Complete AI Training
Sign inGet my AI kit

Your job's AI kit

Get your AI kit

Tell us who you are and what you do. We show you your kit right away and email you the link: skills, prompts, AI agents, MCP servers and courses for your job.

500+ jobs ready, and we make a kit for any other job. No payment needed to look.

Share

Certification

Certification in Building and Deploying Scalable AI Agent Systems with Python

Get certified in LangGraph Fundamentals to expertly design, build, and scale AI agent systems in Python, create multi-agent workflows with persistent state, and deploy adaptive, production-ready AI solutions for real-world challenges.

Exam of 10 to 20 questionsCertificate for LinkedInIntermediate · Expert, technical
IT and developmentProduct developmentScience and research
Take the exam

The exam

Take the certification exam

Multiple-choice questions about the course. Pass with 70% or more and your certificate is issued at once, with a public page and an "Add to LinkedIn" button.

Get certifiedAlready a member? Sign in

What the exam covers

2 questions from each of the 7 chapters of the course

14 multiple-choice questions, drawn fresh for every attempt. Pass with 70% or more.

  1. 01Overview and core features2:31
  2. 02Examples and IDE choice2:29
  3. 03Environment setup2:45
  4. 04Getting an LLM API key1:14
  5. 05Simple LangGraph chatbot14:21
  6. 06Generating the visual graph1:05
  7. 07Complex chatbot with routing22:23
About the course

The "LangGraph Fundamentals: Build Scalable AI Agent Systems with Python" certification empowers you to design, build, and scale robust multi-agent workflows using LangGraph. By mastering these skills, you'll gain increased productivity, a competitive advantage, and adaptability for future career growth in the field of AI systems. Enroll today to take full control of your AI agent solutions and unlock new opportunities in real-world applications.

This certification covers the following topics:

  • LangGraph vs. LangChain and Llama Index: Key Differences and Use Cases
  • Graph-Based Workflow Representation: Core Concepts
  • Nodes and Edges in LangGraph: Structure and Functionality
  • State Management for Persistent and Adaptive Agents
  • Implementing Conditional Routing for Adaptive Agents
  • Building Multi-Agent Systems and Collaboration Strategies
  • Practical Implementation of LangGraph in Python
  • Defining and Managing State with TypedDict and Annotations
  • Writing Node Functions for Modular Agent Workflows
  • Constructing and Compiling Graphs: Nodes, Edges, and Routers
  • Working with LLMs: Initialization, Invocation, and Structured Output
  • Advanced Patterns: Subgraphs, Graph as Data, and Reusability

Jobs this certification suits

Our AI checked this certification against 500 jobs; these get the most out of it. Each job links to its learning path.

AI Engineers