Beginner

A complete hands-on guide to LangGraph's core building blocks: StateGraph and custom graphs, state reducers with add_messages, checkpointing with InMemorySaver, human-in-the-loop with interrupt and Command, and dependency injection in tools with ToolRuntime (state, context, store).

How to extend LLM capabilities with Tools and Agents. Three progressive levels: defining tools with @tool, understanding the manual ReAct loop with bind_tools and ToolMessage, and automating everything with create_react_agent from LangGraph. Covers sequential vs parallel tool calls, stateless LLM behavior, and how agents manage conversation history automatically.

RAG solves two fundamental LLM limitations: hallucination and knowledge cutoff. A complete hands-on guide covering the full pipeline — document loading, text splitting, HuggingFace embeddings, InMemoryVectorStore, retriever, and a full LCEL RAG chain. Free, no API cost needed.

How to chain LangChain components with the pipe operator | to build clean, modular, streamable pipelines. Covers the Runnable interface (invoke, batch, stream), RunnablePassthrough, RunnableParallel, RunnableLambda, and two real use cases: word count chain and subject-based routing.

The 4 fundamental concepts you need to master before anything else in LangChain: Models (invoke, stream, batch), Messages (System, Human, AI), Prompt Templates, and Output Parsers (StrOutputParser, with_structured_output). Built on top of OpenRouter — free tier, no credit card needed.