Course Information
Course Overview
Use free tools: LangChain, LangGraph, ChromaDB & Llama 3 to build agents with memory, web search, then go agentic
What you'll learn
Build a complete AI agent from scratch using 100% open-source tools with no API costs
Understand the fundamental differences between simple chatbots and intelligent agentic AI systems
Implement web search capabilities so your agent can access real-time information from the internet
Create persistent memory systems using ChromaDB vector database for conversation history
Add file upload functionality to analyze PDFs and text documents with AI
Master the ReAct pattern (Reasoning + Acting) for intelligent decision-making
Implement chain-of-thought prompting for complex problem-solving
Build self-correction loops where agents validate and improve their own responses
Design agentic workflows using LangGraph with state machines and conditional routing
Run large language models locally using Ollama (llama3.2) with complete privacy
Create interactive chat interfaces with Streamlit for production-ready applications
Implement semantic search and vector embeddings for intelligent memory retrieval
Build autonomous agents that choose tools, plan actions, and execute tasks independently
Customize agent personality and behavior through advanced prompt engineering
Course Description
Stop paying for expensive AI APIs. Start building your own intelligent agents.
This comprehensive course teaches you how to build agentic AI systems from the ground up using modern open-source technologies. Unlike simple chatbots, agentic AI can reason, plan, use tools, remember conversations, and make autonomous decisions—all running locally on your machine with zero API costs.
What Makes This Course Different?
- 100% Open Source - No proprietary APIs, no vendor lock-in, no recurring costs
- Complete Source Code Included - Every lecture comes with fully working code you can download and customize
- Hands-On Practice Exercises - Carefully designed exercises that enhance your skills and add powerful features to your agent Production-Ready Skills - Build real applications, not toy examples.
- Local Development - Everything runs on your laptop with full data privacy
- Modern AI Stack - Learn the tools used by professional AI engineers today
What You'll Build
By the end of this course, you'll have created a fully functional agentic AI application with these capabilities:
- Web Search Integration - Agent searches DuckDuckGo for current information automatically
- Document Analysis - Upload PDFs or text files and ask questions about their content
- Persistent Memory - Conversations are remembered across sessions using vector database technology
- Intelligent Decision Making - Agent decides when to search, when to analyze files, or when to use existing knowledge
- Self-Correction - Validates its own answers and refines them if needed
- Autonomous Planning - Uses the ReAct pattern to reason before taking action
- State Management - Built with LangGraph for complex multi-step workflows
- User Sessions - Multiple users can have separate conversations with persistent history
- Customizable Personality - Change agent behavior through prompt engineering
Technologies You'll Master
AI & Machine Learning:
Ollama (Local LLM Runtime)
LangChain (Agent Framework)
LangGraph (State Machine Workflows)
llama3.2 (Open Source Language Model)
Vector Databases & Memory:
ChromaDB (Vector Database)
Embeddings and Semantic Search
Sentence Transformers
Web Development:
Streamlit (UI Framework)
Python (Programming Language)
API Integration (OpenLibrary, DuckDuckGo)
Agent Patterns:
ReAct (Reasoning + Acting)
Chain-of-Thought Prompting
Self-Correction Loops
Autonomous Decision Making
Who This Course Is For
- Python developers who want to build AI applications without expensive APIs
- Data scientists looking to add AI agent development to their skillset
- Software engineers interested in practical AI implementation
- Tech entrepreneurs building AI-powered products
- Students learning modern AI development techniques
- Professionals wanting to understand how intelligent agents work
- Anyone interested in building privacy-focused AI applications
- No prior AI experience required - we start from fundamentals and build up to advanced concepts step by step.
Prerequisites
Basic Python programming knowledge (variables, functions, loops)
Familiarity with command line/terminal
A computer with at least 8GB RAM (16GB recommended for better performance)
Willingness to learn and experiment
What Makes This Course Unique
Practice Exercises Included - Each major section includes hands-on exercises designed to deepen your understanding and add real functionality to your agent. Solutions are provided so you can verify your work.
Complete Source Code - Download working code for every single lecture. No guessing, no incomplete examples—just production-ready code you can run immediately.
Regular Updates - As AI technology evolves, this course will be updated with new techniques and tools.
From Theory to Practice - We don't just explain concepts—we build real, working applications you can deploy and customize.
Modern Best Practices - Learn the patterns and techniques used by professional AI engineers in 2024 and beyond.
Course Outcomes
By completing this course, you will:
Understand how modern AI agents work under the hood
Build production-ready agentic AI applications
Implement advanced AI patterns like ReAct and Chain-of-Thought
Master vector databases and semantic search
Create agents that can search the web, analyze files, and remember conversations
Design autonomous systems that make intelligent decisions
Save hundreds of dollars in API costs by running AI locally
Have portfolio projects to showcase your AI development skills
Join Thousands of Students Building the Future of AI
Enroll now and start building intelligent AI agents today. With our 30-day money-back guarantee, you have nothing to lose and everything to gain.
Stop using AI. Start building it.
Course Content
- 9 section(s)
- 31 lecture(s)
- Section 1 Understanding AI Agents
- Section 2 Key Components of AI Agents
- Section 3 Setting Up Your Environment
- Section 4 Agent Architectures: From Reactive to Autonomous
- Section 5 Designing Your Agent's Architecture
- Section 6 Understanding Vector Database: How they differ from Traditional Database Systems
- Section 7 Hands-On: Building Your First AI Agent Step by Step
- Section 8 Customize and Extend Your Agent: Internet-Agent with Memory and File Upload
- Section 9 Advanced: From AI Agents to Agentic AI using LangGraph
What You’ll Learn
- Build intelligent AI agents from scratch using Python, Ollama, LangChain & LangGraph-no API costs, runs locally with complete privacy control, Master agentic patterns: ReAct reasoning, chain-of-thought prompting & self-correction loops to create agents that think and decide autonomously, Implement web search, file analysis & persistent memory using ChromaDB vector database-build agents that remember conversations across sessions, Create production-ready AI applications with Streamlit UI, autonomous decision-making & multi-step workflows-portfolio projects included
Skills covered in this course
Reviews
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GGaurav Rana
Do we have to memorize this complex code since I'm a beginner but have an idea about what a function does as well as about modules of Python, but this code really scares me!
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JJack Ilmonen
It seems very clear and easy to follow! awesome!
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DDanushka Liyanage
Nice and simple. Very easy to grasp explanations for basically anyone.
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PPushpak
Very well made. detailed