# Instagram Ingest Scraper (`industrial_nightfall/instagram-ingest-scraper`) Actor Downloads an Instagram reel/post/carousel, transcribes any video with faster-whisper, and returns structured metadata + media so an AI agent (via the Apify MCP) can extract text and file it into Notion. - **URL**: https://apify.com/industrial\_nightfall/instagram-ingest-scraper.md - **Developed by:** [Riddhiman](https://apify.com/industrial_nightfall) (community) - **Categories:** Social media - **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $10.00 / 1,000 results 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 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. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## Instagram Ingest Scraper An [Apify Actor](https://apify.com/actors) that downloads an Instagram reel, post, or carousel, transcribes any video with [faster-whisper](https://github.com/SYSTRAN/faster-whisper), and returns structured metadata + media so an AI agent can extract the real text and file it somewhere (Notion, in the companion project below). This Actor only does the **scrape + transcribe** step — it does not interpret content or write anywhere itself. It's built to be driven by an AI agent over the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp), typically the [`instagram-ingest-apify`](https://github.com/riddhimaaan/instagram-ingest-apify) Claude Code plugin, though nothing here is Claude-specific — any MCP client (or the plain Apify API) can call it. ### Input | Field | Required | Notes | |---|---|---| | `instagramUrl` | yes | A `/reel/`, `/reels/`, `/p/`, or `/tv/` URL. | | `cookies` | no | Netscape-format `cookies.txt` content for a logged-in Instagram account. Falls back to the `IG_COOKIES` Actor environment variable if omitted — set that once in Actor Settings to avoid passing cookies on every call. | | `whisperModel` | no | `tiny` / `base` (default) / `small` / `medium`. | ### Output One dataset item per run: ```json { "shortcode": "Da5lLU3y3h9", "url": "https://www.instagram.com/reel/Da5lLU3y3h9/", "type": "reel", "author": "someuser", "caption": "...", "posted_at": "2026-07-17 15:54:02", "likes": 705, "page_count": 1, "has_video": true, "transcript": "[01.mp4]\n...", "pages": [{"index": 1, "kind": "video", "kvsKey": "01.mp4"}], "frames": [] } ```` - `transcript` is a plain string (or `null`) — already usable as-is, no file fetch needed. - Each `pages` entry's `kvsKey` points to the actual downloaded media file in this run's Key-Value Store. Fetch it with `get-key-value-store-record` (Apify MCP) or the REST API — either way you get back a signed URL good for a direct, unauthenticated download. - `frames` is always an empty array for compatibility. Extract frames locally from the original MP4 referenced by the `pages` entry instead of uploading JPEGs to Apify. ### Why cookies are needed Instagram blocks anonymous scraping. Export cookies from a logged-in (ideally burner) account via the [Cookie-Editor](https://chromewebstore.google.com/detail/cookie-editor/hlkenndednhfkekhgcdicdfddnkalmdm) browser extension → **Export as Netscape**. Either pass that content as the `cookies` input on each call, or set it once as the `IG_COOKIES` environment variable in this Actor's own Settings (Apify Console) if you're running your own deployment of it. ### Local development ```bash python -m venv .venv .venv/bin/pip install -r requirements.txt # .venv\Scripts\pip on Windows echo '{"instagramUrl": "https://www.instagram.com/reel/XXXXXXXXX/"}' > storage/key_value_stores/default/INPUT.json IG_COOKIES="$(cat /path/to/cookies.txt)" .venv/bin/python -m my_actor ``` Deploy with the [Apify CLI](https://docs.apify.com/cli): ```bash apify login apify push ``` ### License MIT — see [`LICENSE`](LICENSE). # Actor input Schema ## `instagramUrl` (type: `string`): A reel, post, or carousel URL (/reel/, /reels/, /p/, or /tv/). ## `cookies` (type: `string`): Netscape-format cookies.txt content for a logged-in Instagram account. Leave blank to use the IG\_COOKIES Actor environment variable set once in Actor settings — that's the recommended path so you never paste cookies per run. ## `whisperModel` (type: `string`): faster-whisper model size for reel transcription. ## Actor input object example ```json { "instagramUrl": "https://www.instagram.com/reel/XXXXXXXXX/", "whisperModel": "base" } ``` # Actor output Schema ## `dataset` (type: `string`): Structured post metadata + transcript + pointers to every downloaded media record. ## `media` (type: `string`): Every downloaded page, including the complete reel MP4, stored in the default key-value store. # 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 = { "instagramUrl": "https://www.instagram.com/reel/XXXXXXXXX/" }; // Run the Actor and wait for it to finish const run = await client.actor("industrial_nightfall/instagram-ingest-scraper").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 = { "instagramUrl": "https://www.instagram.com/reel/XXXXXXXXX/" } # Run the Actor and wait for it to finish run = client.actor("industrial_nightfall/instagram-ingest-scraper").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 '{ "instagramUrl": "https://www.instagram.com/reel/XXXXXXXXX/" }' | apify call industrial_nightfall/instagram-ingest-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=industrial_nightfall/instagram-ingest-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Instagram Ingest Scraper", "description": "Downloads an Instagram reel/post/carousel, transcribes any video with faster-whisper, and returns structured metadata + media so an AI agent (via the Apify MCP) can extract text and file it into Notion.", "version": "0.1", "x-build-id": "MSIGXfzSp5i8tUQV2" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/industrial_nightfall~instagram-ingest-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-industrial_nightfall-instagram-ingest-scraper", "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/industrial_nightfall~instagram-ingest-scraper/runs": { "post": { "operationId": "runs-sync-industrial_nightfall-instagram-ingest-scraper", "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/industrial_nightfall~instagram-ingest-scraper/run-sync": { "post": { "operationId": "run-sync-industrial_nightfall-instagram-ingest-scraper", "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": [ "instagramUrl" ], "properties": { "instagramUrl": { "title": "Instagram URL", "type": "string", "description": "A reel, post, or carousel URL (/reel/, /reels/, /p/, or /tv/)." }, "cookies": { "title": "Instagram cookies (optional override)", "type": "string", "description": "Netscape-format cookies.txt content for a logged-in Instagram account. Leave blank to use the IG_COOKIES Actor environment variable set once in Actor settings — that's the recommended path so you never paste cookies per run." }, "whisperModel": { "title": "Whisper model", "enum": [ "tiny", "base", "small", "medium" ], "type": "string", "description": "faster-whisper model size for reel transcription.", "default": "base" } } }, "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 } } } } } } } } } } ```