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Learn LangGraph from scratch by building small, clear Python graph examples step by step. In this video, you'll learn the fundamentals of LangGraph in a practical way; not just theory, and not just copy-paste code. We'll start from the simplest possible graph, where the state is just a string, then gradually move into typed state, conditional routing, model calls, message state, tool binding, tool execution, agent loops, and memory. This project is designed as a crash course and refresher for AI engineers, Python developers, and students who want to understand how LangGraph actually works under the hood. Instead of jumping directly into a large agent application, every file in this repo focuses on one concept at a time. You can read each graph, run it, change it, and see exactly how the state moves from node to node. By the end, you'll understand how to build LangGraph workflows that can route state, call language models, use tools, loop between model and tool nodes, and persist conversation state with memory. Timestamps 0:00 - 1:42 Intro 1:42 - 4:34 How to study this course 4:34 - 6:31 What is LangGraph? 6:31 - 20:05 Project setup 20:05 - 32:00 Fundamentals of graph 32:00 - 2:17:10 LangGraph concepts + graphs What you'll learn What LangGraph is and why it matters How graph-based AI workflows work How to create a minimal LangGraph graph How state moves through nodes and edges How to define state with a plain string How to define state with TypedDict How to create nodes that transform state How to connect nodes using START and END How to use conditional edges for routing How to call an OpenAI chat model inside a graph node How to use MessagesState for chat applications How LangGraph accumulates messages across a conversation How to create tools with the @tool decorator How to bind tools to a language model How a model decides when to request a tool How ToolNode executes tool calls How tools_condition controls graph flow How to build a simple tool-calling agent loop How to add in-memory checkpointing with MemorySaver How thread_id is used to keep memory separated How to run graphs locally with Python How to inspect graphs with LangGraph Dev Project features Beginner-friendly LangGraph crash course Numbered examples that build on each other Minimal graph using string state TypedDict state example Conditional edge routing example LLM-powered graph node example MessagesState chat example Model tool-binding example ToolNode execution example Simple math agent with multiple tools Agent loop that returns to the model after tool execution Memory example using LangGraph checkpointers LangGraph API configuration through langgraph.json uv-based Python project setup OpenAI integration through LangChain OpenAI Clean project structure for learning and revision Project architecture User input StateGraph Node function State update Edge or conditional edge Optional ChatOpenAI model call Optional model-requested tool call ToolNode execution Updated MessagesState Final graph state Agent workflow MessagesState ChatOpenAI with bound tools tools_condition ToolNode Return to the chat model Final assistant answer Tech stack Python 3.12+ LangGraph LangChain LangChain OpenAI OpenAI dotenv uv VS Code GitHub Why this project matters Most AI tutorials jump straight into a chatbot or a finished agent app. This project slows things down and shows the building blocks that make those agent systems work: state, nodes, edges, routing, messages, tools, loops, and memory. If you're learning AI agents, LangChain, LangGraph, backend engineering, or how to build real AI applications in Python, this course gives you the foundation you need before moving into larger agentic systems. Once you understand these examples, concepts like multi-agent systems, long-running workflows, human-in-the-loop agents, memory, persistence, dynamic routing, tool orchestration, and production agent backends become much easier to reason about. Links below GitHub Repo: https://github.com/Mohamad-Hachem/LangGraph_ACL OpenAI Dashboard: https://platform.openai.com/home LangGraph: https://www.langchain.com/langgraph LangChain: https://www.langchain.com/ uv: https://docs.astral.sh/uv/ Time for studying: https://pomofocus.io/ #AI #LangGraph #LangChain #Python #AIAgents #LLM #OpenAI #SoftwareEngineering #BackendEngineering #AIProjects #PromptEngineering #PortfolioProject #ArtificialIntelligence #PythonDeveloper #AgenticAI #AIAutomation #AIEngineering

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