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The ReAct (Reasoning + Acting) pattern is one of the most influential agent architectures. It interleaves explicit reasoning steps (thoughts) with concrete actions, producing a transparent chain of thought-action-observation that is easy to debug and understand. This lesson covers the pattern in depth, implements it from scratch, and discusses when to use it.
ReAct stands for Reasoning and Acting. The core idea is simple: before taking an action, the agent explicitly writes down its reasoning. After observing the result, it reasons again.
Thought: I need to find the current population of France.
Action: web_search("population of France 2025")
Observation: France has a population of approximately 68.4 million.
Thought: I now have the answer.
Action: finish("The population of France is approximately 68.4 million.")
This is in contrast to simpler approaches where the model directly outputs an action without explaining its reasoning.
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