  Managing software dependencies is a crucial maintenance task in software
development and is becoming a rapidly growing research field, especially in
light of the significant increase in software supply chain attacks. Specialized
expertise and substantial developer effort are required to fully comprehend
dependencies and reveal hidden properties about the dependencies (e.g., number
of dependencies, dependency chains, depth of dependencies).
  Recent advancements in Large Language Models (LLMs) allow the retrieval of
information from various data sources for response generation, thus providing a
new opportunity to uniquely manage software dependencies. To highlight the
potential of this technology, we present~\tool, a proof-of-concept Retrieval
Augmented Generation (RAG) approach that constructs direct and transitive
dependencies of software packages as a Knowledge Graph (KG) in four popular
software ecosystems. DepsRAG can answer user questions about software
dependencies by automatically generating necessary queries to retrieve
information from the KG, and then augmenting the input of LLMs with the
retrieved information. DepsRAG can also perform Web search to answer questions
that the LLM cannot directly answer via the KG. We identify tangible benefits
that DepsRAG can offer and discuss its limitations.
