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Artificial Intelligence
Artificial Intelligence
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DeepLearning.AI 2

AI Agents in LangGraph

  • up to 1 hour
  • Intermediate

Learn about LangGraph’s components and how they enable the development, debugging, and maintenance of AI agents. This course will teach you to build an agent from scratch using Python and an LLM, and then rebuild it using LangGraph, enhancing agent knowledge and 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 understand the division of tasks between the LLM and the code around it, implement agentic search to retrieve multiple answers in a predictable format, and incorporate persistence in agents for state management. Additionally, you will develop an agent for essay writing, replicating the workflow of a researcher, and learn to incorporate human-in-the-loop into agent systems.

  • 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?

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.

Tech Professionals

Professionals in the tech industry aiming to enhance their skills in AI agent development and integration.

Why should you take this course?

Artificial Intelligence

This course offers key benefits such as learning to build and control AI agents using LangGraph, understanding agentic search, and implementing state management. It is ideal for Python developers, AI enthusiasts, and tech professionals looking to advance 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 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, enhancing the agent’s built-in knowledge.
Persistence in Agents
Implement persistence in agents, enabling 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 Essay Writing Agent
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 a Co-Founder and CEO at LangChain. He has experience in sports, machine learning, software engineering, and statistics.

  • Rotem Weiss

    Founder, Tavily AI

    Rotem Weiss is the founder of Tavily AI, a cutting-edge real-time search engine designed to augment the contextual capabilities of Large Language Models (LLMs). In addition, he has launched two open-source initiatives, GPT-Researcher and GPT-Newspaper, which have significantly contributed to the field by introducing novel multi-agent LLM architectures and pioneering LLM-based web research methodologies.

Upcoming cohorts

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