Tools

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. Trace tool calls and debug errors with LangSmith—follow the tracing quickstart to get set up.

Create tools

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.

Prefer snake_case for tool names (e.g., web_search instead of Web Search). Some model providers have issues with or reject names containing spaces or special characters with errors. Sticking to alphanumeric characters, underscores, and hyphens helps to improve compatibility across providers.

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.

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.

While thread_id (passed via config={"configurable": {"thread_id": ...}}) scopes the conversation: message history and checkpoints, context carries per-run data your tools and middleware read at invocation time. In production you typically pass both together: a stable thread_id per conversation, and a context object on every invoke.

Tools can access an agent’s runtime context through the config parameter. Pass context alongside a thread_id so the conversation is persisted across turns:

Google

import * as z from "zod";
import { ChatOpenAI } from "@langchain/openai";
import { createAgent, tool } 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: "google-genai:gemini-3.5-flash" }),
  tools: [getUserName],
  contextSchema,
});

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

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-5.4" }),
  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: [e-mail]",
    },
  ],
});

// 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: [e-mail]

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(),
    }),
  }
);

Execution info

Access thread ID, run ID, and retry state from within a tool via runtime.execution_info:

import { tool } from "langchain";
import * as z from "zod";

const logExecutionContext = tool(
  async (_input, runtime) => {
    const info = runtime.executionInfo;
    console.log(`Thread: ${info.threadId}, Run: ${info.runId}`);  
    console.log(`Attempt: ${info.nodeAttempt}`);
    return "done";
  },
  {
    name: "log_execution_context",
    description: "Log execution identity information.",
    schema: z.object({}),
  }
);

Requires deepagents>=1.9.0 (or @langchain/langgraph>=1.2.8).

Server info

When your tool runs on LangGraph Server, access the assistant ID, graph ID, and authenticated user via runtime.server_info:

import { tool } from "langchain";
import * as z from "zod";

const getAssistantScopedData = tool(
  async (_input, runtime) => {
    const server = runtime.serverInfo;
    if (server != null) {
      console.log(`Assistant: ${server.assistantId}, Graph: ${server.graphId}`);  
      if (server.user != null) {
        console.log(`User: ${server.user.identity}`);  
      }
    }
    return "done";
  },
  {
    name: "get_assistant_scoped_data",
    description: "Fetch data scoped to the current assistant.",
    schema: z.object({}),
  }
);

serverInfo is null when the tool is not running on LangGraph Server.

Requires deepagents>=1.9.0 (or @langchain/langgraph>=1.2.8).

Tool execution

In LangChain, tools are used by agents (for example via create_agent) and tool error handling is configured through middleware.

For LangGraph workflows, tool execution is handled by ToolNode. See ToolNode.

Tool return values

You can choose different return values for your tools:

• Return a string for human-readable results.
• Return an object for structured results the model should parse.
• Return a Command with optional message when you need to write to state.

Return a string

Return a string when the tool should provide plain text for the model to read and use in its next response.

import { tool } from "langchain";
import * as z from "zod";

const getWeather = tool(({ city }) => `It is currently sunny in ${city}.`, {
  name: "get_weather",
  description: "Get weather for a city.",
  schema: z.object({ city: z.string() }),
});

Behavior:

• The return value is converted to a ToolMessage.
• The model sees that text and decides what to do next.
• No agent state fields are changed unless the model or another tool does so later.

Use this when the result is naturally human-readable text.

Return an object

Return an object (for example, a dict) when your tool produces structured data that the model should inspect.

import { tool } from "langchain";
import * as z from "zod";

const getWeatherData = tool(
  ({ city }) => ({
    city,
    temperature_c: 22,
    conditions: "sunny",
  }),
  {
    name: "get_weather_data",
    description: "Get structured weather data for a city.",
    schema: z.object({ city: z.string() }),
  },
);

Behavior:

• The object is serialized and sent back as tool output.
• The model can read specific fields and reason over them.
• Like string returns, this does not directly update graph state.

Use this when downstream reasoning benefits from explicit fields instead of free-form text.

Return a Command

Return a Command when the tool needs to update graph state (for example, setting user preferences or app state). You can return a Command with or without including a ToolMessage. If the model needs to see that the tool succeeded (for example, to confirm a preference change), include a ToolMessage in the update, using runtime.tool_call_id for the tool_call_id parameter.

import { tool, ToolMessage, type ToolRuntime } from "langchain";
import { Command } from "@langchain/langgraph";
import * as z from "zod";

const setLanguage = tool(
  async ({ language }, config: ToolRuntime) => {
    return new Command({
      update: {
        preferredLanguage: language,
        messages: [
          new ToolMessage({
            content: `Language set to ${language}.`,
            tool_call_id: config.toolCallId,
          }),
        ],
      },
    });
  },
  {
    name: "set_language",
    description: "Set the preferred response language.",
    schema: z.object({ language: z.string() }),
  },
);

Behavior:

• The command updates state using update.
• Updated state is available to subsequent steps in the same run.
• Use reducers for fields that may be updated by parallel tool calls.

Use this when the tool is not just returning data, but also mutating agent state.

Error handling

Handle tool errors using LangChain agent middleware to retry failed tool calls or return custom error messages:

Google

import { createAgent, createMiddleware, ToolMessage } from "langchain";

const handleToolErrors = createMiddleware({
  name: "HandleToolErrors",
  wrapToolCall: async (request, handler) => {
    try {
      return await handler(request);
    } catch (error) {
      return new ToolMessage({
        content: `Tool error: Please check your input and try again. (${error})`,
        tool_call_id: request.toolCall.id!,
      });
    }
  },
});

const agent = createAgent({
  model: "google-genai:gemini-3.5-flash",
  tools: [],
  middleware: [handleToolErrors],
});

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.

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.

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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