Your cart is empty.
Keep shopping
LangChain for AI Agents: Build Reliable Agents with Tools
New
99 students
LangChain for AI Agents: Build Reliable Agents with Tools
Learn LangChain from scratch: chat models, structured output, tool calling, guardrails and tracing for production agents
Last updated 9/2026
English
What you'll learn
- Build a complete LangChain agent from scratch that calls real tools to accomplish multi-step tasks
- Control LLM behavior with system prompts and structured output so your agent returns clean, validated data every time
- Add guardrails and tracing to catch unsafe actions and debug failures before they reach users
- Apply architecture principles that turn a fragile demo into a production-ready agent
- Explain what an AI agent is, how the reason–act–observe loop works, and when a plain LLM call is the better choice
- Define your own LangChain tools and understand how an agent decides which one to call and when
- Make tool calling reliable by understanding how it works under the hood and where it typically breaks
- Recognize the most common reasons agent projects fail and design around them from day one
Explore related topics
Course content
6 sections • 18 lectures • 3h 26m total length
Requirements
- Working knowledge of Python (functions, classes, working with dictionaries and JSON)
- An API key for an LLM provider such as OpenAI or Anthropic (free tiers are enough for the exercises)
- A computer with Python 3.10+ and a code editor installed
- No prior experience with LangChain or AI agents is required — we start from the fundamentals
Description
Most AI agent tutorials stop at a demo that works once. This course takes you from your first LangChain call to a complete agent that uses real tools, returns structured output, and keeps working when things go wrong.
What you'll build
By the end of the course you'll have a working LangChain agent that reasons about a task, chooses the right tool, calls it, and returns clean, validated data — with guardrails that block unsafe actions and tracing that shows you exactly why any step failed. Every concept is taught by building, not just explaining.
What's inside
AI agent fundamentals — what an agent actually is, how the reason–act–observe loop works, when a plain LLM call is the better choice, and why most agent projects fail
LangChain essentials — chat models, system prompts, message types, and structured output that other systems can safely consume
Tools and tool calling — how agents decide which tool to use, how to define your own, and what makes tool calling reliable under the hood
From demo to production — architecture principles, guardrails, and tracing that turn a prototype into something you can trust
Each section ends with a written reference so you can revisit the key ideas without rewatching the videos.
Who this course is for
This course is designed for Python developers who have never built an agent, as well as engineers who have tried and watched their agent break. No prior LangChain experience is needed — a working knowledge of Python and an LLM API key are enough.
Why this course
It's short and focused: no filler, no 20-hour marathon. You learn one framework properly, build one complete agent, and leave with a mental model you can reuse in any project — whether you go on to multi-step workflows, retrieval, or evaluation, the foundations you build here carry over.
Enroll now and build your first reliable AI agent with LangChain.
Who this course is for:
- Python developers who want to build their first AI agent and need a clear, practical starting point
- Software engineers moving beyond simple ChatGPT-style API calls to agents that reason, act, and use tools
- Backend developers who need agents that return structured, predictable output they can integrate into existing systems
- Anyone who has tried building an agent, watched it break, and wants to learn how to make one that holds up
- Anyone who wants a short, focused introduction to LangChain agents before committing to a longer, broader course