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Build a Multi-Tool AI Agent Using LangChain and IBM Granite

BeginnerGuided Project

Agentic AI is very popular. Learn to build a responsive AI agent using LangChain’s tool calling with IBM's Granite model in 30 minutes. This beginner-friendly hands-on project guides you through combining key tools like live weather data fetchers, YouTube content handlers, and search utilities. Learn to design a multi-tool workflow that delivers dynamic, real-time outputs using LangChain and watsonx.ai. Ideal for developers seeking applied experience in orchestrating intelligent agents with LLMs and external APIs.

Language

  • English

Topic

  • Artificial Intelligence

Skills You Will Learn

  • LangChain, LLM, Granite, Generative AI, Tool Calling

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 30 minutes

Platform

  • SkillsNetwork

Last Update

  • July 3, 2025
About this Guided Project
Imagine building an AI agent that doesn’t just respond with pre-trained knowledge—but thinks, reasons, and takes action using live data. That’s exactly the journey you’re about to embark on in this guided project.

You’ll start by diving into the world of LangChain, a framework designed to empower large language models with tool calling abilities. Paired with the enterprise-grade intelligence of IBM’s Granite models through watsonx.ai, you won’t just be working with static LLMs—you’ll be training your agent to interact with the world.
Your mission? To craft an agent that can:
  • Check the weather for any city in real-time,
  • Search YouTube for relevant videos based on user prompts,
  • And even perform an iconic search, pulling context-rich information about public figures, places, and events.
Each feature you build will feel like teaching your AI how to use a new sense—sight, memory, or intuition. And thanks to LangChain’s ReAct framework (Reasoning + Acting), your agent won’t just call these tools randomly. It will evaluate the problem, decide which tool is best suited, call it, interpret the result, and return a well-reasoned answer. Step by step, you’ll watch your agent evolve into a thoughtful assistant capable of chaining together multiple tools to solve complex queries—almost like a detective gathering clues before solving a case.

A Look at the Project Ahead

After completing this lab you will be able to:

  • Understand the concept of tool calling (function calling).
  • Explain the purpose of the LangChain framework
  • Implement a LangChain agent using Granite-3B-Instruct model.
  • Integrate and configure prebuilt tools in LangChain

What You'll Need

Let your learners know what technology and skills they'll need prior to starting this guided project. Remember that the IBM Skills Network Labs environment comes with many things pre-installed (e.g. Docker) to save them the hassle of setting everything up. Also note that this platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer or Safari.

Instructors

Jigisha Barbhaya

Data Scientist

I am a Data scientist at IBM and Lead instructor at Skills network. I love to learn and educate. I have completed my MSc(Computer Application) specialisation in Data science from Symbiosis University.

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Contributors

Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

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Anna Gutowska

AI Engineer & Developer Advocate, IBM

Anna is an AI engineer and technical content writer with a passion for AI, data science, and education. With a background in Computer Science and Cognitive Science, she is particularly interested in the intriguing parallels between AI agents and human cognition and decision-making. Anna’s work is dedicated to empowering developers to effectively leverage advanced AI technologies in a user-friendly way for developers of all experience levels. Driven by her curiosity and commitment to innovation, she has concentrated much of her efforts on developing AI agents. Anna believes that these agentic systems can be utilized to free human resources for more complex and impactful tasks, rather than routine activities. Passionate about both sharing her expertise and learning from others, she holds the conviction that the best educators are those who continually seek knowledge.

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Karan Goswami

Data Scientist

I am a dedicated Data Scientist and an AI enthusiast, currently working at IBM's Skills Builder Network. Learning how some simple mathematical operations could be used to make predictions and discover patterns sparked my curiosity, leading me to explore the exciting world of AI. Over the years, I’ve gained hands-on experience in building scalable AI solutions, fine-tuning models, and extracting meaningful insights from complex datasets. I'm driven by a desire to apply these skills to solve real-world problems and make a meaningful impact through AI.

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