tools

A guide to defining functions that an AI agent can ask your application to run. Each tool includes rules describing the inputs it accepts, and tools can be written by you or taken from LangChain integrations.

In plain words
What is it for?
Use it to add functions such as calculations or other application-specific actions to an agent, and to control which inputs the model can provide.
Why use it?
It provides a consistent way for a language model to request real application actions with structured inputs. This keeps tool definitions and their input rules together.

Agent

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add agents/langchain-ai/langgraphjs/tools
Clone the repo
git clone --depth 1 https://github.com/langchain-ai/langgraphjs
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,520 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00000 $0.02520
Opus 5 $0.00000 $0.01260
Sonnet 5 $0.00000 $0.00504
Haiku 4.5 $0.00000 $0.00252

Measured yesterday against content hash 6bf6a0e2da5b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

tools scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

docs/docs/agents/tools.md · 340 lines

How it starts

The opening of the file, as written. The whole thing — 340 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Tools

Tools are a way to encapsulate a function and its input schema in a way that can be passed to a chat model that supports tool calling. This allows the model to request the execution of this function with specific inputs.

You can either define your own tools or use prebuilt integrations that LangChain provides.

Define tools

You create tools using the tool function:

import { createReactAgent } from "@langchain/langgraph/prebuilt";
import { initChatModel } from "langchain/chat_models/universal";
// highlight-next-line
import { tool } from "@langchain/core/tools";
import { z } from "zod";

const multiply = tool(
  async (input: { a: number; b: number }) => {
    return input.a * input.b;
  },
  {
    name: "multiply",
    schema: z.object({
      a: z.number().describe("First operand"),
      b: z.number().describe("Second operand"),
    }),
    description: "Multiply two numbers.",
  }
);

const llm = await initChatModel("anthropic:claude-3-7-sonnet-latest");
const agent = createReactAgent({
  llm,
  tools: [multiply],
});

For additional customization, refer to the custom tools guide.

Hide arguments from the model

Some tools require runtime-only arguments (e.g., user ID or session context) that should not be controllable by the model.

You can put these arguments in the state or config of the agent, and access this information inside the tool:

import { z } from "zod";
import { tool, type ToolRuntime } from "@langchain/core/tools";
// highlight-next-line
import { MessagesAnnotation } from "@langchain/langgraph";

const myTool = tool(
  async (
    input: {
      // This will be populated by an LLM
      toolArg: string;
    },
    // access runtime values from the second argument
    // highlight-next-line
    runtime: ToolRuntime<typeof MessagesAnnotation.State>
  ) => {
    // Fetch the current agent state
    // highlight-next-line
    const state = runtime.state;
    doSomethingWithState(state.messages);
    doSomethingWithConfig(runtime.config);
    // ...
  },
  {
    name: "myTool",
    schema: z.object({
      myToolArg: z.number().describe("Tool arg"),
    }),
    description: "My tool.",
  }
);

Read the full file on GitHub · 340 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 340 lines · 0 tokens per session scan A 6bf6a0e2da5b

Subscribe to this mod's changes

tools is an agent published in the GitHub repository langchain-ai/langgraphjs (3,242 stars, last pushed 5d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,520 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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