The ReAct Pattern

Imagine a detective who writes in a notebook before every move. Before interviewing a witness, they jot down what they hope to learn and why. After the interview, they write what they found out. Before searching the crime scene, they note their theory. After searching, they record what they discovered. This back-and-forth between writing and doing — reasoning, then acting, then reasoning again about the result, then acting again — is the detective's method. It is also the heart of the ReAct pattern.

What ReAct Stands For

ReAct is a pattern introduced by AI researchers in 2022 as a way to structure how language-model agents handle multi-step tasks. The name combines two words: Reasoning and Acting. In the ReAct pattern, an agent alternates between two distinct move types on every turn. Thought: a reasoning step in which the agent thinks out loud about the current situation, what it knows, what it needs to find out, and what to do next. This thought is not an action — it is internal reasoning made visible. Action: a specific operation the agent performs — calling a tool, producing output, or making a decision — based on the thought that immediately preceded it. After the action, the environment returns an observation. That observation triggers a new thought, which triggers a new action — and the cycle continues until the task is complete.

A ReAct Loop in Action

Here is a simplified ReAct sequence for the task 'What was the most-watched TV show in the US in 2023?' Thought 1: I do not have reliable memory of 2023 TV ratings. I need an external source to answer this accurately. I will search for it. Action 1: search('most-watched TV show US 2023') Observation 1: Results show multiple sources citing a specific popular drama series as number one in total viewers. Thought 2: Multiple sources agree on the same show, which increases my confidence. I also notice the figure refers to live-plus-same-day ratings — I should note this qualifier in my answer. Action 2: produce_answer('According to live-plus-same-day Nielsen ratings, the most-watched show in the US in 2023 was...') Each Thought explains the reasoning before the Action. Each Observation informs the next Thought. Nothing happens without a reason, and every reason is recorded.

ReAct vs. The Basic Agent Loop

ReAct is not a replacement for the basic Perceive-Think-Act-Observe loop — it is a specific, structured implementation of it. The basic loop describes the general cycle that all agents follow. ReAct specifies exactly how that loop works for language-model agents: by making every Think stage a written Thought and ensuring every Act is justified by the Thought that preceded it. This structure has made ReAct one of the most widely adopted patterns in production AI agent systems. Frameworks like LangChain, AutoGen, and OpenAI's function-calling interface all implement variations of the ReAct pattern because it produces agents that are more accurate, more debuggable, and more trustworthy than agents that act without explicit reasoning.