TikTok Profile Scraper with Tor Fallback

An Apify Actor that collects public profile data from TikTok accounts using Selenium (headless Chrome). If a profile fails with a direct connection, the Actor automatically retries it through the Tor network with a fresh identity.

Quick Start

Install the Apify CLI, then run the Actor locally:

$apify run

Push it to the Apify Console:

apify login # first, log in if you haven't already

apify push

Input

Field	Type	Description
users	array (required)	TikTok usernames (e.g. @tiktok or tiktok) or full profile URLs. Duplicates are removed automatically.
useTorFallback	boolean	Retry failed profiles through Tor. Default: true.

Example:

{
  "users": ["@tiktok", "https://www.tiktok.com/@charlidamelio"],
  "useTorFallback": true
}

Output

Each profile is stored as one item in the dataset:

Field	Description
id	TikTok user ID
uniqueId	Username
nickname	Display name
secUid	Secure user ID
signature	Bio text
verified	Whether the account is verified
createTime	Account creation time (Unix timestamp)
followers	Number of followers
following	Number of accounts followed
likes	Total likes received
videos	Number of videos
avatarLarger, avatarMedium, avatarThumb	Avatar image URLs

Profiles that fail on every attempt are skipped and logged; the run status message shows how many succeeded (for example, "8 of 10 profiles scraped").

How it works

1. main.py reads and normalizes the input, then starts Tor in the background (unless disabled) and waits until it is fully bootstrapped. If Tor cannot start, the Actor continues with direct connections only.
2. For each profile, headless Chrome opens the profile page and reads the embedded __UNIVERSAL_DATA_FOR_REHYDRATION__ JSON to extract the account data.
3. If the direct attempt fails (timeout, empty data, browser error), the profile is retried through Tor. Each Tor attempt uses a new browser and a new Tor identity (via the control port). The number of attempts is set by TOR_MAX_RETRIES in tiktok_scraper.py (default: 3).
4. Results are pushed to the dataset with Actor.push_data.

Project Structure

.actor/
├── actor.json            # Actor config: name, version, schema paths
├── dataset_schema.json   # Structure and table view of the output data
└── input_schema.json     # Input validation & Console form definition
my_actor/
├── __init__.py
├── __main__.py           # Entry point (python -m my_actor)
├── main.py               # Actor orchestrator
├── tiktok_scraper.py     # Selenium scraping logic with Tor fallback
└── tor_runner.py         # Starts Tor inside the container
Dockerfile                # apify/actor-python-selenium image + Tor
requirements.txt          # apify, selenium, stem

What's included

• Apify SDK - toolkit for building Actors
• Selenium - headless Chrome automation (Chrome and chromedriver come with the base image)
• Tor - fallback network for failed profiles
• Stem - Tor control library used to request a new identity
• Input schema and Dataset schema - validated input and a readable output table

Notes

• Tor exit nodes are often blocked by TikTok, so the fallback may not always succeed. For higher reliability you can pass an Apify Proxy to Chrome through --proxy-server instead.
• TikTok may change its page structure at any time, which can break data extraction.
• Only public profile data is collected. Make sure your use complies with TikTok's terms and with the laws that apply to you.

Resources

• Quick Start guide for building your first Actor

• Actor definition documentation

• Apify SDK for Python documentation

• Python tutorials in Academy directory on your local machine.

  $apify pull <ActorId>
  

Documentation reference

To learn more about Apify and Actors, take a look at the following resources:

• Apify SDK for JavaScript documentation
• Apify SDK for Python documentation
• Apify Platform documentation
• Join our developer community on Discord