Langchain & Langgraph: Build Production Ai Agents In Python

Langchain & Langgraph: Build Production Ai Agents In Python
Published 8/2026
Created by George Paterakis
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 33 Lectures ( 6h 26m ) | Size: 3.8 GB
Build reliable AI agents with LangChain, LangGraph, RAG, LangSmith, FastAPI, Docker, memory, tools, evaluation.
What you'll learn
Understand how AI agents, agent loops, tools, and agentic workflows work.
Build AI applications with LangChain and Python.
Work with LLM messages, prompts, streaming, structured outputs, and tool calling.
Create custom tools and connect AI agents to external systems and APIs.
Build complete tool-using AI agents from scratch.
Identify common AI agent failure modes and design more reliable agent architectures.
Build stateful AI agents and workflows using LangGraph.
Implement conditional routing, branching, parallel execution, and subgraphs.
Add short-term and persistent memory to AI agents.
Implement human-in-the-loop approval for sensitive agent actions.
Build RAG agents that retrieve and reason over private or internal knowledge.
Improve retrieval using query rewriting and agentic retrieval strategies.
Generate grounded answers with sources and citations.
Trace and debug AI agents using LangSmith.
Build evaluation datasets and systematically evaluate agent performance.
Compare different prompts, models, tools, and agent versions.
Expose AI agents through production APIs using FastAPI.
Package and run agent applications using Docker.
Add validation, guardrails, secure tool access, and least-privilege permissions.
Monitor agent latency, failures, token usage, and costs in production.
Build a complete production-ready AI Support and Operations Agent.
Deploy and operate AI agents in real-world environments.
Requirements
Basic knowledge of Python is recommended.
You should be comfortable with functions, classes, dictionaries, lists, and installing Python packages.
No previous experience with LangChain or LangGraph is required.
No previous AI or machine-learning knowledge is required.
No previous experience building AI agents is required.
A computer capable of running Python and a code editor such as VS Code.
Access to an LLM API will be useful for following the practical exercises.
Basic familiarity with APIs, Git, Docker, or web development can be helpful, but is not required.
Description
Learn how to buildproduction-ready AI agents in Python using LangChain and LangGraph.
This course takes you from the fundamentals of AI agents all the way to building, evaluating, securing, and deploying complete agentic applications. You donot need previous AI or machine-learning experience. If you are comfortable with Python, you can follow the course from the beginning.
We start by understanding what AI agents actually are, how the agent loop works, when to use agents instead of traditional workflows, and how tools allow language models to interact with external systems.
You will then learn the essential building blocks ofLangChain, including models, messages, prompts, streaming, structured outputs, and tool calling. From there, you will create your first real agent and learn why naive agent implementations often fail in production.
The course then moves intoLangGraph, where you will build more reliable and controllable agent architectures using state, nodes, edges, routing, parallel execution, subgraphs, persistence, memory, and human-in-the-loop approval.
You will also buildRAG agents capable of retrieving information from internal knowledge bases, improving search queries, reasoning over retrieved context, and generating grounded answers with citations.
But building an agent is only half the problem.
You will learn how to useLangSmith to trace agent execution, debug failures, create evaluation datasets, measure performance, and compare different versions of your agent.
Finally, we will cover the engineering required to move from a prototype to production:FastAPI, Docker, input validation, guardrails, secure tool design, least-privilege access, monitoring, cost control, deployment, and incident handling.
Throughout the course, you will progressively build components that come together in the final capstone: a completeAI Support and Operations Agent.
By the end of the course, you will understand not only how to build agents with LangChain and LangGraph, but also how to make themreliable, observable, secure, and production-ready.
What you will build and learn
AI agents and agentic workflows
LangChain fundamentals
LLM tool calling
Structured outputs
Streaming responses
Custom Python tools
Reliable agent loops
LangGraph state and workflows
Conditional routing
Parallel execution
Subgraphs
Conversation memory
Persistent state
Human-in-the-loop approval
Retrieval-Augmented Generation (RAG)
Agentic RAG
Query rewriting and retrieval
Grounded answers and citations
LangSmith tracing and debugging
Agent evaluation
FastAPI APIs
Docker containers
Validation and guardrails
Secure agent architecture
Production monitoring
Cost management
Deployment strategies
The focus throughout the course is practical engineering. Instead of only showing isolated examples, we will examinewhy agent systems fail and how to design them properly.
Who this course is for
Python developers who want to learn how to build modern AI agents.
Software engineers who want to move from simple LLM applications to production agent systems.
Backend developers interested in integrating AI agents into existing products and APIs.
AI engineers who want practical experience with LangChain and LangGraph.
Developers who have experimented with ᑕᕼᗩTGᑭT or LLM APIs and want to understand agent architecture properly.
Developers building RAG applications who want to extend them with agentic reasoning and tools.
Engineers who want to learn how to evaluate and debug agents using LangSmith.
Developers interested in agent memory, workflows, human approval, tool calling, and multi-step reasoning.
Technical founders and indie developers building AI-powered applications.
Students who know Python and want a practical introduction to modern AI application development.
Developers who already know the basics of LangChain but want to learn how to build reliable, secure, and deployable AI agents.
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