LangChain (Python): Complete Step-by-Step
Course Curriculum
16 modules designed to master the subject.
Module 1: Environment Setup and Basics
Set up your Python environment, install LangChain, and make your first model call.
Module 1 Lesson 1: Python and Virtual Environments
The starting point. How to set up a clean, isolated Python environment for your LangChain projects.
Module 1 Lesson 2: Installing LangChain
The core installation. Learning about LangChain's modular package structure and how to install the base library.
Module 1 Lesson 3: Installing Model Providers
Connecting to the brains. How to install specialized packages for OpenAI, Anthropic, and local model providers.
Module 1 Lesson 4: API Keys and Environment Variables
Security First. How to securely manage your API keys using .env files and prevent accidental leaks.
Module 1 Lesson 5: First LangChain Run
The Hello World of AI. Initializing your first chat model and making a successful invocation.
Module 1 Wrap-up: Verification and First Call
Hands-on: Verify your complete installation and take the Module 1 challenge.
Module 2: Models in LangChain
Understand model abstractions, chat vs. completion models, and streaming responses.
Module 2 Lesson 1: What is a Model Abstraction?
The Power of Agnosticism. Why LangChain uses wrappers to ensure you can switch models without rewriting your code.
Module 2 Lesson 2: Chat vs. Completion Models
Messages vs. Strings. Understanding the different ways LLMs process input and why Chat models are the modern standard.
Module 2 Lesson 3: Streaming Responses
Zero Latency UX. How to use LangChain's .stream() method to display text as it's being generated.
Module 2 Lesson 4: Batching Requests
Parallel Processing. How to use .batch() to send multiple independent queries to an LLM at once.
Module 2 Lesson 5: Provider-Agnostic Setup
Hands-on: Implementing a model switcher that allows you to change the 'Brain' of your app via a simple configuration.
Module 2 Wrap-up: Designing the Brain
Reviewing model abstractions, streaming, and batching. Your final check before moving to Prompts.
Module 3: Prompts and Prompt Templates
Master prompt engineering with templates, few-shot prompting, and versioning.
Module 3 Lesson 1: Prompt Fundamentals
The Art of Instruction. Learning the basic principles of prompt engineering: Context, Task, and Format.
Module 3 Lesson 2: PromptTemplate (String Abstraction)
From Static to Dynamic. Using PromptTemplate to create reusable instructions with variables.
Module 3 Lesson 3: ChatPromptTemplate (Messaging Abstraction)
The Agent's Blueprint. How to create templates for multi-role conversations (System, Human, AI).
Module 3 Lesson 4: Few-Shot Prompting
Leading by Example. How to provide the model with sample Q&A pairs to enforce style and accuracy.
Module 3 Lesson 5: Prompt Versioning and the Hub
Prompts as Code. How to version-control your instructions and use the LangChain Hub to share and pull best-in-class prompts.
Module 3 Wrap-up: Your Reusable Library
Hands-on: Build a reusable prompt library and implement a few-shot dynamic classifier.
Module 4: Chains (LLM Workflows)
Learn to compose multiple LLM steps into structured workflows using LCEL and chains.
Module 4 Lesson 1: What are Chains?
The Connection Logic. Understanding how LangChain 'links' prompts, models, and output parsers into a single executable object.
Module 4 Lesson 2: Single-Step Chains (LCEL Basics)
Piping the Brain. Learning how to use the | operator to create your first executable LangChain Expression Language chain.
Module 4 Lesson 3: Sequential Chains
Multi-Step reasoning. How to pipe the output of one LLM call directly into the input of a second LLM call.
Module 4 Lesson 4: Router Chains
Dynamic Decision Making. How to use an LLM to decide which sub-chain should handle a specific user request.
Module 4 Lesson 5: Chain Composition and Aggregation
Parallel Reasoning. How to run multiple chains at the same time and combine their results into a final synthesized answer.
Module 4 Wrap-up: Designing Complex Flows
Hands-on: Build a Parallel-Sequential Research Chain that writes, reviews, and translates a report.
Module 5: Document Loading and Text Splitting
Ingest data from various sources and split it into optimized chunks for RAG.
Module 5 Lesson 1: Introduction to Document Loaders
Inbound Data. How LangChain standardizes the mess of real-world file formats into a single 'Document' object.
Module 5 Lesson 2: PDF and Web Loaders
Handling the Web. How to scrape data from websites and extract text from multi-page PDF documents.
Module 5 Lesson 3: Text Splitters and Chunking
Optimizing for Logic. Why we must split long documents into smaller 'Chunks' to fit within LLM context windows.
Module 5 Lesson 4: Special Case Splitters (Code & Markdown)
Context-Aware Splitting. How to split Python code by functions and Markdown by headers to maintain semantic integrity.
Module 5 Wrap-up: Processing Your Knowledge Base
Hands-on: Build a pipeline that loads a multi-page PDF and splits it into optimized chunks.
Module 6: Embeddings and Vector Stores
Transform text into vectors and store them for efficient similarity search.
Module 6 Lesson 1: What are Embeddings?
The Math of Meaning. How to turn human words into a list of numbers that represent their semantic soul.
Module 6 Lesson 2: Embedding Providers (Cloud vs. Local)
Choosing your engine. Comparing OpenAI cloud embeddings with local HuggingFace models for speed and privacy.
Module 6 Lesson 3: Introduction to Vector Stores
The Semantic Database. How to store thousands of vectors so you can search them in milliseconds.
Module 6 Lesson 4: Similarity Search and k-Values
Fine-Tuning Retrieval. Learning how to control how many results (k) your vector store returns and what 'Score' means.
Module 6 Wrap-up: Storing Your AI's Memory
Hands-on: Build a local knowledge base using ChromaDB and perform semantic queries.
Module 7: Retrieval-Augmented Generation (RAG)
Build systems that ground LLM responses in your specific external data.
Module 7 Lesson 1: Why RAG Matters
Fighting Hallucinations. Understanding the architectural pattern of grounding AI responses in factual, retrieved context.
Module 7 Lesson 2: The Retriever Interface
The Search Object. How LangChain standardizes vector store lookups into a 'Retriever' that can be used in any chain.
Module 7 Lesson 3: Building the RAG Chain
Piping Facts. Putting it all together into a single LCEL chain that retrieves context and generates an answer.
Module 7 Lesson 4: Designing RAG Prompts
The Art of Grounding. How to write the perfect system prompt to ensure your AI stays factual and cites its sources.
Module 7 Wrap-up: Building a Q&A Bot
Hands-on: Finalize your first production-ready RAG system over your own local documents.
Module 8: Memory and Conversation State
Add persistent memory to your agents and chatbots to handle multi-turn conversations.
Module 8 Lesson 1: Why Memory is Needed
Breaking the Amnesia. Understanding why LLMs are stateless and how we provide 'history' to simulate a conversation.
Module 8 Lesson 2: ConversationBufferMemory
The Raw Transcript. Using the simplest memory type to keep a literal record of every message in a conversation.
Module 8 Lesson 3: Summary Memory
Dense context. How to use an LLM to periodically summarize a conversation to keep the memory footprint small.
Module 8 Lesson 4: External Memory (Redis & Postgres)
Production State. How to move your memory from local RAM to persistent databases for multi-user applications.
Module 8 Wrap-up: Conversations that Stick
Hands-on: Build a persistent chatbot that remembers your name across different CLI sessions.
Module 9: Tools and Function Calling
Define and integrate external tools to give your models superpowers.
Module 9 Lesson 1: What are Tools?
The Agent's Hands. Understanding how to give an LLM the ability to execute code and interact with the physical world.
Module 9 Lesson 2: Defining Custom Tools (The @tool Decorator)
Creating Superpowers. How to turn any Python function into a LangChain tool using a simple decorator.
Module 9 Lesson 3: Built-in LangChain Tools
Instant Capabilities. Exploring the library of pre-made tools for web search, calculation, and database interaction.
Module 9 Wrap-up: Giving Your Agent Powers
Hands-on: Build a toolbox for an agent that can multiply numbers and search Wikipedia.
Module 10: Agents (Reasoning + Tools)
Build autonomous agents that can reason about tasks and select appropriate tools.
Module 10 Lesson 1: Agent Fundamentals
The Autonomous Mind. Understanding the difference between a static Chain and a dynamic Agent that makes its own decisions.
Module 10 Lesson 2: The Agent Loop and Tool Selection
How the Agent decides. Deep dive into the mechanics of tool selection and processing tool outputs.
Module 10 Lesson 3: Safety Limits (Max Iterations & Timeouts)
Guarding the Budget. How to prevent your agents from getting stuck in infinite loops and burning through your API credits.
Module 10 Wrap-up: Building Your First Agent
Hands-on: Combine tools, memory, and reasoning into a single autonomous Research Agent.
Module 11: Structured Output and Parsers
Force LLMs to return reliable, machine-readable data using output parsers.
Module 11 Lesson 1: Why Structured Output Matters
From Stories to Schema. Why production AI must return machine-readable data (JSON) to interact with other software systems.
Module 11 Lesson 2: Output Parsers (Pydantic & JSON)
Parsing the Mess. Learning how to use OutputParsers to extract structured data even from older or less capable models.
Module 11 Wrap-up: Getting Reliable Data
Hands-on: Build an Information Extraction agent that converts raw text into a clean Python object.
Module 12: Middleware and Callbacks
Inspect and intercept agent actions using the callback system for logging and monitoring.
Module 12 Lesson 1: Introduction to Callbacks
Listening to the Chain. How to use the Callback system to intercept events like 'LLM Start' or 'Tool End' for logging and UI updates.
Module 12 Lesson 2: Implementing Callbacks in Chains
Connecting the Hearths. How to pass your custom handlers to models, tools, and executors to start capturing events.
Module 12 Wrap-up: Inspecting the Machine
Hands-on: Build a Cost-Monitoring Callback that calculates and prints the price of every AI request.
Module 13: Debugging and Observability
Trace chain execution and debug complex agentic decisions using LangSmith.
Module 13 Lesson 1: Debugging Chains
Finding the Bug. Techniques and tools for identifying where a multi-step chain is failing or hallucinating.
Module 13 Lesson 2: LangSmith Overview
The Cloud Control Room. Introduction to LangSmith, the enterprise platform for tracing and optimizing your AI applications.
Module 13 Wrap-up: Professional Observability
Hands-on: Set up LangSmith and trace a complex multi-tool agentic decision.
Module 14: Production Patterns
Best practices for API design, caching, retries, and security in LLM apps.
Module 14 Lesson 1: API Design with FastAPI
Connecting to the Frontend. How to wrap your LangChain apps in a professional REST API using FastAPI.
Module 14 Lesson 2: Caching and Retries
Stability and Speed. How to use caching to save money on redundant queries and retries to handle common network errors.
Module 14 Wrap-up: Shipping to Millions
Hands-on: Create a production-ready FastAPI endpoint with caching and retry logic.
Module 15: Deployment
Package, containerize, and deploy your LangChain applications to the cloud.
Module 15 Lesson 1: Packaging LangChain Apps
From Script to Service. How to organize your code and dependencies for reliable deployment on any server.
Module 15 Lesson 2: Dockerizing LangChain
Isolation at Scale. How to create a Docker container for your AI app to ensure it runs everywhere from AWS to Azure.
Module 15 Wrap-up: Your App on the Internet
Hands-on: Finalize your deployment package and learn about cloud hosting options for LangChain apps.
Capstone Project: Agentic RAG System
Build a full-stack, production-ready RAG agent with memory, tools, and a REST API.
Course Overview
Format
Self-paced reading
Duration
Approx 6-8 hours
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