LangChain ReAct

LangChain ReAct is the built-in agent strategy that mixes Reasoning and Action steps, letting a large language model think through a problem, choose a tool, and use the tool’s output in the same prompt loop. A ReAct agent in LangChain receives the user query, “thinks” in natural language, selects an action such as a web search, code execution, or vector lookup, executes it, and then reflects on the result before deciding the next move. This iterative chain continues until the agent declares “Final Answer.” The pattern is robust for tasks that need fresh data, multi-step logic, or error recovery—like computing stock P/E ratios or crafting travel itineraries from live APIs. Developers register tools with simple Python decorators, set stop conditions, and rely on LangChain’s callback system for streaming tokens, tracing, and cost limits. Because reasoning traces are preserved, teams can audit decisions, tune prompts, and add guardrails, turning LangChain ReAct into a transparent, production-ready backbone for autonomous AI workflows.