# Hacker News Scraper: Stories, Comments & Users (`enezli/hacker-news-scraper`) Actor

Scrape Hacker News stories, comments and user profiles as clean JSON with points, author, timestamps and direct links. No API key required.

- **URL**: https://apify.com/enezli/hacker-news-scraper.md
- **Developed by:** [Turgay NANTA](https://apify.com/enezli) (community)
- **Categories:** Agents, MCP servers, Developer tools
- **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

Pay per event

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

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

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — 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

## Hacker News Scraper

Search Hacker News and export stories, comments and user data as clean JSON: points, author, timestamps and direct links. One click, no required fields, no LLM.

### What it does

Hacker News is where a large part of the software industry argues, launches and changes its mind in public. This actor searches **the full HN archive** — not just the front page — and returns stories or comments as clean JSON: title, external link, HN discussion link, author, points, comment count and timestamp. Search by relevance or by date, filter by score, or pull the current front page with one setting. Comments come back **with their parent story's title attached**, so a comment is never delivered without context. No API key, no login, no proxy.

**Why this one:**

- **Searches the **entire archive**, not only the front page**
- **Stories **and** comments from one actor, with parent-story context preserved**
- **Relevance or date ordering, plus a minimum-score filter**
- **Reads the open HN search index** — no API key, no login, no proxy
- **No LLM anywhere** — deterministic output, predictable costs, no hallucinated fields
- **Clean by default** — canonical URLs (tracking parameters stripped), parsed numbers, merged duplicates

### Quick start (no code)

1. Click **Try for free** / **Start** — every field has a working default, nothing is required.
2. (Optional) change **query** to what you need.
3. Open the **Dataset** tab when the run finishes → export as JSON, CSV or Excel.

### Input

| Field | Required | Default | Description |
|---|---|---|---|
| `query` | no | `artificial intelligence` | What to search for. Leave empty and set Search type to `front_page` to pull the current front page instead. |
| `searchType` | no | `story` | `story`, `comment`, `front_page`, `ask_hn`, `show_hn` or `poll`. |
| `sortByDate` | no | `false` | Off: best matches first. On: newest first — what you want for scheduled monitoring. |
| `minPoints` | no | `0` | Only return items at or above this score. 0 means no filter. |
| `maxResults` | no | `20` | Maximum clean results (capped at 500) |
| `enrich` | no | `false` | Deterministic enrichment per record — see below |
| `monitor` | no | `false` | Compare with the previous run, flag NEW records only |

Example input:

```json
{
  "query": "rust",
  "searchType": "story",
  "minPoints": 100,
  "maxResults": 50
}
```

### Output

Real example record (from a live run):

```json
{
  "id": "22238335",
  "title": "Why Discord is switching from Go to Rust",
  "url": "https://blog.discordapp.com/why-discord-is-switching-from-go-to-rust-a190bbca2b1f",
  "discussion_url": "https://news.ycombinator.com/item?id=22238335",
  "seller": "Sikul",
  "points": 1582,
  "num_comments": 642,
  "created_at": "2020-02-04T17:30:40Z",
  "tags": ["story", "author_Sikul", "story_22238335"],
  "completeness": 0.5
}
```

The final `_summary` row carries run totals (`total_clean`, `deduped`, `enriched`); in monitor mode a `_changes` row lists keys new since the last run.

**Field reference**

| Field | Meaning |
|---|---|
| `id` | Hacker News item id (stable, deduplication key) |
| `title` | Story title. On comment records this is the **parent story's** title, so comments arrive with context |
| `url` | External link when the story points off-site, otherwise the HN discussion URL — always filled |
| `discussion_url` | The HN discussion page, always present alongside the external link |
| `seller` | HN username of the author. The field is named `seller` because the shared schema maps author to that slot — the value is the account name |
| `points / num_comments` | Score and comment count at scrape time |
| `created_at / tags` | ISO timestamp and Algolia tags (`story`, `comment`, `front_page`, `author_<name>`) |
| `story_id / description` | Parent story id and the comment or post text, when the item has one |
| `price` / `price_text` | Parsed numeric value + original text, when the source publishes one |
| `completeness` | 0–1 filled-fields score (with `enrich`) |

### Use cases

- **Launch monitoring** — watch every mention of your product or competitor, sorted by date, on a schedule.
- **Developer research** — measure how a language, framework or company is discussed over years, not just today.
- **Trend detection** — pull `front_page` on a schedule and track what reaches it and how fast.
- **Sentiment and argument mining** — export the comment tree of a story and analyze it in your own stack.
- **AI agents** — let an agent answer 'what did HN say about X?' with real posts and scores via MCP.

### Enrichment (optional, charged only when it produces something)

Set `enrich: true` and every record additionally gets: e-mail addresses extracted from the description (when present), the canonical `domain` of the record's URL, and a `completeness` score (0–1, how many core fields are filled). Deterministic — the same input always yields the same output — and you are **only charged for records that actually got enriched**. Records where enrichment adds nothing are free.

### Monitor mode — change alerts on a schedule

Set `monitor: true` and the actor compares the current run with the previous one (per-actor named storage) and flags **only NEW records**. Combine with [Apify Schedules](https://docs.apify.com/platform/schedules) for a daily/hourly watch: the `_changes` summary row lists what appeared since the last run, and the `change-alert` event is charged **per new record only** — an unchanged run costs you almost nothing.

### Use it from your code

**Python**

```python
from apify_client import ApifyClient

client = ApifyClient("<YOUR_API_TOKEN>")
run = client.actor("EnezLi/hacker-news-scraper").call(run_input={  "query": "rust",  "searchType": "story",  "minPoints": 100,  "maxResults": 50 })
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)
```

**JavaScript**

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

const client = new ApifyClient({ token: '<YOUR_API_TOKEN>' });
const { defaultDatasetId } = await client.actor('EnezLi/hacker-news-scraper').call({  "query": "rust",  "searchType": "story",  "minPoints": 100,  "maxResults": 50 });
const { items } = await client.dataset(defaultDatasetId).listItems();
console.log(items);
```

**curl**

```bash
curl -X POST "https://api.apify.com/v2/acts/EnezLi~hacker-news-scraper/run-sync-get-dataset-items?token=<YOUR_API_TOKEN>" \
  -H "Content-Type: application/json" \
  -d '{  "query": "rust",  "searchType": "story",  "minPoints": 100,  "maxResults": 50 }'
```

### Use it with AI agents (MCP)

This actor is agent-ready: it appears in Apify's **AGENTS / MCP servers** catalog, so any MCP-capable assistant (Claude, custom agents, LangGraph tools) can discover and call it with a one-line tool call — zero required fields means an agent can run it safely with defaults. Connect your agent to the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp) and ask for live data in natural language.

### Pricing — Pay-Per-Event, start is free

| Event | When charged |
|---|---|
| Actor start | **Free ($0)** — try it with one click |
| `result` | Per clean result returned |
| `enrichment` | Only per record that actually got enriched |
| `change-alert` | Monitor mode: per NEW record since the previous run |

No subscription, no minimum. Volume discounts apply automatically through Apify account tiers (up to −44% on GOLD). Typical run cost example: 20 results ≈ a few cents total — you can predict your bill from the numbers above before you run.

### Is this legal?

This actor collects **publicly available data only** — the same information any visitor sees in a browser, via public endpoints. It does not bypass logins, collect private personal data, or store credentials. You are responsible for using the output in compliance with the source site's terms and the laws that apply to you (e.g. GDPR when the output contains personal data).

This Actor is not affiliated with, endorsed by, or connected to Hacker News or Y Combinator. All product names are trademarks of their respective owners.

### Support & feedback

Found a bug, need another field, or want a variant for a related platform? **Open an issue on the Issues tab** — issues are monitored and answered, and frequently-requested fields get added to the standard output. The actor is maintained as part of a scraper family built on one shared, tested core (bugs fixed once are fixed everywhere).

### Changelog

- **0.1** (2026-07) — initial public release: search, dedup, optional enrichment, monitor mode, PPE pricing.

### Limitations (honest ones)

Public Hacker News content only. Scores and comment counts are snapshots at scrape time, not live values. Deleted or flagged items are not returned, and comment threads come back as flat records rather than a nested tree.

### FAQ

**Can I get comments, not just stories?**

Yes — set Search type to `comment`. Each comment carries its parent story's title and id, so you never get a wall of contextless text.

**How do I pull the front page?**

Set Search type to `front_page` and leave the query empty. The query field is then not sent at all, so nothing narrows the result.

**Why is the author in a field called `seller`?**

All actors in this family share one normalization core, and that core maps the author role to `seller`. The value is the HN username. The dataset schema documents this explicitly so nothing is surprising downstream.

**Are points and comment counts live?**

They are the values at the moment of scraping. HN scores keep moving, so for tracking over time run on a schedule and keep the timestamps.

**Do I need an API key or account on the source platform?**

No. The actor uses public endpoints — you only need your Apify account.

**Does it use AI / an LLM?**

No. The core is fully deterministic: same input, same output, no hallucinations, no per-token costs.

**Can I run it on a schedule?**

Yes — use Apify Schedules; combine with monitor mode to pay only for what's new.

**What's the maximum number of results?**

500 per run (memory-safe cap). Run multiple queries or schedule runs for more.

**How is my bill calculated?**

Only from the events in the Pricing table — start is free, and there is no subscription.

# Actor input Schema

## `query` (type: `string`):

What to search for on Hacker News. Leave empty and set Search type to 'front\_page' to pull the current front page instead of searching.

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

Maximum number of items to return (capped at 500). Results are paginated 100 at a time.

## `enrich` (type: `boolean`):

Adds deterministic enrichment per record: extracted emails, canonical domain and a completeness score. Charged per enriched record (see Pricing).

## `monitor` (type: `boolean`):

Compares this run with the previous one and flags NEW records only. Ideal for scheduled runs — get alerted when something changes. Charged per change.

## `searchType` (type: `string`):

What to pull. 'story' returns submissions, 'comment' returns comments (each one carries its parent story's title so it is never contextless), 'front\_page' returns the current front page, and ask\_hn / show\_hn / poll return those specific post types.

## `sortByDate` (type: `boolean`):

Off: best-matching items first. On: newest first, which is what you want for scheduled monitoring runs.

## `minPoints` (type: `integer`):

Only return items at or above this score. 0 means no filter.

## Actor input object example

```json
{
  "query": "artificial intelligence",
  "maxResults": 20,
  "enrich": false,
  "monitor": false,
  "searchType": "story",
  "sortByDate": false,
  "minPoints": 0
}
```

# Actor output Schema

## `results` (type: `string`):

Clean, normalized and de-duplicated records scraped from Hacker News, as JSON. Each item follows the dataset schema (see storages.dataset.fields in actor.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 = {
    "query": "artificial intelligence"
};

// Run the Actor and wait for it to finish
const run = await client.actor("enezli/hacker-news-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 = { "query": "artificial intelligence" }

# Run the Actor and wait for it to finish
run = client.actor("enezli/hacker-news-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 '{
  "query": "artificial intelligence"
}' |
apify call enezli/hacker-news-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,enezli/hacker-news-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/wLti1hDVwN6cArmmc/builds/y0TffCeqXYYkcP6FJ/openapi.json