landscape flow visuals. These visuals, intricately representing landscape metabolisms, leverage visual problem-solving with the Chain-of-Thoughts LLM reasoning framework of staging prompts.