Scweet — Twitter/X Scraper

Extract tweets from search results and profile timelines into JSON, CSV, and XLSX. No API key, no cookies, no account setup — just configure your query and Scweet handles the rest.

Run on Apify | Open-Source Library

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What Scweet Does

• Search and profile scraping — query X/Twitter by keywords, hashtags, users, engagement, date range, location, and more, or scrape profile timelines. Run both in a single job.
• Zero configuration — no cookies, no proxies, nothing to manage. Just set your query and Scweet handles the rest.
• Deduplicated billing — you only pay for unique tweets. Every item is deduplicated before it reaches your dataset.
• Production-grade reliability — automatic retries, adaptive rate limiting, and built-in resilience keep runs stable at scale.

Use cases

Brand monitoring, lead generation, market research, academic datasets, OSINT, content strategy.

Pricing

Plan	Per 1,000 tweets	Run-start fee
Free	$3.00	$0.006
Starter	$0.30	$0.0006
Scale	$0.28	$0.0006
Business	$0.25	$0.0006

Apify provides monthly free platform credit (commonly $5/month). You only pay for unique, deduplicated tweets.

Free tier is for evaluation, not production. Higher free pricing reduces abuse and keeps paid tiers low.

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Quick Start

1. Open Scweet on Apify.
2. Paste one of the inputs below.
3. Run the Actor.
4. Export your dataset (JSON, CSV, or XLSX).

Search

{
  "source_mode": "search",
  "search_query": "bitcoin lang:en from:elonmusk -filter:replies min_faves:100",
  "since": "2025-02-17",
  "until": "2026-02-19",
  "max_items": 1000
}

Profile timeline

{
  "source_mode": "profiles",
  "profile_urls": [
    "https://x.com/elonmusk",
    "@apify"
  ],
  "max_items": 1000
}

Combined (search + profiles)

{
  "source_mode": "auto",
  "all_words": ["ai", "agent"],
  "since": "2025-02-17",
  "profile_urls": ["https://x.com/apify"],
  "search_sort": "Top",
  "max_items": 500
}

Input can be partial — omitted fields use defaults. The Apify Console opens with sample prefill values (search_query="from:elonmusk", profile_urls=["@elonmusk"]). Replace or remove them before running your own job.

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Input Reference

Source modes

Mode	Behavior
auto (default)	Runs whichever paths have input. If both search and profile inputs are present, both run.
search	Search path only. Profile input is ignored.
profiles	Profile timeline path only. Search input is ignored.

Core fields

Field	Type	Description
source_mode	string	auto, search, or profiles (default: auto)
search_query	string	Raw advanced query string. Operator reference
profile_urls	array	Handles or profile URLs (@handle, x.com/<handle>, twitter.com/<handle>)
max_items	integer	Global tweet target per run (default: 1000)
since	string	Start date or UTC timestamp
until	string	End date or UTC timestamp
search_sort	string	Top or Latest (default: Top)

Search builder fields

Instead of writing a raw search_query, you can use structured fields that Scweet combines into a query automatically:

Category	Fields
Keywords	all_words, any_words, exact_phrases, exclude_words
Users	from_users, to_users, mentioning_users
Hashtags	hashtags_any, hashtags_exclude
Language	lang (e.g. en, fr, ar)
Tweet type	tweet_type: all, originals_only, replies_only, retweets_only, exclude_replies, exclude_retweets
Filters	verified_only, blue_verified_only, has_images, has_videos, has_links, has_mentions, has_hashtags
Engagement	min_likes, min_replies, min_retweets
Location	place, geocode (lat,lon,radius), near, within

Defaults and limits

• max_items is global per run, not per profile.
• Minimum run size: if max_items < 100, it is auto-adjusted to 100.
• Unknown input keys are rejected.
• If both since and until are missing, lookback defaults to 4 years (Top) or 180 days (Latest).
• Location filters depend on X/Twitter metadata and can be approximate.
• Free plan guardrails: 1000 tweets/day, 10 runs/day, minimum 60s between runs.

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Output

Results are stored in the Apify dataset, deduplicated by tweet ID. Export as JSON, CSV, or XLSX.

Fields

Field	Description
id	Tweet ID
text	Tweet text
handle	Author handle
tweet_url	Direct link to tweet
favorite_count, retweet_count, reply_count, quote_count, bookmark_count, view_count	Engagement metrics
created_at	Tweet creation time
collected_at_utc	Collection timestamp (UTC)
lang	Language
conversation_id	Thread/conversation ID
in_reply_to_status_id, in_reply_to_user_id, in_reply_to_screen_name	Reply references
quoted_status_id	Quoted tweet ID
is_quote, is_reply	Convenience flags
source_root	search or profile_url
source_value	Effective query or normalized profile URL
user	Nested author object (handle, name, followers, bio, etc.)
tweet	Nested tweet details (media, entities, edit history, etc.)

Example (top-level fields)

{
  "id": "1996300676012376299",
  "handle": "FTB_Team",
  "text": "We dug through the first month of StoneBlock 4...",
  "favorite_count": 71,
  "retweet_count": 5,
  "reply_count": 10,
  "view_count": "9695",
  "tweet_url": "https://x.com/FTB_Team/status/1996300676012376299",
  "created_at": "Wed Dec 03 19:29:05 +0000 2025",
  "collected_at_utc": "2026-04-07T15:58:33.539740+00:00",
  "lang": "en",
  "source_root": "search",
  "source_value": "(sample OR query) lang:en",
  "is_quote": false,
  "is_reply": false,
  "user": { "handle": "FTB_Team", "name": "Feed The Beast", "followers_count": 43367, "..." : "..." },
  "tweet": { "media": ["..."], "entities": { "..." : "..." }, "..." : "..." }
}

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Tips

• Use Top sort (default). Top consistently returns more tweets than Latest for the same query and time range. Latest is useful when you need strict reverse-chronological order, but tends to return fewer total results because X's search index is less exhaustive in that mode.
• Use wider time ranges. The wider the since/until window, the more tweets X will surface. Scweet automatically splits wide ranges into parallel sub-intervals, so a large window does not slow down the run.
• Start broad, then narrow. If you get fewer tweets than expected, try removing restrictive filters (min_likes, tweet_type, lang) one at a time to see which one is limiting results.
• For large volumes from a single profile, use search instead of profile mode. Profile mode (source_mode=profiles) is best for recent activity. For thousands of tweets from a specific user, use source_mode=search with from_users: ["handle"] (or search_query: "from:handle"), a wide since/until range, and search_sort: "Latest" for more complete results.
• Date filters are handled internally. Scweet converts since and until to precise Unix-timestamp operators under the hood. You can keep using human-readable dates (e.g. "since": "2025-01-01").
• Combine structured fields with search_query. You can use search_query for advanced operators not covered by the builder fields (e.g. filter:media, -filter:replies) and add structured fields like from_users or min_likes on top — Scweet merges them into a single query.

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How It Works

Scweet uses X's internal GraphQL API — the same endpoints the X website uses. No official Twitter API key or developer account is needed.

You configure a query and a target. Scweet handles everything else — authentication, proxies, pacing, retries, and deduplication. If something fails mid-run, work is automatically retried so you get complete results without manual intervention.

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FAQ

Do I need a Twitter API key? No. Scweet uses X's internal GraphQL API — no developer account or API key required.

Do I need to provide cookies or an account? No. Account management, proxies, and rate limiting are all handled automatically.

Can I scrape private accounts? No. Only publicly visible content is accessible.

What export formats are supported? JSON, CSV, and XLSX — download directly from the Apify dataset tab.

Is there a free tier? Yes — Apify provides monthly free platform credit. The actor's free plan allows up to 1,000 tweets/day and 10 runs/day.

Power users: the full open-source Python library is at github.com/Altimis/Scweet — pip install Scweet for programmatic access, async support, resume, and full control over accounts and proxies.

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Support

For help with query tuning, limits, or workflow design, contact us on the Actor page or open an issue in the open-source repository.

Responsible usage: Use this Actor lawfully and ethically. Comply with applicable platform terms and local regulations. Scweet applies adaptive rate limiting — repeatedly running queries that return zero results will trigger progressively longer cooldowns.

Privacy: Run metadata (user ID, timestamps, input payload, counters) may be stored for rate limiting, support, and stability. This data is used internally and is not shared with third parties.