Packt

Building AI Agents with Python and LangChain

Packt

Building AI Agents with Python and LangChain

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

5 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

5 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Build and deploy AI agents with LangChain

  • Implement memory features in AI agents for dynamic conversations

  • Develop a web app that hosts your AI agent

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Recently updated!

September 2026

Assessments

5 assignments

Taught in English

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There are 6 modules in this course

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.

What's included

6 videos

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.

What's included

4 videos1 assignment

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.

What's included

5 videos1 assignment

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.

What's included

5 videos1 assignment

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.

What's included

9 videos1 assignment

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.

What's included

6 videos1 assignment

Instructor

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