# AI YouTube Transcript Analyzer (`hexa-api/youtube-transcript-ai-analyzer`) Actor Prompt + YouTube URL -> { ❤️‍🔥 } - **URL**: https://apify.com/hexa-api/youtube-transcript-ai-analyzer.md - **Developed by:** [Hexa API](https://apify.com/hexa-api) (community) - **Categories:** AI, Videos, Open source - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: 5.00 out of 5 stars ## Pricing $25.00 / 1,000 transcript analyses 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 ## AI YouTube Transcript Analyzer Turn one YouTube video into one sharp answer. `Video URL + Prompt = { "answer": "❤️‍🔥" }` No setup maze. No prompt engineering circus. No transcript cleanup work on your side. Drop in a YouTube URL, tell the Actor what you want, and it will: - fetch the transcript - understand the full video - chunk long videos safely behind the scenes - return either plain text or strict JSON ### Input ```json { "videoUrl": "https://www.youtube.com/watch?v=dQw4w9WgXcQ", "prompt": "Summarize the video in 3 concise bullet points.", "jsonschema": { "title": "video_summary", "type": "object", "additionalProperties": false, "properties": { "topic": { "type": "string" }, "highlights": { "type": "array", "items": { "type": "string" } } }, "required": ["topic", "highlights"] } } ```` ### What You Get - One YouTube video per run - One custom prompt per run - Plain text output, or strict JSON with your schema - Clean dataset output shaped like `{ "result": ... }` ### Pricing, But Human We price by **transcript analysis**. - Most short videos usually fit into **1 transcript analysis** - A short video with `jsonschema` still usually fits into **1 transcript analysis** - If the video is long, we automatically split the transcript into chunks behind the scenes - Each processed chunk counts as **1 transcript analysis** - After all chunks are processed, the final combined answer counts as **1 more transcript analysis** Big video? No panic. We cut the transcript into safe pieces, analyze every piece, and stitch the final answer back together. The goal is simple: **no lost context, no dropped data, no messy partial answer**. ### Example Dataset Item ```json { "result": { "topic": "Product strategy", "highlights": [ "The speaker explains...", "The video compares...", "The conclusion emphasizes..." ] } } ``` ### Example OUTPUT Record ```json { "summary": { "requested": 1, "succeeded": 1, "failed": 0, "chunkCount": 3, "fallbackUsed": false, "llmCallCount": 4, "aiCallsRequested": 4, "aiCallsCharged": 4, "chargeLimitReached": false, "finalModel": "mistralai/mistral-medium-3-instruct" }, "error": null, "meta": { "pricing": { "eventName": "ai-call", "requestedAiCalls": 4, "chargedAiCalls": 4, "chargeLimitReached": false } } } ``` ### Environment Variables - `WORKER_BASE_URL` required - `WORKER_API_TOKEN` optional - `REQUEST_TIMEOUT_MS` optional ### Apify Pricing Setup Set this Actor to **Pay per event** and add an event named `ai-call`. You choose the price of that event. Inside Apify, the event name stays `ai-call`. For users, that event means **1 transcript analysis**. If you want pricing to be based on **only real transcript analyses**, remove the synthetic events you do not want: - remove `apify-default-dataset-item` - remove `apify-actor-start` That means: - `1 ai-call event = 1 transcript analysis` - you choose the price of each transcript analysis - long videos cost more only because they truly require more analysis work # Actor input Schema ## `videoUrl` (type: `string`): One direct YouTube video URL. Supports watch, shorts, embed, and youtu.be links. ## `prompt` (type: `string`): The analysis instruction that will be applied to the transcript. ## `jsonschema` (type: `object`): Optional strict JSON schema for structured LLM output. ## Actor input object example ```json { "videoUrl": "https://www.youtube.com/watch?v=dQw4w9WgXcQ", "prompt": "Summarize the video in one sentence.", "jsonschema": { "title": "video_summary", "type": "object", "additionalProperties": false, "properties": { "summary": { "type": "string" } }, "required": [ "summary" ] } } ``` # Actor output Schema ## `results` (type: `string`): Successful LLM results written to the run's default dataset. Each item contains a single `result` field. ## `runOutput` (type: `string`): The OUTPUT record in the default key-value store containing the run summary, worker metadata, and any error details. # 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 = { "videoUrl": "https://www.youtube.com/watch?v=dQw4w9WgXcQ", "prompt": "Summarize the video in one sentence.", "jsonschema": { "title": "video_summary", "type": "object", "additionalProperties": false, "properties": { "summary": { "type": "string" } }, "required": [ "summary" ] } }; // Run the Actor and wait for it to finish const run = await client.actor("hexa-api/youtube-transcript-ai-analyzer").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 = { "videoUrl": "https://www.youtube.com/watch?v=dQw4w9WgXcQ", "prompt": "Summarize the video in one sentence.", "jsonschema": { "title": "video_summary", "type": "object", "additionalProperties": False, "properties": { "summary": { "type": "string" } }, "required": ["summary"], }, } # Run the Actor and wait for it to finish run = client.actor("hexa-api/youtube-transcript-ai-analyzer").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 '{ "videoUrl": "https://www.youtube.com/watch?v=dQw4w9WgXcQ", "prompt": "Summarize the video in one sentence.", "jsonschema": { "title": "video_summary", "type": "object", "additionalProperties": false, "properties": { "summary": { "type": "string" } }, "required": [ "summary" ] } }' | apify call hexa-api/youtube-transcript-ai-analyzer --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=hexa-api/youtube-transcript-ai-analyzer", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "AI YouTube Transcript Analyzer", "description": "Prompt + YouTube URL -> { ❤️‍🔥 }", "version": "0.1", "x-build-id": "7atztZ0EjbIbd8iJD" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/hexa-api~youtube-transcript-ai-analyzer/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-hexa-api-youtube-transcript-ai-analyzer", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/hexa-api~youtube-transcript-ai-analyzer/runs": { "post": { "operationId": "runs-sync-hexa-api-youtube-transcript-ai-analyzer", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/hexa-api~youtube-transcript-ai-analyzer/run-sync": { "post": { "operationId": "run-sync-hexa-api-youtube-transcript-ai-analyzer", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "videoUrl", "prompt" ], "properties": { "videoUrl": { "title": "YouTube video URL", "type": "string", "description": "One direct YouTube video URL. Supports watch, shorts, embed, and youtu.be links." }, "prompt": { "title": "Prompt", "type": "string", "description": "The analysis instruction that will be applied to the transcript." }, "jsonschema": { "title": "JSON schema", "type": "object", "description": "Optional strict JSON schema for structured LLM output." } } }, "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 } } } } } } } } } } ```