Course curriculum

  • 1
    Introduction to Tools and Tool Calling
    • Introduction to Agents and Tools
    • Hands-On: Creating Tools in LangChain - Part I - Introduction
    • Hands-On: Creating Tools in LangChain - Part II - Using Tools
    • Hands-On: Creating Tools in LangChain - Part III - Creating Custom Tools
    • Introduction to Tool or Function Calling
    • Hands-on: Tool Calling using LLMs in LangChain
    • Course Handouts
    • Quiz
    • Reading Resources: Understanding LangChain's Agent Framework
  • 2
    Essentials of AI Agents with LangChain
    • Introduction to LangChain for AI Agents
    • Core Components of LangChain Agents
    • Types of AI Agents in LangChain
    • Hands-On: Build a Tool-Calling Agentic AI Research Assistant
    • Quiz
    • Reading: Agent Types in LangChain
  • 3
    Memory and Conversational Agents
    • Memory and Conversational Agents
    • Hands-On: Build a Multi-User Conversational Tool-Calling Agentic AI Research Assistant
    • Quiz
    • Reading Resource: Understanding Memory in LangChain
  • 4
    Project: Build a Text2SQL AI Agent
    • Project Introduction
    • What is Text2SQL?
    • AI Agents vs. AI Workflows
    • Text2SQL AI Workflow System Architecture
    • Hands-On: Build a Text2SQL AI Workflow System - Part I - Project Setup
    • Hands-On: Build a Text2SQL AI Workflow System - Part II - Project Implementation
    • Text2SQL ReAct Agentic AI System Architecture
    • Hands-On: Build a Text2SQL ReAct Agentic AI System - Project Setup -Implementation
    • Quiz
    • Reading Resources: Building a Conversational AI SQL Assistant with LangChain, GROQ, and Streamlit
  • 5
    Project: Build a Financial Analyst AI Agent
    • Project Introduction
    • Financial Analysis Essentials
    • Key Financial Data Platforms
    • Setup Access to Financial Data Platforms
    • OpenBB Platform: Introduction by the OpenBB Team
    • Financial Analyst ReAct Agentic AI System Architecture
    • Hands-On: Part I - Project Setup
    • Hands-On: Part II - Creating Financial Tools
    • Hands-On: Part III - Implementing Agentic System
    • Quiz
    • Reading Resources: ReAct Logic in LangChain
    • Assignment: Building an intelligent Travel Assistant AI

Course Description

In today’s AI-driven world, tools are the cornerstone of building smart, functional agents. This course dives deep into the use of LangChain for developing tool-enabled AI agents, guiding you from foundational concepts to advanced real-world applications.

Through practical, hands-on sessions, you’ll learn:

  • The fundamentals of tools and tool calling in AI systems.

  • Step-by-step creation of tools in LangChain.

  • Practical implementation of tool calling using LLMs in LangChain for real-world scenarios.

By the end of this course, you’ll be equipped with the skills and knowledge to design, build, and optimize intelligent AI agents with LangChain, tailored to solve complex tasks effectively

Who Should Enroll

  • Individuals eager to explore the potential of LangChain and tools in building smart AI agents.

  • Professionals looking to enhance their skills in creating tool-enabled, task-specific AI systems.

  • Learners who want to gain practical, hands-on experience with LangChain and LLM-based tools.

  • Those seeking to design scalable, intelligent AI agents for real-world applications.

Key Takeaways from the course

  • Understand the fundamentals of tools and their role in enabling AI agents.

  • Gain hands-on experience in creating and using tools within LangChain.

  • Learn the concept of tool or function calling to enhance agent capabilities.

  • Explore practical, real-world applications of tool calling using LLMs.

  • Master the integration of LangChain tools for building scalable, task-specific AI agents.

About the Instructor

Dipanjan Sarkar - Head of Community and Principal AI Scientist, Analytics Vidhya

Dipanjan Sarkar is a distinguished Lead Data Scientist, Published Author, and Consultant, having a decade of extensive expertise in Machine Learning, Deep Learning, Generative AI, Computer Vision, and Natural Language Processing. His leadership spans Fortune 100 enterprises to startups, crafting end-to-end data products and pioneering Generative AI upskilling programs. A seasoned mentor, Dipanjan advises a diverse clientele, from novices to C-suite executives and PhDs, across Advanced Analytics, Product Development, and Artificial Intelligence. His recognitions include "Top 10 Data Scientists in India, 2020," "40 under 40 Data Scientists, 2021," "Google Developer Expert in Machine Learning, 2019," and "Top 50 AI Thought Leaders, Global AI Hub, 2022," alongside global accolades and a Google Champion Innovator title in Cloud AI/ML, 2022.
About the Instructor

FAQ

  • What is LangChain, and why is it important for building AI agents?

    LangChain is a powerful framework that simplifies the development of AI agents by integrating tools and enabling advanced task-specific functionalities with LLMs.

  • What are tools and tool calling in LangChain?

    Tools in LangChain enable AI agents to perform specific tasks, while tool calling allows agents to dynamically execute functions or workflows, enhancing their capabilities.

  • What hands-on skills will I gain?

    You’ll learn to create and use tools in LangChain, implement tool calling with LLMs, and build scalable, task-specific AI agents through practical exercises.

  • Why should I learn to build AI agents with LangChain?

    LangChain simplifies the development of intelligent, tool-enabled AI systems that can automate complex tasks and deliver real-world value efficiently.

  • How does this course prepare me for real-world applications?

    By providing hands-on experience with LangChain, you’ll learn to implement practical solutions like tool creation and function calling for dynamic, task-specific agents.

  • Will I receive a certificate upon completing this course?

    Yes, participants will receive a certificate of completion after successfully finishing the course and assessments.