Building AI Agents with Langchain! (Crash Course 2026)

Learn the fundamentals of LangChain and AI agents in Python, with a practical focus on understanding how language models can interact with tools, external functions, and structured workflows.

This course is designed as an introduction to the core concepts behind modern AI agents and the LangChain framework. Rather than trying to cover every part of the broader agent ecosystem, the course focuses on the building blocks you need to understand before moving into more advanced topics.

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 how the basic agent loop works. We will look at how a model can receive a request, decide whether it needs additional information or an external capability, call a tool, observe the result, and continue until it can produce a useful response.

An important part of the course is tool calling.

Modern language models can do much more than generate text. By connecting them to tools, we can allow them to interact with Python functions, APIs, databases, search systems, internal services, and other components of an application.

You will learn how tool calling works conceptually and how LangChain helps organize this interaction. We will create tools in Python, define the information a model needs in order to use them, and explore how tools can be combined with language models to create simple agentic applications.

From there, we will cover the main LangChain fundamentals that are useful when building these systems.

You will work with language models through LangChain's interfaces and understand concepts such as messages, prompts, model invocation, streaming, structured outputs, and chaining different components together.

The goal is not simply to memorize LangChain methods. Instead, the course aims to give you a clear mental model of how the different pieces fit together.

For example, you will see how a typical application can move from a user message to a model request, from the model to a tool call, from the tool back to the model, and finally to the response presented to the user.

We will also spend time looking at structured outputs. In many applications, we do not want an LLM to return arbitrary text. We may instead want it to return information following a particular schema that can be processed reliably by Python code or another part of an application.

Another topic covered is streaming, which allows model responses to be returned progressively rather than waiting for an entire generation to complete.

Once these fundamentals are in place, we will use them to build simple AI agents with LangChain.

You will see how LangChain can combine a model with a collection of tools and allow the model to choose which capability to use depending on the user's request. We will examine the basic execution flow and some of the design considerations involved when giving an AI system access to external functionality.

We will also discuss why tool definitions matter, why clear inputs and outputs are useful, and why developers should think carefully about what capabilities an agent is allowed to access.

The course remains intentionally focused on the introductory layer of the LangChain ecosystem. It does not attempt to cover every advanced architecture or production deployment pattern. Instead, it provides a foundation for understanding LangChain, tool calling, and the core ideas behind agentic applications.

By the end of the course, you should be familiar with the main concepts involved in building a basic LangChain application and have practical experience connecting language models to Python tools.

What we will cover

• AI agent fundamentals

• The basic agent loop

• Agents compared with traditional LLM applications

• LangChain fundamentals

• Working with language models in LangChain

• Messages and model interactions

• Prompt construction

• Model invocation

• Streaming responses

• Structured outputs

• Tool calling fundamentals

• Creating tools with Python

• Tool inputs and outputs

• Connecting tools to language models

• Allowing models to select tools

• Handling tool results

• Building simple LangChain agents

• Understanding the flow of an agent execution

• Designing clear and useful tools

• Basic considerations around agent capabilities and tool access

• Organizing simple agentic applications in Python

The emphasis throughout the course is on understanding the fundamentals through practical examples.

If you already know Python and want to understand how LangChain works, how LLM tool calling is implemented, and how the basic ideas behind AI agents translate into code, this course provides a focused starting point.