Building RAG Agents with LangGraph and LangServe

Modern AI applications require reliable reasoning, external knowledge retrieval, and robust deployment pipelines. This course provides a practical, written introduction to constructing intelligent retrieval-augmented generation systems and serving local language models efficiently. You will start with core artificial intelligence terminology, agent foundations, and the basics of local model deployment before advancing to graph-based agent orchestration. What you'll learn: Understand core concepts of retrieval-augmented generation, embeddings, and vector stores; Build cyclical, stateful AI agent workflows using LangGraph; Integrate local language models to run private, secure agent pipelines; Configure retrieval strategies to ground model responses in real data; Deploy and expose agent APIs effectively using LangServe; Apply industry best practices for state management, structured outputs, and debugging. The curriculum begins with fundamental definitions and progresses step-by-step into hands-on code walkthroughs and architectural patterns. Designed specifically for developers and AI enthusiasts new to agentic frameworks, this text-based guide requires only basic Python familiarity. Start reading today to build and serve production-ready language model agents with confidence.