Threads Search Post Scraper

Search public Threads posts via threads.com/search and export thread-level results (with all posts in each thread) into your Apify Dataset and Key-Value Store.

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What this actor does

• Accepts:

  • text queries (single string or array)
  • optional date filters (before_date, after_date)
  • optional author filter (from_author, username without @)

• Groups results by thread:

  • one dataset item per thread (type: "thread")
  • includes root_post and full posts array for that thread

• Works only with public content, no login or cookies required

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Input

Example input

{
  "text": ["te", "hola"],
  "before_date": "2025-11-12",
  "after_date": "2025-11-01",
  "from_author": "taylorswift",
  "timeoutMs": 15000
}

Behavior

• If text is:

  • a string → one search
  • an array → one search per query value

• If text is missing or empty:

  • the actor still performs one search using only date/author filters (e.g. “all posts by this author in this time range”).

Input fields

Field	Type	Required	Notes
text	string	string[]	No
before_date	string	No	Upper date filter, passed as before_date query param (YYYY-MM-DD).
after_date	string	No	Lower date filter, passed as after_date query param (YYYY-MM-DD).
from_author	string	No	Filter by Threads username (without @).
timeoutMs	number	No	Per-request timeout (default 15000).

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Output

Dataset items

The actor produces several item types.

1) Thread items (main output)

Each search result thread becomes one item:

{
  "type": "thread",
  "thread_id": "3763948739171019822",
  "query": "te",
  "root_post": {
    "id": "3763948739171019822_63028951785",
    "pk": "3763948739171019822",
    "user": {
      "id": "63028951785",
      "username": "_sebastiansantillan",
      "full_name": "Sebastián",
      "profile_pic_url": "https://instagram.cdn.example/sample.jpg",
      "is_verified": true
    },
    "caption": {
      "text": "Hola 🫣❤️"
    },
    "code": "DQ8O2-fEWwu",
    "like_count": 133,
    "taken_at": 1762917676
    /* ...other raw fields from Threads... */
  },
  "posts": [
    {
      "id": "3763948739171019822_63028951785",
      "pk": "3763948739171019822",
      "user": {
        "id": "63028951785",
        "username": "_sebastiansantillan"
      },
      "caption": { "text": "Hola 🫣❤️" },
      "code": "DQ8O2-fEWwu",
      "like_count": 133
      /* ...same structure as root_post... */
    }
    /* replies / other posts in the same thread, if any */
  ]
}

Notes:

• thread_id is taken from the thread node or the first post in the thread.
• root_post is the first post in thread_items.
• posts includes all posts in the thread (root + replies, etc.).
• Post objects preserve most original fields from Threads.

Key-Value Store: SUMMARY.json

After each run, the actor stores a summary:

{
  "totalSearches": 2,
  "totalThreads": 10,
  "totalPosts": 25,
  "text": ["te", "hola"],
  "collectedAt": "2025-11-12T14:05:10.789Z"
}

• totalSearches – number of unique search URLs executed
• totalThreads – number of unique threads across all results
• totalPosts – number of unique posts across all threads
• text – distinct non-empty query strings used in this run

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Quick start (no code)

1. Open the actor in Apify → click Run

2. Paste your JSON input (see example above)

3. Start the run

4. When finished:

   • Open Dataset → view/export threads as JSON/CSV/XLSX
   • Open Key-Value Store → download SUMMARY.json

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API Usage

Start a run

curl -X POST "https://api.apify.com/v2/acts/trantus~threads-search-post-scraper/runs?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "text": ["te", "hola"],
    "before_date": "2025-11-12",
    "after_date": "2020-11-01",
    "from_author": "zuck",
  }'

Fetch last run dataset items

$curl "https://api.apify.com/v2/acts/trantus~threads-search-post-scraper/runs/last/dataset/items?token=YOUR_TOKEN"

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Node.js Example

import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with API token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    text: ["te", "hola"],
    before_date: "2025-11-12",
    after_date: "2020-11-01",
    from_author: "zuck"
}

(async () => {
    // Run the Actor and wait for it to finish
    const run = await client.actor("lSboJckrYDrGheK8W").call(input);

    // Fetch and print Actor results from the run's dataset (if any)
    console.log('Results from dataset');
    const { items } = await client.dataset(run.defaultDatasetId).listItems();
    items.forEach((item) => {
        console.dir(item);
    });
})();

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FAQ

Q: Is this for profiles or posts?

A: This actor is for searching posts (threads) via threads.com/search. If you need profile metadata, use a dedicated profile-scraper actor.

Q: What exactly is a “thread” item?

A: A thread is the main post plus any replies / follow-up posts grouped by thread_items. Each dataset item contains one thread with all its posts.

Q: Can I filter by author only?

A: Yes. You can omit text and pass only from_author (and optional dates); the actor will still perform a single filtered search.

Q: What date format should I use?

A: Use "YYYY-MM-DD" strings, e.g. "2025-11-12". The actor sends them as before_date / after_date query parameters.

Q: Do I need a Threads account or cookies?

A: No. The actor only uses publicly available search results.

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Changelog

• 1.0.0 – Initial release: Threads search scraper, thread-level items, summary statistics.

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Sample run (demo)

Input

{
  "text": "te",
  "before_date": "2025-11-12",
  "after_date": "2025-11-01"
}

Sample Dataset items

[
  {
    "type": "thread",
    "thread_id": "3763948739171019822",
    "query": "te",
    "root_post": {
      "id": "3763948739171019822_63028951785",
      "user": {
        "id": "63028951785",
        "username": "_sebastiansantillan"
      },
      "caption": { "text": "Hola 🫣❤️" }
    },
    "posts": [
      {
        "id": "3763948739171019822_63028951785",
        "caption": { "text": "Hola 🫣❤️" }
      }
    ]
  }
]

Sample SUMMARY.json

{
  "totalSearches": 1,
  "totalThreads": 1,
  "totalPosts": 1,
  "text": ["te"],
  "collectedAt": "2025-11-12T14:05:10Z"
}

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Happy scraping! 🚀