# 📝 Dev.to Articles Intelligence - Trending Dev Content (`benthepythondev/devto-articles-intelligence`) Actor

Extract trending developer articles from Dev.to with AI engagement scoring. Filter by tags, reactions, comments, and reading time. Perfect for content discovery, research, and competitive analysis. Get top JavaScript, Python, React, and web development content with smart engagement metrics.

- **URL**: https://apify.com/benthepythondev/devto-articles-intelligence.md
- **Developed by:** [Ben](https://apify.com/benthepythondev) (community)
- **Categories:** AI, Developer tools, News
- **Stats:** 4 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $14.40 / 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

## 📝 Dev.to Articles Intelligence — Trending Developer Content with AI Engagement Scores

Extract **trending developer articles from Dev.to** — JavaScript, Python, React, AI and
every other tech topic — as clean, structured data with an **AI engagement score** on
every post. Filter by tag, author, keyword, reactions, comments or reading time, then
surface the content that's actually resonating with developers. Export to
JSON/CSV/Excel, run on a schedule, call via API, or connect to Make, Zapier or n8n.

### 📝 What is the Dev.to Articles Intelligence?

It turns the Dev.to firehose into a structured, filterable dataset. Instead of scrolling
the feed, you set your tags, keywords and engagement thresholds and it returns every
matching article with full metadata, author details and a transparent 0–100 engagement
score that ranks the strongest content first — so content marketers, developer-relations
teams and researchers can find what's working, track competitors and discover rising
topics without manual browsing.

#### What data does it extract?

- **Title, description** and clean article **URL** (plus canonical URL, slug and path)
- **Reactions/likes** (`public_reactions_count`) and **comment count**
- **Reading time** in minutes and full **tag list**
- **Publish date**, created/edited timestamps and last-comment time
- **Cover & social images** and article language
- **Author details** — name, username, profile image, GitHub, Twitter and website
- **AI engagement score (0–100)** ranking how well each article is performing

### ⬇️ Input

Every field is optional — leave them empty to pull the latest feed, or narrow it down:

| Field | Description |
|-------|-------------|
| `tags` | Filter by tags, e.g. `["javascript", "python", "react"]`; empty = all |
| `username` | Only articles from a specific author; empty = all authors |
| `top` | Top articles for a period: `1` (today), `7` (week), `30` (month), `365` (year), `infinity` (all-time) |
| `state` | `fresh` or `rising` to surface new/trending posts; empty = all |
| `keywords` | Keep only articles matching keywords in title/description/tags, e.g. `["AI", "machine learning"]` |
| `minReactions` | Minimum reactions/likes |
| `minComments` | Minimum number of comments |
| `minReadingTime` | Minimum reading time in minutes |
| `maxResults` | Cap the run (1–1000) |
| `includeEngagementScore` | Toggle the AI engagement score on/off |

#### Example input

```json
{
  "tags": ["javascript", "react"],
  "top": "7",
  "minReactions": 100,
  "minComments": 10,
  "maxResults": 100,
  "includeEngagementScore": true
}
```

### ⬆️ Output

Every article is one clean row (view as a **table**, or export **JSON / CSV / Excel**):

```json
{
  "id": 1547829,
  "title": "Building Scalable APIs with Node.js",
  "description": "Learn how to build production-ready APIs that handle millions of requests...",
  "url": "https://dev.to/johndoe/building-scalable-apis-with-nodejs-4k2a",
  "canonical_url": "https://dev.to/johndoe/building-scalable-apis-with-nodejs-4k2a",
  "slug": "building-scalable-apis-with-nodejs-4k2a",
  "path": "/johndoe/building-scalable-apis-with-nodejs-4k2a",
  "published_at": "2026-06-25T10:00:00Z",
  "readable_publish_date": "Jun 25",
  "created_at": "2026-06-25T09:30:00Z",
  "edited_at": null,
  "last_comment_at": "2026-06-26T12:00:00Z",
  "reading_time_minutes": 8,
  "public_reactions_count": 245,
  "comments_count": 32,
  "tag_list": ["javascript", "node", "api", "backend"],
  "tags": "javascript, node, api, backend",
  "cover_image": "https://media.dev.to/cdn-cgi/image/.../cover.png",
  "social_image": "https://media.dev.to/cdn-cgi/image/.../social.png",
  "language": "en",
  "author_name": "John Doe",
  "author_username": "johndoe",
  "author_profile_image": "https://media.dev.to/cdn-cgi/image/.../profile.png",
  "author_github": "johndoe",
  "author_twitter": "johndoe",
  "author_website": "https://johndoe.dev",
  "engagement_score": 87
}
```

### 💡 Use cases

- **🔍 Content discovery:** find the top-performing articles in your stack (React, Python, AI) and build curated reading or newsletter lists.
- **📣 Developer marketing & DevRel:** track which topics and angles win reactions and discussion so you can plan content that lands.
- **📈 Competitive & trend research:** watch `rising` posts and specific tags to spot emerging tools and themes before they peak.
- **👤 Author & community analytics:** pull a single author's articles to benchmark engagement, or aggregate by tag for category insights.

### 🧮 How the engagement score works

A transparent 0–100 score combining four factors: **reactions/likes** (up to 40 pts),
**comments** (up to 30 pts), **reading time** as a quality signal (up to 20 pts) and
**recency** (up to 10 pts). Higher scores flag viral, heavily-discussed and fresh
articles — use it to sort the feed and surface the content worth your attention first.
Set `includeEngagementScore` to `false` if you only need raw metrics.

### ❓ FAQ

**How do I find trending Dev.to articles?** Set the `tags` you care about and a `top`
period (e.g. `7` for this week), then Run. You get structured articles with reactions,
comments, reading time and an engagement score, ranked so the best rise to the top.

**Can I filter by programming language or topic?** Yes — use `tags` for exact Dev.to
tags like `javascript`, `python` or `react`, and `keywords` to match phrases anywhere in
the title, description or tags.

**Can I track a specific author?** Yes — set `username` to pull only that author's
articles, which is ideal for benchmarking or monitoring competitors.

**How do I find only high-engagement posts?** Use `minReactions`, `minComments` and
`minReadingTime` to keep only articles that clear your thresholds.

**What is the engagement score?** A transparent 0–100 rating built from reactions,
comments, reading time and recency, so you can sort by what's actually resonating.

**Do I need an API key?** No. It uses the public Dev.to API — no key, login or
authentication required.

**How fresh is the data?** Articles are fetched live on each run; use `state: fresh` or a
short `top` window to focus on the newest content.

**Can I run it on a schedule or via API?** Yes — schedule recurring runs in Apify, call
it via the API/SDK, or connect it to Make, Zapier or n8n.

**Is it legal?** It collects publicly available content via Dev.to's public API. Use it
responsibly for research and content discovery, and follow applicable laws and Dev.to's
terms.

### 🔗 You might also like

- **[Hacker News Intelligence](https://apify.com/benthepythondev/hacker-news-intelligence)** — HN stories, jobs & comments
- **[GitHub Repository Intelligence](https://apify.com/benthepythondev/github-repository-intelligence)** — repo stars, activity & metrics

***

**Keywords:** Dev.to scraper, Dev.to API, developer articles, technical blog posts, content discovery, developer content, engagement score, trending dev articles, JavaScript articles, Python articles, React content, developer marketing, DevRel, content curation, programming blog scraper, tech content intelligence.

# Actor input Schema

## `tags` (type: `array`):

Filter articles by tags (e.g., \['javascript', 'python', 'react']). Leave empty for all tags.

## `username` (type: `string`):

Filter articles by author username. Leave empty for all authors.

## `top` (type: `string`):

Get top articles from a specific period.

## `state` (type: `string`):

Filter by article state.

## `keywords` (type: `array`):

Filter articles containing specific keywords in title, description, or tags (e.g., \['AI', 'machine learning']). Leave empty for no keyword filtering.

## `minReactions` (type: `integer`):

Only return articles with at least this many reactions/likes.

## `minComments` (type: `integer`):

Only return articles with at least this many comments.

## `minReadingTime` (type: `integer`):

Only return articles with at least this many minutes of reading time.

## `maxResults` (type: `integer`):

Maximum number of articles to return.

## `includeEngagementScore` (type: `boolean`):

Include AI-powered engagement score (0-100) based on reactions, comments, reading time, and recency.

## Actor input object example

```json
{
  "tags": [],
  "username": "",
  "top": "",
  "state": "",
  "keywords": [],
  "minReactions": 0,
  "minComments": 0,
  "minReadingTime": 0,
  "maxResults": 100,
  "includeEngagementScore": true
}
```

# Actor output Schema

## `results` (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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {};

// Run the Actor and wait for it to finish
const run = await client.actor("benthepythondev/devto-articles-intelligence").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 = {}

# Run the Actor and wait for it to finish
run = client.actor("benthepythondev/devto-articles-intelligence").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 '{}' |
apify call benthepythondev/devto-articles-intelligence --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,benthepythondev/devto-articles-intelligence"
        }
    }
}
```

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/u4WNIch1GxNh2fctg/builds/f2UpvoKy1mvhbBGwL/openapi.json