100 free credits. No credit card required
LinkedIn Search Posts API
Scrape LinkedIn Search Posts data with one API call. Finds public LinkedIn posts, feed updates, and Pulse articles via Google Search. Returns description, author, media, like count, comment count, and published date when LinkedIn exposes them publicly. Best-effort against Google's index, not a complete native LinkedIn search. Use `date_posted` to narrow to recent posts and `page` to walk deeper. **Many posts in one call.** Pass `limit` (1 to 200) with a `query` and the call reads as many pages as it needs, removes duplicates, and returns up to that many posts in relevance order, charged 1 credit for every 5 posts returned, so 200 posts is 40 credits. Repeat the identical call inside the 2-minute cache window and it is served from cache for nothing. The response carries a `walk` summary saying how many pages were read and why the call stopped. **Reading one member's back catalogue.** Pass `from_member` on its own, with no `query`, and page through the result: this is the only way to reach a member's posts older than the 100 that /v1/linkedin/profile/posts can return, and unlike that endpoint it hands you a real `next_cursor` that keeps advancing. Get the urn from `author.ext.urn` on /v1/linkedin/profile. Those results come back newest-first; `sort_by=relevance` is rejected on a subject-only call because there is no query for them to be relevant to. **This is not a complete history, and how much it returns varies by member.** It reads a public search index, so it holds whatever that index holds for a given profile and no more. Measured 06/09/2026: one prolific member exhausted at 395 posts, while a customer walking his own profile of 800+ posts reached 223. Neither number is a limit you can raise by paging harder; the walk simply ends when the index runs out. Treat the depth as unknown until you walk it, and do not design around a fixed figure. For a member whose /v1/linkedin/profile/posts call already returns fewer posts than the `limit` you asked for, that feed is their whole history and this endpoint returns less, not more.
Last updated October 2026Maintained by the SocialCrawl team
Returns public LinkedIn posts and Pulse articles matching a keyword, with each result's text, author, media, like and comment counts, and date.
Use it for broad keyword monitoring; it reads public search results, so treat coverage as best effort rather than complete.
Searching 68 platforms in parallel
·
TikTok·
Instagram·
YouTube·
Facebook·
X·LinkedIn·
Reddit·
Threads·
Pinterest·
Twitch·
Truth Social·
Snapchat·
Kick·
Bluesky·
Kwai·
Rumble·
Spotify·
Apple Music·TikTok Shop·
Amazon Shop·
Google Shopping·
Trustpilot·
TripAdvisor·
Yelp·
Linktree·
Komi·
Pillar·
lnk.bio·Facebook Ads·Google Ads·LinkedIn Ads·Google Search·Google News·
Finance·
Polymarket·
Tavily·
Hacker News·
GitHub·
Perplexity·
Naver·
Utility·
Universal Search
TikTok·Instagram·YouTube·Facebook·X·LinkedIn·Reddit·Threads·Pinterest·Twitch·Truth Social·Snapchat·Kick·Bluesky·Kwai·Rumble·Spotify·Apple Music·TikTok Shop·Amazon Shop·Google Shopping·Trustpilot·TripAdvisor·Yelp·Linktree·Komi·Pillar·lnk.bio·Facebook Ads·Google Ads·LinkedIn Ads·Google Search·Google News·Finance·Polymarket·Tavily·Hacker News·GitHub·Perplexity·Naver·Utility·Universal Search·TikTok·Instagram·YouTube·Facebook·X·LinkedIn·Reddit·Threads·Pinterest·Twitch·Truth Social·Snapchat·Kick·Bluesky·Kwai·Rumble·Spotify·Apple Music·TikTok Shop·Amazon Shop·Google Shopping·Trustpilot·TripAdvisor·Yelp·Linktree·Komi·Pillar·lnk.bio·Facebook Ads·Google Ads·LinkedIn Ads·Google Search·Google News·Finance·Polymarket·Tavily·Hacker News·GitHub·Perplexity·Naver·Utility·Universal Search
TikTok·Instagram·YouTube·Facebook·X·LinkedIn·Reddit·Threads·Pinterest·Twitch·Truth Social·Snapchat·Kick·Bluesky·Kwai·Rumble·Spotify·Apple Music·TikTok Shop·Amazon Shop·Google Shopping·Trustpilot·TripAdvisor·Yelp·Linktree·Komi·Pillar·lnk.bio·Facebook Ads·Google Ads·LinkedIn Ads·Google Search·Google News·Finance·Polymarket·Tavily·Hacker News·GitHub·Perplexity·Naver·Utility·Universal Search·TikTok·Instagram·YouTube·Facebook·X·LinkedIn·Reddit·Threads·Pinterest·Twitch·Truth Social·Snapchat·Kick·Bluesky·Kwai·Rumble·Spotify·Apple Music·TikTok Shop·Amazon Shop·Google Shopping·Trustpilot·TripAdvisor·Yelp·Linktree·Komi·Pillar·lnk.bio·Facebook Ads·Google Ads·LinkedIn Ads·Google Search·Google News·Finance·Polymarket·Tavily·Hacker News·GitHub·Perplexity·Naver·Utility·Universal Search
TikTok·Instagram·YouTube·Facebook·X·LinkedIn·Reddit·Threads·Pinterest·Twitch·Truth Social·Snapchat·Kick·Bluesky·Kwai·Rumble·Spotify·Apple Music·TikTok Shop·Amazon Shop·Google Shopping·Trustpilot·TripAdvisor·Yelp·Linktree·Komi·Pillar·lnk.bio·Facebook Ads·Google Ads·LinkedIn Ads·Google Search·Google News·Finance·Polymarket·Tavily·Hacker News·GitHub·Perplexity·Naver·Utility·Universal Search·TikTok·Instagram·YouTube·Facebook·X·LinkedIn·Reddit·Threads·Pinterest·Twitch·Truth Social·Snapchat·Kick·Bluesky·Kwai·Rumble·Spotify·Apple Music·TikTok Shop·Amazon Shop·Google Shopping·Trustpilot·TripAdvisor·Yelp·Linktree·Komi·Pillar·lnk.bio·Facebook Ads·Google Ads·LinkedIn Ads·Google Search·Google News·Finance·Polymarket·Tavily·Hacker News·GitHub·Perplexity·Naver·Utility·Universal Search
TikTok·Instagram·YouTube·Facebook·X·LinkedIn·Reddit·Threads·Pinterest·Twitch·Truth Social·Snapchat·Kick·Bluesky·Kwai·Rumble·Spotify·Apple Music·TikTok Shop·Amazon Shop·Google Shopping·Trustpilot·TripAdvisor·Yelp·Linktree·Komi·Pillar·lnk.bio·Facebook Ads·Google Ads·LinkedIn Ads·Google Search·Google News·Finance·Polymarket·Tavily·Hacker News·GitHub·Perplexity·Naver·Utility·Universal SearchLinkedIn API
What can you do with the Search Posts API?
The Search Posts endpoint gives you structured LinkedIn data with computed fields in a single request. No scraping infrastructure to build or maintain.
Example Request
curl -H "x-api-key: YOUR_API_KEY" \
"https://www.socialcrawl.dev/v1/linkedin/search/posts?query=ai+agents&date_posted=past_week&label=sponsored&relevance=filter"import requests
response = requests.get(
"https://www.socialcrawl.dev/v1/linkedin/search/posts",
params={
'query': 'ai agents',
'date_posted': 'past_week',
'label': 'sponsored',
'relevance': 'filter',
},
headers={"x-api-key": "YOUR_API_KEY"},
)
data = response.json()const response = await fetch(
"https://www.socialcrawl.dev/v1/linkedin/search/posts?query=ai+agents&date_posted=past_week&label=sponsored&relevance=filter",
{
headers: { "x-api-key": "YOUR_API_KEY" },
},
);
const data = await response.json();Parameters worth sending
label=sponsoredMarks each post that is paid or sponsored, at no extra credits; intent and niche are free too.relevance=filterDrops rows that are not about your query, at no extra credits; relevance=score keeps them and adds a score.date_posted=past_weekOnly posts from this time window.
Parameters
| Parameter | Required | Description |
|---|---|---|
| page | No | 1-based page number of Google results. Defaults to 1. |
| sort_by | No | Result ordering: `relevance` (default) or `date_posted`. On a subject-only call (`from_member` or `from_company` with no `query`) the results are ordered by date and `relevance` is rejected, because there is no query for them to be relevant to. (date_posted | relevance) |
| date_posted | No | Date filter based on Google-indexed results. One of `past_24h`, `past_week`, `past_month` (underscores, not hyphens). Note the JOB lanes use `past_24_hours` for the same concept; the two vocabularies are not interchangeable. (past_24h | past_week | past_month) |
| content_type | No | Narrow to one kind of post. (videos | photos | jobs | live_videos | documents | collaborative_articles) |
| from_company | No | Filter to posts from a company, by numeric LinkedIn company id (for example `1035`). A company slug or URL is rejected; read the id from `author.id` on /v1/linkedin/company. |
| from_member | No | Filter to posts from one member, by their LinkedIn member urn — the bare `ACoAA…` value published as `author.ext.urn` by /v1/linkedin/profile. A profile URL, a public slug (`williamhgates`) or a `urn:li:fsd_profile:`-prefixed value is rejected, because the source cannot use any of them. Pass this on its own, with no `query`, to walk one member's back catalogue. |
| query | No | Keyword or phrase to search for. Optional when `from_member` or `from_company` is set: omit it to get that subject's posts unfiltered, and note that supplying a real content word narrows the results to posts matching it. |
| limit | No | Return up to this many posts in one call, 1 to 200. The call reads as many pages as it needs, removes duplicates, and is charged 1 credit for every 5 posts returned (200 posts = 40 credits). Repeating the identical call inside the 2-minute cache window is free. Requires `query`. Cannot be combined with `page` or `cursor`: the result always starts at the top and `pagination.next_cursor` is null. `pagination.has_more` says whether more posts were available when the call stopped, and `walk.stopped` says why it stopped (`limit`, `exhausted`, `deadline` or `sources_unavailable`). |
| relevance | No | Optional. Without this param every row already carries computed.relevance against your query, free ({ p, sense, depth, spam }: is this row about what your query means, or a different thing that shares its words?), and nothing is dropped or reordered. score asks for it explicitly and waits for every row; filter also drops the rows that are not about your query, and lists their ids in data.relevance.dropped_ids. Your query is the topic; nothing to configure. A row that could not be judged is never dropped and carries relevance: null. Pagination is unchanged, so a filtered page can hold fewer rows. Free with your query as the topic; with relevant_to it adds 1 credit per started 25 newly judged rows (rows already judged for the same topic are free, and so is a cached page). (score | filter) |
| relevance_threshold | No | Optional, only with relevance. The probability (0 to 1) a row must reach to be kept by relevance=filter. Default 0.5. Lower keeps more rows, higher keeps fewer. |
| relevant_to | No | Optional, only with relevance. Up to 200 characters describing what you mean, used as the topic instead of the query. Use it when the query is ambiguous, for example query=cleopatra with relevant_to=Cleopatra, the IGT slot game. |
| judgments | No | Optional, on (the default) or off. By default every row gains free SocialCrawl judgments (computed.labels, and computed.relevance on search endpoints), reported in data.labels (mode default) and data.relevance (origin default), each with a status (complete, partial or skipped) and pending: the rows still being judged when the page was sent, which carry null now and are filled on your next call or cached read. Default judgments never add credits, never change an existing field, and never drop or reorder a row. off returns the page exactly as before, with none of those keys. label=none does the same. (on | off) |
| dry_run | No | Optional. When 1, return a cost preview for this labelled or relevance-filtered request without fetching the page or judging any row. data.estimate reports rows_expected, rows_cached, label_credits_min, label_credits_max and base_credits. 0 credits charged. (1) |
| fit | No | Optional. When goal, keep the rows and fields needed for the goal you pass in goal= (plus any that are uncertain, and the first and last), and replace the rest with a stub. data.held_back lists the held ids and a recall id that re-reads the full page from cache at no extra charge. Without this param the page is unchanged. (goal) |
| goal | No | Required by fit=goal. What you are trying to do, in your own words, up to 300 characters. |
| fit_tokens | No | Optional, only with fit=goal. Soft cap on how much of the page to keep, in tokens. Uncertain blocks and the first and last block are kept even if they exceed it. |
| label | No | Optional CSV of SocialCrawl labels to add to every post. Without this param every page already carries sponsored, intent and niche, free; label= adds the labels you name to them (the defaults keep running). sponsored, intent and niche are free when asked for too; mention, quality, intent with offer=, and injection add 1 credit per started 25 newly judged posts. judgments=off (or label=none) turns the default labels off. sponsored: is the post a paid or gifted promotion (p, 0 to 1), did it carry a disclosure marker such as #ad or 광고 (disclosed), is it likely paid with no marker (undisclosed), and which of the accounts it mentions does it promote (brand, or null). These are signals to review, never a finding. mention (needs brand=): is the post about that brand rather than something that shares its name (about_brand, 0 to 1), how it feels about the brand on five levels (sentiment_level 0 to 4 and sentiment_score 0 to 1, null when the post is not about the brand), is it sarcastic, which aspect it talks about (taste_or_quality, price_or_value, availability_or_delivery, health_or_safety, advertising_or_campaign, customer_service, none), and did the author buy or use it (first_hand). intent: what the author is mainly doing (label: asking_for_recommendation, comparing_options, switching_away, complaining, promoting, news_or_discussion, other, or null when unsure, with confidence), whether they read as a potential buyer rather than a seller (buyer, seller), how pressing the need is (urgency, 0 to 3), and, when you pass offer=, whether your offer would plausibly help them (fits_offer). niche: which of the 33 niches of the published taxonomy sc-niche-v1 the caption belongs to, or personal_no_niche, or other (label, confidence, taxonomy), with label null when the caption is too thin to tell or the pick is unsure. It reads the caption only, not the video. quality: how much checkable detail the caption carries (fact_density 0 to 3), whether it mainly asks for likes, replies, shares, follows or tags (engagement_bait, 0 to 1), whether it is written to provoke anger as a way to get engagement (rage_bait, 0 to 1; about the writing, never the side taken), whether it only repeats someone else's news or view (secondhand, 0 to 1), and what the post is mainly doing (post_aim: inform, opinion, sell, entertain, provoke, other). exclude=engagement_bait drops posts whose engagement_bait is 0.8 or higher. injection flags text that addresses an AI system and tries to direct it (flagged, p); it never drops or rewrites a row. A post that could not be judged carries labels: null. Posts already labelled for anyone are free, and so is a cached page. data.labels reports what was judged and billed. |
| exclude | No | Optional CSV (engagement_bait), only with label=quality. Drops posts whose engagement_bait probability is 0.8 or higher and lists their ids in data.labels.dropped_ids. A post that could not be judged is never dropped. |
| brand | No | Required by label=mention, ignored otherwise. The brand or product the posts are judged against, up to 300 characters, for example brand=Buldak. Without it label=mention is skipped with the warning label_mention_needs_brand and is not billed. |
| brand_description | No | Optional, with label=mention. One plain sentence saying what the brand is, for example brand_description=Samyang's spicy instant noodle brand. Use it when the name is also an ordinary word or another company's name. |
| offer | No | Optional, with label=intent. One or two plain sentences saying what you sell, up to 300 characters, for example offer=A web design agency that builds websites for small businesses. Adds fits_offer to every post; without it fits_offer is null. |
| label_evidence | No | Optional, only with label=. When 1, every labelled row also carries computed.labels_evidence.<preset> = { quote, sentence_index }: the sentence in the row that most clearly shows the label, copied verbatim. Absent or null when no single sentence shows it. (1) |
| since | No | Only rows published on or after this date: YYYY-MM-DD (midnight UTC) or an ISO 8601 timestamp. Rows older than it are left off each page. This list is not in date order, so the walk does not stop at the date: keep following `next_cursor`, and each page costs what a page costs even when every row on it is filtered out. A row with no date is kept. `published_after`, `start_date`, `from_date`, `publish_time` (`yesterday`, `this-week`, `this-month`, `last-3-months`, `last-6-months`) and `max_age_days` are accepted and applied as `since`. |
| until | No | Only rows published on or before this date: YYYY-MM-DD (that whole UTC day is included) or an ISO 8601 timestamp. Newer rows are left off each page and the walk does not stop, so older pages follow. A row with no date is kept. `published_before`, `end_date` and `to_date` are accepted and applied as `until`. |
Example Response
What does the LinkedIn Search Posts API return?
Every response follows one unified schema. Here is a real, unmodified response body, so you can see the exact fields you get back before spending a credit.
Example response
{
"success": true,
"platform": "linkedin",
"endpoint": "/v1/linkedin/search/posts",
"data": {
"items": [
{
"post": {
"id": "7510831414699933696",
"url": "https://www.linkedin.com/feed/update/urn:li:activity:7510831414699933696",
"content": {
"text": "Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted).",
"media_urls": null,
"thumbnail_url": null,
"duration_seconds": null
},
"author": {
"username": "user_c16612",
"display_name": "user_222f59",
"avatar_url": "https://example.com/avatars/user_5e45ac.png",
"verified": null
},
"engagement": {
"views": null,
"likes": 0,
"comments": 0,
"shares": 0,
"saves": null
},
"flags": {
"nsfw": null,
"spoiler": null,
"pinned": null,
"deleted": false
},
"published_at": "2026-09-29T22:42:33.764Z",
"ext": {
"reaction_counts": [],
"author_headline": "user_1634e3"
}
},
"computed": {
"engagement_rate": null,
"language": "en",
"content_category": "travel",
"estimated_reach": null,
"relevance": {
"p": 0.96,
"sense": "target",
"depth": 3,
"spam": 0.1
},
"labels": {
"sponsored": {
"p": 0.02,
"disclosed": false,
"undisclosed": false,
"brand": null
},
"intent": {
"label": "news_or_discussion",
"confidence": 1,
"buyer": false,
"seller": 0.18,
"fits_offer": null,
"urgency": 0.26
},
"niche": {
"label": "technology",
"confidence": 1,
"taxonomy": "sc-niche-v1"
}
}
}
},
{
"post": {
"id": "7510861527219044352",
"url": "https://www.linkedin.com/feed/update/urn:li:activity:7510861527219044352",
"content": {
"text": "Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted).",
"media_urls": null,
"thumbnail_url": null,
"duration_seconds": null
},
"author": {
"username": "user_d9c51e",
"display_name": "user_f468d7",
"avatar_url": "https://example.com/avatars/user_82b488.png",
"verified": null
},
"engagement": {
"views": null,
"likes": 7,
"comments": 13,
"shares": 2,
"saves": null
},
"flags": {
"nsfw": null,
"spoiler": null,
"pinned": null,
"deleted": false
},
"published_at": "2026-09-30T00:42:13.148Z",
"ext": {
"reaction_counts": [
{
"count": 7,
"type": "LIKE"
}
],
"author_headline": "user_89064d"
}
},
"computed": {
"engagement_rate": null,
"language": "en",
"content_category": "other",
"estimated_reach": null,
"relevance": {
"p": 0.84,
"sense": "target",
"depth": 1.07,
"spam": 0.05
},
"labels": {
"sponsored": {
"p": 0.02,
"disclosed": false,
"undisclosed": false,
"brand": null
},
"intent": {
"label": "news_or_discussion",
"confidence": 1,
"buyer": false,
"seller": 0.2,
"fits_offer": null,
"urgency": 1.54
},
"niche": {
"label": "technology",
"confidence": 0.99,
"taxonomy": "sc-niche-v1"
}
}
}
}
],
"walk": {
"requested": 2,
"returned": 2,
"pages_attempted": 1,
"pages_served": 0,
"duplicates_removed": 0,
"stopped": "limit"
},
"relevance": {
"mode": "score",
"topic_source": "query",
"rows": 2,
"scored": 2,
"cached": 0,
"kept": 2,
"dropped": 0,
"dropped_ids": [],
"unjudged": 0,
"too_short": 0,
"threshold": 0.5,
"extra_credits": 0,
"methodology_version": "relevance/2",
"origin": "default",
"pending": 0,
"pending_ids": [],
"status": "complete"
},
"labels": {
"presets": [
"sponsored",
"intent"
],
"rows": 2,
"labelled": 2,
"cached": 0,
"skipped": 0,
"unjudged": 0,
"dropped": 0,
"dropped_ids": [],
"extra_credits": 0,
"methodology_version": "labels/1",
"mode": "default",
"status": "complete",
"pending": 0,
"pending_ids": []
},
"dropped": 0
},
"credits_used": 1,
"request_id": "req_example000000",
"cached": false,
"pagination": {
"next_cursor": null,
"has_more": true,
"page_size": 2
},
"credits_remaining": 9999,
"source": "captured",
"captured_at": "2026-10-02T07:31:50.586Z",
"redacted": true
}Live sample illustrating the unified response shape. Field values reflect the record you query.
API Details
How does the LinkedIn Search Posts API work?
Send a GET request with your API key and get back clean, structured JSON in our unified schema. Supported computed fields are populated when the source provides the required inputs.
Method
GET
Response
JSON
Developer First
How do you scrape social media data in seconds?
The fastest social media scraping API for developers. Scrape profiles, posts, comments, and analytics from 68 platforms covering 10B+ monthly active users.
One schema, every platform
Query 68 platforms with identical response structures. Write your integration once.
Computed fields, not just scraped
When an endpoint supports these metrics and the source provides the required inputs, the normalized record includes engagement_rate, estimated_reach, content_category, and language. Ready to use.
See your data before you code
Visual Data Explorer. Paste any URL, get rich result cards, sortable tables, CSV export.
import requests
response = requests.get(
'https://www.socialcrawl.dev/v1/tiktok/profile',
params={'handle': 'charlidamelio'},
headers={'x-api-key': 'sc_YOUR_API_KEY'}
)
data = response.json()[ .JSON ]
{
"success": true,
"platform": "tiktok",
"data": {
"author": {
"username": "charlidamelio",
"followers": 152400000
},
"engagement": {
"likes": 12400000000,
"engagement_rate": 0.087
},
"metadata": {
"language": "en",
"content_category": "lifestyle"
}
}
}FAQ
Have a question? We got answers
Find answers to frequently asked questions about SocialCrawl's API, pricing, and capabilities.
Contact usHow do I search public LinkedIn posts with an API?
Send a GET request to /v1/linkedin/search/posts with a query keyword. SocialCrawl finds public LinkedIn posts, feed updates, and Pulse articles through Google's index and returns each with author, media, like count, comment count, and published date where public.
Is this native LinkedIn search or best-effort?
It is best-effort against Google's index, not exhaustive native LinkedIn search. It surfaces public posts and Pulse articles that Google has indexed, so coverage is broad but not guaranteed to include every matching post.
What filters does LinkedIn post search support?
Beyond query, you can add date_posted, sort_by, content_type, from_company, and from_member, plus page to paginate. from_member takes a member urn (the ACoAA... value on author.ext.urn from /v1/linkedin/profile) and from_company takes a numeric company id. Pass either one on its own, with no query, to page through that subject's posts, which is how you reach posts older than the 100 that /v1/linkedin/profile/posts returns. This reads a public search index, so it is not a complete archive and how far back it reaches varies by member: walk it to find out, and do not design around a fixed number.
How much does the LinkedIn Post Search API cost?
Each page of results costs 5 credits on the advanced tier, regardless of how many posts return. New accounts get 100 free credits with no credit card, enough for 20 searches to test coverage.
What fields come back for each post?
Where the post is public, you get the description, author, media, like count, comment count, and published date. Some fields can be missing when Google's index does not expose them, so treat counts as best-effort.
How do I paginate post search results?
Add the page query parameter (integer) and increment it to load more results. Start at page 1 and continue until the response returns an empty list, which marks the end of the indexed matches.
Ask AI about SocialCrawl
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