The exploration of building a web research agent evolved into developing a simple yet efficient and customizable retriever, highlighting an effective approach for web research applications. Initially inspired by the popularity of tools like gpt-researcher and AI search engines, the project aimed to create an agent capable of autonomously scouring the web. However, the realization of AI's unique ability to conduct parallel searches led to the development of a retriever that synthesizes information from multiple sources using a LangChain framework. This retriever leverages large language models (LLMs) to generate search queries, execute searches, and index relevant documents into a vectorstore, enabling streamlined retrieval augmented generation. The project underscores the potential benefits of agentic properties for further refinement and provides a Streamlit interface for configuration with various LLMs, vectorstores, and search tools, emphasizing the merits of privacy, configurability, and observability in lightweight web research tools.