Agents in LangChain

Agents in LangChain are autonomous loops in which a large language model (LLM) reasons about a task, selects a tool, executes it, and returns the result back to its hint until it reaches a final answer. Each agent implements the ReAct loop — Thought → Action → Observe — so it can search the web, query a database, run Python, or call an API without hard-coding the steps. Developers register custom tools with the @tool decorator, choose an agent type (Zero-Shot, Conversational, Plan-Execute), and set guards for rate limits or PII. LangChain manages JSON I/O, retries, stream tokens, and cost tracking via callback handlers. Memory modules allow agents to recall previous moves, while router chains allow multiple agents to collaborate. By abstracting the selection and execution of tools, agents in LangChain transform LLMs from static chatbots into dynamic co-pilots that write reports, triage tickets, or orchestrate Retrieval-Augmented Generation (RAG) pipelines—all with just a few lines of Python code.