Build an AI Agent from Scratch with LangGraph

About this course

Most AI agent tutorials start with a framework and a magic create_agent() call. When the agent loops forever, leaks data or answers with confidence from the wrong document, you are left guessing what happened inside.

In this course you build the agent yourself first. You write the agent loop in plain Python: a short loop that calls a model, runs tools and feeds the results back. Then you rebuild the same agent with LangGraph, so every node, edge and checkpoint has something you already understand behind it.

Every chapter has two parts:- From scratch: a small Python package, tiny_agents, that grows with the course: tools and function calling, the ReAct loop, structured answers, RAG, memory, planning, a code sandbox, multi-agent teams, evaluation, guardrails and tracing. - The LangGraph version: the same idea as a graph: state and reducers, ToolNode, conditional edges and Send, agentic RAG, checkpointers and long-term memory, plan-and-execute, human-in-the-loop with interrupt(), supervisors, handoffs and subgraphs, testing, retries, caching and durable execution.

What makes it different- Real runs, honest numbers. Every result in the videos comes from a real model call, recorded and replayable. When the model skips an instruction, falls for a prompt injection or loops for eight calls, the course shows it, explains why from the trace, and fixes it. - Free to follow. Examples run on open-weight gpt-oss models through Groq's free tier, or on OpenAI. The last chapter runs the agent on small open models on a laptop with Ollama. - The 2026 stack. MCP tools, the A2A protocol, the OpenAI Agents SDK and PydanticAI compared with LangGraph, prompt-injection defences, cost per answer, and a FastAPI service in a Docker container.

What is inside- 15 sections, 64 short lectures, about 1 hour 50 minutes of focused video.

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