Udemy - Agno Agentic AI with MCP

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Free Download Udemy - Agno Agentic AI with MCP
Published 5/2025
Created by Rahul Raj
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 65 Lectures ( 4h 53m ) | Size: 2 GB

Agentic AI with Agno and MCP Crash Course Crash Course
What you'll learn
Learn how to use the MCP (Message, Chain, Plan) protocol to build structured, intelligent agents.
Gain hands-on experience developing financial agents that can analyze data and automate real-world tasks.
Understand how to integrate memory into your agents so they can recall past interactions and make informed decisions.
Using the Agno Playground, you'll simulate and visualize agent workflows in an interactive environment.
Create multi-agent systems where agents collaborate, delegate tasks, and solve problems together.
Requirements
Python basic
Description
Unlock the power of Agentic AI with this focused, hands-on course designed to teach you how to build intelligent financial agents using the Agno Agentic Framework and the MCP (Message, Chain, Plan) Protocol. Whether you're an AI enthusiast, developer, or a financial tech innovator, this course will help you grasp the next evolution of AI workflows through practical projects.You'll dive into how Agno simplifies the development of autonomous, goal-driven agents using structured planning and communication patterns. Learn to design financial agents that can analyze market data, execute tasks, and retain knowledge using Memory Agents. Explore the Agno Playground, a visual environment to test and simulate agents with dynamic prompts and task flows.The course emphasizes multi-agent collaboration where agents communicate and coordinate as teams to solve complex problems. Using real-world financial use cases, you'll create agents that not only act independently but also plan, reflect, and delegate in multi-agent environments.What you'll build:A smart Financial Analyst Agent using Agno & MCPA Memory-augmented agent capable of recall and reasoningA Multi-Agent system where agents collaborate on financial tasksUse of Agno Playground to simulate and visualize workflowsNo prior experience in agent frameworks is required-just a basic understanding of Python and AI concepts.Start your journey into the world of Agentic AI today and build systems that think, plan, and act-autonomously.
Who this course is for
Agentic AI
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Agno Agentic Ai With Mcp
Published 5/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.79 GB | Duration: 4h 53m​

Agentic AI with Agno and MCP Crash Course Crash Course

What you'll learn

Learn how to use the MCP (Message, Chain, Plan) protocol to build structured, intelligent agents.

Gain hands-on experience developing financial agents that can analyze data and automate real-world tasks.

Understand how to integrate memory into your agents so they can recall past interactions and make informed decisions.

Using the Agno Playground, you'll simulate and visualize agent workflows in an interactive environment.

Create multi-agent systems where agents collaborate, delegate tasks, and solve problems together.

Requirements

Python basic

Description

Unlock the power of Agentic AI with this focused, hands-on course designed to teach you how to build intelligent financial agents using the Agno Agentic Framework and the MCP (Message, Chain, Plan) Protocol. Whether you're an AI enthusiast, developer, or a financial tech innovator, this course will help you grasp the next evolution of AI workflows through practical projects.You'll dive into how Agno simplifies the development of autonomous, goal-driven agents using structured planning and communication patterns. Learn to design financial agents that can analyze market data, execute tasks, and retain knowledge using Memory Agents. Explore the Agno Playground, a visual environment to test and simulate agents with dynamic prompts and task flows.The course emphasizes multi-agent collaboration where agents communicate and coordinate as teams to solve complex problems. Using real-world financial use cases, you'll create agents that not only act independently but also plan, reflect, and delegate in multi-agent environments.What you'll build:A smart Financial Analyst Agent using Agno & MCPA Memory-augmented agent capable of recall and reasoningA Multi-Agent system where agents collaborate on financial tasksUse of Agno Playground to simulate and visualize workflowsNo prior experience in agent frameworks is required-just a basic understanding of Python and AI concepts.Start your journey into the world of Agentic AI today and build systems that think, plan, and act-autonomously.

Overview

Section 1: Introduction

Lecture 1 Agent Introduction

Lecture 2 Agno Agent Framework Introduction

Lecture 3 Open AI API Key

Lecture 4 Visual studio code install

Lecture 5 Project Setup with UV

Lecture 6 UV installation

Lecture 7 Run Program with UV

Lecture 8 All Code Resources

Section 2: Project Agno Financial assistant

Lecture 9 Agno Financial assistant

Section 3: Agno memory Agent

Lecture 10 Memory Agent Overview

Lecture 11 VectorDB Chunking

Lecture 12 Agno memory Agent

Lecture 13 Agno Memory Agent Demo

Section 4: Agno Multi Agent

Lecture 14 Agno Multi Agent Intro

Lecture 15 Yahoo Finance and Web Agent

Lecture 16 Demo Yahoo Finance Agent

Lecture 17 Demo Web Agent

Section 5: Agno Playground

Lecture 18 Agno Playground intro

Lecture 19 Playground Finance and Web Agent

Lecture 20 PlayGround Demo Finance Agent

Lecture 21 Playground Demo Web Agent

Section 6: MCP Protocol Overview

Lecture 22 MCP Protocol Intro

Lecture 23 MCP vs Non MCP

Lecture 24 MCP Communication between components

Section 7: Project: Agno Agent with MCP

Lecture 25 MCP Architecture Intro

Lecture 26 Agno MCP server Intro

Lecture 27 Agno MCP server code

Lecture 28 Agno MCP Client Code

Lecture 29 Demo Agno MCP

Section 8: Appendix- Python

Lecture 30 Internal Working of Python

Lecture 31 Data Type Number

Lecture 32 Data Type String

Lecture 33 Data Type Boolean

Lecture 34 Operator Python

Lecture 35 Collection List

Lecture 36 Collection Tuple

Lecture 37 Collection Set

Lecture 38 Dictionary

Lecture 39 If Else Condition

Lecture 40 While Loop

Lecture 41 For Loop

Lecture 42 Function Intro

Lecture 43 Function Code

Lecture 44 Lambda Function

Lecture 45 Array Function

Lecture 46 Class Introduction

Lecture 47 Class Init

Lecture 48 Class str

Lecture 49 Class and Function

Lecture 50 Inheritance Intro

Lecture 51 Inheritance with Init

Lecture 52 Iterator

Lecture 53 Polymorphism

Lecture 54 Scope Python

Lecture 55 Modules Python

Lecture 56 dates python

Lecture 57 math modules

Lecture 58 regular expression

Lecture 59 json object

Lecture 60 Pip package

Lecture 61 Exception handling

Lecture 62 User Input

Lecture 63 String format

Lecture 64 File read

Lecture 65 File write

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