Developing AI Agents with LangGraph, RAG, and FastAPI

Moving beyond simple LLM prompts is essential for building software that can independently solve complex tasks. This text-based course guides you through designing, testing, and deploying production-ready AI agents and multi-agent workflows. You will transition from writing basic scripts to engineering sophisticated AI systems that manage state, coordinate multiple specialized agents, and retrieve contextual knowledge efficiently. Through written explanations and practical code snippets, you will gain the skills to build resilient AI services that can handle real-world workloads. What you'll learn: - Understand the core concepts of agentic workflows, state machines, and Retrieval-Augmented Generation (RAG). - Build stateful, multi-agent systems with conditional routing and human-in-the-loop patterns using LangGraph. - Implement advanced RAG pipelines using vector databases like Chroma, hybrid search, and contextual compression. - Develop secure, asynchronous APIs using FastAPI to serve your AI agents to external applications. - Apply modern Python practices including type hints, Pydantic data validation, and automated testing with pytest. - Configure application monitoring with LangSmith tracing and containerize your setup using Docker. The course begins with foundational AI agent concepts, Python type hints, and basic chain construction. You will then progress to complex multi-agent orchestration, advanced retrieval patterns, and robust deployment workflows. This course is designed for Python developers and aspiring AI engineers who want to build real-world AI systems. No prior experience with LangChain or LangGraph is required, though a basic understanding of Python is recommended. Start reading today to build and deploy your own production-grade AI agents.