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 âš¡ Understand how AI agents, agent loops, tools, and agentic workflows work. âš¡ Build AI applications with LangChain and Python. âš¡ Work with LLM messages, prompts, streaming, structured outputs, and tool calling. âš¡ Create custom tools and connect AI agents to external systems and APIs. âš¡ Build complete tool-using AI agents from scratch. âš¡ Identify common AI agent failure modes and design more reliable agent architectures. âš¡ Build stateful AI agents and workflows using LangGraph. âš¡ Implement conditional routing, branching, parallel execution, and subgraphs. âš¡ Add short-term and persistent memory to AI agents. âš¡ Implement human-in-the-loop approval for sensitive agent actions. âš¡ Build RAG agents that retrieve and reason over private or internal knowledge. âš¡ Improve retrieval using query rewriting and agentic retrieval strategies. âš¡ Generate grounded answers with sources and citations. âš¡ Trace and debug AI agents using LangSmith. âš¡ Build evaluation datasets and systematically evaluate agent performance. âš¡ Compare different prompts, models, tools, and agent versions. âš¡ Expose AI agents through production APIs using FastAPI. âš¡ Package and run agent applications using Docker. âš¡ Add validation, guardrails, secure tool access, and least-privilege permissions. âš¡ Monitor agent latency, failures, token usage, and costs in production. âš¡ Build a complete production-ready AI Support and Operations Agent. âš¡ Deploy and operate AI agents in real-world environments. Requirements â— Basic knowledge of Python is recommended. â— You should be comfortable with functions, classes, dictionaries, lists, and installing Python packages. â— No previous experience with LangChain or LangGraph is required. â— No previous AI or machine-learning knowledge is required. â— No previous experience building AI agents is required. â— A computer capable of running Python and a code editor such as VS Code. â— Access to an LLM API will be useful for following the practical exercises. â— Basic familiarity with APIs, Git, Docker, or web development can be helpful, but is not required.