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Tools extend what agents can do—letting them fetch real-time data, execute code, query external databases, and take actions in the world. Under the hood, tools are callable functions with well-defined inputs and outputs that get passed to a chat model. The model decides when to invoke a tool based on the conversation context, and what input arguments to provide.
For details on how models handle tool calls, see Tool calling.

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Create tools

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Basic tool definition

The simplest way to create a tool is by importing the tool function from the langchain package. You can use zod to define the tool’s input schema: 
import * as z from "zod"
import { tool } from "langchain"

const searchDatabase = tool(
  ({ query, limit }) => `Found ${limit} results for '${query}'`,
  {
    name: "search_database",
    description: "Search the customer database for records matching the query.",
    schema: z.object({
      query: z.string().describe("Search terms to look for"),
      limit: z.number().describe("Maximum number of results to return"),
    }),
  }
);
Server-side tool use: Some chat models feature built-in tools (web search, code interpreters) that are executed server-side. See Server-side tool use for details.

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Access context

Tools are most powerful when they can access runtime information like conversation history, user data, and persistent memory. This section covers how to access and update this information from within your tools.

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Context

Context provides immutable configuration data that is passed at invocation time. Use it for user IDs, session details, or application-specific settings that shouldn’t change during a conversation. Tools can access an agent’s runtime context through the config parameter: 
import * as z from "zod"
import { ChatOpenAI } from "@langchain/openai"
import { createAgent } from "langchain"

const getUserName = tool(
  (_, config) => {
    return config.context.user_name
  },
  {
    name: "get_user_name",
    description: "Get the user's name.",
    schema: z.object({}),
  }
);

const contextSchema = z.object({
  user_name: z.string(),
});

const agent = createAgent({
  model: new ChatOpenAI({ model: "gpt-4.1" }),
  tools: [getUserName],
  contextSchema,
});

const result = await agent.invoke(
  {
    messages: [{ role: "user", content: "What is my name?" }]
  },
  {
    context: { user_name: "John Smith" }
  }
);

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Long-term memory (Store)

The BaseStore provides persistent storage that survives across conversations. Unlike state (short-term memory), data saved to the store remains available in future sessions. Access the store through config.store. The store uses a namespace/key pattern to organize data: 
import * as z from "zod";
import { createAgent, tool } from "langchain";
import { InMemoryStore } from "@langchain/langgraph";
import { ChatOpenAI } from "@langchain/openai";

const store = new InMemoryStore();

// Access memory
const getUserInfo = tool(
  async ({ user_id }) => {
    const value = await store.get(["users"], user_id);
    console.log("get_user_info", user_id, value);
    return value;
  },
  {
    name: "get_user_info",
    description: "Look up user info.",
    schema: z.object({
      user_id: z.string(),
    }),
  }
);

// Update memory
const saveUserInfo = tool(
  async ({ user_id, name, age, email }) => {
    console.log("save_user_info", user_id, name, age, email);
    await store.put(["users"], user_id, { name, age, email });
    return "Successfully saved user info.";
  },
  {
    name: "save_user_info",
    description: "Save user info.",
    schema: z.object({
      user_id: z.string(),
      name: z.string(),
      age: z.number(),
      email: z.string(),
    }),
  }
);

const agent = createAgent({
  model: new ChatOpenAI({ model: "gpt-4.1" }),
  tools: [getUserInfo, saveUserInfo],
  store,
});

// First session: save user info
await agent.invoke({
  messages: [
    {
      role: "user",
      content: "Save the following user: userid: abc123, name: Foo, age: 25, email: foo@langchain.dev",
    },
  ],
});

// Second session: get user info
const result = await agent.invoke({
  messages: [
    { role: "user", content: "Get user info for user with id 'abc123'" },
  ],
});

console.log(result);
// Here is the user info for user with ID "abc123":
// - Name: Foo
// - Age: 25
// - Email: foo@langchain.dev

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Stream writer

Stream real-time updates from tools during execution. This is useful for providing progress feedback to users during long-running operations. Use config.writer to emit custom updates: 
import * as z from "zod";
import { tool, ToolRuntime } from "langchain";

const getWeather = tool(
  ({ city }, config: ToolRuntime) => {
    const writer = config.writer;

    // Stream custom updates as the tool executes
    if (writer) {
      writer(`Looking up data for city: ${city}`);
      writer(`Acquired data for city: ${city}`);
    }

    return `It's always sunny in ${city}!`;
  },
  {
    name: "get_weather",
    description: "Get weather for a given city.",
    schema: z.object({
      city: z.string(),
    }),
  }
);

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ToolNode

ToolNode is a prebuilt node that executes tools in LangGraph workflows. It handles parallel tool execution, error handling, and state injection automatically.
For custom workflows where you need fine-grained control over tool execution patterns, use ToolNode instead of @[create_agent]. It’s the building block that powers agent tool execution.

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Basic usage

import { ToolNode } from "@langchain/langgraph/prebuilt";
import { tool } from "@langchain/core/tools";
import * as z from "zod";

const search = tool(
  ({ query }) => `Results for: ${query}`,
  {
    name: "search",
    description: "Search for information.",
    schema: z.object({ query: z.string() }),
  }
);

const calculator = tool(
  ({ expression }) => String(eval(expression)),
  {
    name: "calculator",
    description: "Evaluate a math expression.",
    schema: z.object({ expression: z.string() }),
  }
);

// Create the ToolNode with your tools
const toolNode = new ToolNode([search, calculator]);

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Error handling

Configure how tool errors are handled. See the ToolNode API reference for all options. 
import { ToolNode } from "@langchain/langgraph/prebuilt";

// Default behavior
const toolNode = new ToolNode(tools);

// Catch all errors
const toolNode = new ToolNode(tools, { handleToolErrors: true });

// Custom error message
const toolNode = new ToolNode(tools, {
  handleToolErrors: "Something went wrong, please try again."
});

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Route with tools_condition

Use @[tools_condition] for conditional routing based on whether the LLM made tool calls: 
import { ToolNode, toolsCondition } from "@langchain/langgraph/prebuilt";
import { StateGraph, MessagesAnnotation } from "@langchain/langgraph";

const builder = new StateGraph(MessagesAnnotation)
  .addNode("llm", callLlm)
  .addNode("tools", new ToolNode(tools))
  .addEdge("__start__", "llm")
  .addConditionalEdges("llm", toolsCondition)  // Routes to "tools" or "__end__"
  .addEdge("tools", "llm");

const graph = builder.compile();

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State injection

Tools can access the current graph state through @[ToolRuntime]: For more details on accessing state, context, and long-term memory from tools, see Access context.

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Prebuilt tools

LangChain provides a large collection of prebuilt tools and toolkits for common tasks like web search, code interpretation, database access, and more. These ready-to-use tools can be directly integrated into your agents without writing custom code. See the tools and toolkits integration page for a complete list of available tools organized by category.

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Server-side tool use

Some chat models feature built-in tools that are executed server-side by the model provider. These include capabilities like web search and code interpreters that don’t require you to define or host the tool logic. Refer to the individual chat model integration pages and the tool calling documentation for details on enabling and using these built-in tools.
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