Build a ReAct Agent from Scratch in Python (No Framework)

Frameworks like LangChain, LlamaIndex, and the Claude Agent SDK make it easy to ship an agent in an afternoon. But the abstractions hide where the real engineering decisions live: how you structure the prompt, when you stop the loop, what you do when a tool throws, how you keep the model from hallucinating tool names. The fastest way to internalise those decisions is to write the loop yourself, once, end to end.

This guide walks through a complete ReAct (Reason + Act) agent in roughly 200 lines of Python with zero agent dependencies. We talk to the model via the official Anthropic SDK, but the same pattern works against any chat completions API. By the end you will have a working agent that searches the web, reads files, does math, recovers from tool errors, and stops when it has actually answered the question rather than when it runs out of patience.