LangChain Agent

LangChain Agent is an autonomous loop where a large language model (LLM) reasons about a user request, chooses a tool, acts on that tool, and feeds the result back into its prompt until it reaches a final answer. The agent follows the ReAct pattern—Thought → Action → Observation—so it can search the web, query SQL, run Python, or call an API without hard-coding the steps. Developers register functions with the @tool decorator, select an agent type (Zero-Shot, Conversational, Plan-Execute), and pass the tool list to initialize_agent(). LangChain handles JSON I/O, retries, streaming tokens, and cost tracking through callback hooks, while guardrails redact PII and enforce rate limits. Memory modules let agents recall prior turns, and router chains let multiple agents collaborate. By abstracting tool selection and execution, a LangChain Agent transforms a static chatbot into a dynamic copilot that drafts reports, triages tickets, or orchestrates Retrieval-Augmented Generation (RAG) pipelines—all in a few lines of code.