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Artificial Intelligence
Artificial Intelligence
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DeepLearning.AI

AI Agents in LangGraph

  • up to 1 hour
  • Intermediate

Learn to build and maintain AI agents using LangGraph, an extension of the popular LangChain framework. This course covers the development of controllable agents, agentic search, and state management, providing you with the skills to enhance AI agent performance.

  • LangGraph components
  • Agentic search
  • State management
  • Human-in-the-loop systems
  • Flow-based applications

Overview

In this course, you will learn to build an agent from scratch using Python and an LLM, and then rebuild it using LangGraph. You will explore LangGraph’s components and how to combine them to build flow-based applications. Additionally, you will learn about agentic search, which returns multiple answers in an agent-friendly format, enhancing the agent’s built-in knowledge. The course also covers implementing persistence in agents, enabling state management across multiple threads, conversation switching, and the ability to reload previous states. You will incorporate human-in-the-loop into agent systems and develop an agent for essay writing, replicating the workflow of a researcher working on this task.

  • Web Streamline Icon: https://streamlinehq.com
    Online
    course location
  • Layers 1 Streamline Icon: https://streamlinehq.com
    English
    course language
  • Self-paced
    course format
  • Live classes
    delivered online

Who is this course for?

Intermediate Python Developers

Individuals with intermediate Python knowledge looking to create more controllable agents using the LangGraph open source framework.

AI Enthusiasts

People interested in learning about the latest advancements in AI agent development and how to implement them.

Software Developers

Professionals aiming to enhance their skills in building and maintaining AI agents using LangGraph.

Why should you take this course?

Artificial Intelligence

This course offers key benefits such as learning to build and maintain AI agents using LangGraph, understanding agentic search, and implementing state management. It is ideal for intermediate Python developers, AI enthusiasts, and software developers looking to enhance their skills in AI agent development.

Pre-Requisites

1 / 3

  • Intermediate knowledge of Python

  • Basic understanding of AI and machine learning concepts

  • Familiarity with open source frameworks

What will you learn?

Introduction to LangGraph
Learn about LangGraph’s components and how they enable the development, debugging, and maintenance of AI agents.
Building an Agent from Scratch
Build an agent from scratch using Python and an LLM, understanding the division of tasks between the LLM and the code around it.
Implementing Agents with LangGraph
Rebuild the agent using LangGraph, learning about its components and how to combine them to build flow-based applications.
Agentic Search
Learn how agentic search retrieves multiple answers in a predictable format, unlike traditional search engines that return links.
Implementing Persistence in Agents
Enable state management across multiple threads, conversation switching, and the ability to reload previous states.
Human-in-the-Loop Systems
Incorporate human-in-the-loop into agent systems to enhance their performance and reliability.
Developing an Agent for Essay Writing
Develop an agent for essay writing, replicating the workflow of a researcher working on this task.

Meet your instructors

  • Harrison Chase

    Co-Founder and CEO, LangChain

    Harrison Chase is the Co-Founder and CEO of LangChain, a company that combines sports, machine learning, software engineering, and statistics. He is an expert in natural language processing and has a deep understanding of how to apply it to real-world problems.

  • Rotem Weiss

    Founder, Tavily AI

    Rotem Weiss is a tech entrepreneur who founded Tavily AI, a real-time search engine for LLMs. He is also the founder of open-source projects GPT-Researcher and GPT-Newspaper.

Upcoming cohorts

  • Dates

    start now

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