Multi-agent LLM architectures, such as LangChain and AutoGen, largely assume English as the lingua franca for internal inter-agent communication, even when the end-user task is non-English. We fill this gap by evaluating a two-agent extraction-answer core, with an additional back-translation agent in the English-forced condition, across four typologically diverse languages (Hindi, Chinese, Spanish, Arabic; n=300 per language) using the Aya-23-8B model. We compare a native-language pipeline to an English-forced one (which incorporates a final back-translation step from English to the user’s language). We discover a statistically significant English-Forcing Tax (surviving a strict Bonferroni correction) that isolates the cost of English routing from general multi-agent orchestration overhead. Forcing inter-agent communication through English reduces Exact Match accuracy by 13.0 percentage points (Spanish) up to 30.6 percentage points (Hindi) compared to native-language multi-agent execution. Using chrF scores as a diagnostic measure of English-reference lexical overlap, we find that lower overlap is strongly associated with pipeline failure, consistent with translation loss being an important contributor to the observed performance drop. These findings suggest a compelling case for native-language routing in agent frameworks when the source and target languages are typologically distant, reducing a compounding translation tax.

Story building is an important part of language and overall development of a child. Developing an interactive and artificial intelligence (AI) based solution to create stories for children is an open and challenging problem. Methods combining large language models (LLMs) and knowledge graphs (KGs) have further enabled high quality and coherent story generation. In this work, we present a platform, Story Yarn, developed for interactive story creation for children. We customise a KG, using children stories, which captures relationships between components of stories. This customised KG is then used along with LLM to collaboratively create a story. We have also built a simple app to facilitate user interaction. This platform can aid the creative development of children, and can be used at home or in schools.