How to Integrate Google Search Tool in LangGraph | Agentic LLM Search

In this video of LangGraph course series, you’ll learn how to integrate a Google-style web search tool into LangGraph and enable agentic LLM search, meaning your model can decide when to search the web for real-time information instead of hallucinating confidently like that one friend who’s never wrong (but always is 😄).

This video demonstrates how to use Tavily Search as a web search tool inside LangGraph, wire it into an agent workflow, and allow the LLM to automatically call the tool when fresh data is required—because static knowledge is so 2021, and even your LLM deserves to stay updated 🧠🌐.

By the end of this tutorial, you’ll understand how to build search-powered AI agents using LangGraph, apply tool calling logic, and design smarter systems that fetch live information on demand—basically turning your LLM from a bookworm into a research intern who actually Googles things 📚➡️🔍.

👉 Topics covered in this video:

What is Agentic Search in LangGraph

Adding a Web / Google Search Tool using Tavily

Letting the LLM decide when to call the search tool

Handling search results inside the agent flow

Real-world use cases for search-enabled AI agents

👍 If you’re enjoying the LangGraph series, like, subscribe, and comment—it helps the channel grow faster than an over-fitted model on a tiny dataset 😄.

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#GoogleSearchTool #TavilySearch #LangChain #ToolCalling
#LLMAgents #GenerativeAI #RealtimeLLM #AIEngineering
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