# Monitoring Runner (`apify/monitoring-runner`) Actor

The monitoring runner is a part of the Apify Monitoring Suite (apify/monitoring). See its readme for more information and how to use this.

- **URL**: https://apify.com/apify/monitoring-runner.md
- **Developed by:** [Apify](https://apify.com/apify) (Apify)
- **Categories:** Developer tools
- **Stats:** 142 total users, 26 monthly users, 99.2% runs succeeded, 16 bookmarks
- **User rating**: 5.00 out of 5 stars

## 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?

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

## Monitoring runner

This actor is a part of Apify Monitoring Suite [apify/monitoring](https://apify.com/apify/monitoring).
Its main responsibility is running checkers and reporters.

# Actor input Schema

## `projectName` (type: `string`):

Name of your monitoring suite. It will be used in notifications and to identify related resources in the Apify dashboard.

## `targetType` (type: `string`):

Only one type of target can be monitored by a single monitoring suite. If you want to watch more types, create more monitoring suites.

## `targetPatternList` (type: `array`):

Regular expressions that will be matched against selected actors / tasks or datasets under your Apify account. All matching targets will then be monitored by this monitoring suite. This is typically also the fastest way to select a single target. Just type its full name. Datasets are going to be automatically group by these patterns when dashboard statistics is counted for dataset target type.

## `targetList` (type: `array`):

If for whatever reason the Target name pattern option does not suit you, targets can also be specified by providing their IDs, as found in your Apify dashboard.

## `limit` (type: `integer`):

Last items to be checked. If 100 is passed, only last hundred runs/datasets will be checked.

## `saveHistory` (type: `boolean`):

All monitoring runs results are saved in named dataset

## `resourceStatsCollector` (type: `boolean`):

Works with Subject type 'Actor', 'Task' and 'Named dateaset', Gets information about the runs TODO: LINK TO PA

## `duplicationChecker` (type: `boolean`):

Checks for duplicates based on defined unique fields. Based on 'https://apify.com/lukaskrivka/duplications-checker'

## `schemaValidatorChecker` (type: `boolean`):

Validates dataset items based on the Type check library

## `runStatusChecker` (type: `boolean`):

Validates run status

## `checkers` (type: `array`):

Ids of checker actors or tasks that needs

## `checkersInput` (type: `object`):

Pass input to checkers

## `dashboard` (type: `boolean`):

Works with Subject type 'Actor', 'Task' 'Named dataset'. Starts dashboard actor. LINK TO PA

## `emailReporter` (type: `boolean`):

Sends report to mail inbox. LINK TO PA for input

## `slackReporter` (type: `boolean`):

Sends report to custom slack channel. LINK TO PA for input

## `reporters` (type: `array`):

Id of reporter actors or tasks

## `reportersInput` (type: `object`):

Pass input to checkers

## `frequency` (type: `string`):

If passed the resources will be filtered by the frequency of the task. For example if you pass MONTHLY only resources modified last month will be collected.

## Actor input object example

```json
{
  "targetType": "ACTOR",
  "targetPatternList": [],
  "targetList": [],
  "limit": 1000,
  "saveHistory": false,
  "checkers": [],
  "checkersInput": {
    "RESOURCE_STATS_CHECKER": {},
    "DEDUPLICATION_CHECKER": {
      "uniqueKey": "email"
    },
    "SCHEMA_VALIDATOR_CHECKER": {
      "options": [
        {
          "resourceList": [
            "id",
            "id"
          ],
          "resourceRegex": "",
          "minItemCount": 100,
          "maxItemCount": 5000,
          "validationSchema": "{ address: String, open: Boolean }"
        }
      ]
    }
  },
  "reporters": [],
  "reportersInput": {
    "EMAIL_REPORTER": {
      "sendMailInput": {
        "to": "info@apify.com",
        "subject": "My monitoring task report"
      }
    },
    "SLACK_REPORTER": {
      "slackInput": {
        "token": "Your token",
        "message": "Hey, look what I have done!",
        "channel": "#monitoring"
      }
    },
    "DASHBOARD_REPORTER": {
      "notifyOnUpdate": true
    }
  }
}
```

# 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 = {
    "limit": 1000,
    "checkersInput": {
        "RESOURCE_STATS_CHECKER": {},
        "DEDUPLICATION_CHECKER": {
            "uniqueKey": "email"
        },
        "SCHEMA_VALIDATOR_CHECKER": {
            "options": [
                {
                    "resourceList": [
                        "id",
                        "id"
                    ],
                    "resourceRegex": "",
                    "minItemCount": 100,
                    "maxItemCount": 5000,
                    "validationSchema": "{ address: String, open: Boolean }"
                }
            ]
        }
    },
    "reportersInput": {
        "EMAIL_REPORTER": {
            "sendMailInput": {
                "to": "info@apify.com",
                "subject": "My monitoring task report"
            }
        },
        "SLACK_REPORTER": {
            "slackInput": {
                "token": "Your token",
                "message": "Hey, look what I have done!",
                "channel": "#monitoring"
            }
        },
        "DASHBOARD_REPORTER": {
            "notifyOnUpdate": true
        }
    }
};

// Run the Actor and wait for it to finish
const run = await client.actor("apify/monitoring-runner").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 = {
    "limit": 1000,
    "checkersInput": {
        "RESOURCE_STATS_CHECKER": {},
        "DEDUPLICATION_CHECKER": { "uniqueKey": "email" },
        "SCHEMA_VALIDATOR_CHECKER": { "options": [{
                    "resourceList": [
                        "id",
                        "id",
                    ],
                    "resourceRegex": "",
                    "minItemCount": 100,
                    "maxItemCount": 5000,
                    "validationSchema": "{ address: String, open: Boolean }",
                }] },
    },
    "reportersInput": {
        "EMAIL_REPORTER": { "sendMailInput": {
                "to": "info@apify.com",
                "subject": "My monitoring task report",
            } },
        "SLACK_REPORTER": { "slackInput": {
                "token": "Your token",
                "message": "Hey, look what I have done!",
                "channel": "#monitoring",
            } },
        "DASHBOARD_REPORTER": { "notifyOnUpdate": True },
    },
}

# Run the Actor and wait for it to finish
run = client.actor("apify/monitoring-runner").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 '{
  "limit": 1000,
  "checkersInput": {
    "RESOURCE_STATS_CHECKER": {},
    "DEDUPLICATION_CHECKER": {
      "uniqueKey": "email"
    },
    "SCHEMA_VALIDATOR_CHECKER": {
      "options": [
        {
          "resourceList": [
            "id",
            "id"
          ],
          "resourceRegex": "",
          "minItemCount": 100,
          "maxItemCount": 5000,
          "validationSchema": "{ address: String, open: Boolean }"
        }
      ]
    }
  },
  "reportersInput": {
    "EMAIL_REPORTER": {
      "sendMailInput": {
        "to": "info@apify.com",
        "subject": "My monitoring task report"
      }
    },
    "SLACK_REPORTER": {
      "slackInput": {
        "token": "Your token",
        "message": "Hey, look what I have done!",
        "channel": "#monitoring"
      }
    },
    "DASHBOARD_REPORTER": {
      "notifyOnUpdate": true
    }
  }
}' |
apify call apify/monitoring-runner --silent --output-dataset

```

## MCP server setup

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

```

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/0GDMKg9U4e1g4Awdq/builds/NsoMmOtydmedVKZXR/openapi.json