Software development is increasingly reliant on open-source libraries, but managing these dependencies can be a real headache. From security vulnerabilities to compatibility issues, ensuring a smooth integration process is crucial. Imagine having an AI assistant that could analyze your project's dependencies, identify potential risks, and even suggest solutions. That's the promise of DEPSRAG, a new multi-agent framework designed to revolutionize how developers handle software dependencies. DEPSRAG constructs a knowledge graph of your project's dependencies, including both direct and transitive dependencies. This comprehensive view allows developers to interact with DEPSRAG through a conversational interface, asking questions about potential risks, key packages, and dependency paths. What sets DEPSRAG apart is its ability to tap into external resources like vulnerability databases and the web, providing real-time insights that go beyond static analysis. The magic behind DEPSRAG lies in its multi-agent system. It uses Retrieval-Augmented Generation (RAG) to enhance queries with relevant information from the knowledge graph and external sources. A 'Critic-Agent' feedback loop ensures the accuracy and clarity of the AI's responses, refining the results through iterative reasoning and validation. This approach significantly improves the reliability of the system, addressing the common issue of 'hallucinations' in large language models. In early tests, DEPSRAG demonstrated a threefold increase in accuracy with the Critic-Agent mechanism. This suggests that AI-powered tools like DEPSRAG could significantly streamline the software development lifecycle, reducing the time and effort required for dependency management. While challenges remain, such as optimizing the Critic-Agent interaction and expanding the system's capabilities, DEPSRAG represents a significant step towards more intelligent and efficient software development.