# YouTube Comments Extractor (`skilled_glee/youtube-comments-extractor`) Actor

Extract YouTube video comments as structured JSON: author, text, like count, timestamp, reply count. Give it video URLs, get back per-comment records. Deterministic extraction, no AI, no browser. One failed video never kills the batch.

- **URL**: https://apify.com/skilled_glee/youtube-comments-extractor.md
- **Developed by:** [EchoEmpire LLC](https://apify.com/skilled_glee) (community)
- **Categories:** Videos, Developer tools, Social media
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$1.50 / 1,000 comment extracteds

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

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

## 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

## YouTube Comments Extractor

**YouTube video URLs in, structured comments out.**

### What it does

Extracts comments from YouTube videos with yt-dlp (no browser, no AI). Each saved comment is one flat dataset record with author, text, like count, timestamp, and reply count. Up to 500 comments per video (`maxComments`, default 50). Records can include replies as well as top-level comments, in the order yt-dlp returns them. Requests go through Apify residential proxy when it is available to the run.

### Input

```json
{
  "videoUrls": ["https://www.youtube.com/watch?v=VIDEO_ID"],
  "maxComments": 100
}
```

`videoUrls` accepts watch, youtu.be, embed, or shorts links, or bare video IDs. If `videoUrls` is empty, the Actor falls back to one example video (`dQw4w9WgXcQ`) and charges for those comments normally, so always pass the videos you want.

### Output

One dataset record per comment (not one record per video):

```json
{
  "videoId": "VIDEO_ID",
  "videoTitle": "Video title",
  "videoUrl": "https://www.youtube.com/watch?v=VIDEO_ID",
  "id": "UgxCOMMENT_ID",
  "author": "@Username",
  "text": "Great video!",
  "likes": 42,
  "publishedAt": 1759320000,
  "replyCount": 3,
  "status": "ok"
}
```

- `likes` is the like count YouTube reports (YouTube may round it).
- `publishedAt` is a Unix timestamp in seconds as reported by yt-dlp. YouTube shows relative times ("2 days ago"), so this value is approximate.
- `text` is truncated to 5,000 characters. Comments with empty text are skipped.

A video that fails or has no retrievable comments produces one record `{"videoId": "...", "status": "error", "error": "..."}` (`{"input": "...", "status": "error", ...}` for an unparseable URL). The key-value store record `run-summary` holds `requested`, `videosSucceeded`, `videosFailed`, `billable_units`, `ppe_event`, and `maxComments`.

### Use cases

- Sentiment analysis
- Community engagement monitoring
- Content feedback collection
- Social listening pipelines

### Pricing

**$0.0015 per comment extracted** ($1.50 per 1,000 comments)

Charged once per `status: "ok"` comment record saved. Error records (failed video, disabled or no retrievable comments, bad URL) are not charged.

### Limitations

- Only comments YouTube shows publicly can be returned; deleted or hidden comments are not accessible
- Maximum 500 comments per video
- YouTube can rate-limit or bot-wall requests; affected videos return error records

### Failure behavior

Each video is processed independently. A failed video produces an error record and the batch continues.

# Actor input Schema

## `maxComments` (type: `integer`):

Maximum comments fetched per video.

## `videoUrls` (type: `array`):

YouTube video URLs (watch, youtu.be, embed, or shorts links) or bare video IDs.

## Actor input object example

```json
{
  "maxComments": 50,
  "videoUrls": [
    "https://www.youtube.com/watch?v=dQw4w9WgXcQ"
  ]
}
```

# Actor output Schema

## `videoId` (type: `string`):

No description

## `videoTitle` (type: `string`):

No description

## `videoUrl` (type: `string`):

No description

## `id` (type: `string`):

No description

## `author` (type: `string`):

No description

## `text` (type: `string`):

No description

## `likes` (type: `string`):

No description

## `replyCount` (type: `string`):

No description

## `status` (type: `string`):

No description

## `error` (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 = {
    "videoUrls": [
        "https://www.youtube.com/watch?v=dQw4w9WgXcQ"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("skilled_glee/youtube-comments-extractor").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 = { "videoUrls": ["https://www.youtube.com/watch?v=dQw4w9WgXcQ"] }

# Run the Actor and wait for it to finish
run = client.actor("skilled_glee/youtube-comments-extractor").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 '{
  "videoUrls": [
    "https://www.youtube.com/watch?v=dQw4w9WgXcQ"
  ]
}' |
apify call skilled_glee/youtube-comments-extractor --silent --output-dataset

```

## MCP server setup

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

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/w776T3ZhVqleUind4/builds/pi6SaCBoZgvtV90sd/openapi.json