What is the ReAct framework for AI agents?
ReAct or Reasoning plus Acting is a framework that interleaves reasoning traces with action execution in AI agents. At each step, the agent produces a thought explaining its reasoning about the current situation, takes an action such as calling a tool or querying a database, receives an observation from the action result, and uses this observation to inform the next thought and action cycle. This think-act-observe loop continues until the agent determines the task is complete. ReAct improves agent reliability by making the reasoning process explicit and auditable. It helps the agent plan multi-step tasks, recover from errors by reasoning about unexpected observations, and provide transparent explanations of its decision process. Compared to chain-of-thought alone, ReAct grounds reasoning in real observations rather than speculation. LangChain and LlamaIndex provide built-in ReAct agent implementations that work with various LLM providers.