LangGraph Tools & Tool Calling Tutorial | Restaurant Finder

Описание: Hello everyone, welcome back to SummarizedAI!

In this video, we will understand Tools and Tool Calling in LangGraph and see how they help AI agents interact with external systems.

Large Language Models are powerful, but they cannot always perform certain tasks reliably on their own, such as:

• Calling external APIs to fetch real-time data
• Querying databases
• Running code
• Interacting with external systems

This is where Tools come into the picture.

You can think of a tool as a specialized helper that the LangGraph workflow can call whenever the LLM needs additional information or capabilities.

In this video, we will walk through a complete LangGraph workflow and understand how tool calling works step-by-step.

What you will learn in this video:
1. What Tools are in LangGraph
2. Why AI Agents need Tool Calling
3. How shared state works inside a graph
4. How LLM decides when to call a tool
5. Step-by-step LangGraph workflow explanation

Real Use Case Covered:

We will build a Restaurant Recommendation Assistant that helps users find good restaurants near their location.

The workflow will:
1. Store the user's query and location in a shared state
2. Call an external restaurant search tool (API)
3. Retrieve real-time restaurant information
4. Format the results into a clean and structured response
5. Return the final recommendation to the user

This example will help you clearly understand how tools integrate with AI agents in LangGraph.

GitHub Code Reference:
You can find the complete implementation here:
https://github.com/toimrank/summarize...

If you enjoy learning about AI Agents, RAG, LangGraph, and LLM systems, make sure to like this video, subscribe to the channel and hare it with others who want to learn AI.

More exciting AI engineering tutorials coming soon!

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