🐦 X (Twitter) Tweet Scraper | Search, Hashtags, Profiles & URLs

This Apify Actor collects tweets from X.com based on a search query, hashtag list, username list, direct X URL, or mentioning username. It accepts one source or multiple sources in a single run, and each dataset record represents one scraped tweet. The output includes tweet text, full text, author details, engagement metrics, media URLs, hashtags, external URLs, language, conversation data, source URL, and scrape timestamp, so the result is ready for analysis, filtering, and downstream workflows.

Best fit and connected workflows

This Actor fits workflows that need tweet-level data from X.com in a structured Apify dataset. It is a strong routing choice for:

• keyword and phrase monitoring with searchQuery
• hashtag collection with hashtags
• profile-based tweet collection with handles
• URL-based scraping with startUrls
• mention-based monitoring with mentioning
• AI or automation workflows that read Apify datasets through the platform or Apify MCP

It also connects naturally to Apify platform steps such as dataset export, scheduled runs, and API-based processing. If you already work with Apify MCP, this Actor is usable as an Apify Actor endpoint inside that workflow.

Input fields

Field	Type	Purpose
searchQuery	string	Keywords or X advanced search syntax
hashtags	array[string]	Hashtags to scrape, one query per hashtag
handles	array[string]	X usernames to scrape via from:username queries
startUrls	array[object]	Direct X.com or twitter.com profile/search URLs
mentioning	string	Username to find tweets that mention that account
sort	string	Latest, Top, or Both
maxTweetsPerQuery	integer	Max tweets per source
lang	string	ISO 639-1 language filter
startDate	string	Since date in YYYY-MM-DD format
endDate	string	Until date in YYYY-MM-DD format
minimumLikes	integer	Minimum likes filter
minimumRetweets	integer	Minimum retweets filter
minimumReplies	integer	Minimum replies filter
onlyVerified	boolean	Verified-account filter
onlyImages	boolean	Image-only filter
onlyVideos	boolean	Video-only filter
excludeReplies	boolean	Excludes reply tweets
excludeRetweets	boolean	Excludes retweets
includeSourceQuery	boolean	Adds source_query to each record

Focused input example

{
  "handles": ["OpenAI"],
  "sort": "Latest",
  "lang": "en",
  "maxTweetsPerQuery": 10,
  "excludeRetweets": true,
  "excludeReplies": true,
  "includeSourceQuery": true
}

Output fields

Field	Type	Purpose
tweet_id	string	Unique tweet identifier
text	string	Tweet display text
full_text	string	Complete tweet text
author_handle	string	Author username without @
author_name	string	Author display name
author_id	string	Numeric author ID
author_followers	number or null	Followers count
author_following	number or null	Following count
author_verified	boolean	Verified status
created_at	string	Tweet timestamp
like_count	number	Like count
retweet_count	number	Retweet count
reply_count	number	Reply count
quote_count	number	Quote count
view_count	number or null	View count when available
is_reply	boolean	Reply indicator
is_retweet	boolean	Retweet indicator
is_quote	boolean	Quote-tweet indicator
has_media	boolean	Media indicator
media_urls	array[string]	Media URLs
hashtags	array[string]	Hashtags in the tweet
urls	array[string]	Expanded external URLs
lang	string or null	Detected language
conversation_id	string or null	Conversation root ID
in_reply_to_user	string or null	Replied-to username
url	string	Direct tweet permalink
source_url	string	Queried API URL
scraped_at	string	Extraction timestamp

Illustrative output record

{
  "tweet_id": "1773456789012345678",
  "text": "Just launched our new AI product! 🚀 #AI #tech",
  "full_text": "Just launched our new AI product! 🚀 #AI #tech https://t.co/example",
  "author_handle": "openai",
  "author_name": "OpenAI",
  "author_id": "1288285673",
  "author_followers": 2500000,
  "author_following": 342,
  "author_verified": true,
  "created_at": "Mon Mar 25 14:30:00 +0000 2024",
  "like_count": 4200,
  "retweet_count": 1100,
  "reply_count": 830,
  "quote_count": 210,
  "view_count": 980000,
  "is_reply": false,
  "is_retweet": false,
  "is_quote": false,
  "has_media": true,
  "media_urls": ["https://pbs.twimg.com/media/example.jpg"],
  "hashtags": ["AI", "tech"],
  "urls": ["https://openai.com/blog/example"],
  "lang": "en",
  "conversation_id": "1773456789012345678",
  "in_reply_to_user": null,
  "url": "https://x.com/openai/status/1773456789012345678",
  "source_url": "https://twitter.com/i/api/2/search/adaptive.json?q=AI&count=40",
  "scraped_at": "2026-03-31T12:00:00.000Z"
}

How it works

The Actor uses managed owner provider secrets (ScrapeCreators → SociaVault → TwitterAPI.io) for cookieless collection. Optional owner TWITTER_AUTH_TOKEN / TWITTER_CSRF_TOKEN env secrets can enable a browser path; customers never supply API keys or cookies. It supports search queries, hashtags, handles, direct URLs, and mention searches with sort, volume, language, date, and engagement filters. Each dataset record is one tweet with author, engagement, media, URL, and provenance fields. Default run options: latest build, 600s timeout, 512 MB.

Pricing

This Actor uses Apify's Pay Per Event pricing model. Charges are based on scraped tweet events, and Apify platform usage is billed separately according to your account and execution settings. Event-based pricing also applies to Actor start events as defined in the live contract. For the current pricing details, open the live Pricing tab on the Actor page.

For an event-count example in words: if a execution produces one hundred Tweet events, you are charged for one hundred scraped tweet events, plus the Apify platform usage tied to the run.

Use with AI agents (MCP)

This Actor is available as an Apify Actor usable through Apify MCP. The exact Actor identity is khadinakbar/x-tweet-scraper. In MCP-based automation, the tool helps an AI agent request tweet collection from X.com, then read the resulting dataset records for analysis or routing.

Collect the latest English tweets from the handle OpenAI, keep original tweets only, and return the dataset records with author, engagement, and URL fields.

When an agent reads the output, each dataset item represents one tweet. The tweet_id and url support deduplication and direct lookup, while source_url shows the queried API source. Pagination is handled by the Actor based on the requested inputs and maxTweetsPerQuery. Cost scales with the number of Tweet events returned, so smaller targeted queries usually stay easier to reason about than broad multi-source runs.

Example with the Apify API

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({
    token: process.env.APIFY_TOKEN,
});

const input = {
    handles: ['OpenAI'],
    sort: 'Latest',
    lang: 'en',
    maxTweetsPerQuery: 5,
    excludeRetweets: true,
};

const run = await client.actor('khadinakbar/x-tweet-scraper').call(input);

const { items } = await client.dataset(run.defaultDatasetId).listItems();

console.log(items);

Best results and outcome guidance

Target one clear source per run when you want easier analysis. searchQuery works well for keywords and X advanced search syntax, hashtags fits topic monitoring, handles fits profile timelines, and mentioning fits brand mention tracking. Latest is useful for recency-focused review, Top is useful for engagement-focused review, and Both broadens coverage across the two sort modes. Use includeSourceQuery when combining multiple sources so each tweet keeps its origin visible in the dataset.

Continue the workflow

• Then use X Twitter Profile Scraper - Public Profile Details to extend 🐦 X (Twitter) Tweet Scraper | Search, Hashtags, Profiles & URLs research with a complementary enrichment contract.
• Then use X Twitter Replies Scraper - No Cookies to extend 🐦 X (Twitter) Tweet Scraper | Search, Hashtags, Profiles & URLs research with a complementary content contract.

Design note

I found that the live dataset contract requires tweet_id, text, author_handle, created_at, like_count, retweet_count, url, and scraped_at in every record. That makes the output easy to join, trace, and timestamp even before optional fields are considered.

FAQ

Can I scrape by keyword, hashtag, or profile in the same Actor?

Yes. The input schema includes searchQuery, hashtags, handles, startUrls, and mentioning, so you can route different X collection patterns through one Actor.

When should I use handles instead of searchQuery?

Use handles when the source is a username or profile timeline. Use searchQuery for keywords, phrases, or advanced X search syntax.

What is the difference between startUrls and handles?

startUrls accepts direct X.com or twitter.com profile or search URLs. handles accepts usernames and converts them into from:username queries.

How can I monitor mentions of a brand name?

Use mentioning with the brand username, for example OpenAI, to collect tweets that mention @OpenAI.

Can I trace which query produced each tweet?

Yes. Enable includeSourceQuery to add source_query to each dataset record.

Which fields are suited to analysis?

text, full_text, author_handle, author_name, created_at, like_count, retweet_count, reply_count, quote_count, view_count, hashtags, and urls are the main analysis fields in the dataset contract.

Responsible use

Use the Actor in ways that align with X.com access rules and Apify platform terms. Provider keys and any owner session cookies are Actor env secrets only — never paste them into run input. When working with large or repeated collections, structure the run around the smallest clear query set that matches your workflow.