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RAG Agents with LangChain & LangGraph: A Practical Guide
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110 students
RAG Agents with LangChain & LangGraph: A Practical Guide
Ground your AI agents in real data: build a full RAG agent in LangChain, then rebuild it as a LangGraph workflow
Last updated 9/2026
English
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- Explain what RAG is, why LLMs need it, and when retrieval is the right fix for hallucinations and outdated knowledge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
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Course content
5 sections • 11 lectures • 1h 49m total length
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Explain what RAG is, why LLMs need it, and when retrieval is the right fix for hallucinations and outdated knowledge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. Developers who have made a basic "chat with your documents" demo and want to understand what is actually happening under the hood. Basic familiarity with LangChain is enough; exposure to LangGraph helps but is not required.
Why take it
It is short and practical: two theory sections, two builds, and a pipeline that works with any documents you swap in. You will be able to diagnose a bad answer instead of just rewriting the prompt.
Enroll now and build an agent that answers from your data.
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