Onyx, an AI assistant, enhances productivity by connecting documented knowledge from various sources using large language models (LLMs) to answer complex enterprise-related questions. They have developed an advanced search method called Agent Search, which deconstructs broad questions into narrower sub-questions, composes initial answers, and refines them based on relevant documents and learned facts. The blog outlines their experience in implementing this flow using LangGraph, an open-source agentic framework, which offers features like parallelism and streaming. Onyx selected LangGraph due to its alignment with their logical flow and its potential for future enhancements, despite some concerns about third-party dependencies and state management. Their implementation emphasizes code organization, state management, and the use of subgraphs for parallel processing, with plans to further develop their system for more agentic capabilities in the future.