# Twitter (X) Grok AI Prompt Scraper (`fastcrawler/twitter-x-grok-ai-prompt-scraper`) Actor
Powerful API to interact with the Twitter (X) Ask Grok endpoint. Automate sending queries to Grok AI and extracting its real-time responses. Ideal for developers and businesses needing to leverage Grok's unique AI capabilities for analytics, content generation, and automated workflows.
- **URL**: https://apify.com/fastcrawler/twitter-x-grok-ai-prompt-scraper.md
- **Developed by:** [fastcrawler](https://apify.com/fastcrawler) (community)
- **Categories:** AI, Agents, Social media
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet
## Pricing
from $3.00 / 1,000 grokresults
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/platform/actors/running/actors-in-store#pay-per-event
## What's an Apify Actor?
Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases.
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.
Actors are written with capital "A".
## 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.
The best way to integrate Actors is as follows.
In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md):
```bash
npm install apify-client
```
In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md):
```bash
pip install apify-client
```
In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md):
````bash
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash
In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md).
If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md).
For usage examples, see the [API](#api) section below.
For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).
# README
## Twitter (X) Grok AI Prompt Scraper
Powerful API to interact with the Twitter (X) Ask Grok endpoint. Automate sending queries to Grok AI (`grok-3-latest`) and extracting its real-time responses. Ideal for developers and businesses needing to leverage Grok's unique AI capabilities for analytics, content generation, and automated workflows.
### 🚀 Features
- **Seamless Interaction**: Connect directly with the latest `grok-3-latest` model.
- **Conversation Continuity**: Maintain conversation context by passing a `conversation_id`.
- **Proxy Support**: Use custom proxies for reliable and secure requests.
- **Easy Integration**: Simple JSON payload structure, easy to integrate into any workflow.
### 💻 Input / 使用示例
To interact with this Actor, you need to provide the input parameters as a JSON object.
Here is a complete example of how to structure your input payload:
```json
{
"prompt": "hello",
"cookie": "auth_token=7908...",
"conversation_id": "2076320454898278729",
"proxy": "http://username:password@ip:port"
}
````
#### 📝 Parameters Explanation (参数说明)
| Parameter | Type | Required | Description |
| :--- | :--- | :---: | :--- |
| **`prompt`** | `String` | **Yes** | The text or question you want to send to Grok AI.
*(您想向 Grok AI 发送的提示词或问题。)* |
| **`cookie`** | `String` | **Yes** | Your Twitter (X) authentication cookie, essential for authorization (e.g., `auth_token=...`).
*(您的 Twitter (X) 身份验证 Cookie，主要为 auth\_token。)* |
| **`conversation_id`** | `String` | No | Include this if you want to continue an existing conversation thread with Grok.
*(如果您想继续之前的对话，请提供此对话 ID。)* |
| **`proxy`** | `String` | No | Custom proxy URL to route your requests, formatted as `http://username:password@ip:port`.
*(自定义代理地址，用于路由请求。)* |
### 🛠️ How to Run
1. Navigate to the **Input** tab in your Apify Console.
2. Enter your `prompt` and valid Twitter `cookie`.
3. (Optional) Provide a `conversation_id` or `proxy` if needed.
4. Click **Start** and wait for the Actor to extract Grok's response.
***
*Crafted by fastcrawler*
# Actor input Schema
## `prompt` (type: `string`):
The text or question you want to send to Grok AI.
## `cookie` (type: `string`):
Your Twitter (X) authentication cookie (e.g., auth\_token=7908...).
## `conversation_id` (type: `string`):
Include this if you want to continue an existing conversation thread with Grok.
## `proxy` (type: `string`):
Custom proxy URL (e.g., http://username:password@ip:port). Leave blank if not needed.
## Actor input object example
```json
{
"prompt": "hello",
"cookie": "auth_token="
}
```
# Actor output Schema
## `overview` (type: `string`):
No description
# 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 '' with your token
const client = new ApifyClient({
token: '',
});
// Prepare Actor input
const input = {
"prompt": "hello",
"cookie": "auth_token="
};
// Run the Actor and wait for it to finish
const run = await client.actor("fastcrawler/twitter-x-grok-ai-prompt-scraper").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 '' with your token.
client = ApifyClient("")
# Prepare the Actor input
run_input = {
"prompt": "hello",
"cookie": "auth_token=",
}
# Run the Actor and wait for it to finish
run = client.actor("fastcrawler/twitter-x-grok-ai-prompt-scraper").call(run_input=run_input)
# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item)
# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start
```
## CLI example
```bash
echo '{
"prompt": "hello",
"cookie": "auth_token="
}' |
apify call fastcrawler/twitter-x-grok-ai-prompt-scraper --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=fastcrawler/twitter-x-grok-ai-prompt-scraper",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "Twitter (X) Grok AI Prompt Scraper",
"description": "Powerful API to interact with the Twitter (X) Ask Grok endpoint. Automate sending queries to Grok AI and extracting its real-time responses. Ideal for developers and businesses needing to leverage Grok's unique AI capabilities for analytics, content generation, and automated workflows.",
"version": "0.0",
"x-build-id": "yP37HxpZrTT5Fonev"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/fastcrawler~twitter-x-grok-ai-prompt-scraper/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-fastcrawler-twitter-x-grok-ai-prompt-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/fastcrawler~twitter-x-grok-ai-prompt-scraper/runs": {
"post": {
"operationId": "runs-sync-fastcrawler-twitter-x-grok-ai-prompt-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/fastcrawler~twitter-x-grok-ai-prompt-scraper/run-sync": {
"post": {
"operationId": "run-sync-fastcrawler-twitter-x-grok-ai-prompt-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
}
},
"components": {
"schemas": {
"inputSchema": {
"type": "object",
"required": [
"prompt",
"cookie"
],
"properties": {
"prompt": {
"title": "Prompt",
"type": "string",
"description": "The text or question you want to send to Grok AI."
},
"cookie": {
"title": "Twitter Cookie",
"type": "string",
"description": "Your Twitter (X) authentication cookie (e.g., auth_token=7908...)."
},
"conversation_id": {
"title": "Conversation ID (Optional)",
"type": "string",
"description": "Include this if you want to continue an existing conversation thread with Grok."
},
"proxy": {
"title": "Proxy URL (Optional)",
"type": "string",
"description": "Custom proxy URL (e.g., http://username:password@ip:port). Leave blank if not needed."
}
}
},
"runsResponseSchema": {
"type": "object",
"properties": {
"data": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"actId": {
"type": "string"
},
"userId": {
"type": "string"
},
"startedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"finishedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"status": {
"type": "string",
"example": "READY"
},
"meta": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"example": "API"
},
"userAgent": {
"type": "string"
}
}
},
"stats": {
"type": "object",
"properties": {
"inputBodyLen": {
"type": "integer",
"example": 2000
},
"rebootCount": {
"type": "integer",
"example": 0
},
"restartCount": {
"type": "integer",
"example": 0
},
"resurrectCount": {
"type": "integer",
"example": 0
},
"computeUnits": {
"type": "integer",
"example": 0
}
}
},
"options": {
"type": "object",
"properties": {
"build": {
"type": "string",
"example": "latest"
},
"timeoutSecs": {
"type": "integer",
"example": 300
},
"memoryMbytes": {
"type": "integer",
"example": 1024
},
"diskMbytes": {
"type": "integer",
"example": 2048
}
}
},
"buildId": {
"type": "string"
},
"defaultKeyValueStoreId": {
"type": "string"
},
"defaultDatasetId": {
"type": "string"
},
"defaultRequestQueueId": {
"type": "string"
},
"buildNumber": {
"type": "string",
"example": "1.0.0"
},
"containerUrl": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "integer",
"example": 1
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
},
"usageTotalUsd": {
"type": "number",
"example": 0.00005
},
"usageUsd": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "number",
"example": 0.00005
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
}
}
}
}
}
}
}
}
```