What is Agentic RAG? Building Agents with Qdrant

Agentic Retrieval Augmented Generation (RAG) is an advanced approach that combines traditional RAG with agent systems, enabling a more dynamic and flexible response generation process. Unlike standard RAG, which follows a linear path, agentic RAG allows agents to make decisions about when and how to use external knowledge sources, such as querying a vector database like Qdrant, to gather the necessary context for generating responses. Agents in this system can take multiple, non-linear steps, and even employ features like query expansion and quality judgment to improve information retrieval. Various frameworks support the development of agentic RAG systems, including LangGraph, CrewAI, AutoGen, and OpenAI Swarm, each offering unique strengths such as multi-agent support, memory systems, and tool integrations. While LangGraph and CrewAI are more established, offering extensive features and integrations, AutoGen and OpenAI Swarm are more experimental and lightweight, focusing on agent coordination through message exchanges. Choosing the right framework depends on factors like existing tech stack, project needs, and desired level of human involvement. Qdrant plays a pivotal role in these systems by providing robust semantic search capabilities, and users can easily start building agentic RAG systems using Qdrant's managed service.