Dive into the world of agentic AI development with Allen and Mark as they explore LangGraph, a powerful state management system for building dynamic and complex AI agents with LangChain. Discover how LangGraph simplifies agent design, handles state transitions, integrates tools, and enables robust error handling – all while keeping the LLM at the heart of your application.
Further Info:
* https://github.com/langchain-ai/langgraphjs
* https://github.com/langchain-ai/langgraphjs-studio-starter/
* https://vodo-drive.com/
Timeline:
0:00 - Introduction
1:05 - What is LangGraph and how does it work?
2:38 - Understanding Nodes and Tools in LangGraph
4:54 - Navigating Conditional Edges and State Transitions
7:00 - Real-world example: LangGraph in action
12:56 - State Management in LangGraph: Maintaining Context
14:24 - Comparing LangGraph to Jovo and Voiceflow for Conversational Design
17:11 - Handling both freeform and directed conversations with LangGraph
20:40 - Vodo Drive: A deeper dive into a real-world LangGraph implementation
26:31 - The LLM's role: Tool Selection, Execution, and Output Generation
29:51 - Managing Latency and Optimizing LLM Calls
32:08 - Working with JSON responses: Tools vs Human-Readable Output
36:04 - The bigger picture: LangGraph's role in building robust AI agents
Don't forget to subscribe and leave your comments below, especially if you're using LangGraph in your own projects!
#GenAI #GenerativeAI #LangChain #LangGraph #AIAgents #Agents
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