Agentic AI: Build AI Agents with LangGraph, CrewAI & MCP

Name: Agentic AI: Build AI Agents with LangGraph, CrewAI & MCP
Rating: 4.6 (183 reviews)

Agentic AI Bootcamp: Multi-Agent Systems using LangChain. Build Real-World Agents using Python, CrewAI, OpenAI, MCP

Created byTechLynk Selenium | DevOps | GenAI | Cloud

Last updated 12/2025

English

What you'll learn

Master Agentic AI concepts: Planning, Reasoning, Tool Use, and Memory.
Build complex stateful workflows using LangGraph and LangChain.
Create Multi-Agent Systems where AI agents collaborate using CrewAI and AutoGen.
Implement Anthropic's MCP (Model Context Protocol) to standardise agent tool use.
Connect LLMs to the internet, databases, and APIs using Function Calling.
Develop robust agents with Human-in-the-loop approval workflows.
Deploy 8 Real-World Projects including Research Assistants and Coding Agents.

Course content

18 sections • 83 lectures • 21h 30m total length

Introduction to the Course3:55

Build Your First Agentic AI Workflow22:37
Building Effective Agents7:44
LLM Workflow Design Patterns7:42
LLM Workflow Design Patterns II7:37
Course Chat with Students and Tech Community0:22
Code Location : Hands-On Setup0:07
Understanding Agent vs Workflow Patterns9:42
Orchestrating Multiple LLMs: Comparing Multiple LLMs13:26
Resources vs. Tools: Two Ways to Enhance LLM Capabilities in Agentic AI8:35
Build a Chatbot That Acts Like You31:56
Pydantic: Data Validation and Settings Management Made Easy24:05
A Multi LLM Pipeline: Use LLM to Evaluate LLM17:20
A Multi LLM Pipeline: Use LLM to Evaluate LLM - ChatBot Re-Evaluate18:42
Intelligent Agents: Function Execution and Alerts with LLMs16:25
Under the Hood: How LLMs Handle Tool and Function Calls18:16
Building AI Assistants: Handling Unknowns with Tooling18:33
Building AI Assistants: Handling Unknowns with Tooling II24:04
Building AI Assistants: Handling Unknowns with Tooling - DeepSeek9:48
Deploying Your Digital Avatar AI Agent10:59

Async IO in Python: The Secret to Scalable AI Agent Workflows12:25
Async IO in Python: The Secret to Scalable AI Agent Workflows II28:57
Demystifying the OpenAI Agents SDK10:45
Explore the OpenAI agent sdk, its core primitives—agents, handoffs, guardrails—and how to build modular, hierarchical agent systems with tool calls, input validation, and end-to-end tracing.
Building Blocks of OpenAI Agent SDK: Agent, Runner and Trace18:25
Explore the three building blocks of the OpenAI agent SDK—agent, runner, and trace—and learn how these components handle prompts, memory, tools, and execution with any llm.

Building Intelligent Sales Outreach Agents with Agent SDK16:12
Parallel Generation + AI-Based Selection of Best Email10:34
Orchestrating Tools with Agents — End-to-End AI Sales Execution37:07
Orchestrate tools with agents for ai sales execution, wrapping functions and agents as tools, set up sendgrid, and empower a sales manager to coordinate three agents and send the email.
Tools vs. Handoffs in Multi-Agent Architectures15:40
Tools vs. Handoffs in Multi-Agent Architectures - Final Version11:55

Supercharged Web Agents: Building LLMs That Search Like Pros18:10
Planning with Precision: Structuring AI Output Using Pydantic17:04
Async Mastermind: Designing End-to-End Research Pipelines13:37
Design an end-to-end asynchronous research pipeline that executes planner-generated queries in parallel, creates a markdown report, and emails the results via a dedicated agent flow.
Async Mastermind: Designing End-to-End Research Pipelines - Final Execution18:44
Plug-and-Play AI: Crafting Modular Research Systems with Gradio26:14
Visual Research Control: Monitoring Autonomous Agents with Gradio18:25

Unlocking Crew AI: Build Collaborative AI Agent Teams from Scratch15:45
The Crew AI Blueprint: Agents, Tasks & Processing Modes Made Simple10:35
Plug & Play Intelligence: Integrating Multiple LLMs with LightLLM + Crew AI23:02
Learn to integrate multiple llms into a crew ai project using light lm, with centralized rate limits, logging, and failover, plus project setup and yaml configuration for agents and tasks.
ThinkBot Clash: CrewAI-Powered Debate System15:03
Design and implement a think bot within a fresh crew I project, building an argue bot and verdict bot to handle make case, break case, and render verdict tasks.
ThinkBot Clash: CrewAI-Powered Debate System II25:12

Stock Whisperer: Build a Crew AI-Powered Investment Picker6:17
Build a multi-agent ai-powered investment picker called stock whisper, evaluating stock fundamentals and technicals, using structured pedantic json outputs and a hierarchical workflow to deliver actionable recommendations.
Data That Talks: Structuring Agent Output with Pydantic + Crew AI12:06
Data That Talks: Structuring Agent Output with Pydantic + Crew AI - II19:29
Toolsmith Mode: Craft Custom Alerts & JSON Tools for Crew AI17:17
Memory Mastery: Equip Crew AI with Vector Search & SQL Recall16:59
Equip crew AI with memory using vector search and SQL recall to enable memory-based agents, covering short term to long term, contextual, entity, and user memories.
Implement Memory Mastery: Equip Crew AI with Vector Search & SQL Recall17:41

Requirements

Basic knowledge of Python programming (variables, functions, classes).
Understanding of APIs and RESTful services (basic level).
Familiarity with Large Language Models (LLMs) concepts (like OpenAI, Hugging Face models, etc.).

Description

Is 2025 the year you master the next evolution of Artificial Intelligence?

Say goodbye to passive chatbots and hello to Agentic AI. The future belongs to intelligent agents that can reason, plan, execute code, and collaborate to solve complex real-world problems autonomously.

Welcome to the Agentic AI: Build AI Agents with LangGraph, CrewAI & MCP, the most comprehensive and hands-on guide to building production-grade Autonomous AI Agents.

Whether you are a Python developer, Data Scientist, or AI Engineer, this course is your roadmap to mastering the leading frameworks: LangGraph, CrewAI, MCP, and the OpenAI Agents SDK.

Why This Course?
Unlike other courses that focus on theory, we focus on Engineering. You won't just learn about agents; you will build 8 distinctive, portfolio-ready Agentic Applications from scratch.

What You Will Master:

Agentic Fundamentals: Understand the shift from Prompt Engineering to Flow Engineering and Cognitive Architectures.
The Big 3 Frameworks: Deep dive into LangGraph (for control), CrewAI (for role-playing teams), and Microsoft AutoGen (for conversational swarms).
New Frontiers: Be among the first to implement Anthropic’s Model Context Protocol (MCP) for standardized tool connections.
Advanced Capabilities: Implement Memory (Short-term/Long-term), Human-in-the-loop systems, and RAG (Retrieval Augmented Generation) for agents.
Multi-Agent Orchestration: Learn how to make multiple AI agents communicate, debate, and achieve shared goals.

The Projects You Will Build:

Autonomous Research Agent: An agent that browses the web, summarizes news, and writes reports.
Coding & Debugging Agent: An AI pair programmer that writes and executes its own code.
Customer Support Swarm: A multi-agent system handling tickets, routing, and responses.
Data Analysis Droid: An agent capable of ingesting CSVs, analyzing trends, and generating charts.
Personal Executive Assistant: Connects to calendars and email to manage your life.

Tech Stack Covered:

Languages: Python (3.10+)
Frameworks: LangChain, LangGraph, CrewAI, OpenAI SDK
LLMs: OpenAI (GPT), Anthropic (Claude), Groq, and Local LLMs (Ollama)
Tools: Tavily Search, ChromaDB, Docker

Don't let the AI revolution pass you by. Join the Agentic AI revolution today and start building the autonomous workforce of tomorrow!

Who this course is for:

Developers who want to move beyond basic RAG pipelines.
Entrepreneurs building AI-SaaS products.
Engineers looking to implement Multi-Agent Systems in enterprise environments.

Agentic AI: Build AI Agents with LangGraph, CrewAI & MCP

What you'll learn

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Course content

Introduction to the Course1 lecture • 4min

Installation of Tools & Environment SetUp2 lectures • 28min

Agentic AI Foundation Week19 lectures • 4hr 28min

Async programming and OpenAI Agent SDK fundamentals4 lectures • 1hr 11min

Building, Structuring, and Executing AI Agents5 lectures • 1hr 31min

Safety, Guardrails, and Multi-Model Integration4 lectures • 1hr 19min

Research-Focused Autonomous Agents6 lectures • 1hr 52min

Kickoff to Crew AI – Build Smart Teams of AI Agents5 lectures • 1hr 30min

Smarter Together – Building Real-World Multi-Agent AI Systems3 lectures • 45min

Smart Money & Smarter Agents – Financial Intelligence with Crew AI6 lectures • 1hr 30min

Requirements

Description

Who this course is for: