Content moderation for LangChain

Arcjet content moderation detects harmful content in untrusted text before it is stored, displayed, or forwarded. It is a Guard rule – call it from guard() / Guard, not protect().

What is Arcjet?

Arcjet is the runtime security platform that ships with your code. Enforce budgets, stop prompt injection, detect bots, and protect personal information with Arcjet's AI security building blocks.

Quick start

This example screens inbound text with content moderation before the agent continues.

We assume you already have a LangChain project set up. For helper options and denial behavior, see the LangChain agent guard.

Install the dependencies:

# Export your Arcjet API key from https://console.arcjet.com
export ARCJET_KEY="ajkey_..."
export ARCJET_ENV=development

pip install "arcjet[langchain-agents]" langchain langchain-openai

Create the example:

agent.py

import os

from arcjet.guard import ModerateContent, launch_arcjet

arcjet = launch_arcjet(key=os.environ["ARCJET_KEY"])
moderate = ModerateContent()


async def screen_message(text: str) -> None:
    decision = await arcjet.guard(
        label="message.received",
        rules=[moderate(text)],
    )
    if decision.conclusion == "DENY" and decision.reason == "MODERATE_CONTENT":
        raise RuntimeError("Harmful content detected – rephrase your message")

Then start or invoke the agent with a test prompt.

Requests appear in your Arcjet dashboard in real time.

Language examples

JavaScript / TypeScript

npm install @arcjet/guard

import { launchArcjet, moderateContent } from "@arcjet/guard";

const arcjet = launchArcjet({ key: process.env.ARCJET_KEY! });
const moderate = moderateContent();

const decision = await arcjet.guard({
  label: "tools.chat",
  rules: [moderate(userMessage)],
});

if (decision.conclusion === "DENY" && decision.reason === "MODERATE_CONTENT") {
  throw new Error("Harmful content detected – rephrase your message");
}

const result = moderate.result(decision);
// `detected` is true when harmful content was found. Billing is undefined
// when the service does not report usage. Content moderation uses text_units.
console.log(result?.detected, result?.billing?.unit, result?.billing?.count);

Python

pip install arcjet

import os

from arcjet.guard import ModerateContent, launch_arcjet

arcjet = launch_arcjet(key=os.environ["ARCJET_KEY"])
moderate = ModerateContent()

decision = await arcjet.guard(
    label="llm.output",
    rules=[moderate(text)],
)

if decision.conclusion == "DENY" and decision.reason == "MODERATE_CONTENT":
    raise RuntimeError("Harmful content detected – rephrase your message")

result = moderate.result(decision)
# `detected` is True when harmful content was found. Billing is None
# when the service does not report usage. Content moderation uses text_units.
print(result.detected if result else None)
if result and result.billing:
    print(result.billing.unit, result.billing.count)

Go

go get github.com/arcjet/arcjet-go@latest

moderation, err := arcjet.GuardModerateContent(arcjet.GuardModerateContentOptions{
  Mode: arcjet.ModeLive, // required
})
if err != nil {
  return err
}

decision, err := guard.Guard(ctx, arcjet.GuardRequest{
  Label: "tools.generate",
  Rules: []arcjet.GuardRuleInput{moderation.Text(userMessage)},
})
if err != nil {
  return err
}
if decision.IsDenied() && decision.Reason == arcjet.ReasonModerateContent {
  return errors.New("content flagged by moderation")
}

// Billing is optional. Content moderation usage is measured in text_units.
if result := moderation.Result(decision); result != nil && result.Billing != nil {
  fmt.Printf("charged %d %s\n", result.Billing.Count, result.Billing.Unit)
}

Set Mode on every Guard rule. An empty Mode returns ErrInvalidMode.

Keep the response generic. Do not leak detector details or explain exactly what was flagged.

What next?

Content moderation intro When to use content moderation and how the result is shaped.

Agent guards Protect tool calls and other actions without an HTTP request.

Prompt injection detection Block hostile instructions before they reach your model.

Sensitive information Detect PII locally before it leaves your application.

Discussion