AI Youtube Transcript API API ID: 12410

You can query Youtube videos with a prompt, Youtube video URL and JSON Schema as input, structured data as output.
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Turn any YouTube video into AI-ready output using just a video URL and a prompt.

This API fetches the transcript, analyzes the content, and returns a final answer in plain text or strict JSON. It is ideal for summarization, extraction, classification, review analysis, and structured data generation from YouTube videos.

Short videos are processed in one pass. Long videos are automatically chunked and merged in the background so no transcript data is lost.

Input:

  • videoUrl
  • prompt
  • jsonschema (optional)

Output:

  • Plain text or structured JSON
  • Transcript-based AI results
  • One simple workflow for both short and long videos

Video URL + Prompt = { "answer": "❤️‍🔥" }

Zyla Listing Tone
Analyze YouTube videos with AI in one simple API call.

Send a YouTube video URL, your prompt, and an optional JSON schema. The API retrieves the transcript, processes the full video content, and returns a clean AI-generated response in text or structured JSON.

Built for developers who want reliable transcript-based AI outputs without handling transcript fetching, chunking, or response shaping on their own.

Why it works well:

  • Supports custom prompts
  • Supports strict JSON schema outputs
  • Handles long videos automatically
  • Returns automation-friendly responses
  • Great for extraction, summarization, categorization, and insight generation

Perfect for:

  • AI workflow builders
  • SaaS products
  • Data extraction tools
  • Research automation
  • Review and content analysis

Simple in, powerful out:

Video URL + Prompt = { "answer": "structured json" }

API Documentation

Endpoints


Fetches a YouTube video transcript, runs AI on the full transcript with your prompt, and returns the result as plain text or strict JSON.



                                                                            
POST https://pr197-testing.zylalabs.com/api/12410/ai+youtube+transcript+api/23479/analyze+youtube+transcript
                                                                            
                                                                        

Analyze Youtube Transcript - Endpoint Features

Object Description
Request Body [Required] Json

Free test requests remaining: 3 of 3.


INPUT PARAMETERS


API EXAMPLE RESPONSE

{
  "success": true,
  "result": {
    "inputUrl": "https://www.youtube.com/watch?v=tzruGtCBK9o",
    "videoId": "tzruGtCBK9o",
    "videoUrl": "https://www.youtube.com/watch?v=tzruGtCBK9o",
    "status": "ok",
    "output": {
      "positives": [
        "High-quality audio peripherals, living up to Sennheiser's legacy.",
        "Significant leap in design and quality compared to previous models.",
        "Beautiful hard case with useful accessories included.",
        "Minimalistic and unobtrusive design.",
        "Well-built construction with fabric headband and faux leather ear cups.",
        "Effective touch controls for various functions.",
        "Long battery life of up to 60 hours, ideal for travelers.",
        "Good active noise cancellation for typical commute noises.",
        "Excellent sound quality with decent clarity in mids and highs.",
        "Visual EQ in the Sennheiser app allows for a balanced and high-quality listening experience.",
        "Sufficient audio quality for most genres and users."
      ],
      "negatives": [
        "Headphones are not the lightest, though they do not feel cheap.",
        "Construction is a bit stiff and bulky.",
        "Active noise cancellation can be fatiguing over long periods.",
        "Transparency mode is not very effective when audio is playing.",
        "Uncomfortable fit for long periods due to tight headband and ear cups pinching ears."
      ]
    }
  },
  "meta": {
    "primaryModel": "mistralai/mistral-medium-3-instruct",
    "fallbackModel": "z-ai/glm4.7",
    "finalModel": "mistralai/mistral-medium-3-instruct",
    "outputMode": "json_schema",
    "fallbackUsed": false,
    "chunkCount": 1,
    "transcriptSegmentCount": 0,
    "transcriptCharacterCount": 5021,
    "llmCallCount": 1,
    "successfulLlmCallCount": 1,
    "llmAttemptCount": 1,
    "modelsUsed": [
      "mistralai/mistral-medium-3-instruct"
    ]
  }
}

Analyze Youtube Transcript - CODE SNIPPETS


curl --location --request POST 'https://zylalabs.com/api/12410/ai+youtube+transcript+api/23479/analyze+youtube+transcript' --header 'Authorization: Bearer YOUR_API_KEY' 

--data-raw '{
  "videoUrl": "https://www.youtube.com/watch?v=tzruGtCBK9o",
  "prompt": "Find the main positive and negative points mentioned in this video about the product, service, or topic being discussed. Use only transcript-supported evidence.",
  "jsonschema": {
    "title": "sentiment_points",
    "type": "object",
    "additionalProperties": false,
    "properties": {
      "positives": {
        "type": "array",
        "items": {
          "type": "string"
        }
      },
      "negatives": {
        "type": "array",
        "items": {
          "type": "string"
        }
      }
    }
  }
}'

    

API Access Key & Authentication

After signing up, every developer is assigned a personal API access key, a unique combination of letters and digits provided to access to our API endpoint. To authenticate with the AI Youtube Transcript API simply include your bearer token in the Authorization header.
Headers
Header Description
Authorization [Required] Should be Bearer access_key. See "Your API Access Key" above when you are subscribed.

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AI Youtube Transcript API FAQs

The API returns structured data derived from YouTube video transcripts, including both pure text and strictly formatted JSON. The output contains insights based on the provided prompt and the video's content.

The key fields in the response include `success`, `result`, `inputUrl`, `videoId`, `videoUrl`, `status`, and `output`. The `output` field contains the processed insights based on the video transcript.

Users can customize their requests by providing a specific `prompt` along with the `videoUrl`. An optional `jsonschema` can also be included to define the desired structure of the output.

The response data is organized in a JSON format with a top-level object containing `success` status and a `result` object. The `result` includes metadata about the video and the processed `output` insights.

The API provides insights such as summaries, sentiment analysis, content categorization, and key points extracted from the video transcript, making it suitable for various analytical tasks.

Data accuracy is maintained through automated transcription processes and AI analysis, ensuring that the output reflects the content of the video accurately. Continuous improvements in AI models also enhance quality.

Typical use cases include content summarization, sentiment analysis for comments, data extraction for research, and generating structured insights for SaaS applications, enhancing user engagement and understanding.

Users can utilize the returned data by parsing the JSON output to extract relevant insights, integrating them into applications for analytics, or using them for content creation and research purposes.

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