RAG Agents with LangChain & LangGraph: A Practical Guide
 

RAG Agents with LangChain & LangGraph: A Practical Guide
Last updated 9/2026
Created by Anton Voroniuk• 1.250.000+ Students, Anton Voroniuk Support, George Paterakis
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 11 Lectures ( 1h 49m ) | Size: 688 MB

Ground your AI agents in real data: build a full RAG agent in LangChain, then rebuild it as a LangGraph workflow

What you'll learn
⚡ Build a complete RAG agent in LangChain that retrieves relevant documents and generates grounded, source-based answers
⚡ Rebuild the same RAG agent as a LangGraph workflow, with retrieval as an explicit, controllable step in the graph
⚡ Understand how chunking, embeddings, retrieval, and grounding work together and how each one affects answer quality
⚡ Explain what RAG is, why LLMs need it, and when retrieval is the right fix for hallucinations and outdated knowledge
⚡ Decide where a retrieval step belongs inside an agent's reason-act-observe loop
⚡ Compare a plain LangChain RAG implementation with a graph-based design and choose the right one for your project
⚡ Explain what an AI agent is, how it makes decisions, and when a single LLM call is the better choice
⚡ Recognize the most common ways agent projects fail and apply design habits that prevent them

Requirements
❗ Working knowledge of Python (functions, classes, working with lists and dictionaries)
❗ Basic familiarity with LangChain - you should have called a chat model and ideally defined a tool at least once
❗ Some exposure to LangGraph is helpful for the final build but not required; the graph is explained step by step
❗ An API key for an LLM provider such as OpenAI or Anthropic, plus access to an embedding model (free tiers are enough)
❗ A computer with Python 3.10+ and a code editor installed

Description
Ask an LLM about a document written yesterday and you get a confident, detailed, wrong answer. The model only knows what it saw in training. Retrieval-Augmented Generation fixes this by letting the agent look things up before it responds, and this course teaches you to build a RAG agent that answers from your own data.

What you will learn
✨ Explain what RAG is, why LLMs need it, and when retrieval is the right fix for hallucinations and outdated knowledge

✨ Understand how chunking, embeddings, retrieval, and grounding work together and how each affects answer quality

✨ Build a complete RAG agent in LangChain, from loading documents to a source-based answer

✨ Rebuild the same agent as a LangGraph workflow, with retrieval as an explicit, controllable step

✨ Choose between a plain LangChain implementation and a graph-based design for your own project

Inside the course
After a short grounding in agent fundamentals, the course explains the full RAG pipeline in plain terms before any code is written. Then come two hands-on builds: the same RAG agent first in LangChain, then rebuilt in LangGraph. Building it twice is deliberate: you will understand both implementations well enough to pick the right one for your needs. Written references close each section.

Who this course is for
⭐ Python developers who want their agent to answer from their own documents instead of guessing
⭐ AI engineers building assistants that need current, verifiable knowledge rather than what the model memorized in training
⭐ Developers who have built a basic "chat with your PDF" demo and want to understand what's actually happening under the hood
⭐ Backend engineers who need to ground LLM output in internal data such as docs, tickets, or knowledge bases
⭐ Anyone who wants a short, practical introduction to RAG before committing to a longer, broader course