In today’s AI-driven landscape, companies increasingly rely on Large Language Models (LLMs) to build intelligent applications for automation, customer interaction, data analysis, and more. LangChain and LangGraph provide powerful tools to streamline the development of such applications. LangChain is an open-source framework that simplifies LLM application development by offering modular, user-friendly components. It allows seamless integration with models like OpenAI’s GPT, Hugging Face models, Google Bard, and PaLM, among others. With LangChain, you can create solutions for text summarization, question answering, structured data analysis, and more.
LangGraph extends LangChain’ s capabilities by enabling complex, multi-step workflows with AI agents, supporting stateful applications, decision-making, and adaptive reasoning. Companies use LangGraph to build scalable, intelligent systems that require structured execution flows, such as chatbots, autonomous agents, and Retrieval-Augmented Generation (RAG) pipelines.
This hands-on course covers LangChain fundamentals, data connections, vector databases, memory, chains, and agent-based workflows. You’ll also explore LangGraph’ s role in building AI-powered applications with structured execution logic. By the end of this course, you’ll be equipped to create cutting-edge AI applications that leverage LLMs effectively.
Duration: 3 Days
Course Code: BDT406
Learning Objectives:
After this course, you will be able to:
What is LangChain?
Installing LangChain and setting up environment
Creating and using prompts
Hands-on: Writing a basic prompt with LangChain
Model Inputs and Outputs
Understanding model inputs and outputs
Connecting LangChain with an LLM
Using prompt templates (zero-shot, one-shot, few-shot learning)
Hands-on: Experimenting with model inputs and outputs
Data Connections and Vector Stores
Connecting LLMs to external data
Using document loaders and transformers
Working with text embeddings and vector databases (FAISS, ChromaDB)
Hands-on: Implementing a vector database for retrieval
Using Chains for Application Logic
Understanding LLM chains
Building sequential and router-based chains
Hands-on: Developing a LangChain pipeline
Memory in LangChain
Introduction to memory and its use cases
Types of memory and memory buffers
Hands-on: Implementing chat memory for contextual conversations
Building AI Agents with LangChain
Introduction to AI Agents and their applications
Understanding ReAct framework in LangChain
Exploring agent tools and executions
Hands-on: Building an agent-based application
Introduction to LangGraph
What is LangGraph?
Why use LangGraph with AI workflows?
Flow engineering and LangGraph components
Building a Reflection Agent
What is a Reflection Agent?
Creating a reflection chain and defining LangGraph graphs
Understanding State Graphs
Hands-on: Implementing a Reflection Agent
Developing a Reflexion Agent
Understanding Reflexion agent for iterative improvement
Build Actor, Revisor agents and integrate message graph
Integrating search tools (Tavily, others)
Hands-on: Creating a Reflexion Agent with LangGraph
Advanced RAG (Retrieval-Augmented Generation)
Constructing an advanced RAG workflow
Using nodes and states for decision making
Leveraging predefined LangChain prompt templates
Implementing Self-RAG for reducing hallucinations
Working with Adaptive RAG for dynamic retrieval
Hands-on: Build an advanced RAG system
Persistence & State Management in LangGraph
Using state persistence in LangGraph
Implementing memory-saving techniques and human-in-loop interventions
Storing State using SQLite saver
Hands-on: Using persistence in LangGraph application
LangGraph Ecosystem & Cloud Deployment
Setting up LangGraph Studio (IDE)
LangGraph Cloud API (set up local environment)
Using LangGraph Cloud API: Threads, Runs, Assistants
Hands-on: Install ecosystem on local machine (Docker required)
