Building AI Agents with Python and LangChain

Packt via Coursera

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

6 weeks, 1 hour a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

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Overview

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Explore the world of AI agent creation using LangChain, a powerful framework for building intelligent agents. This course takes you from the basics of AI agents to advanced features like memory integration, web apps, and web search capabilities. Perfect for developers looking to build real-world AI solutions. In this course, you will embark on an exciting journey of building intelligent AI agents with LangChain. Starting with the fundamentals, you'll learn how to create a weather agent that can retrieve live weather data and interact with users dynamically. As the course progresses, you’ll enhance your agent with tools that enable it to perform multiple tasks and interact with users more naturally. The course continues by introducing you to memory functionality, allowing the agent to retain and recall past conversations. You'll explore both transient memory and persistent database memory using SQLite and PostgreSQL, giving your agent the ability to hold context across interactions. This section emphasizes how memory can make AI agents smarter and more responsive. In the final part of the course, you will take your agent to the web, building a full-fledged web app where your AI agent will serve real-time responses. You’ll also integrate advanced features like GPS-based location services and web search capabilities to extend the agent’s usefulness in real-world scenarios. By the end of this course, you will have the skills to create versatile, memory-empowered AI agents ready to tackle a wide range of challenges. This course is ideal for developers with a basic understanding of Python who are looking to build intelligent agents using LangChain. If you’re interested in AI, machine learning, or web development, and you want to take your skills to the next level, this course will help you understand how to build and deploy powerful AI-driven systems. No prior experience with LangChain is required, but familiarity with Python is recommended. The course uses a hands-on approach, focusing on real-world applications of LangChain in AI agent development and its craft to build. By the end, you’ll have developed a comprehensive AI solution that integrates memory, web interfaces, and advanced features like web search. This course is based on Building AI Agents with Python and LangChain, by PythonHow, Ardit Sulce. This Course is licensed and distributed by Packt. All rights reserved. Packt is one of the world's most prolific publishers of cutting-edge technical content. For over two decades we've made it our mission to curate and publish the knowledge of only the very best technical experts. We focus on real-world courses that help our customers get the job done, with coverage that extends across a wide range of established and cutting-edge technical topics. If you're an individual or an organisation that embraces learning by doing, Packt is the perfect fit for you.

Syllabus

Building a Weather AI Agent with LangChain
- This module guides learners through the process of creating a weather AI agent using LangChain, covering the fundamentals of AI agents, tool integration, and prompt design. It emphasizes practical skills in building and refining AI systems for real-world applications.
Building a Real-World AI Agent
- This module teaches how to build a functional AI agent that retrieves and processes real-world data, including weather information and user location. Learners will develop skills in integrating APIs, handling dynamic data, and improving agent output quality. The focus is on practical implementation and best practices in code structure.
Agents with Memory
- This module explores how AI agents can use memory to enhance user interactions, manage multi-conversation contexts, and maintain continuous dialogue. Learners will gain insight into the technical implementation of memory systems and their impact on agent behavior. The focus is on practical strategies for improving agent responsiveness and contextual awareness.
Agents with Persistent Database Memory
- This module explores how to implement persistent memory systems for AI agents using SQLite and PostgreSQL databases. Learners will gain hands-on experience in saving and retrieving conversation history, configuring database architectures, and deploying solutions on Supabase. The focus is on building scalable and reliable memory systems for conversational AI.
Build a Real-World AI Agent Web App
- This module guides learners through the process of transforming an AI agent into a fully functional web application. It covers setting up the web app environment, designing a user interface, implementing HTTP requests, managing sessions, and integrating features like GPS location. By the end, learners will be able to build a responsive, interactive chat-based weather application using Flask.
Build AI Agents with Web Search Capabilities
- This module introduces learners to building AI agents with web search capabilities. It covers setting up tools, integrating Tavily, and implementing web search functions to generate articles. Learners will gain practical skills in coding and AI system development.