NutriCHAT: A Reasoning-Driven large language model agent with Expert-Designed tools for Knowledge-Grounded poultry nutrition Assistance

• The first large language model agent was developed in the poultry nutrition domain. • NutriCHAT achieved 83. 87% improvement over GPT-4o in expert evaluation. • Four specialized tools integrated 17, 017 nutrition parameters from trusted sources. • Operating cost of 0. 0124 per query with 61. 90% lower hallucination than GPT-4o. The integration of generative artificial intelligence (AI) in poultry nutrition can enhance decision-making, support feed design and improve animal health. This study introduces NutriCHAT, a domain-specific AI agent for poultry nutrition, built on a novel hybrid architecture combining ReAct (Reasoning + Acting) framework with Retrieval-Augmented Generation (RAG). NutriCHAT incorporates four expert-designed tools: a Feed Ingredient Bank (integrating 12, 100 nutritional parameters from 100 feedstuffs and 20 amino acids, including composition, digestibility, and energy values), a Definitions Tool (600 poultry definitions), a Nutrient Requirements Tool (855 parameters representing Ross and Cobb broiler nutrition requirements across phases, genetic lines, and weight targets), and a Performance Management Tool (3462 parameters of six production parameters of Ross and Cobb broilers). NutriCHAT was evaluated against GPT-4o operating in standalone mode, and four additional LLMs (Grok-beta, GPT-4o mini, GPT-3. 5 Turbo, and Gemini 2. 0 Flash) using 120 queries assessed on correctness, precision, and scientific depth metrics (5-point Likert scale). Expert evaluation showed 83. 87% overall improvement compared to GPT-4o. SelfCheckGPT-NLI analysis demonstrated that NutriCHAT-200 achieved a 61. 90% reduction in hallucination score compared to GPT-4o. Ablation studies confirmed ReAct’s role in correctness and precision, and including RAG component in NutriCHAT contributed to improvement in scientific depth. Cost analysis showed an average query cost of 0. 0124 for NutriCHAT, with average inference time of 6. 89 s per query. This work demonstrates feasibility for data-driven poultry nutrition management by retrieving data from trusted knowledge bases, advancing precision agriculture via computational tools for sustainable production.