React LangChain agent

The React LangChain agent is LangChain’s implementation of the Reason-and-Act (ReAct) paradigm, where a large language model alternates between internal reasoning and external tool usage until it solves a problem. In each cycle, the agent “thinks” in natural language, selects a registered tool — an API call, an SQL query, a web search, a Python function — executes it, observes the result, and updates its plan. The cycle continues until the agent outputs a final answer or reaches a stopping condition. Developers create tools with the @tool decorator, pass them to initialize_agent(“react”) , and let the framework handle JSON I/O, schema validation, retries, and stream tokens. Built-in callback hooks track every thought and action, allowing for cost tracking and rapid debugging, while limiters edit PII and enforce rate limits. React agents excel at multi-step tasks: generating reports from real-time data, troubleshooting code, or orchestrating Retrieval-Augmented Generation (RAG) workflows. Because the agent’s chain of reasoning is transparent, teams can validate decisions, inject domain context, and customize hints, turning React Agent LangChain into a safe and explainable foundation for autonomous AI applications.