# Instagram Post Image Analyzer - Vision Tagging (`seemuapps/instagram-post-image-analyzer`) Actor Analyze the images on any Instagram profile or post with vision models. Get a description, content category, detected objects and brands, on-image text, brand safety, and suggested hashtags for every post. - **URL**: https://apify.com/seemuapps/instagram-post-image-analyzer.md - **Developed by:** [Andrew](https://apify.com/seemuapps) (community) - **Categories:** Social media, AI, Developer tools - **Stats:** 1 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $18.00 / 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 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 ## Instagram Post Image Analyzer See what is actually in your Instagram images, at scale. Point it at a profile or a list of posts and a vision model describes every image, detects objects and brands, reads on-image text, flags brand safety, and suggests hashtags. ### What you get For every post: - Post basics: shortcode, URL, media type, caption, like and comment counts, thumbnail URL, author - **Description**: one sentence describing the image - **Category**: the content category - **Objects**: notable objects detected in the image - **Brands**: visible brands or logos - **On-image text**: any text read from the image - **Brand safety**: safe, review, or risky - **Suggested hashtags**: relevant hashtags for the image ### Use cases - Auto tag and categorize a creator's or competitor's feed - Detect product placements and brand logos across posts - Build searchable image catalogs from Instagram content - Brand safety screening before a partnership - Generate hashtag ideas from real post imagery ### How to use 1. Enter a **Username** to analyze recent posts, or paste specific **Post URLs** 2. Set **Max posts** when using a username 3. Optionally change the **Vision model** 4. Run the actor. Each post becomes one row in the **Dataset** tab ### Output example ```json { "shortcode": "DZ8cY6xAElr", "postUrl": "https://www.instagram.com/p/DZ8cY6xAElr/", "mediaType": "video", "category": "wildlife", "description": "A close-up of a baby sea turtle swimming through clear turquoise water above a sandy seabed.", "objects": ["baby sea turtle", "water", "seabed"], "brands": [], "onImageText": [], "brandSafety": "safe", "suggestedHashtags": ["BabySeaTurtle", "OceanLife", "WildlifePhotography", "MarineLife", "SeaTurtle"] } ```` ### Notes - For videos and Reels, the cover image is analyzed. - Posts without an available image are returned with empty analysis fields so no post is silently dropped. - The default model is a fast, low cost vision model. Any vision model that accepts image input can be used. # Actor input Schema ## `username` (type: `string`): Instagram username whose recent posts to analyze (with or without @). Leave empty if you provide Post URLs instead. ## `postUrls` (type: `array`): Specific Instagram post or Reel URLs or shortcodes to analyze. If set, these are used instead of the username's recent posts. ## `maxPosts` (type: `integer`): Maximum number of posts to analyze when using a username. 0 means no cap. ## `model` (type: `string`): Vision model used to analyze each image. A fast, low cost model is recommended. ## Actor input object example ```json { "username": "natgeo", "postUrls": [], "maxPosts": 12, "model": "google/gemini-2.5-flash" } ``` # Actor output Schema ## `results` (type: `string`): One record per post. Fields: postId, shortcode, postUrl, mediaType, caption, likeCount, commentCount, thumbnailUrl, authorUsername, description, category, objects, brands, onImageText, brandSafety, suggestedHashtags. # 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 = { "username": "natgeo", "postUrls": [] }; // Run the Actor and wait for it to finish const run = await client.actor("seemuapps/instagram-post-image-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 = { "username": "natgeo", "postUrls": [], } # Run the Actor and wait for it to finish run = client.actor("seemuapps/instagram-post-image-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 '{ "username": "natgeo", "postUrls": [] }' | apify call seemuapps/instagram-post-image-analyzer --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=seemuapps/instagram-post-image-analyzer", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Instagram Post Image Analyzer - Vision Tagging", "description": "Analyze the images on any Instagram profile or post with vision models. Get a description, content category, detected objects and brands, on-image text, brand safety, and suggested hashtags for every post.", "version": "1.0", "x-build-id": "iwBQGUcVtdOyumihi" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/seemuapps~instagram-post-image-analyzer/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-seemuapps-instagram-post-image-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/seemuapps~instagram-post-image-analyzer/runs": { "post": { "operationId": "runs-sync-seemuapps-instagram-post-image-analyzer", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/seemuapps~instagram-post-image-analyzer/run-sync": { "post": { "operationId": "run-sync-seemuapps-instagram-post-image-analyzer", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "username": { "title": "Username", "type": "string", "description": "Instagram username whose recent posts to analyze (with or without @). Leave empty if you provide Post URLs instead." }, "postUrls": { "title": "Post URLs (optional)", "type": "array", "description": "Specific Instagram post or Reel URLs or shortcodes to analyze. If set, these are used instead of the username's recent posts.", "items": { "type": "string" } }, "maxPosts": { "title": "Max posts", "minimum": 0, "type": "integer", "description": "Maximum number of posts to analyze when using a username. 0 means no cap.", "default": 12 }, "model": { "title": "Vision model", "enum": [ "google/gemini-2.5-flash", "google/gemini-2.5-pro", "openai/gpt-4o-mini", "openai/gpt-4.1", "anthropic/claude-sonnet-4.6" ], "type": "string", "description": "Vision model used to analyze each image. A fast, low cost model is recommended.", "default": "google/gemini-2.5-flash" } } }, "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 } } } } } } } } } } ```