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Getting Started with LangGraph

Published by O'Reilly Media, Inc.

Beginner content levelBeginner

Hands-on introduction to LangGraph for building multi-agent workflows

Course outcomes

  • Understand the core features of LangGraph
  • Learn how to integrate LangGraph with the LangChain ecosystem
  • Develop skills to build multi-agent applications using LangGraph
  • Manage state across multiple interactions and dynamic routing in workflows
  • Explore practical applications and advanced workflows powered by LangGraph, such as multi-agent research workflows, scheduler agents, and multi-agent coding workflows

Course description

Join expert Lucas Soares for an in-depth, practical introduction to LangGraph, a framework designed to work in conjunction with LangChain for building multi-agent workflows powered by large language models like ChatGPT. You’ll learn how to create complex, multi-agent applications that leverage LangGraph and the LangChain ecosystem and how to implement state management and dynamic routing within LangGraph workflows. You’ll also get a hands-on introduction to building applications such as scheduler and coder agents.

What you’ll learn and how you can apply it

  • Understand the absolute basics of LangChain the core features of LangGraph
  • Learn how LangGraph integrates with LangChain seamlessly
  • Create multi-agent applications using LangGraph
  • Implement state management and dynamic routing within LangGraph workflows
  • Explore practical applications and case studies involving LangGraph

This live event is for you because...

  • You’re a software engineer or developer
  • You’re a data scientist or machine learning engineer
  • You’re interested in learning about large language models, specifically ChatGPT, and how to build applications.

Prerequisites

  • Familiarity with Python programming
  • A computer with Python installed locally or set up on a Google Colab notebook
  • Some knowledge of machine learning concepts
  • Basic understanding of LLMs
  • Basic understanding of LangChain

Recommended preparation:

Recommended follow-up:

Schedule

The time frames are only estimates and may vary according to how the class is progressing.

Introduction to LangGraph (30 minutes)

  • Presentation: Introduction to LangChain absolute basics; introduction to LangGraph—from persistence to configuration
  • Hands-on exercise: Build an agent workflow
  • Q&A
  • Break

Integrating design patterns in LangGraph (30 minutes)

  • Presentation: Subgraphs, branching, and human-in-the-loop; tool calling, dynamism, and structured formats
  • Hands-on exercise: Create an agentic research workflow
  • Q&A
  • Break

LangGraph Studio for Debugging and Visualization (40 minutes)

  • Presentation: Demo of Real-time visualization, monitoring, and debugging in LangGraph Studio
  • Hands-on Exercise: Observing multi-agent interactions in LangGraph Studio
  • Q&A and Break

Agentic Rag with LangGraph (40 minutes)

  • Presentation: Collaboration, supervision and hierarchy; RAG concepts with LangGraph
  • Hands-on exercise: Build a coding agent; build an RAG agent over webpage
  • Q&A
  • Break

Building Local Agentic Workflows with LangGraph (50 minutes)

  • Presentation: Building a web navigation agent; building agents that can schedule and execute tasks autonomously
  • Hands-on exercise: Build a planner agent that can integrate with or without human-in-the-loop
  • Q&A

LangGraph Deployment (50 minutes)

  • Presentation: Deploying a LangGraph Application
  • Hands-on exercise: Best practices and template code for deploying langgraph applications
  • Q&A

Your Instructor

  • Lucas Soares

    Lucas Soares is a machine learning engineer who has worked at K1 Digital and Biometrid, where he developed computer vision and NLP models for applications such as document verification, OCR-based applications, and recommender systems. Lucas has also developed various ML models, including neural networks, Siamese networks, convolutional neural networks, LSTMs, and genetic algorithms.

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Skills covered

  • Large Language Models (LLMs)
  • Graph Analytics