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LangChain v1.0Welcome to the new LangChain documentation! If you encounter any issues or have feedback, please open an issue so we can improve. Archived v0 documentation can be found here.See the release notes and migration guide for a complete list of changes and instructions on how to upgrade your code.
This quickstart takes you from a simple setup to a fully functional AI agent in just a few minutes.

​
Build a basic agent

Start by creating a simple agent that can answer questions and call tools. The agent will use Claude Sonnet 4.5 as its language model, a basic weather function as a tool, and a simple prompt to guide its behavior. 
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: "anthropic:claude-sonnet-4-5",
  tools: [getWeather],
});

console.log(
  await agent.invoke({
    messages: [{ role: "user", content: "What's the weather in Tokyo?" }],
  })
);
For this example, you will need to set up a Claude (Anthropic) account and get an API key. Then, set the ANTHROPIC_API_KEY environment variable in your terminal.

​
Build a real-world agent

Next, build a practical weather forecasting agent that demonstrates key production concepts:
  1. Detailed system prompts for better agent behavior
  2. Create tools that integrate with external data
  3. Model configuration for consistent responses
  4. Structured output for predictable results
  5. Conversational memory for chat-like interactions
  6. Create and run the agent create a fully functional agent
Let’s walk through each step:
1

Define the system prompt

The system prompt defines your agent’s role and behavior. Keep it specific and actionable:
const systemPrompt = `You are an expert weather forecaster, who speaks in puns.

You have access to two tools:

- get_weather_for_location: use this to get the weather for a specific location
- get_user_location: use this to get the user's location

If a user asks you for the weather, make sure you know the location. If you can tell from the question that they mean wherever they are, use the get_user_location tool to find their location.`;
2

Create tools

Tools are functions your agent can call. Oftentimes tools will want to connect to external systems, and will rely on runtime configuration to do so. Notice here how the getUserLocation tool does exactly that:
import { type Runtime } from "@langchain/langgraph";
import { tool } from "langchain";
import * as z from "zod";

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

type AgentRuntime = Runtime<{ user_id: string }>;

const getUserLocation = tool(
  (_, config: AgentRuntime) => {
    const { user_id } = config.context;
    return user_id === "1" ? "Florida" : "SF";
  },
  {
    name: "get_user_location",
    description: "Retrieve user information based on user ID",
  }
);
Zod is a library for validating and parsing pre-defined schemas. You can use it to define the input schema for your tools to make sure the agent only calls the tool with the correct arguments.Alternatively, you can define the schema property as a JSON schema object. Keep in mind that JSON schemas won’t be validated at runtime.
const getWeather = tool(
  ({ city }) => `It's always sunny in ${city}!`,
  {
    name: "get_weather_for_location",
    description: "Get the weather for a given city",
    schema: {
      type: "object",
      properties: {
        city: {
          type: "string",
          description: "The city to get the weather for"
        }
      },
      required: ["city"]
    },
  }
);
3

Configure your model

Set up your language model with the right parameters for your use case:
import { initChatModel } from "langchain";

const model = await initChatModel(
  "anthropic:claude-sonnet-4-5",
  { temperature: 0.5, timeout: 10, maxTokens: 1000 }
);
4

Define response format

Optionally, define a structured response format if you need the agent responses to match a specific schema.
const responseFormat = z.object({
  punny_response: z.string(),
  weather_conditions: z.string().optional(),
});
5

Add memory

Add memory to your agent to maintain state across interactions. This allows the agent to remember previous conversations and context.
import { MemorySaver } from "@langchain/langgraph";

const checkpointer = new MemorySaver();
In production, use a persistent checkpointer that saves to a database. See Add and manage memory for more details.
6

Create and run the agent

Now assemble your agent with all the components and run it!
import { createAgent } from "langchain";

const agent = createAgent({
  model: "anthropic:claude-sonnet-4-5",
  prompt: systemPrompt,
  tools: [getUserLocation, getWeather],
  responseFormat,
  checkpointer,
});

// `thread_id` is a unique identifier for a given conversation.
const config = {
  configurable: { thread_id: "1" },
  context: { user_id: "1" },
};

const response = await agent.invoke(
  { messages: [{ role: "user", content: "what is the weather outside?" }] },
  config
);
console.log(response.structuredResponse);
// {
//   punny_response: "Florida is still having a 'sun-derful' day ...",
//   weather_conditions: "It's always sunny in Florida!"
// }

// Note that we can continue the conversation using the same `thread_id`.
const thankYouResponse = await agent.invoke(
  { messages: [{ role: "user", content: "thank you!" }] },
  config
);
console.log(thankYouResponse.structuredResponse);
// {
//   punny_response: "You're 'thund-erfully' welcome! ...",
//   weather_conditions: undefined
// }
Congratulations! You now have an AI agent that can:
  • Understand context and remember conversations
  • Use multiple tools intelligently
  • Provide structured responses in a consistent format
  • Handle user-specific information through context
  • Maintain conversation state across interactions
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