Build an AI Agent From Scratch in Python (No LangChain)

About this course

This course contains the use of artificial intelligence.

An AI agent is just an LLM in a loop that chooses its own next action. Once you can write that loop — about twenty lines of plain Python — every "agent framework" on the market stops being magic.

This course is the un-magic. You build your own agent framework from scratch, one concept at a time, and walk away understanding every line of it — and able to read LangChain, CrewAI, or AutoGen like an open book.

What you'll build, across nine short modules: the reusable agent loop with a correct stopping condition; tool use from scratch (parse, a registry and dispatch, and graceful handling when the model invents a tool); three kinds of memory — working, episodic, and semantic retrieval you build with your own cosine similarity in numpy; planning with ReAct, reflection-and-retry, and a real beam-search tree-of-thoughts; multi-agent systems with a coordinator and a delegation cap so it can never loop forever; evaluation — the part almost no course teaches — a harness, the three metrics that matter, a failure taxonomy, and regression diffs; production hardening with cost and step caps, structured logging, and tool guardrails; and a capstone that consolidates it into one clean API and builds a flagship agent end to end.

Honest about scope: "from scratch" means you build everything that turns a model into an agent. The one black box we keep is the language model and its embeddings, called through a thin wrapper. It's about 1,300 lines of pure Python — numpy and a provider client only. No LangChain, no vector database.

You need solid Python and to have called an LLM API once. You do not need any machine-learning math — the one bit, a dot product, is taught from scratch. By the end you won't just have working code; you'll have the judgment to design agents that don't burn money or fail silently, and you'll understand what every framework on the market is really doing.

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