Udemy - LangChain and LangGraph - Build Production AI Agents in P...

LangChain & LangGraph: Build Production AI Agents in Python

https://WebToolTip.com

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.