# LinkedIn Post Search Scraper · No Cookies · $1.50/1k ✅ (`linkedintel/linkedin-post-search-scraper`) Actor Search LinkedIn posts by keyword. Track competitor messaging, viral content, industry conversations. No cookies, no login. $1.50/1k pay-per-post. - **URL**: https://apify.com/linkedintel/linkedin-post-search-scraper.md - **Developed by:** [LinkedIntel](https://apify.com/linkedintel) (community) - **Categories:** Marketing, Social media - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing $3.00 / 1,000 post search result extracteds 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 ## LinkedIn Post Search Scraper · No Cookies · $1.50/1k Search LinkedIn posts by keyword. Track competitor messaging, viral content, industry conversations — without logging in, without managing cookies, without browser sessions. **Pay-per-result pricing: $1.50 per 1,000 posts.** No subscription, no monthly minimum, no charge for empty queries. --- ### What you get — 27 fields per post Every post returned is a `searchPost` record with this shape: ```json { "type": "searchPost", "sourceQuery": "AI launch", "postId": "7204318726155931648", "postUrn": "urn:li:activity:7204318726155931648", "postUrl": "https://www.linkedin.com/posts/...", "authorName": "Jane Founder", "authorHeadline": "Building autonomous agents @ Acme", "authorUrl": "https://www.linkedin.com/in/jane-founder/", "authorUrn": "urn:li:fsd_profile:ABC123", "authorType": "person", "text": "Just shipped our biggest AI release of 2026...", "textLength": 412, "postedAt": "2026-05-18T14:32:00+00:00", "language": "en", "reactionCount": 487, "likeCount": 312, "empathyCount": 24, "praiseCount": 95, "interestCount": 42, "funnyCount": 3, "appreciationCount": 11, "maybeCount": 0, "commentCount": 38, "shareCount": 6, "repostCount": 14, "mediaType": "image", "mediaUrl": "https://media.licdn.com/dms/...", "isRepost": false, "originalAuthorName": null, "hashtagsFound": ["ai", "agents", "shipit"], "mentions": ["acmecorp", "openai"], "scrapedAt": "2026-05-19T08:12:44+00:00" } ```` Each run also emits one `summary` record at the end with per-query counts, and a `diagnostic` record per query if zero posts come back. ### Top use cases #### 1. Track competitor messaging in real time Search `"YourCompetitor launch"`, `"acme AI assistant"`, `" raises"` — every time a customer, employee, partner, or analyst posts about them, you see the post, the engagement, and the context. #### 2. Find viral content in your category Sort by `top`, filter by date, get the LinkedIn posts that actually won attention this quarter on your topic. Tear them down. Reverse-engineer the hook. #### 3. Industry pulse & market research Monthly snapshot of `"generative AI launch"`, `"climate tech series A"`, `"developer productivity"` — count posts, top authors, dominant themes, reaction patterns. #### 4. Lead-gen signal mining Search `"hiring head of marketing"`, `"looking for vendor"`, `"recommend a tool for"` and pipe the raw posts to your CRM as warm intent signals. #### 5. PR & brand monitoring Watch `""`, `""`, `""` weekly — find the posts you don't want to miss, the customer-generated content you can amplify, and the criticism you want to address. #### 6. Sales prospecting by topic Search `"shopify migration"`, `"databricks pricing"`, `"hubspot alternative"` — get the people actively posting about those decisions, with their headline + profile URL. Better than a cold list. #### 7. Conference & event tracking Free-form queries like `"SaaStr 2026 keynote"`, `"AWS reInvent"`, `"web summit takeaways"` pull the actual posts from speakers, sponsors, and attendees. ### Difference from our hashtag scraper We ship two related actors. Pick by use case: | Use case | Use this actor (Post Search) | Use [Hashtag Scraper](https://apify.com/linkedintel/linkedin-hashtag-scraper) | |---|---|---| | Free-form keyword like `"AI launch"` or `"series A"` | YES | no (would only match `#ailaunch`) | | Hashtag like `#aiagents`, `#blackfriday` | works, but use Hashtag Scraper | YES (purpose-built) | | Multi-word competitor mentions | YES | no | | Author bio search (`"head of marketing"`) | YES | no | | Tracking a clean campaign hashtag | works | YES (per-tag dedup) | You can run both — they share dedup keys (postId), so unifying datasets is trivial. ### Pricing — why $1.50 / 1,000 posts wins | Tool | Pricing | Monthly minimum | LinkedIn-specific | |---|---|---|---| | **Brand24** | $99 / mo (Personal) → $499 / mo | Yes — fixed | Multi-channel; LinkedIn is one of many | | **Mention** | $41 / mo → $179 / mo | Yes — fixed | Multi-channel | | **Sprinklr** | Enterprise — "contact sales" | Yes — typically $1k+ / mo | Multi-channel | | **harvestapi linkedin-post-search** | $4 / 1,000 (search page) + $4 / 1,000 (full post) = $8 effective | None | LinkedIn-only | | **Our [a20 LinkedIn Search](https://apify.com/linkedintel/linkedin-search-scraper-no-cookies) (Posts entity)** | $1.50 / 1,000 posts | None | LinkedIn-only | | **This actor** | **$1.50 / 1,000 posts** | **None** | **LinkedIn-only, post-search-purpose-built** | Apify gives every user **$5 of free platform credit per month** — that's ~3,300 posts/month free before you ever pay a cent. ### Example recipes #### Track a competitor launch — last 7 days ```json { "searchQueries": ["YourAICompetitor launch", "YourAICompetitor pricing"], "sortBy": "recent", "dateFrom": "2026-05-12", "maxPostsPerQuery": 200 } ``` #### Top-performing AI launch posts of 2026 ```json { "searchQueries": ["AI launch", "generative AI startup", "LLM product"], "sortBy": "top", "dateFrom": "2026-01-01", "maxPostsPerQuery": 500 } ``` #### Only-company-authored fundraising announcements ```json { "searchQueries": ["Series A announcement", "we raised"], "authorTypeFilter": "company", "sortBy": "recent", "maxPostsPerQuery": 300 } ``` #### English-only Black Friday chatter ```json { "searchQueries": ["black friday deal", "cyber monday launch"], "languagesFilter": ["en"], "sortBy": "top", "maxPostsPerQuery": 300 } ``` #### Pulse-check around CES week ```json { "searchQueries": ["CES 2026 keynote", "CES 2026 announcement"], "dateFrom": "2026-01-06", "dateTo": "2026-01-12", "maxPostsPerQuery": 1000 } ``` ### How it works 1. You provide one or more free-form keyword queries (multi-word OK — `"head of marketing AI"` is one query). 2. The actor sends each query to LinkedIn's public post-search endpoint via a managed cookieless data partnership. 3. For each query, it paginates results until it hits `maxPostsPerQuery` (or the data runs out). 4. Posts are deduplicated **across queries by post ID** — if `"AI launch"` and `"generative AI"` both surface the same post, you're billed once. 5. Date, language, and author-type filters are applied client-side. 6. The final dataset is a flat list of `searchPost` records plus a single `summary` row. **No login. No cookies. No browser sessions. No account ban risk.** The actor uses a managed data partnership for cookieless LinkedIn access — your LinkedIn account never touches this. ### FAQ **Q: How is this different from a generic web-search actor?** We hit LinkedIn's public post-search endpoint directly, return structured 27-field records with engagement breakdowns, and dedup across queries. A generic search actor would give you HTML you'd have to parse. **Q: How fresh is the data?** Posts surface within minutes of being published on LinkedIn, just like they would for any logged-out visitor browsing search results. **Q: Does `sortBy: top` always work?** LinkedIn supports `top` for most queries. For long-tail / low-volume keywords it may auto-fall-back to relevance. Either way, you get posts. **Q: What's the max posts per query?** 1,000, capped at the vendor level. To go deeper, schedule the actor (daily, hourly) and append. **Q: Why are some reaction breakdown fields missing?** LinkedIn returns reaction breakdown only when a post has enough engagement to split it. Posts with single-digit reactions usually only get `reactionCount` populated, not per-type counts. **Q: Can I run multiple post-search scrapes in parallel?** Yes — kick off as many runs as you want on the Apify platform. Each one is billed independently per-record. **Q: Will this scrape comments under a post?** No — for that, run our [LinkedIn Post Comments Scraper](https://apify.com/linkedintel/cheapest-linkedin-comments-scraper-no-cookies-1-1k) ($1/1k) on the post URLs this actor returns. ### Compliance & disclaimer This actor scrapes **publicly accessible LinkedIn post-search results** — the same content any logged-out visitor sees by browsing `linkedin.com/search/results/content/?keywords=...`. - No private profile data, no gated content, no DMs, no anything-behind-login. - LinkedIn user content is subject to LinkedIn's Terms of Use and applicable copyright laws. - Customers are responsible for compliance with applicable data protection laws (GDPR, CCPA, etc.) when processing scraped data — particularly when post text contains personal information. - **Not affiliated with, endorsed by, or sponsored by LinkedIn Corporation or Microsoft Corporation.** "LinkedIn" is a trademark of LinkedIn Corporation, used here solely for descriptive purposes. # Actor input Schema ## `searchQueries` (type: `array`): Free-form keyword phrases (e.g. 'AI launch', 'marketing automation playbook', 'series A announcement'). Each query is a separate search. ## `maxPostsPerQuery` (type: `integer`): Hard cap on posts returned per query. ## `sortBy` (type: `string`): Most recent or top-performing posts. (Vendor may auto-fallback to relevance for some popular queries.) ## `authorTypeFilter` (type: `string`): Filter by post author type — any, person, or company. ## `dateFrom` (type: `string`): ISO date (YYYY-MM-DD). Only return posts published on or after this date. ## `dateTo` (type: `string`): ISO date (YYYY-MM-DD). Only return posts published on or before this date. ## `languagesFilter` (type: `array`): ISO 639-1 language codes (e.g. 'en', 'es', 'fr'). Empty = all languages. ## Actor input object example ```json { "searchQueries": [ "AI launch", "generative AI startup" ], "maxPostsPerQuery": 50, "sortBy": "recent", "authorTypeFilter": "any", "languagesFilter": [] } ``` # 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 = { "searchQueries": [ "AI launch", "generative AI startup" ], "languagesFilter": [] }; // Run the Actor and wait for it to finish const run = await client.actor("linkedintel/linkedin-post-search-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 = { "searchQueries": [ "AI launch", "generative AI startup", ], "languagesFilter": [], } # Run the Actor and wait for it to finish run = client.actor("linkedintel/linkedin-post-search-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 '{ "searchQueries": [ "AI launch", "generative AI startup" ], "languagesFilter": [] }' | apify call linkedintel/linkedin-post-search-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=linkedintel/linkedin-post-search-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Post Search Scraper · No Cookies · $1.50/1k ✅", "description": "Search LinkedIn posts by keyword. Track competitor messaging, viral content, industry conversations. No cookies, no login. $1.50/1k pay-per-post.", "version": "1.0", "x-build-id": "PvW2hrMxuABwuHukR" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/linkedintel~linkedin-post-search-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-linkedintel-linkedin-post-search-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/linkedintel~linkedin-post-search-scraper/runs": { "post": { "operationId": "runs-sync-linkedintel-linkedin-post-search-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/linkedintel~linkedin-post-search-scraper/run-sync": { "post": { "operationId": "run-sync-linkedintel-linkedin-post-search-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "searchQueries" ], "properties": { "searchQueries": { "title": "Search queries", "type": "array", "description": "Free-form keyword phrases (e.g. 'AI launch', 'marketing automation playbook', 'series A announcement'). Each query is a separate search.", "items": { "type": "string" } }, "maxPostsPerQuery": { "title": "Max posts per query", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Hard cap on posts returned per query.", "default": 50 }, "sortBy": { "title": "Sort order", "enum": [ "recent", "top" ], "type": "string", "description": "Most recent or top-performing posts. (Vendor may auto-fallback to relevance for some popular queries.)", "default": "recent" }, "authorTypeFilter": { "title": "Author type", "enum": [ "any", "person", "company" ], "type": "string", "description": "Filter by post author type — any, person, or company.", "default": "any" }, "dateFrom": { "title": "Posted after (optional)", "pattern": "^\\d{4}-\\d{2}-\\d{2}$", "type": "string", "description": "ISO date (YYYY-MM-DD). Only return posts published on or after this date." }, "dateTo": { "title": "Posted before (optional)", "pattern": "^\\d{4}-\\d{2}-\\d{2}$", "type": "string", "description": "ISO date (YYYY-MM-DD). Only return posts published on or before this date." }, "languagesFilter": { "title": "Languages filter (optional)", "type": "array", "description": "ISO 639-1 language codes (e.g. 'en', 'es', 'fr'). Empty = all languages.", "items": { "type": "string" } } } }, "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 } } } } } } } } } } ```