Free online courseLangGraph Masterclass: Build LLM Agents, RAG, Memory, Multi-Agent Systems and Streaming
Duration of the online course: 8 hours and 42 minutes
New
Free LangGraph course to build LLM agents, RAG, memory, multi-agent systems, and streaming workflows with practical, project-based lessons.
In this free course, learn about
Foundations and Autonomy in LLM Apps
Agents and Why LangGraph
Reflection Agents with MessageGraph
Structured Outputs and Reflexion Agent Build
StateGraph and State Transformation
ReAct Agents Rebuilt with LangGraph
Chatbots, Tools, and Memory
Human-in-the-Loop Workflows
RAG Systems in LangGraph
Multi-Agent Systems and Streaming
Capstone: Full-Stack LangGraph Application
Course Description
LangGraph Masterclass is a free, hands-on course in Technology and Programming focused on Artificial Intelligence and Machine Learning. It guides you through building reliable LLM applications with LangGraph, moving from foundational concepts to production-style agent systems.
You will learn how to design agent workflows with clear levels of autonomy, understand the limitations of common ReAct-style patterns, and implement reflection and reflexion approaches that improve output quality through structured reasoning and iterative revision. Along the way, you will work with structured LLM outputs and tracing practices to make behavior easier to debug and improve.
The course also covers stateful graph design, including StateGraph concepts, manual and declarative state transformations, and practical node construction. You will build chat experiences that progress from basic interactions to tool-augmented assistants, and then to memory-enabled conversations using checkpointing so your agent can persist context across turns.
For real-world safety and control, you will explore human-in-the-loop patterns that support pausing, resuming, reviewing tool calls, and handling multi-turn conversations. Retrieval-augmented generation is addressed through classification-driven retrieval, RAG-powered tool calling, and advanced multi-step reasoning to improve factual grounding.
Finally, you will step into multi-agent systems with supervisor-style orchestration and learn streaming fundamentals for responsive user experiences. A capstone project ties everything together by building and deploying a complete graph-based application with an API layer, streaming support, and a frontend walkthrough.
Course content
Video class: LangGraph Crash Course #1 - Introduction04m
Exercise: According to the course overview, what is the main reason for learning LangGraph instead of only using LangChain?
Video class: LangGraph Crash Course #2 - Levels of Autonomy in LLM applications16m
Video class: LangGraph Crash Course #3 - Agents05m
Exercise: In the ReAct agent pattern, what is the correct loop of steps used to solve a problem?
Video class: LangGraph Crash Course #4 - Agents30m
Exercise: Why did the agent initially fail when asked to create a tweet about today’s weather, and what fixed it?
Video class: LangGraph Crash Course #5 - Drawbacks of React Agents09m
Exercise: Why does LangGraph come into the picture compared to ReAct agents and chains?
Exercise: In a reflection agent system, what is the main purpose of alternating between the generate node and the reflect node?
Video class: LangGraph Crash Course #9.5 - Structured LLM Outputs15m
Exercise: Which approach is described as the first way to force an LLM to return structured output and also perform validation on required fields and data types?
Exercise: In the execute_tools node, what is the main purpose of using the tool_call_id when creating the final ToolMessage?
Video class: LangGraph Crash Course #14 - Reflexion Agent - Building Graph20m
Exercise: In a reflexion agent graph, what is the purpose of counting ToolMessages in the event loop?
Video class: LangGraph Crash Course #15 - What is StateGraph?11m
Exercise: In a LangGraph StateGraph, what is the main benefit of using a custom state instead of a MessageGraph?
Video class: LangGraph Crash Course #16 - Manual State Transformation04m
Exercise: When adding a history list to a custom state, how is the latest count appended to the existing list?
Video class: LangGraph Crash Course #17 - Declarative Annotated State Transformation04m
Video class: LangGraph Crash Course #18 - ReAct using LangGraph - Overview13m
Exercise: When rebuilding a ReAct agent with LangGraph, what is the main component being eliminated to gain full control over the LLM-tool looping and avoid infinite loops?
Video class: LangGraph Crash Course #19 - ReAct using LangGraph - Reasoning Runnable03m
Exercise: When building a ReAct agent reasoning runnable, which inputs are required by the create_react_agent method?
Video class: LangGraph Crash Course #20 - ReAct using LangGraph - State06m
Exercise: Which three state properties are needed to build the described ReAct agent system?
Video class: LangGraph Crash Course #21 - ReAct using LangGraph - Building Nodes03m
Exercise: In a ReAct-style LangGraph setup, what is appended to intermediate_steps after the ACT node runs a tool?
Video class: LangGraph Crash Course #21.5 - ToolExecutor (Deprecated)02m
Exercise: In the updated ACT node implementation, how is a tool executed without using the deprecated ToolExecutor?
Video class: LangGraph Crash Course #22 - ReAct using LangGraph - Final Graph06m
Exercise: In a ReAct-style LangGraph using a conditional edge after the Reason node, when should the graph transition to the END state?
Video class: LangGraph Crash Course #23 - ReAct using LangGraph - LangSmith Tracing03m
Exercise: In a ReAct-style LangGraph agent trace, what does the ACT node primarily do after the reason node outputs an agent action?
Video class: LangGraph Crash Course #24 - Chatbot - Introduction01m
Exercise: What is the key characteristic of the first basic chatbot being built?
Video class: LangGraph Crash Course #25 - Chatbot - Basic10m
Exercise: Why does the basic LangGraph chatbot fail to answer a follow-up like What’s my name? after you previously said Hi I’m Harish?
Video class: LangGraph Crash Course #26 - Chatbot - With Tools15m
Exercise: In a tool-enabled LangGraph chatbot, what does it usually mean when the LLM response has empty content but includes a non-empty tool_calls field?
Video class: LangGraph Crash Course #27 - Chatbot - With Memory (What is Checkpointer?)15m
Exercise: What two things are needed to add persistence (memory) to a LangGraph chatbot across invocations?
Video class: LangGraph Crash Course #28 - Chatbot - With SqliteSaver Checkpointer07m
Exercise: What change enables true persistence so a conversation can be resumed after closing and reopening the app?
Video class: LangGraph Crash Course #29 - Human In The Loop - Introduction12m
Exercise: Why is LangGraph’s interrupt preferred over Python’s input() for serious human-in-the-loop workflows?
Video class: LangGraph Crash Course #30 - Human In The Loop - Command Class04m
Exercise: In LangGraph, what is the main purpose of returning a Command from a node?
Video class: LangGraph Crash Course #31 - Human In The Loop - Resume Graph10m
Exercise: In an interrupted LangGraph run, why is a memory/checkpointer (e.g., MemorySaver) needed?
Video class: LangGraph Crash Course #32 - Human In The Loop - Review Tool Calls09m
Exercise: What is the primary purpose of using interrupt_before in the compile step when building a human-in-the-loop tool approval flow?
Video class: LangGraph Crash Course #33 - Human In The Loop - Multi-turn Conversations06m
Exercise: In the multi-turn LinkedIn post agent, what causes the graph to stop looping and reach the end node?
Video class: LangGraph Crash Course #34 - RAGs - Introduction11m
Exercise: In a basic RAG pipeline, what is the main job of the retriever component?
Exercise: In a LangGraph agent setup with a retriever tool and an off-topic tool, what condition determines whether the flow goes to the tools node or ends?
Exercise: In a FastAPI streaming chat endpoint using Server-Sent Events (SSE), what media type must be set on the StreamingResponse to follow the SSE protocol?
Exercise: In the NextJS chat UI, which browser API is used to connect to a Server-Sent Events (SSE) endpoint and receive streamed chunks from the agent?
Video class: LangGraph Crash Course #45 - Capstone Project - Final Deployment12m
Exercise: In the deployment workflow shown, what is the main purpose of pushing the Docker image to Docker Hub before deploying on a cloud platform like Render?
