# \[NO COOKIES] LinkedIn Post Fetcher (`scary_good_apis/linkedin-post-fetcher`) Actor Retrieve detailed information about LinkedIn posts and their authors by ID (URN), up to 100 per run. No cookies required. - **URL**: https://apify.com/scary\_good\_apis/linkedin-post-fetcher.md - **Developed by:** [Scary Good APIs](https://apify.com/scary_good_apis) (community) - **Categories:** Social media, Lead generation - **Stats:** 2 total users, 2 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $5.00 / 1,000 posts 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/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 ## LinkedIn Post Fetcher --- ### ๐Ÿš€ Features - **Pay-Per-Result Pricing** - Pay only for posts delivered - **Fetch Any Post by URN** - Complete, real-time data for any public LinkedIn post - **Batch Fetching** - Up to 100 posts per run - **Rich Data Extraction** - Full post content, media, engagement breakdown, and author profile - **No Cookies Required** - No LinkedIn account, session cookies, or account risk ### ๐Ÿ’ฐ Pricing #### Pay-Per-Result Model - **LinkedIn Posts**: $0.005 per post fetched - **No per-run fee** - You only pay for the posts you retrieve - **No infrastructure costs** - You only pay for actual results delivered, not the Actor runtime - **No failed fetches charged** - API errors, deleted posts, and unsupported posts are free - **Budget-aware** - Set a maximum cost per run and the actor only fetches what fits your budget #### Example Costs | Fetch Type | Cost | |------------|------| | Single post fetch | **$0.005** | | Batch of 100 posts | **$0.50** | ### ๐Ÿ“Š Output Data #### LinkedIn Post Data Includes: - **Author Information**: Name, profile URL, type (Person/Company), follower count, location - **Post Content**: Full text, media (images, videos, documents) - **Engagement Metrics**: Likes, comments, shares, reactions breakdown - **Metadata**: Timestamp, URN, permissions #### Multiple Output Views: 1. **Post Details** - Main table with key post information 2. **Engagement Metrics** - Detailed reaction and engagement data 3. **PPR Billing Details** - Transparent billing metadata 4. **Media Content** - Attached images, videos, and documents #### Example Raw Output ```json { "urn": "urn:li:ugcPost:7478186847056306177", "shareUrn": "urn:li:ugcPost:7478186847056306177", "approxPublishedAt": "1w", "timestamp": 1782938682331, "engagement": { "comments": 408, "reactions": { "LIKE": 3812, "PRAISE": 0, "EMPATHY": 0, "INTEREST": 0, "APPRECIATION": 0, "ENTERTAINMENT": 0 }, "shares": 0 }, "content": { "edited": false, "text": "Weโ€™re entering a new stage of the fight against Alzheimerโ€™s. Faster diagnoses and more efficient research are on the horizon, and Iโ€™m optimistic theyโ€™ll give people living with the disease and their loved ones more time and more hope.", "textAttributes": [], "quotePost": false, "quotedPost": null, "hasImage": false, "hasMultipleImages": false, "hasVideo": true, "hasDocument": false, "hasLink": false, "isCelebration": false, "isJobPosting": false, "media": { "images": [], "videos": [ { "contentUrl": "https://dms.licdn.com/playlist/vid/v2/D4E05AQErexIPDJOoLw/mp4-720p-30fp-crf28/B4EZ8fb1aMJYB8-/0/1782938834003?e=2147483647&v=beta&t=a5JkoXTsfBT_5iwQ8cP8bi7Z3oa_SFaL01UEsG29Zjk", "thumbnailUrl": "https://media.licdn.com/dms/image/v2/D4E05AQErexIPDJOoLw/videocover-high/B4EZ8fb1aMJYBM-/0/1782938829370?e=2147483647&v=beta&t=SXguHtyArCOXnZU2pEFCYKZQZSe1--Ltrj-mo7vwD2g", "duration": "PT1M", "width": 720, "height": 1280 } ], "articles": [] }, "jobPostingDetails": null }, "group": null, "permissions": { "canComment": true, "canReact": true, "canShare": true, "canMessage": true }, "poster": { "type": "Person", "name": "Bill Gates", "title": null, "profileUrl": "https://www.linkedin.com/in/williamhgates", "profileImage": "https://media.licdn.com/dms/image/v2/D5603AQF-RYZP55jmXA/profile-displayphoto-shrink_200_200/B56ZRi8g.aGsAY-/0/1736826818802?e=2147483647&v=beta&t=bKWfN6UwwtiCqFWsG7rBELbd48qJOAMLdxhBzzkJV0k", "urn": "urn:li:member:251749025", "location": { "country": "US", "state": null, "city": null }, "followersCount": 40496286, "currentPosition": { "companyUrn": "urn:li:organization:8736", "vanityId": "gates-foundation", "name": "Gates Foundation", "minEmployeeCount": 4207, "industry": "Non-profit Organizations", "jobLocation": { "country": "US", "state": "WA", "city": "Seattle" }, "domain": "https://www.gatesfoundation.org/about/careers", "locationRaw": "Seattle", "description": "Guided by the belief that every life has equal value, the Gates Foundation works to help all people lead healthy, productive lives. In developing countries, we work with partners toโ€ฏcreate impactful solutions so that people can take charge of their futures and achieve their full potential. In the United States, we aim to ensure that everyoneโ€”especially those with the fewest resourcesโ€”has access to the opportunities needed to succeed in school and life.โ€ฏBased in Seattle, Washington, the foundation is led by CEO Mark Suzman, under the direction of Bill Gates and our governing board. โ€ฏ ", "followers": 1511910, "slogan": null, "type": "Nonprofit", "yearFounded": null, "tags": [ "Global Health", "Global Development", "US Education", "Gender Equality" ] }, "isFullDetails": true, "version": 1 }, "fromCache": true } ```` ### โš™๏ธ How to Use #### Finding a Post's URN Every LinkedIn post has a URN in its URL. Open the post and copy it from the address bar: ```text https://www.linkedin.com/feed/update/urn:li:activity:7480183920727134209/ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ URN โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ ``` Both `urn:li:activity:...` and `urn:li:ugcPost:...` formats are supported. #### Example Input Pass 1โ€“100 URNs per run: ```json { "urns": [ "urn:li:ugcPost:7478186847056306177", "urn:li:activity:7480183920727134209" ] } ``` This fetches the full post data โ€” content, media, engagement, and author profile โ€” for each URN. #### Partial Failures If some URNs can't be fetched (deleted posts, unsupported content), the run still succeeds with the posts that worked โ€” you're never charged for failures. Failed URNs and their reasons are saved to the `failed_urns` record in the run's key-value store. ### ๐Ÿ“ˆ Use Cases #### Post Monitoring - Track engagement on your own or competitors' posts over time - Re-fetch posts on a schedule to build engagement time series - Detect edits and content changes #### Lead Generation - Enrich post URNs from other sources with full author profiles - Qualify leads by follower count, company, and role - Feed complete post data into your CRM or outreach tools #### Market Research - Analyze engagement on specific industry posts - Capture company announcements with full context - Study reaction breakdowns on viral content #### Content Analysis - Pull complete media and text for archived analysis - Compare engagement across specific posts - Verify post metadata and publish timing ### ๐Ÿ›ก๏ธ Data Quality & Reliability - **Real-time Data** - Direct from LinkedIn - **Complete Profiles** - Full author and company information - **Accurate Metrics** - Real engagement counts and timestamps - **Rate Limited** - Respects LinkedIn's usage policies - **Error Handling** - Robust retry logic and error reporting ### ๐Ÿ“‹ Data Export All data is available in multiple formats: - **JSON** - Full structured data with all fields - **CSV** - Flattened data perfect for spreadsheet analysis - **Excel** - Formatted tables with proper data types - **API Access** - Programmatic access via Apify API ### ๐Ÿ”— Resources - [Data Export Options](https://docs.apify.com/platform/storage/dataset) # Actor input Schema ## `urns` (type: `array`): LinkedIn post URNs (Uniform Resource Names) to fetch, up to 100 per run. Format: urn:li:activity:7480183920727134209 or urn:li:ugcPost:7478186847056306177. ## Actor input object example ```json { "urns": [ "urn:li:ugcPost:7478186847056306177" ] } ``` # Actor output Schema ## `results` (type: `string`): No description # 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 = { "urns": [ "urn:li:ugcPost:7478186847056306177" ] }; // Run the Actor and wait for it to finish const run = await client.actor("scary_good_apis/linkedin-post-fetcher").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ 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); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ 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 = { "urns": ["urn:li:ugcPost:7478186847056306177"] } # Run the Actor and wait for it to finish run = client.actor("scary_good_apis/linkedin-post-fetcher").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "urns": [ "urn:li:ugcPost:7478186847056306177" ] }' | apify call scary_good_apis/linkedin-post-fetcher --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scary_good_apis/linkedin-post-fetcher", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "[NO COOKIES] LinkedIn Post Fetcher", "description": "Retrieve detailed information about LinkedIn posts and their authors by ID (URN), up to 100 per run. No cookies required.", "version": "1.0", "x-build-id": "0fHJvJegETFHtvnEe" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scary_good_apis~linkedin-post-fetcher/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scary_good_apis-linkedin-post-fetcher", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scary_good_apis~linkedin-post-fetcher/runs": { "post": { "operationId": "runs-sync-scary_good_apis-linkedin-post-fetcher", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scary_good_apis~linkedin-post-fetcher/run-sync": { "post": { "operationId": "run-sync-scary_good_apis-linkedin-post-fetcher", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "urns" ], "properties": { "urns": { "title": "LinkedIn Post URNs", "minItems": 1, "maxItems": 100, "uniqueItems": true, "type": "array", "description": "LinkedIn post URNs (Uniform Resource Names) to fetch, up to 100 per run. Format: urn:li:activity:7480183920727134209 or urn:li:ugcPost:7478186847056306177.", "items": { "type": "string", "pattern": "^urn:([^:]*:){2}[^:]*$" } } } }, "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 } } } } } } } } } } ```