ReAct Prompting

ReAct (Reasoning + Acting) is a prompting framework where an AI alternates between Thought steps (reasoning about the current state) and Action steps (performing an operation like search or tool call), then processes Observations (results of actions) before the next thought. This interleaved loop enables AI to solve multi-step tasks that require both planning and real-time information gathering. It was introduced in a 2022 paper and underpins most AI agent frameworks.

Chain-of-thought prompting produces a reasoning trace that leads directly to an answer from existing knowledge. ReAct interleaves reasoning with actions — the AI can fetch external information, use tools, and incorporate new observations into its reasoning at each step. CoT is for reasoning over known information; ReAct is for tasks that require gathering information, using tools, or taking actions in a multi-step loop. ReAct is effectively CoT extended with agency.

Use ReAct for tasks requiring external information retrieval, multi-step planning with decision points, tool-use workflows, or any task where the AI needs to take intermediate actions and incorporate their results. If your task can be answered from the model's existing knowledge, chain-of-thought is sufficient. ReAct adds value specifically when actions (search, calculate, read, API call) are required as part of the reasoning process.

LangChain's ReAct agent implements the ReAct framework with programmatic tool integration. The underlying prompting pattern (Thought/Action/Observation loop) is the same. LangChain handles the plumbing: parsing action outputs, routing them to the right tool, and feeding observations back. Understanding the ReAct prompt structure helps you debug LangChain agents, customise their behaviour, and design better tools for them to use.

ReAct chains can fail in several ways: the model can get stuck in loops, misparse tool outputs, or compound errors over many steps. Each additional step amplifies any earlier reasoning errors. Token costs increase with chain length. For tasks with many steps (10+), the model's attention to early context degrades. Best practices: keep each action atomic, add explicit stop conditions, validate critical observations before proceeding, and test the chain on representative inputs before deploying.