👨‍🏫 Tutorial Build AI Agents CrewAI, LangGraph, OpenAI SDK, Autogen, MCP

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Build AI Agents CrewAI, LangGraph, OpenAI SDK, Autogen, MCP
Published 6/2026
Created by Deesa Technologies
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 77 Lectures ( 23h 50m ) | Size: 15.7 GB
End-to-end AI agent bootcamp - from your first LLM call to multi-agent systems you can deploy at work

What you'll learn
LLM fundamentals: tokens, cost, latency, capabilities & limitations
Prompt engineering that controls output (JSON, classification, extraction)
Agent design patterns: tool use, multi-step reasoning, failure modes
OpenAI Agents SDK for research copilots, meeting notes → action items, support bots
CrewAI multi-agent orchestration for software teams, product launches & hiring
LangGraph stateful workflows with human-in-the-loop approval
AutoGen collaborative code review and problem-solving agents
MCP integration for databases, PDFs, and live market data
Guardrails to prevent unsafe or unreliable AI outputs
Observability: logging, tracing, failure simulation & quality evaluation
Deployment: FastAPI, webhooks, cost optimization
Requirements
Basic Python knowledge
Curiosity and willingness to build - no ML PhD required
Description
Stop watching AI demos. Start building AI systems companies actually ship.
This hands-on bootcamp takes you from your first LLM API call toproduction-grade multi-agent systems - using the tools shaping AI engineering today:OpenAI Agents SDK, CrewAI, LangGraph, AutoGen, and MCP.
You won't just learn theory. You'lldesign, build, debug, secure, and deploy real AI applications in Python.
What you'll build
29 exciting AI projects that we will design from scratch
AI research assistant, email generator & chatbot (Module 0)
Prompt playground, JSON formatter & email classifier
Resume analyzer, data extractor & SQL assistant
Web search agent & multi-step task agent
OpenAI SDK: research copilot, meeting notes → action items, support bot
CrewAI flagship: AI software engineering team (Developer → Reviewer → Tester)
LangGraph customer support & human-in-the-loop approval workflows
AutoGen collaborative code review & problem-solving agents
MCP: internal data assistant, knowledge base agent, trading simulation
Debug dashboard, failure simulator & output evaluator
GitHub AI PR code reviewer (FastAPI + webhooks)
This course doesn't just teach you how to design and build AI systems - it also shows you how and when AI fails, and what to do about it. It's a production-ready bootcamp that takes you all the way to deployment, including a GitHub AI PR reviewer you can run in real time and watch interact with live pull requests.By Deesa Technologies - production-focused courses built by engineers who ship real systems.
Enroll now and build your first AI agent in Module 0.
Who this course is for
Python developers who want to move into AI engineering
Software engineers building internal AI tools at work
Data professionals who need practical agent workflows, not theory-only content
Career switchers targeting AI Engineer / ML Engineer / Agent Developer roles
Anyone tired of copy-paste ᑕᕼᗩTGᑭT tutorials who wants real system design skills
Homepage

https://www.udemy.com/course/build-ai-agents-crewai-langgraph-openai-sdk-autogen-mcp

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