# Epoch Ai Notable Models Scraper (`kawsar/epoch-ai-notable-models-scraper`) Actor

Download and export notable AI models from Epoch AI into clean datasets. Filter models by organization or publication year, and easily export your data to JSON, CSV, or Excel for seamless AI research tracking

- **URL**: https://apify.com/kawsar/epoch-ai-notable-models-scraper.md
- **Developed by:** [Kawsar](https://apify.com/kawsar) (community)
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $2.99 / 1,000 results

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

## Epoch AI Notable Models Database Scraper

![Epoch AI Notable Models Database Scraper cover](https://images.bigibyte.com/cdn-cgi/image/width=1260,height=540,fit=cover,format=webp/apify-actors-covers/epoch-ai-notable-models-scraper-cover.png)

Collect notable AI models from Epoch AI into a clean, structured dataset. Extract model names, organizations, publication dates, domains, tasks, and related information effortlessly. Set your collection rules, adjust item limits, and export data in JSON, CSV, or Excel formats.

### What it does

This actor gathers Epoch AI's notable AI models and organizes each entry neatly into structured dataset rows. You can extract the full dataset or filter items by organization and publication year. Every record includes timestamp metadata to keep track of your snapshot updates over time.

### Modes

- **All notable models**: Collects all available model records up to your specified maximum limit.
- **Filter**: Keeps records matching selected organizations and optional publication year cutoffs.

### Input example

```json
{
  "mode": "all",
  "maxItems": 50,
  "requestDelaySecs": 1
}

```

Filter mode example

```json
{
  "mode": "filter",
  "organizations": ["OpenAI", "Google DeepMind"],
  "sinceYear": 2023,
  "maxItems": 100
}

```

### Output example

```json
{
  "Model": "Gemini 4 Argon",
  "Organization": "Google DeepMind",
  "Publication date": "2026-09-30",
  "Domain": "Language,Multimodal,Vision",
  "Task": "Language modeling/generation,Question answering",
  "Confidence": "Confident",
  "Link": "[https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-4-argon/](https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-4-argon/)",
  "Reference": "Gemini 4 Argon: our next era of frontier intelligence",
  "Abstract": "Gemini 4 Argon delivers frontier performance in complex workflows across real-world software engineering, enterprise knowledge work like leg…",
  "Organization categorization": "Industry",
  "Country (of organization)": "United States of America",
  "Notability criteria": "Discretionary",
  "Model accessibility": "Limited access",
  "scraped_at": "2026-10-26T00:00:00+00:00"
}

```

The output dataset provides structured details covering Model, Organization, Publication date, Domain, Task, Confidence, Link, and Timestamp.

### Use cases

- **AI Landscape Tracking**: Build and maintain an updated spreadsheet of notable AI models as new developments are documented.
- **Organization Comparison**: Filter by top AI labs like OpenAI, Google DeepMind, and Anthropic for structured side-by-side analysis.
- **Research & Intelligence**: Analyze publication trends, capability domains, and primary tasks across artificial intelligence developments.
- **Automated Data Snapshots**: Run periodic extractions to build historical datasets and record changes over time.

### FAQ

**Who is this for?**

Researchers, market analysts, and engineering teams looking for structured dataset exports on notable AI models without manual copying.

**What formats can I export?**

Download your data in JSON, CSV, Excel, or any standard dataset export format supported on the platform.

**Can I run it on an automated schedule?**

Yes. You can schedule regular runs to refresh your datasets automatically over time.

**What happens if a run encounters an error?**

The actor records an error entry describing the issue so your pipeline can handle it smoothly without breaking.

### Trademark Notice

Epoch AI and related names, logos, and trademarks belong to their respective owners. Their use in this documentation is solely to identify the subject matter of the dataset and does not imply affiliation, sponsorship, endorsement, or partnership.

### Disclaimer

This tool operates independently and is not affiliated with, endorsed by, or officially connected to Epoch AI. Users are responsible for ensuring their usage complies with relevant terms, rate limits, and licensing terms.

### Is web scraping legal?

This actor extracts publicly accessible data without bypassing security controls, paywalls, or protected systems. While collecting public web data is widely permitted, compliance depends on how data is gathered and used:

- **Personal data**: When collecting identifiers like emails, phone numbers, or social handles, ensure compliance with applicable data protection laws such as GDPR or CCPA.
- **Copyrighted content**: Public text, images, and visual assets are often protected by intellectual property rights. Avoid directly duplicating or re-distributing full creative works.
- **Platform terms & rate limits**: Practice responsible data extraction by avoiding aggressive traffic spikes, adhering to rate limits, and respecting published platform policies.
- **Disclaimer**: This summary serves informational purposes only and does not constitute formal legal counsel. Seek professional legal advice for specific compliance queries.

For a detailed guide, see Apify's legal overview: [Is web scraping legal?](https://blog.apify.com/is-web-scraping-legal/)

# Actor input Schema

## `mode` (type: `string`):

What to scrape. All downloads every model. Filter keeps rows by organization and year.

## `organizations` (type: `array`):

Organization names to keep in filter mode. Matching is case-insensitive. Leave empty to skip this filter.

## `sinceYear` (type: `integer`):

In filter mode, keep models published from this year onward. Leave empty to skip.

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

Stop after this many successful rows. Hard max is 1000.

## `requestDelaySecs` (type: `number`):

Seconds to wait between requests.

## `userAgent` (type: `string`):

User-Agent header sent with each request.

## `timeoutSecs` (type: `integer`):

Overall run budget in seconds.

## `requestTimeoutSecs` (type: `integer`):

Per-request timeout in seconds.

## `proxyConfiguration` (type: `object`):

Optional proxy. Leave off unless you need it.

## Actor input object example

```json
{
  "mode": "all",
  "organizations": [],
  "maxItems": 100,
  "requestDelaySecs": 1,
  "userAgent": "ActorResearch/1.0",
  "timeoutSecs": 300,
  "requestTimeoutSecs": 30,
  "proxyConfiguration": {
    "useApifyProxy": false
  }
}
```

# Actor output Schema

## `overview` (type: `string`):

Dataset rows in the overview table.

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

Full dataset for this run.

## `items` (type: `string`):

Dataset items as JSON.

# 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 = {
    "organizations": []
};

// Run the Actor and wait for it to finish
const run = await client.actor("kawsar/epoch-ai-notable-models-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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "organizations": [] }

# Run the Actor and wait for it to finish
run = client.actor("kawsar/epoch-ai-notable-models-scraper").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 '{
  "organizations": []
}' |
apify call kawsar/epoch-ai-notable-models-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,kawsar/epoch-ai-notable-models-scraper"
        }
    }
}
```

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/LAAPqywKYU9kYSJ2d/builds/oXxrePHgPxbyGmHui/openapi.json