A Quick Introduction to LangGraph for Building Agentic Workflows Using GPT-4o-Mini
In this video, we'll talk about the basics of LangGraph, a framework for building powerful agentic workflows based on large language models. We'll use OpenAI's gpt-4o-mini as the base model for our examples and walk through the basics of langgraph and some interesting concepts and ideas around agentic systems!
Disclaimer: (Yes my face did get cut off in the video...I only realized that after having recorded...living and learning folks...)
📚 Chapters:
00:00 - Introduction to LangGraph: Welcome and overview of the video
00:05 - What is LangGraph?
00:10 - Overview of Agentic Systems
1:13 - The Agent Loop: Explanation of the agent loop concept
5:53 - Key Components of Agentic Systems: Essential elements like memory, planning, and tool calling
6:42 - Modeling Agentic Workflows as graphs
7:53 - Core Principles of LangGraph: Controllability, human-in-the-loop, and streaming-first design
11:22 - Components of LangGraph: Explanation of nodes, edges, and states in LangGraph
13:09 - Example Implementation: Step-by-step construction of a basic agent workflow in LangGraph
19:30 - Nodes in LangGraph: Functionality, execution, and special nodes
21:22 - Edges in LangGraph: Different types of edges and their purposes
23:09 - Integrating Edges: Practical example of adding edges to the graph
27:46 - Inspecting Final State: Reviewing the final output and state of the graph
29:33 - Agent Loop Outline: Summary of the basic agent loop
31:44 - Full Example - Research Agent: Detailed demo of a research agent use case
42:38 - Agentic Patterns: Overview of agentic patterns like plan and execute, supervisor, and multi-agent workflow
45:13 - Conclusion: Final thoughts and encouragement to explore LangGraph and related resources
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