# YouTube Video to Article (`hikayatlabs/youtube-video-to-article`) Actor
Transforms YouTube videos into longform blog articles using multimodal LLMs. Extracts video transcripts, interprets content semantically, and synthesizes a coherent longform article.
- **URL**: https://apify.com/hikayatlabs/youtube-video-to-article.md
- **Developed by:** [Hikayat Labs](https://apify.com/hikayatlabs) (community)
- **Categories:** AI, Automation, Social media
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks
- **User rating**: No ratings yet
## Pricing
from $100.00 / 1,000 articles
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/platform/actors/running/actors-in-store#pay-per-event
## What's an Apify Actor?
Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases.
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.
In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md):
```bash
npm install apify-client
```
In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md):
```bash
pip install apify-client
```
In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md):
````bash
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash
In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md).
If your project is in a different language, use 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
## YouTube Video to Article
> ⚠️ **ALPHA SOFTWARE** — Expect breaking changes, bugs, and evolving features. [Report issues →](#your-feedback)
**YouTube Video to Article** transforms YouTube videos into longform articles using multimodal AI. It extracts video transcripts, interprets content semantically, and synthesizes a coherent article.
Works best with educational content, talks, interviews, and explainers.
> **Language support:** Only English is supported at the moment. Videos must have English subtitles or transcripts (manual, not auto-generated). Videos in other languages will return a `NO_TRANSCRIPT` error.
### How it works
Three-stage pipeline:
1. **Extract** — Invoke [YouTube Scraper](https://apify.com/streamers/youtube-scraper) to fetch video metadata and transcript
2. **Interpret** — Semantic analysis of transcript content (key arguments, structure, claims)
3. **Synthesize** — Generate a structured article grounded in the source material
Processing takes ~20–40 seconds per video, depending on transcript length.
### Cost
Pay-per-event pricing:
| Event | Cost |
|-------|------|
| YouTube extraction (via [YouTube Scraper](https://apify.com/streamers/youtube-scraper)) | Billed by that actor — see its pricing |
| Article synthesis | ~$0.1 per article |
### Input
```json
{
"videoUrls": [
{ "url": "https://www.youtube.com/watch?v=dQw4w9WgXcQ" },
{ "url": "https://www.youtube.com/watch?v=abc123" }
],
"synthesisTemplate": "article",
"synthesisGuide": "Write in a neutral journalistic tone",
"includeExtracted": false,
"includeInterpretation": false
}
````
#### Parameters
| Parameter | Type | Required | Default | Description |
|-----------|------|----------|---------|-------------|
| `videoUrls` | array | ✅ | — | YouTube video URLs to process. Each item is an object with `url` (required). |
| `synthesisTemplate` | string | No | `"article"` | Preset output style. Options: `article`, `newsletter`, `social_thread`, `none`. |
| `synthesisGuide` | string | No | — | Custom instructions for tone, focus, style. Appended to template instructions. |
| `includeExtracted` | boolean | No | `false` | Include raw extraction data (metadata + transcript) in output. |
| `includeInterpretation` | boolean | No | `false` | Include semantic blueprint in output. |
#### Synthesis Templates
Templates provide base instructions for the LLM. `synthesisGuide` extends or refines them.
| Template | Output style |
|----------|-------------|
| `article` | General purpose — structured with headings, conversational voice |
| `newsletter` | Concise, scannable, hook intro, call-to-action ending |
| `social_thread` | Punchy, short paragraphs, engaging hooks — LinkedIn/Twitter style |
| `none` | No template — only custom `synthesisGuide` (if any) is sent |
### Output
Each processed video produces one dataset item:
```json
{
"sourceUrl": "https://www.youtube.com/watch?v=dQw4w9WgXcQ",
"title": "Generated Article Title",
"body": "## Markdown article content...",
"wordCount": 1250,
"error": null,
"errorMessage": null
}
```
| Field | Description |
|-------|-------------|
| `sourceUrl` | Original YouTube video URL |
| `title` | Generated article title (plain text) |
| `body` | Full article body in **markdown** |
| `wordCount` | Word count of generated article |
| `error` | Error code if failed: `SCRAPE_FAILED`, `NO_TRANSCRIPT`, `INTERPRETATION_FAILED`, `LLM_ERROR`, `TIMEOUT`, `CONFIG_ERROR`, `INTERNAL_ERROR` |
| `errorMessage` | Detailed error description |
| `extracted` | *(only if `includeExtracted: true`)* Raw video metadata and transcript |
| `interpretation` | *(only if `includeInterpretation: true`)* Semantic blueprint |
Failed videos still produce a dataset item with `error` and `errorMessage` populated. The actor continues processing remaining URLs.
### API Usage
```python
from apify_client import ApifyClient
client = ApifyClient("YOUR_API_TOKEN")
run = client.actor("actor-id").call(
run_input={
"videoUrls": [{"url": "https://www.youtube.com/watch?v=EXAMPLE"}],
"synthesisTemplate": "article"
}
)
for item in client.dataset(run["defaultDatasetId"]).list_items().items:
print(item["title"], item["wordCount"])
```
```javascript
import { ApifyClient } from "apify-client";
const client = new ApifyClient({ token: "YOUR_API_TOKEN" });
const run = await client
.actor("actor-id")
.call({
videoUrls: [{ url: "https://www.youtube.com/watch?v=EXAMPLE" }],
synthesisTemplate: "article",
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
for (const item of items) {
console.log(item.title, item.wordCount);
}
```
```bash
apify call actor-id \
'{"videoUrls":[{"url":"https://www.youtube.com/watch?v=EXAMPLE"}],"synthesisTemplate":"article"}'
```
### FAQ
**What videos work best?** Educational content, talks, interviews, explainers, and any video with a clear English transcript. Music videos and content without English subtitles won't produce useful results.
**What if a video has no English transcript?** The actor returns a `NO_TRANSCRIPT` error for that video and continues processing the rest. Auto-generated subtitles are not used — only manual subtitles are accepted.
**Does it support other languages?** Not yet. The actor currently only processes English transcripts. Support for additional languages is planned.
**Can I customize the output?** Yes — use `synthesisTemplate` for a preset style and `synthesisGuide` for custom instructions on tone, structure, and focus.
**What if a video fails?** The actor continues processing remaining URLs. Check `error` and `errorMessage` in the output for details.
**Can I automate runs?** Yes. Use Apify scheduling, webhooks, or the API.
### Your Feedback
This is alpha software. Bug reports and feature requests help us improve:
- 🐛 [Create an issue](https://apify.com/hikayatlabs/youtube-video-to-article/issues) with the run ID, input parameters, and expected vs actual behavior
***
**By Hikayat Labs**
# Actor input Schema
## `videoUrls` (type: `array`):
One or more YouTube video URLs to process. Each URL can optionally override the global style and output settings. Only English is supported — videos must have manual English subtitles or transcripts. Videos in other languages will fail with a NO\_TRANSCRIPT error.
## `sourceDataset` (type: `string`):
Select an existing dataset to process instead of scraping URLs. The dataset items must be raw output from a YouTube scraper Actor.
## `synthesisTemplate` (type: `string`):
Choose a preset style for the output. The template sets the base instructions sent to the LLM. Per-URL overrides take precedence.
## `synthesisGuide` (type: `string`):
Optional instructions for the LLM on tone, focus, and style (e.g. 'Write in a neutral journalistic tone, focus on factual content, preserve the speaker's key insights').
## `includeExtracted` (type: `boolean`):
If enabled, the output for each URL will include an extracted field containing the raw video metadata and transcript.
## `includeInterpretation` (type: `boolean`):
If enabled, the output for each URL will include an interpretation field containing the semantic blueprint generated from the video transcript.
## Actor input object example
```json
{
"videoUrls": [
{
"url": "https://www.youtube.com/watch?v=EXAMPLE"
}
],
"synthesisTemplate": "article",
"includeExtracted": false,
"includeInterpretation": false
}
```
# Actor output Schema
## `results` (type: `string`):
Dataset containing generated articles with source URLs, titles, bodies, word counts, and processing status
# 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 '' with your token
const client = new ApifyClient({
token: '',
});
// Prepare Actor input
const input = {
"videoUrls": [
{
"url": "https://www.youtube.com/watch?v=EXAMPLE"
}
]
};
// Run the Actor and wait for it to finish
const run = await client.actor("hikayatlabs/youtube-video-to-article").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 '' with your token.
client = ApifyClient("")
# Prepare the Actor input
run_input = { "videoUrls": [{ "url": "https://www.youtube.com/watch?v=EXAMPLE" }] }
# Run the Actor and wait for it to finish
run = client.actor("hikayatlabs/youtube-video-to-article").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 '{
"videoUrls": [
{
"url": "https://www.youtube.com/watch?v=EXAMPLE"
}
]
}' |
apify call hikayatlabs/youtube-video-to-article --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=hikayatlabs/youtube-video-to-article",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "YouTube Video to Article",
"description": "Transforms YouTube videos into longform blog articles using multimodal LLMs. Extracts video transcripts, interprets content semantically, and synthesizes a coherent longform article.",
"version": "0.0",
"x-build-id": "KbyUmrd0UIwb7iRHb"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/hikayatlabs~youtube-video-to-article/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-hikayatlabs-youtube-video-to-article",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/hikayatlabs~youtube-video-to-article/runs": {
"post": {
"operationId": "runs-sync-hikayatlabs-youtube-video-to-article",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/hikayatlabs~youtube-video-to-article/run-sync": {
"post": {
"operationId": "run-sync-hikayatlabs-youtube-video-to-article",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
}
},
"components": {
"schemas": {
"inputSchema": {
"type": "object",
"properties": {
"videoUrls": {
"title": "YouTube Video URLs",
"type": "array",
"description": "One or more YouTube video URLs to process. Each URL can optionally override the global style and output settings. Only English is supported — videos must have manual English subtitles or transcripts. Videos in other languages will fail with a NO_TRANSCRIPT error.",
"items": {
"type": "object",
"required": [
"url"
],
"properties": {
"url": {
"type": "string",
"title": "URL of a web page",
"format": "uri"
}
}
}
},
"sourceDataset": {
"title": "Source Dataset",
"type": "string",
"description": "Select an existing dataset to process instead of scraping URLs. The dataset items must be raw output from a YouTube scraper Actor."
},
"synthesisTemplate": {
"title": "Synthesis Template",
"enum": [
"article",
"article_short",
"article_long",
"newsletter",
"newsletter_short",
"social_thread",
"none"
],
"type": "string",
"description": "Choose a preset style for the output. The template sets the base instructions sent to the LLM. Per-URL overrides take precedence.",
"default": "article"
},
"synthesisGuide": {
"title": "Synthesis Guide",
"type": "string",
"description": "Optional instructions for the LLM on tone, focus, and style (e.g. 'Write in a neutral journalistic tone, focus on factual content, preserve the speaker's key insights')."
},
"includeExtracted": {
"title": "Include Raw Extraction Output",
"type": "boolean",
"description": "If enabled, the output for each URL will include an extracted field containing the raw video metadata and transcript.",
"default": false
},
"includeInterpretation": {
"title": "Include Semantic Interpretation",
"type": "boolean",
"description": "If enabled, the output for each URL will include an interpretation field containing the semantic blueprint generated from the video transcript.",
"default": false
}
}
},
"runsResponseSchema": {
"type": "object",
"properties": {
"data": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"actId": {
"type": "string"
},
"userId": {
"type": "string"
},
"startedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"finishedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"status": {
"type": "string",
"example": "READY"
},
"meta": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"example": "API"
},
"userAgent": {
"type": "string"
}
}
},
"stats": {
"type": "object",
"properties": {
"inputBodyLen": {
"type": "integer",
"example": 2000
},
"rebootCount": {
"type": "integer",
"example": 0
},
"restartCount": {
"type": "integer",
"example": 0
},
"resurrectCount": {
"type": "integer",
"example": 0
},
"computeUnits": {
"type": "integer",
"example": 0
}
}
},
"options": {
"type": "object",
"properties": {
"build": {
"type": "string",
"example": "latest"
},
"timeoutSecs": {
"type": "integer",
"example": 300
},
"memoryMbytes": {
"type": "integer",
"example": 1024
},
"diskMbytes": {
"type": "integer",
"example": 2048
}
}
},
"buildId": {
"type": "string"
},
"defaultKeyValueStoreId": {
"type": "string"
},
"defaultDatasetId": {
"type": "string"
},
"defaultRequestQueueId": {
"type": "string"
},
"buildNumber": {
"type": "string",
"example": "1.0.0"
},
"containerUrl": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "integer",
"example": 1
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
},
"usageTotalUsd": {
"type": "number",
"example": 0.00005
},
"usageUsd": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "number",
"example": 0.00005
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
}
}
}
}
}
}
}
}
```