Foundations of LangGraph

  • BeginnerLevel

  • 111+Students Enrolled

  • 1 Hr 30 MinsDuration

  • 4.6Average Rating

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About this Course

  • Learn the mental model behind LangGraph workflows, including nodes, edges, execution state, and durable reasoning across multi-step AI tasks.
  • Build stateful LangGraph agents that maintain context, coordinate tools, and recover cleanly during long reasoning chains.
  • Explore branching logic and conditional routing to create adaptive AI workflows instead of rigid prompt chains.
  • Integrate external tools inside LangGraph execution while keeping reasoning transparent, testable, and production ready.
  • Complete a hands-on LangGraph capstone by building a multi-agent autonomous research assistant end-to-end.

Course Benefits

  • Gain a clear mental model of LangGraph orchestration for reliable AI execution.
  • Build stateful, multi-step AI agents instead of fragile prompt pipelines.
  • Implement branching logic, memory, and tool usage in production-style workflows.
  • Understand multi-agent coordination patterns used in modern agentic AI systems.
  • Deliver a portfolio-ready LangGraph research assistant project.

Learning Outcomes

LangGraph Basics

Understand how LangGraph extends LangChain for stateful agent workflow

Graph Building Blocks

Build nodes, edges, and shared state for multi-step reasoning.

Branching and Tools

Add conditional routing and tool calling in LangGraph.

Agent Orchestration

Design multi-agent flows using LangGraph execution patterns.

Who Should Enroll

  • Python developers and AI builders ready to move from prompt chains to stateful LangGraph agent workflows.
  • LangChain users seeking structured execution, memory, and branching control through LangGraph.
  • Data scientists and ML engineers building real-world multi-step AI automation systems.
  • Professionals exploring agentic AI architecture for production copilots and assistants.

Course Curriculum

Start with LangGraph fundamentals like nodes, edges, and shared state. Progress to memory, branching, and tool-enabled execution. Conclude by building a multi-agent autonomous research assistant using LangGraph orchestration & retrieval integration.

tools

Understand what LangGraph is and why graph-based execution enables more reliable AI systems than linear chains. You will build your first LangGraph agent workflow, explore nodes, edges, shared state, and implement memory to manage multi-step reasoning effectively.

  1. 1. What is LangGraph?

  2. 2. LangGraph Basics

  3. 3. Building Simple Agent Workflows

  4. 4. State & Memory Management

Move beyond single-agent flows to design structured, controllable LangGraph orchestration with branching and conditional routing. You will also integrate external tools inside LangGraph execution, enabling agents to interact with real data and perform meaningful actions.

  1. 1. Branching & Conditional Flows

  2. 2. Integrating External Tools

Apply everything you learned in LangGraph to build a complete multi-agent autonomous research assistant from scratch.

  1. 1. Introduction to Project

  2. 2. Project: Multi-Agent Research Assistant

Meet the instructor

Our instructor and mentors carry years of experience in data industry

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Huzefa Lohawala

Machine Learning Engineer- Avalara

Huzefa Lohawala is a Data Scientist with 5+ years of experience, specializing in NLP and end-to-end ML products. As an educator with 4+ years and 1,000+ training hours, he has trained 500+ learners in data science and analytics.

Get this Course Now

With this course you’ll get

  • Huzefa Lohawala

    Instructor

  • 4.6

    Average Rating

  • Beginner

    Level

Certificate of completion

Earn a professional certificate upon course completion

  • Industry-Recognized Credential
  • Career Advancement Credential
  • Shareable Achievement
certificate

Frequently Asked Questions

Looking for answers to other questions?

You will learn LangGraph fundamentals, agent state management, routing logic, tool integration, and how to build a multi-agent research assistant.

It is designed for intermediate learners familiar with Python and basic LLM or LangChain concepts.

The course is hands-on, culminating in a real LangGraph multi-agent capstone project.

LangChain provides components, while LangGraph enables stateful execution, branching control, and multi-agent orchestration.

The full learning experience is designed to be completed in 92 minutes.

Yes. You will create a multi-agent autonomous research assistant powered by LangGraph.

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