Introduction to Langchain and LangGraph (Crash Course 2026)

Learn the fundamentals of LangChain, LangGraph, and AI agents in Python in a focused crash course designed to introduce the main concepts behind modern agentic applications.

This course provides a practical introduction to the LangChain ecosystem without trying to cover every advanced feature or production architecture. The goal is to help you understand the most important building blocks first: language models, messages, prompts, tools, agents, workflows, state, and LangGraph.

We begin with the fundamentals of AI agents.

You will learn what an AI agent is, how it differs from a standard language model application, and why tools are such an important part of agentic systems. A normal language model primarily generates text, while an agent can be given access to external capabilities such as Python functions, APIs, search systems, databases, or other application components.

We will look at the basic agent loop and how a model can receive a request, decide whether it needs to use a tool, observe the result, and continue until it can produce a useful response.

From there, we introduce LangChain.

LangChain provides abstractions for working with language models and building applications around them. You will become familiar with some of the core concepts used throughout the framework, including models, messages, prompts, structured outputs, streaming, and tool calling.

The emphasis is on understanding how these components fit together rather than memorizing a large number of APIs.

You will see how a Python application can send messages to a language model, control the structure of its input and output, and connect the model to custom tools.

A significant part of the course focuses on tool calling.

You will learn how tools can be defined in Python and exposed to a language model. We will look at how the model decides when a tool may be useful, how arguments are passed to the tool, and how the result can be returned to the model as part of an agent workflow.

This gives us the foundation required to create a simple LangChain AI agent.

Once the basic agent concepts are clear, the course moves into LangGraph.

LangGraph introduces a graph-based way of organizing AI agents and multi-step workflows. Instead of treating the entire application as one continuous loop, we can represent different operations as separate nodes and explicitly define how execution moves between them.

You will learn the basic concepts of state, nodes, and edges.

State represents the information that needs to move through the application. Nodes perform individual operations, while edges determine which node should execute next.

We will use these concepts to understand how an agent can be represented as a workflow.

The course also introduces conditional routing.

Some workflows follow a fixed sequence, while others need to make decisions based on the current state or the output of a language model. LangGraph allows us to define these different paths and create applications that can move between multiple steps depending on what happens during execution.

We will also look at the relationship between agents and workflows.

An agent typically gives the language model some freedom to decide what action should happen next. A workflow usually gives the developer more explicit control over the sequence of operations.

In practice, many useful AI applications combine both approaches.

LangGraph provides a way to represent this combination clearly. Some parts of a graph can follow deterministic application logic, while other parts can allow a language model to make decisions or select tools.

We will also introduce the idea of memory and stateful applications.

Many AI applications need to maintain conversation history or other information between steps. You will see how state can be used to keep track of messages and intermediate information as an application moves through a LangGraph workflow.

The course is intentionally structured as a crash course.

It does not attempt to cover every feature in LangChain or LangGraph, nor does it go deeply into advanced deployment, evaluation, RAG architectures, multi-agent systems, or large-scale production engineering.

Instead, the goal is to give you a practical overview of the ecosystem and a foundation that makes more advanced LangChain and LangGraph topics easier to understand.

By the end of the course, you should have a clearer understanding of how LangChain and LangGraph work together, how tools connect language models to external functionality, and how basic AI agents can be represented as structured workflows in Python.

What we will cover

• AI agent fundamentals

• The basic agent loop

• Agents compared with standard LLM applications

• Introduction to LangChain

• Working with language models

• Messages and conversations

• Prompts

• Model invocation

• Streaming responses

• Structured outputs

• Tool calling

• Creating custom Python tools

• Connecting tools to language models

• Building a simple LangChain agent

• Understanding agent execution

• Introduction to AI workflows

• Agents compared with workflows

• Introduction to LangGraph

• LangGraph state

• Nodes and edges

• Passing information between nodes

• Conditional routing

• Basic agent loops in LangGraph

• Tool-using agents with LangGraph

• Conversation state

• Basic memory concepts

• Combining deterministic logic with model-driven decisions

• Structuring multi-step AI applications in Python

The focus throughout the course is on building a clear mental model of LangChain, LangGraph, and AI agents through practical examples.

If you are comfortable with Python and want a concise introduction to the LangChain ecosystem in 2026, this course provides a practical starting point before moving into more specialized topics such as RAG, advanced LangGraph workflows, memory systems, evaluation, or production agent architectures.