Agentic AI Masterclass: Building Multi-Agent Systems with AutoGen, LangGraph & CrewAI

  • IntermediateLevel

  • 138+Students Enrolled

  • 3 Hrs Duration

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About this Course

  • This course teaches agentic AI by building multi-agent systems using AutoGen, LangGraph, and CrewAI across real-world use cases.
  • Learn how agents collaborate, communicate, and solve complex problems using modern frameworks designed for scalable AI systems.
  • Build hands-on projects including research assistants and agent teams that demonstrate real-world applications of multi-agent systems.
  • Understand agent architecture, memory, workflows, and tool integrations to design production-ready AI solutions.

Course Benefits

  • Build complete multi-agent AI systems using leading frameworks like AutoGen, LangGraph, and CrewAI.
  • Learn how agents collaborate, communicate, and solve complex real-world problems using structured workflows.
  • Gain hands-on experience building AI agents, research assistants, and multi-agent pipelines.
  • Understand memory, state, and tool integration for building scalable and production-ready AI systems.
  • Develop skills required to build next-generation agentic AI applications used in industry.

Learning Outcomes

Build Multi-Agent System

Design and build agents using AutoGen, LangGraph, CrewAI.

Master Agent Workflows

Create workflows with memory, tools, and state handling.

Develop Real AI Projects

Build research assistants and agent-based applications.

Who Should Enroll

  • AI engineers looking to build multi-agent systems using frameworks like AutoGen, LangGraph, and CrewAI.
  • Developers interested in creating intelligent AI agents with workflows, memory, and tool integrations.
  • Data scientists exploring agentic AI systems and real-world applications beyond simple LLM usage.
  • Professionals building scalable AI applications using multi-agent collaboration and orchestration.

Course Curriculum

Learn how to build multi-agent systems using AutoGen, LangGraph, and CrewAI. This curriculum covers agent architecture, workflows, memory, tools, and hands-on projects including research assistants and agent teams.

toolstoolstools

Learn AutoGen architecture and build agents that communicate and collaborate. Understand agent workflows, multi-model usage, and create a DSA solver using agent teams.

  1. 1. Course Introduction

  2. 2. Setting Environment & Installation

  3. 3. AutoGen: Architecture & Working

  4. 4. Build Your First Agent with AutoGen

  5. 5. How Agents work internally?

  6. 6. Agent in AutoChat

  7. 7. How can we use different model in the agent?

  8. 8. Multimodal Inputs & Structured Output

  9. 9. Teams in Autogen

  10. 10. Project: DSA Solver with Agent Team

Learn LangGraph to build structured agent workflows using state and memory. Create branching flows, integrate tools, and build a multi-agent research assistant.

  1. 1. What is LangGraph?

  2. 2. LangGraph Basics

  3. 3. Building Simple Agent Workflows

  4. 4. State & Memory Management

  5. 5. Branching & Conditional Flows

  6. 6. Integrating External Tools

  7. 7. Project: Multi-Agent Research Assistant

Build multi-agent workflows using CrewAI and understand how agents collaborate with roles, tasks, and memory. Create stateful AI applications and research assistants.

  1. 1. Introduction

  2. 2. Introduction to CrewAI

  3. 3. Core Components of CrewAI - Part 1

  4. 4. Core Components of CrewAI - Part 2

  5. 5. Building Stateful Applications with CrewAI

  6. 6. Research Assistant - Part 1

  7. 7. Research Assistant - Part 2

  8. 8. Flow and States management in CrewAI

  9. 9. Building a Workflow with CrewAI

Get this Course Now

With this course you’ll get

  • 3 Hours

    Duration

  • Mayank Aggarwal & + 2 More

    Instructor

  • Intermediate

    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?

Agentic AI refers to systems where AI agents can plan, act, use tools, and collaborate to complete tasks. Unlike basic LLM usage, these systems operate autonomously and handle complex workflows involving multiple steps and decision-making processes.

Multi-agent systems involve multiple AI agents working together to solve tasks. Each agent can have specific roles, tools, or responsibilities, and they collaborate to produce better results than a single agent working alone.

These frameworks are widely used to build scalable agentic AI systems. Learning them allows you to design complex workflows, manage multiple agents, and build production-ready AI applications beyond simple chatbot implementations.

A basic understanding of AI concepts and large language models is recommended. However, the course explains agentic AI concepts step-by-step, making it accessible for learners transitioning from basic AI to advanced systems.

You will build real-world projects such as a DSA solver using agent teams and a multi-agent research assistant. These projects demonstrate how agents collaborate, use tools, and solve complex tasks.

AutoGen is used to build conversational multi-agent systems where agents can communicate and collaborate. It enables designing agent teams that solve tasks through structured interactions.

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