LangChain ReAct

LangChain ReAct is an agent strategy that allows a large language model (LLM) to reason about a user request and act on it by selecting tools — APIs, code, vector search — then iterating until it reaches a final answer. The agent follows a cycle: Thought → Action → Observe → Thought … → Final Response. Developers wrap any Python function in the @tool decorator, pass a list of tools to initialize_agent(“react”), and LangChain handles JSON IO, input validation, retries, and stream tokens. Callbacks expose each thought and tool call for debugging or tracking costs, while guardrails block dangerous code or PII leaks. ReAct agents excel at multi-step tasks: generating market reports from real data, fixing code with unit tests, or orchestrating Retrieval-Augmented Generation (RAG) workflows. Because the chain of reasoning is transparent, teams can validate decisions, customize hints, and apply policy, turning static chatbots into understandable, autonomous co-pilots with just a few lines of Python code.