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Keep shoppingBuild Autonomous AI Agents with LangChain & LangGraph
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106 students
Build Autonomous AI Agents with LangChain & LangGraph
Build intelligent AI agents using LangChain, LangGraph, Python, tools, memory, and multiagent architectures from scratch
Created byYotta Academy
Last updated 7/2026
English
What you'll learn
- Build intelligent AI agents using LangChain and LangGraph with Python.
- Create autonomous AI agents with memory, tools, and reasoning capabilities using the ReAct architecture.
- Design and implement multi-agent systems where specialized AI agents collaborate to solve complex tasks.
- Integrate external tools, APIs, and custom Python functions into AI agent workflows.
- Implement persistent memory, state management, and human-in-the-loop approval workflows in LangGraph.
- Deploy AI agents using FastAPI, Docker, and Streamlit for real-world applications.
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Course content
10 sections • 40 lectures • 1h 36m total length
Requirements
- Basic knowledge of Python programming is recommended.
- Familiarity with fundamental programming concepts such as variables, functions, loops, and classes.
- No prior experience with LangChain, LangGraph, or AI agents is required—everything is taught step by step.
- Basic understanding of APIs and command-line tools is helpful but not mandatory.
- Enthusiasm to learn and build real-world AI agent applications through hands-on coding.
Description
This course contains the use of artificial intelligence.
Artificial Intelligence is rapidly evolving from simple chatbots to autonomous AI agents capable of reasoning, using tools, maintaining memory, and solving complex tasks with minimal human intervention. As businesses increasingly adopt Agentic AI, skills in frameworks like LangChain and LangGraph have become highly valuable for developers and AI enthusiasts.
In this hands-on course, you'll learn how to build intelligent AI agents from scratch using Python, LangChain, and LangGraph. We'll start with the fundamentals of Agentic AI, exploring how large language models, prompts, memory, and tools work together to create autonomous systems. You'll then build LLM-powered applications, add conversational memory, integrate external tools, and create custom Python tools for automation.
Next, you'll move beyond traditional AI chains and discover how LangGraph enables powerful graph-based workflows. Through practical coding demonstrations, you'll build autonomous agents using the ReAct architecture, implement state management and persistent memory, create human-in-the-loop approval workflows, and develop collaborative multi-agent systems with supervisor and worker agents.
Finally, you'll learn how to deploy your AI agents using FastAPI, containerize them with Docker, and build a simple Streamlit interface for interacting with your applications.
This course focuses on practical implementation rather than theory, with step-by-step demos that help you build real-world AI applications. Whether you're a Python developer, AI engineer, machine learning enthusiast, or software developer looking to enter the world of Agentic AI, this course will give you the knowledge and hands-on experience needed to build, customize, and deploy modern AI agents with confidence.
Who this course is for:
- Python developers who want to build intelligent AI agents using LangChain and LangGraph.
- AI and machine learning enthusiasts interested in Agentic AI and autonomous systems.
- Software developers looking to integrate LLMs, tools, memory, and multi-agent workflows into their applications.
- Students and professionals who want to learn practical AI agent development through hands-on projects.
- Developers looking to deploy production-ready AI agents using FastAPI, Docker, and Streamlit.
- Anyone who understands basic Python and wants to transition from building chatbots to creating autonomous AI agents.