LangGraph Tutorial for Beginners: Build Your First AI Agent

DataCamp · Beginner ·🤖 AI Agents & Automation ·1y ago

Key Takeaways

This video tutorial covers the fundamentals of LangGraph, a framework for building AI agents, and guides viewers through building their first AI agent using Python and integrating tools like OpenAI. The tutorial explores defining AI agents as graphs and nodes, and managing tool calling and custom tools.

Original Description

In this beginner-friendly tutorial, explore the fundamentals of LangGraph and learn how to build your first AI agent. Discover how to define AI agents as graphs and nodes, and integrate powerful tools like OpenAI for enhanced functionality. Whether you're new to AI development or looking to expand your skills, this comprehensive guide will walk you through each step. - Understand the core building blocks of LangGraph - Build a simple AI agent from scratch in Python - Integrate and manage tool calling by adding built-in tools - Create and register custom tools - Compare cloud vs local LLM deployments 📌 Resources & Tutorials Check out our newly released newsletter on Substack — The Median: https://dcthemedian.substack.com Tutorial Notebook: https://www.datacamp.com/datalab/w/3378917b-12bd-4f79-ab48-00ec6e292705/edit Course - Multi-Agent Systems with LangGraph: https://www.datacamp.com/courses/multi-agent-systems-with-langgraph LangChain Tools Documentation: https://python.langchain.com/docs/integrations/tools/ Berkeley Function-Calling Leaderboard: https://gorilla.cs.berkeley.edu/leaderboard.html 📱Follow Us on Social Facebook: https://www.facebook.com/datacampinc/ Twitter: https://x.com/datacamp LinkedIn: https://www.linkedin.com/school/datacampinc/ Instagram: https://www.instagram.com/datacamp/ 📕 Chapters 00:00 Welcome & tutorial roadmap 01:18 LLM vs AI agent • What is LangGraph? 04:41 Flow-chart analogy • Nodes & edges 07:40 Imports & environment setup 10:21 Agent 1 – Define state & build first agent 16:03 Compile, visualise & test Agent 1 22:42 Agent 2 – Add conversational memory 27:30 Union types, context feed & demo 33:29 Agent 3 – Tool calling & conditional edges 40:36 LangChain docs tour • Built-in tools 44:17 Reducer functions & graph visualisation 55:38 Agent 4 – ReAct agent with `create_react_agent` 57:32 Agent 5 – Custom tools & multi-tool agent 1:02:38 Agent 6 – Running local LLMs with Ollama 1:05:55 GPT-4o vs Qwen 2.5 • Pros & co
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Playlist

Uploads from DataCamp · DataCamp · 0 of 60

← Previous Next →
1 SQL Server Tutorial: Date manipulation
SQL Server Tutorial: Date manipulation
DataCamp
 2 R Tutorial: Intermediate Interactive Data Visualization with plotly in R
R Tutorial: Intermediate Interactive Data Visualization with plotly in R
DataCamp
 3 R Tutorial: Adding aesthetics to represent a variable
R Tutorial: Adding aesthetics to represent a variable
DataCamp
 4 R Tutorial: Moving Beyond Simple Interactivity
R Tutorial: Moving Beyond Simple Interactivity
DataCamp
 5 Python Tutorial: Why use ML for marketing? Strategies and use cases
Python Tutorial: Why use ML for marketing? Strategies and use cases
DataCamp
 6 Python Tutorial: Preparation for modeling
Python Tutorial: Preparation for modeling
DataCamp
 7 Python Tutorial: Machine Learning modeling steps
Python Tutorial: Machine Learning modeling steps
DataCamp
 8 R Tutorial: The prior model
R Tutorial: The prior model
DataCamp
 9 R Tutorial: Data & the likelihood
R Tutorial: Data & the likelihood
DataCamp
 10 R Tutorial: The posterior model
R Tutorial: The posterior model
DataCamp
 11 R Tutorial: An Introduction to plotly
R Tutorial: An Introduction to plotly
DataCamp
 12 R Tutorial: Plotting a single variable
R Tutorial: Plotting a single variable
DataCamp
 13 R Tutorial: Bivariate graphics
R Tutorial: Bivariate graphics
DataCamp
 14 Python Tutorial: Customer Segmentation in Python
Python Tutorial: Customer Segmentation in Python
DataCamp
 15 Python Tutorial: Time cohorts
Python Tutorial: Time cohorts
DataCamp
 16 Python Tutorial: Calculate cohort metrics
Python Tutorial: Calculate cohort metrics
DataCamp
 17 Python Tutorial: Cohort analysis visualization
Python Tutorial: Cohort analysis visualization
DataCamp
 18 R Tutorial: Building Dashboards with flexdashboard
R Tutorial: Building Dashboards with flexdashboard
DataCamp
 19 R Tutorial: Anatomy of a flexdashboard
R Tutorial: Anatomy of a flexdashboard
DataCamp
 20 R Tutorial: Layout basics
R Tutorial: Layout basics
DataCamp
 21 R Tutorial: Advanced layouts
R Tutorial: Advanced layouts
DataCamp
 22 Python Tutorial: Time Series Analysis in Python
Python Tutorial: Time Series Analysis in Python
DataCamp
 23 Python Tutorial: Correlation of Two Time Series
Python Tutorial: Correlation of Two Time Series
DataCamp
 24 Python Tutorial: Simple Linear Regressions
Python Tutorial: Simple Linear Regressions
DataCamp
 25 Python Tutorial: Autocorrelation
Python Tutorial: Autocorrelation
DataCamp
 26 R Tutorial: The gapminder dataset
R Tutorial: The gapminder dataset
DataCamp
 27 R Tutorial: The filter verb
R Tutorial: The filter verb
DataCamp
 28 R Tutorial: The arrange verb
R Tutorial: The arrange verb
DataCamp
 29 R Tutorial: The mutate verb
R Tutorial: The mutate verb
DataCamp
 30 R Tutorial: What is cluster analysis?
R Tutorial: What is cluster analysis?
DataCamp
 31 R Tutorial: Distance between two observations
R Tutorial: Distance between two observations
DataCamp
 32 R Tutorial: The importance of scale
R Tutorial: The importance of scale
DataCamp
 33 R Tutorial: Measuring distance for categorical data
R Tutorial: Measuring distance for categorical data
DataCamp
 34 Python Tutorial: Plotting multiple graphs
Python Tutorial: Plotting multiple graphs
DataCamp
 35 Python Tutorial: Customizing axes
Python Tutorial: Customizing axes
DataCamp
 36 Python Tutorial: Legends, annotations, & styles
Python Tutorial: Legends, annotations, & styles
DataCamp
 37 Python Tutorial: Introduction to iterators
Python Tutorial: Introduction to iterators
DataCamp
 38 Python Tutorial: Playing with iterators
Python Tutorial: Playing with iterators
DataCamp
 39 Python Tutorial: Using iterators to load large files into memory
Python Tutorial: Using iterators to load large files into memory
DataCamp
 40 SQL Tutorial: Introduction to Relational Databases in SQL
SQL Tutorial: Introduction to Relational Databases in SQL
DataCamp
 41 SQL Tutorial: Tables: At the core of every database
SQL Tutorial: Tables: At the core of every database
DataCamp
 42 SQL Tutorial: Update your database as the structure changes
SQL Tutorial: Update your database as the structure changes
DataCamp
 43 Python Tutorial: Classification-Tree Learning
Python Tutorial: Classification-Tree Learning
DataCamp
 44 Python Tutorial: Decision-Tree for Classification
Python Tutorial: Decision-Tree for Classification
DataCamp
 45 Python Tutorial: Decision-Tree for Regression
Python Tutorial: Decision-Tree for Regression
DataCamp
 46 Python Tutorial: Census Subject Tables
Python Tutorial: Census Subject Tables
DataCamp
 47 Python Tutorial: Census Geography
Python Tutorial: Census Geography
DataCamp
 48 Python Tutorial: Using the Census API
Python Tutorial: Using the Census API
DataCamp
 49 R Tutorial: A/B Testing in R
R Tutorial: A/B Testing in R
DataCamp
 50 R Tutorial: Baseline Conversion Rates
R Tutorial: Baseline Conversion Rates
DataCamp
 51 R Tutorial: Designing an Experiment - Power Analysis
R Tutorial: Designing an Experiment - Power Analysis
DataCamp
 52 R Tutorial: Introduction to qualitative data
R Tutorial: Introduction to qualitative data
DataCamp
 53 R Tutorial: Understanding your qualitative variables
R Tutorial: Understanding your qualitative variables
DataCamp
 54 R Tutorial: Making Better Plots
R Tutorial: Making Better Plots
DataCamp
 55 SQL Tutorial: OLTP and OLAP
SQL Tutorial: OLTP and OLAP
DataCamp
 56 SQL Tutorial: Storing data
SQL Tutorial: Storing data
DataCamp
 57 SQL Tutorial: Database design
SQL Tutorial: Database design
DataCamp
 58 Python Tutorial: Introduction to spaCy
Python Tutorial: Introduction to spaCy
DataCamp
 59 Python Tutorial: Statistical Models
Python Tutorial: Statistical Models
DataCamp
 60 Python Tutorial: Rule-based Matching
Python Tutorial: Rule-based Matching
DataCamp

This tutorial teaches viewers how to build their first AI agent using LangGraph, covering topics such as defining AI agents as graphs and nodes, integrating tools like OpenAI, and managing tool calling and custom tools. By the end of the tutorial, viewers will be able to build and deploy their own AI agents.

Key Takeaways
  1. Define AI agents as graphs and nodes
  2. Set up the environment and import necessary libraries
  3. Build and compile the first AI agent
  4. Add conversational memory and union types
  5. Integrate tool calling and conditional edges
  6. Create and register custom tools
  7. Deploy AI agents locally and in the cloud
💡 LangGraph provides a flexible framework for building AI agents, allowing developers to define agents as graphs and nodes and integrate powerful tools like OpenAI for enhanced functionality.

Related Reads

📰
7 AI Models, 1 API Key: How to Build a Model-Agnostic SaaS
Learn to build a model-agnostic SaaS that can switch between 7 AI models at runtime without changing application code, avoiding single-vendor lock-in
Dev.to AI
 📰
How Our AI Agents Built a Global E-commerce Customs & Tax Widget Builder in Minutes
Learn how AI agents can build a global e-commerce customs & tax widget in minutes, revolutionizing international online shopping
Dev.to AI
 📰
Devlog: How our AI Agents Built the Micro-Frontend Orchestration Architect to Simplify Micro-Frontend Development
Learn how AI agents can simplify micro-frontend development by building a micro-frontend orchestration architect
Dev.to AI
 📰
How Our AI Agents Built a Universal DSAR Form Builder in Record Time for Global GDPR & CCPA Compliance
Learn how AI agents can accelerate building compliance tools like a Universal DSAR Form Builder for GDPR and CCPA, simplifying data privacy regulation adherence
Dev.to AI

Chapters (15)

Welcome & tutorial roadmap
1:18 LLM vs AI agent • What is LangGraph?
4:41 Flow-chart analogy • Nodes & edges
7:40 Imports & environment setup
10:21 Agent 1 – Define state & build first agent
16:03 Compile, visualise & test Agent 1
22:42 Agent 2 – Add conversational memory
27:30 Union types, context feed & demo
33:29 Agent 3 – Tool calling & conditional edges
40:36 LangChain docs tour • Built-in tools
44:17 Reducer functions & graph visualisation
55:38 Agent 4 – ReAct agent with `create_react_agent`
57:32 Agent 5 – Custom tools & multi-tool agent
1:02:38 Agent 6 – Running local LLMs with Ollama
1:05:55 GPT-4o vs Qwen 2.5 • Pros & co
Up next
What is AI Agents Swarm Explained with Examples
VLR Software Training
Watch →