# AFZU VERIFYER (`user-jxqocdpplm4gwu5iz/afzu-verifyer`) Actor

Email Address Validation: validates if a string contains a valid email.
Email Verification Lookup via SMTP: performs an email verification on the passed email (catchAll detection enabled by default)
MX Validation: checks the

- **URL**: https://apify.com/user-jxqocdpplm4gwu5iz/afzu-verifyer.md
- **Developed by:** [aftab shaikh](https://apify.com/user-jxqocdpplm4gwu5iz) (community)
- **Categories:** AI, Developer tools, Lead generation
- **Stats:** 7 total users, 0 monthly users, 0.0% runs succeeded, 1 bookmarks
- **User rating**: No ratings yet

## Pricing

Pay per usage

This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-usage

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

### LangChain.js template

> LangChain is a framework for developing applications powered by language models.

This example template illustrates how to use LangChain.js with Apify to crawl the web data, vectorize them, and prompt the OpenAI model. All of this is within a single Apify Actor and slightly over a hundred lines of code.

### Included features

- **[Apify SDK](https://docs.apify.com/sdk/js/)** - a toolkit for building [Actors](https://apify.com/actors)
- **[Input schema](https://docs.apify.com/platform/actors/development/input-schema)** - define and easily validate a schema for your actor's input
- **[Langchain.js](https://github.com/hwchase17/langchainjs)** - a framework for developing applications powered by language models
- **[OpenAI](https://openai.com/)** - a powerful language model

### How it works

The code contains the following steps:

1. Crawls given website using [Website Content Crawler](https://apify.com/apify/website-content-crawler) Actor.
2. Vectorizes the data using the [OpenAI](https://openai.com/) API.
3. Caches the vector index in the [key-value store](https://docs.apify.com/platform/storage/key-value-store) so that when you run Actor for the same website again, the cached data are used to speed it up.
4. Data are fed to the OpenAI model using [Langchain.js](https://github.com/hwchase17/langchainjs), and a given query is asked.

### Before you start

To be able to run this template both locally and on the Apify platform, you need to:

- Have an [Apify account](https://console.apify.com/) and sign into it using `apify login` command in your terminal. Without this, you won't be able to run the required [Website Content Crawler](https://apify.com/apify/website-content-crawler) Actor to gather the data.
- Have an [OpenAI](https://openai.com/) account and an API key. This is needed for vectorizing the data and also to be able to prompt the OpenAI model.
  - When running locally store this as OPENAI_API_KEY environment variable (https://docs.apify.com/cli/docs/vars#set-up-environment-variables-in-apify-console).
  - When running on Apify platform, you can simply paste this into the input field in the input UI.

### Production use

> This serves purely as an example of the whole pipeline.

For production use, we recommend you to:

- Separate crawling, data vectorization, and prompting into separate Actors. This way, you can run them independently and scale them separately.
- Replace the local vector store with [Pinecone](https://www.pinecone.io/) or a similar database. See the [LangChain.js docs](https://js.langchain.com/docs/) for more information.

### Resources

- [Pinecone integration](https://apify.com/jan.turon/pinecone-integration) Actor
- [How to use Pinecone with LLMs](https://blog.apify.com/what-is-pinecone-why-use-it-with-llms/)
- [How to use LangChain with OpenAI, Pinecone, and Apify](https://blog.apify.com/how-to-use-langchain/)
- [Integration with Zapier](https://apify.com/integrations), Make, Google Drive and others
- [Video guide on getting data using Apify API](https://www.youtube.com/watch?v=ViYYDHSBAKM)
- A short guide on [how to create web scrapers using code templates](https://www.youtube.com/watch?v=u-i-Korzf8w)

[Web Scraping Data for Generative AI](https://www.youtube.com/watch?v=8uvHH-ocSes)

### Getting started

For complete information [see this article](https://docs.apify.com/platform/actors/development#build-actor-at-apify-console). In short, you will:

1. Build the Actor
2. Run the Actor

### Pull the Actor for local development

If you would like to develop locally, you can pull the existing Actor from Apify console using Apify CLI:

1. Install `apify-cli`

   **Using Homebrew**

   ```bash
   brew install apify-cli
   ```

   **Using NPM**

   ```bash
   npm -g install apify-cli
   ```

2. Pull the Actor by its unique `<ActorId>`, which is one of the following:

   - unique name of the Actor to pull (e.g. "apify/hello-world")
   - or ID of the Actor to pull (e.g. "E2jjCZBezvAZnX8Rb")

   You can find both by clicking on the Actor title at the top of the page, which will open a modal containing both Actor unique name and Actor ID.

   This command will copy the Actor into the current directory on your local machine.

   ```bash
   apify pull <ActorId>
   ```

### Documentation reference

To learn more about Apify and Actors, take a look at the following resources:

- [Apify SDK for JavaScript documentation](https://docs.apify.com/sdk/js)
- [Apify SDK for Python documentation](https://docs.apify.com/sdk/python)
- [Apify Platform documentation](https://docs.apify.com/platform)
- [Join our developer community on Discord](https://discord.com/invite/jyEM2PRvMU)

# Actor input Schema

## `startUrls` (type: `array`):

One or more URLs of pages where the crawler will start. Note that the Actor will additionally only crawl sub-pages of these URLs. For example, for start URL `https://www.example.com/blog`, it will crawl pages like `https://example.com/blog/article-1`, but will skip `https://example.com/docs/something-else`.

## `maxCrawlPages` (type: `integer`):

The maximum number pages to crawl. It includes the start URLs, pagination pages, pages with no content, etc. The crawler will automatically finish after reaching this number. This setting is useful to prevent accidental crawler runaway.

## `openAIApiKey` (type: `string`):

Enter your [OpenAI](https://openai.com/) account and an API key. This is needed for vectorizing the data and also to be able to prompt the OpenAI model.

## `query` (type: `string`):

The query you want to ask the model about the crawled data.

## `forceRecrawl` (type: `boolean`):

If enabled, the data will be re-crawled even if cached vector index is available.

## Actor input object example

```json
{
  "startUrls": [
    {
      "url": "https://wikipedia.com"
    }
  ],
  "maxCrawlPages": 3,
  "query": "What is Wikipedia?",
  "forceRecrawl": false
}
```

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "startUrls": [
        {
            "url": "https://wikipedia.com"
        }
    ],
    "maxCrawlPages": 3,
    "query": "What is Wikipedia?"
};

// Run the Actor and wait for it to finish
const run = await client.actor("user-jxqocdpplm4gwu5iz/afzu-verifyer").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "startUrls": [{ "url": "https://wikipedia.com" }],
    "maxCrawlPages": 3,
    "query": "What is Wikipedia?",
}

# Run the Actor and wait for it to finish
run = client.actor("user-jxqocdpplm4gwu5iz/afzu-verifyer").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "startUrls": [
    {
      "url": "https://wikipedia.com"
    }
  ],
  "maxCrawlPages": 3,
  "query": "What is Wikipedia?"
}' |
apify call user-jxqocdpplm4gwu5iz/afzu-verifyer --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,user-jxqocdpplm4gwu5iz/afzu-verifyer"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/mlTiVZpJYekRcNOlV/builds/JXoF5KCqZwgP0i60s/openapi.json