# 🧪 TikTok Shop Creator Commerce Evidence (`thenetaji/tiktok-shop-creator-commerce-proof-graph`) Actor

Map TikTok Shop products to public showcase videos and creators, with observed engagement, labels, thresholds, and evidence edges for practical commerce research.

- **URL**: https://apify.com/thenetaji/tiktok-shop-creator-commerce-proof-graph.md
- **Developed by:** [The Netaji](https://apify.com/thenetaji) (community)
- **Categories:** E-commerce, Social media, Marketing
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $8.00 / 1,000 product creator graphs

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## TikTok Shop Creator Commerce Proof Graph

Map a TikTok Shop product to the creators and public showcase videos visibly promoting it. Each product row groups repeated creator appearances, totals the observed plays and likes, preserves video evidence, and emits explicit product-to-creator-to-video edges.

### Accepted input

`productUrls` accepts TikTok Shop product page URLs and numeric product IDs. `region` selects the storefront used for both the product and showcase lookups. Qualification thresholds filter the creator nodes without changing the underlying evidence.

```json
{
  "productUrls": ["https://shop.tiktok.com/us/pdp/1730927783781307026"],
  "region": "US",
  "minCreatorViews": 10000,
  "minCreatorVideos": 2,
  "includeVideoRows": true,
  "maxItems": 20
}
```

### Response fields

Each row represents one submitted product:

- `product` contains the product ID, public product URL, name, seller, and shop when available.
- `promotion` contains observed creator and video counts, combined plays and likes, paid-label count, and the thresholds applied.
- `creators` contains deduplicated creator nodes with observed video totals, engagement, top video, and qualification status.
- `edges` connects the product, creator, and video using the relation `showcase_observed`.
- `analysis` reports the product-detail, showcase, and graph outcomes.
- `provenance` records the collection outcome for the row.

```json
{
  "record_type": "creator_commerce_proof",
  "product": {
    "product_id": "1730927783781307026",
    "name": "Example serum",
    "seller_id": "7495516049083828882",
    "shop_name": "Example Beauty"
  },
  "promotion": {
    "status": "observed",
    "creator_count": 2,
    "qualified_creator_count": 1,
    "video_count": 3,
    "total_views": 48200,
    "total_likes": 3100,
    "labeled_video_count": 1,
    "thresholds": {
      "min_creator_views": 10000,
      "min_creator_videos": 2
    }
  },
  "edges": [
    {
      "product_id": "1730927783781307026",
      "creator_id": "6234567890123456789",
      "video_id": "7500167942269619486",
      "relation": "showcase_observed"
    }
  ],
  "analysis": {
    "detail": { "status": "ok" },
    "showcase": { "status": "ok" },
    "graph": { "status": "complete" }
  }
}
```

### Evidence boundary

An edge indicates that a video was publicly associated with the product at collection time. It is not proof of a purchase or attributed sale. Engagement totals are the visible play and like counters on the collected videos.

Duplicate video IDs are counted once. Creator nodes are keyed by creator ID when available and otherwise by the public creator name. A product with a successful empty showcase response returns an observed graph with zero creators and zero videos; absence is not converted into a source error.

### Behaviour on partial results

If part of a product record is unavailable, available identity or showcase evidence is retained and the row remains `partial`. Unavailable promotion arrays remain empty rather than being reported as zero observed promotion.

Video IDs, creator IDs, observed counters, timestamps, and evidence edges provide the durable evidence fields in each row.

# Actor input Schema

## `productUrls` (type: `array`):

TikTok Shop product page URLs or numeric product IDs. Add one per line.

## `region` (type: `string`):

Storefront used to resolve the product and its public showcase videos.

## `minCreatorViews` (type: `integer`):

Minimum combined showcase-video plays for a creator to qualify in the graph.

## `minCreatorVideos` (type: `integer`):

Minimum observed showcase videos for a creator to qualify in the graph.

## `includeVideoRows` (type: `boolean`):

Keep the observed video records inside each creator node. Graph edges remain available when disabled.

## `maxItems` (type: `integer`):

Maximum product graphs to save across the run. Set 0 for no limit.

## Actor input object example

```json
{
  "productUrls": [
    "1730927783781307026"
  ],
  "region": "US",
  "minCreatorViews": 1000,
  "minCreatorVideos": 1,
  "includeVideoRows": true,
  "maxItems": 20
}
```

# Actor output Schema

## `dataset` (type: `string`):

All records scraped by this run

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

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

// Prepare Actor input
const input = {
    "productUrls": [
        "https://shop.tiktok.com/us/pdp/1730927783781307026"
    ],
    "region": "US",
    "minCreatorViews": 1000,
    "minCreatorVideos": 1,
    "includeVideoRows": true,
    "maxItems": 20
};

// Run the Actor and wait for it to finish
const run = await client.actor("thenetaji/tiktok-shop-creator-commerce-proof-graph").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "productUrls": ["https://shop.tiktok.com/us/pdp/1730927783781307026"],
    "region": "US",
    "minCreatorViews": 1000,
    "minCreatorVideos": 1,
    "includeVideoRows": True,
    "maxItems": 20,
}

# Run the Actor and wait for it to finish
run = client.actor("thenetaji/tiktok-shop-creator-commerce-proof-graph").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "productUrls": [
    "https://shop.tiktok.com/us/pdp/1730927783781307026"
  ],
  "region": "US",
  "minCreatorViews": 1000,
  "minCreatorVideos": 1,
  "includeVideoRows": true,
  "maxItems": 20
}' |
apify call thenetaji/tiktok-shop-creator-commerce-proof-graph --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,thenetaji/tiktok-shop-creator-commerce-proof-graph"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/JSrw9Q8dvb2Csl1V1/builds/skY7tMWGbU6l9Z05J/openapi.json