What are Agents in LangChain?

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Agents in LangChain are autonomous AI systems that use a Large Language Model (LLM) as a **reasoning engine** to decide which actions to take and in what order. Unlike simple chains with fixed steps, agents dynamically choose tools, APIs, or functions based on user input and intermediate results — repeating the think → act → observe loop until a final answer is reached. An Agent combines an LLM with a set of Tools and a reasoning strategy (called an AgentExecutor). The LLM acts as the 'brain' — it reads the input, decides which tool to call, observes the result, and either calls another tool or returns a final response. This loop is often referred to as the ReAct (Reasoning + Acting) pattern. **LLM / Chat Model** — The reasoning engine that decides what to do next **Tools** — Functions the agent can call (e.g., web search, calculator, SQL query) **AgentExecutor** — The runtime loop that executes the think → act → observe cycle **Prompt Template** — Instructs the LLM on how to reason and format its actions **Memory (optional)** — Allows the agent to remember past interactions // First install: npm install langchain zod @langchain/openai
import { createAgent, tool } from "langchain";
import * as z from "zod";

const getWeather = tool(
  (input) => `It's always sunny in ${input.city}!`,
  {
    name: "get_weather",
    description: "Get the weather for a given city",
    schema: z.object({
      city: z.string().describe("The city to get the weather for"),
    }),
  }
);

const agent = createAgent({
  model: "gpt-5.4",
  tools: [getWeather],
});

console.log(
  await agent.invoke({
    messages: [{ role: "user", content: "What's the weather in San Francisco?" }],
  })
); ReAct Agent — Reasons step-by-step using Thought → Action → Observation OpenAI Tools Agent — Uses OpenAI's native function/tool calling capability Structured Chat Agent — Handles multi-input tools with structured arguments Self-Ask with Search — Breaks complex questions into sub-questions iteratively Plan-and-Execute Agent — Plans all steps first, then executes them sequentially In LangGraph (the newer LangChain framework for agents), agents are modeled as stateful graphs where each node represents an action or decision point. This gives developers fine-grained control over the agent's flow, making it ideal for complex multi-step and multi-agent workflows.

What are Agents in LangChain?

What are Agents in LangChain?

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