LangChain is a common layer that lets you write against any LLM the same way. And in 1.x, agent development has been consolidated into a single function: create_agent. Hand it a model, some tools, and memory, and you get an agent that decides on its own to call a tool, remembers the conversation, and searches documents when it needs to. This course builds those parts up one at a time, hands on, through 20 practical drills.
Learning follows a roadmap of 7 chapters. Once Section 0 has your environment ready, Section 1 nails the foundation: calling an LLM, streaming, and system prompts. Section 2 covers prompt templates and the technique that pays off most in real work—structured output, receiving the LLM’s reply as a Python type (Pydantic) instead of a string—carried all the way through to building your own review analyzer.
Section 3 is the peak of the course: tools and agents. The “magic” that agents look like to so many learners is taken apart, starting from how a @tool decorator’s docstring becomes the LLM’s instructions. You’ll build an agent that picks between a calculator and a unit converter with create_agent, then peek into the message history to see what the agent thought and which tool it called—the basics of debugging.
