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Langchain AI Agents Course Free

AI Agent Workflows Using LangGraph

Beginner level 3.0 learning hrs 43 Learners

Learn how to build intelligent AI agents using LangGraph, covering agent fundamentals, graph-based workflows, & multi-agent patterns with Python. Langchain AI Agents free course to design stateful, real-world automation systems.

AI Agent Workflows Using LangGraph

Beginner level 3.0 learning hrs 43 Learners
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43 learners enrolled so far

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Skills you will gain

AI agent fundamentals

Graph‑based workflow design

Agent orchestration patterns

Multi-Agent Orchestration

Real-World Automation & Research

Python development skills

Key Highlights

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Master in-demand skills & tools

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About this course

The freeAI Agents in LangGraph course helps you move from basic chatbot ideas to building structured, stateful AI agents that can handle real automation and problem-solving workflows. You will learn what AI agents are, how LangGraph works, and how graph-based architectures turn agent logic into clear, executable workflows. By working through agent fundamentals and the LangGraph architecture, you will understand how agents think, maintain state, and execute tasks across multiple steps rather than responding in isolation.


As you progress, you will gain hands-on experience building AI agents in Python, setting them up correctly, and applying core design principles for reliable execution. You will learn how to design multi-agent systems in which agents collaborate through graph-based workflows, enabling coordination, memory, and decision-making across nodes and edges. By the end of the course, you will be able to design, implement, and orchestrate sophisticated LangGraph agents that solve real-world automation and research problems, giving you practical skills to build scalable, agent-driven systems with confidence.

Course outline

AI Agents in LangGraph Fundamentals

This module introduces AI agents and the LangGraph framework, covering how LangGraph works, its architecture for building and orchestrating complex agentic workflows, and practical examples of agent design and execution.

AI Agent Setup and Implementation

This module introduces the Agent initialization and startup process, followed by hands‑on of building AI agent using agent frameworks and Python, covering key setup, design and execution.

LangGraph Multi‑Agent Patterns

This module introduces the concepts in multiple AI agents using LangGraph, explores how graph‑based workflows enable complex, stateful agent collaboration and recaps the key ideas covered in this course.

Get access to the complete curriculum once you enroll in the course

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Stand out with an industry-recognized certificate

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AI Agent Workflows Using LangGraph

3.0 Hours

Beginner

Sign up with Email
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43 learners enrolled so far

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Get free course content

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Master in-demand skills & tools

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Test your skills with quizzes

Trusted by 10 Million+ Learners globally

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Frequently Asked Questions

Will I receive a certificate upon completing this free course?

Yes, upon successful completion of the course and payment of the certificate fee, you will receive a completion certificate that you can add to your resume.

Is this course free?

Yes, you may enroll in the course and access the course content for free. However, if you wish to obtain a certificate upon completion, a non-refundable fee is applicable.

Who should take this AI Agents in LangGraph course?

This course is suited for anyone interested in building intelligent AI agents and automation workflows. It is especially helpful for developers, engineers, researchers, and professionals who want to design structured, stateful agent systems using modern frameworks.



What will I learn in this Langchain AI Agents Free course?

You will learn the fundamentals of AI agents and how LangGraph enables graph based agent workflows. The course covers: 

  • Agent architecture 
  • Workflow design 
  • Agent execution 
  • Real world automation patterns using LangGraph.


Do I need prior experience with LangGraph or AI agents?

No prior experience with LangGraph is required. The course introduces core agent concepts first and then gradually moves into hands on implementation using Python.

Why is LangGraph important for building AI agents?

LangGraph allows you to design AI agents as structured graphs with nodes and edges. This makes it easier to:

  • Manage state 
  • Enable multi step reasoning 
  • Coordinate multiple agents for complex automation tasks.


What modules are included in this course?

The course includes:

  • AI Agents in LangGraph Fundamentals
  • AI Agent Setup and Implementation
  • LangGraph Multi Agent Patterns


What skills will I gain after completing this course?

You will gain skills in: 

  • AI agent fundamentals 

  • graph-based workflow design 

  • agent orchestration 

  • multi-agent coordination 

  • Python-based agent development.


How does this course help in real-world scenarios?

By the end of the course, you will be able to design and build stateful AI agents for automation, research, and problem-solving. You will confidently use LangGraph to create scalable agent workflows that handle complex, real-world use cases.




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