Actors Scraper

A lightweight actors scraper that collects structured information about published actors and returns clean, filterable datasets. It helps teams explore actor listings, analyze usage metrics, and track performance trends without manual browsing.

     

Created by Bitbash, built to showcase our approach to Scraping and Automation!
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Introduction

This project gathers actor metadata and statistics into a structured dataset that’s easy to filter and analyze. It solves the problem of manually reviewing large actor catalogs by turning them into queryable data. It’s built for developers, analysts, and product teams who need visibility into actor ecosystems.

Structured Actor Discovery

• Collects actor profiles with titles, usernames, and descriptions
• Supports filtering by name, title keywords, and result limits
• Outputs normalized records ready for analytics or storage
• Designed to scale across hundreds or thousands of actors

Features

Feature	Description
Keyword Filtering	Narrow results by username or title for targeted discovery.
Result Limiting	Control output size to match analysis or testing needs.
Rich Metadata	Includes usage stats, categories, and descriptive fields.
Dataset Output	Returns consistent, structured records for easy reuse.

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What Data This Scraper Extracts

Field Name	Field Description
title	Human-readable title of the actor.
name	Unique identifier or slug for the actor.
username	Publisher or owner username.
description	Short summary of what the actor does.
categories	Functional categories associated with the actor.
stats_totalRuns	Total number of runs executed.
stats_totalUsers	Count of unique users.
stats_lastRunStartedAt	Timestamp of the most recent run.
pictureUrl	URL to the actor’s icon image.
currentPricingInfo	Pricing and trial configuration details.

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Example Output

[
  {
    "title": "Y Combinator Companies",
    "name": "y-combinator-companies",
    "username": "prog-party",
    "stats_totalBuilds": 11,
    "stats_totalRuns": 8,
    "stats_totalUsers": 1,
    "stats_lastRunStartedAt": "2025-03-13T21:47:39.490Z",
    "description": "Retrieves Y Combinator company data and returns it as a dataset.",
    "categories": ["AUTOMATION", "LEAD_GENERATION"],
    "pictureUrl": "https://example.com/actor-icon.png"
  }
]

---

Directory Structure Tree

Apify Actors/
├── src/
│   ├── main.py
│   ├── filters.py
│   ├── collectors/
│   │   └── actor_collector.py
│   ├── models/
│   │   └── actor_schema.py
│   └── utils/
│       └── validators.py
├── data/
│   ├── sample_input.json
│   └── sample_output.json
├── requirements.txt
└── README.md

---

Use Cases

• Product teams use it to analyze actor adoption, so they can prioritize integrations.
• Developers use it to discover relevant actors, so they can speed up implementation.
• Market researchers use it to track trends, so they can understand ecosystem growth.
• Automation builders use it to shortlist tools, so they can design workflows faster.

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FAQs

What input does this scraper require? It accepts a JSON configuration where you can define filters such as username, title keywords, and a maximum result limit.

Can I run it without filters? Yes, running it without filters returns a general listing, though applying filters is recommended for performance and relevance.

Is the output suitable for analytics pipelines? Absolutely. The structured dataset format is designed to plug directly into analytics or storage systems.

Does it support large datasets? It’s optimized to handle large result sets, with limit controls to manage performance.

---

Performance Benchmarks and Results

Primary Metric: Processes several hundred actor records per minute under normal conditions.

Reliability Metric: Consistently achieves high success rates with stable data collection across runs.

Efficiency Metric: Minimal memory footprint due to streaming-style data handling.

Quality Metric: High data completeness with consistent field coverage across records.

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