# SpamRescue (`spamrescue/my-actor`) Actor

Spend less time sorting emails and more time getting things done with intelligent inbox protection and organisation.

- **URL**: https://apify.com/spamrescue/my-actor.md
- **Developed by:** [Spam Rescue](https://apify.com/spamrescue) (community)
- **Categories:** Agents, Automation, SEO tools
- **Stats:** 2 total users, 1 monthly users, 14.3% runs succeeded, 0 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/platform/actors/running/actors-in-store#pay-per-usage

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.
Actors are written with capital "A".

## 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.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

### TypeScript BeeAI agent Template

A template for [BeeAI agent](https://beeai.dev/) projects in TypeScript for building AI agents with [Apify Actors](https://apify.com/actors). This template offers a structured setup and an example [ReAct agent](https://react-lm.github.io/) utilizing [Instagram Scraper](https://apify.com/apify/instagram-scraper) and a calculator tool in a workflow context.

#### How it Works

A [ReAct agent](https://react-lm.github.io/) is employed, equipped with tools to respond to user queries. The agent processes a user query, decides on the tools to use, and in what sequence, to achieve the desired outcome. Here, the agent leverages an Instagram Scraper to fetch posts from a profile and a calculator tool to compute sums, such as totaling likes or comments. The agent produces textual and structured output, which is saved to a dataset.

#### LLM provider

The agent talks to its LLM through the [Apify OpenRouter proxy](https://apify.com/apify/openrouter) — an OpenAI-compatible endpoint at `https://openrouter.apify.actor/api/v1` that fronts the full [OpenRouter](https://openrouter.ai) model catalog. Token usage is billed against the user's Apify account (pay-per-event), so **no `OPENAI_API_KEY` or any other provider API key is required**. The Actor authenticates with the proxy using the `APIFY_TOKEN` that the platform injects into every run automatically.

If you'd rather call OpenAI / Anthropic / etc. directly with your own key, swap the `OpenAIChatModel` configuration in `src/main.ts` for a different `baseURL` / `apiKey` / provider adapter — see the [BeeAI backend docs](https://framework.beeai.dev/modules/backend).

#### How to Use

Add or modify tools in `src/tools/calculator.ts` and `src/tools/instagram.ts`, and register them in the agent's tool list in `src/main.ts`. You can also adjust the agent's system prompt or other configuration in `src/main.ts`. For more details, see the [BeeAI framework agents documentation](https://framework.beeai.dev/modules/agents).

##### Pay Per Event

This template uses the [Pay Per Event](https://docs.apify.com/platform/actors/publishing/monetize#pay-per-event-pricing-model) (PPE) monetization model, which provides flexible pricing based on defined events.

To charge users, define events in JSON format and save them on the Apify platform. Here is an example schema with the `task-completed` event:

```json
[
    {
        "task-completed": {
            "eventTitle": "Task completed",
            "eventDescription": "Cost per query answered.",
            "eventPriceUsd": 0.1
        }
    }
]
```

In the Actor, trigger the event with:

```typescript
await Actor.charge({ eventName: 'task-completed' });
```

This approach allows you to programmatically charge users directly from your Actor, covering the costs of execution and related services, such as LLM input/output tokens.

To set up the PPE model for this Actor:

- **Configure Pay Per Event**: establish the Pay Per Event pricing schema in the Actor's **Monetization settings**. First, set the **Pricing model** to `Pay per event` and add the schema. An example schema can be found in [pay\_per\_event.json](.actor/pay_per_event.json).

No provider API key (e.g. `OPENAI_API_KEY`) needs to be configured — LLM costs are billed through the Apify OpenRouter proxy to the user running the Actor.

#### Included Features

- **[Apify SDK](https://docs.apify.com/sdk/js/)** for JavaScript - a toolkit for building Apify [Actors](https://apify.com/actors) and scrapers in JavaScript
- **[Input schema](https://docs.apify.com/platform/actors/development/input-schema)** - define and easily validate a schema for your Actor's input
- **[Dataset](https://docs.apify.com/sdk/js/docs/guides/result-storage#dataset)** - store structured data where each object stored has the same attributes
- **[Key-value store](https://docs.apify.com/platform/storage/key-value-store)** - store any kind of data, such as JSON documents, images, or text files

#### Resources

- [What are AI agents?](https://blog.apify.com/what-are-ai-agents/)
- [TypeScript tutorials in Academy](https://docs.apify.com/academy/node-js)
- [Apify SDK documentation](https://docs.apify.com/sdk/js/)
- [BeeAI framework documentation](https://framework.beeai.dev/introduction/welcome)
- [Integration with Make, GitHub, Zapier, Google Drive, and other apps](https://apify.com/integrations)

### 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

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

Query for the agent.

## `modelName` (type: `string`):

Model ID routed through the Apify OpenRouter proxy (https://apify.com/apify/openrouter). Tokens are charged automatically to your Apify account, so no provider API key is needed.

## `debug` (type: `boolean`):

If enabled, the Actor will run in debug mode and produce more output.

## Actor input object example

```json
{
  "query": "What is the total number of likes and the total number of comments for the latest 10 posts on the @openai Instagram account? From the 10 latest posts, show me the most popular one.",
  "modelName": "deepseek/deepseek-v4-flash",
  "debug": false
}
```

# Actor output Schema

## `results` (type: `string`):

No description

# 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 = {
    "query": "What is the total number of likes and the total number of comments for the latest 10 posts on the @openai Instagram account? From the 10 latest posts, show me the most popular one.",
    "modelName": "deepseek/deepseek-v4-flash"
};

// Run the Actor and wait for it to finish
const run = await client.actor("spamrescue/my-actor").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 = {
    "query": "What is the total number of likes and the total number of comments for the latest 10 posts on the @openai Instagram account? From the 10 latest posts, show me the most popular one.",
    "modelName": "deepseek/deepseek-v4-flash",
}

# Run the Actor and wait for it to finish
run = client.actor("spamrescue/my-actor").call(run_input=run_input)

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

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

```

## CLI example

```bash
echo '{
  "query": "What is the total number of likes and the total number of comments for the latest 10 posts on the @openai Instagram account? From the 10 latest posts, show me the most popular one.",
  "modelName": "deepseek/deepseek-v4-flash"
}' |
apify call spamrescue/my-actor --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=spamrescue/my-actor",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/vJ8WhzXmogSQKX3hp/builds/7vQUDdeqd4gL3ozvc/openapi.json