# Hacker News Search Scraper (`zerobreak/hacker-news-search-scraper`) Actor

Hacker News Search Scraper is a Python tool designed to extract post titles, URLs, author metadata, comment counts, timestamps, and scores from Hacker News search results. It exports structured tech community datasets into clean CSV or JSON formats for trend analysis and research.

- **URL**: https://apify.com/zerobreak/hacker-news-search-scraper.md
- **Developed by:** [ZeroBreak](https://apify.com/zerobreak) (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

### Hacker News Search Scraper — Story Discovery, Comments, and Engagement Metrics

![Hacker News Search Scraper cover](https://images.bigibyte.com/cdn-cgi/image/width=1260,height=540,fit=cover,format=webp/apify-actors-covers/hacker-news-search-scraper-cover.png)

### Overview

Collect Hacker News stories and comments matching your search terms. Choose relevance-based or date-based ordering, refine results with tags and numeric filters, and export structured datasets in JSON, CSV, Excel, or other supported formats.

Each record can include the item title, author, points, comment count, timestamps, tags, and related story information when available.

### Features

- **Keyword search:** Find Hacker News content matching your query.
- **Relevance sorting:** Discover results ranked by search relevance.
- **Date sorting:** Retrieve newer results first.
- **Content filtering:** Narrow results by tags such as stories or comments.
- **Engagement metrics:** Collect points and comment counts when available.
- **Configurable collection:** Set a maximum number of records.
- **Structured exports:** Save search results for analysis and reporting.
- **Scheduled collection:** Refresh search datasets periodically using Apify scheduling.

### Collection Modes

#### 1. Search by Relevance

Find stories and comments ranked according to their relevance to the search query.

#### 2. Search by Date

Retrieve matching stories and comments ordered by date, with newer items appearing first.

### Input Parameters

| Parameter | Type | Default | Description |
|---|---|---|---|
| `mode` | String | `search` | Selects relevance-based search or date-based search. |
| `query` | String | — | Keyword or phrase to search for. |
| `tags` | String | — | Optional content filter, such as `story` or `comment`. |
| `numericFilters` | String | — | Optional numeric conditions, such as `points>100`, when supported by the actor input. |
| `maxItems` | Integer | `100` | Maximum number of records to collect. |
| `requestDelaySecs` | Number | `1` | Configured delay between requests, where applicable. |
| `requestTimeoutSecs` | Integer | `30` | Maximum waiting time for a request, in seconds. |

### Example Input

```json id="p8w5xa"
{
  "mode": "search",
  "query": "python",
  "tags": "story",
  "maxItems": 10,
  "requestDelaySecs": 1,
  "requestTimeoutSecs": 30
}
```

This configuration searches for Python-related stories and collects up to 10 matching records.

### What Data Does This Actor Extract?

The actor collects searchable Hacker News items, including story and comment details, engagement metrics, tags, and timestamps where available.

### Extracted Data Fields

| Field | Description |
|---|---|
| `objectID` | Unique identifier for the search result. |
| `storyTitle` | Title associated with the story, when available. |
| `author` | Username of the item's author. |
| `points` | Points associated with the item. |
| `numComments` | Number of comments associated with the story, when available. |
| `tagsList` | List of tags associated with the result. |
| `children` | Related comment identifiers, when available. |
| `createdAt` | Creation date and time. |
| `createdAtI` | Creation timestamp in Unix format. |
| `storyId` | Identifier of the associated story, when available. |
| `updatedAt` | Last update timestamp, when available. |
| `url` | Link associated with the story, when available. |
| `scrapedAt` | Date and time when the record was collected. |

Some fields may be absent depending on whether a result represents a story, comment, or another item type. The original title field is represented as `storyTitle` in the documented dataset structure.

### Example Output

```json id="r6v9kd"
{
  "tagsList": [
    "story",
    "author_trotterdylan",
    "story_13319904"
  ],
  "author": "trotterdylan",
  "children": [
    13320144,
    13320174
  ],
  "createdAt": "2017-01-04T17:00:39Z",
  "createdAtI": 1483549239,
  "numComments": 451,
  "objectID": "13319904",
  "points": 1411,
  "storyId": 13319904,
  "storyTitle": "Grumpy: Go running Python",
  "updatedAt": "2026-06-14T03:38:24Z",
  "scrapedAt": "2026-10-26T00:00:00+00:00"
}
```

This example illustrates the record structure. Actual values and available fields vary by search result.

### Use Cases

- **Topic research:** Find discussions and stories related to programming, startups, AI, and technology.
- **Trend monitoring:** Track newly published stories matching selected keywords.
- **Engagement analysis:** Identify stories with high point totals or comment counts.
- **Dataset building:** Export searchable Hacker News content for offline analysis.
- **Community research:** Explore topics and discussions across stories and comments.
- **Content discovery:** Find relevant technical discussions and interesting articles.

### Working with Your Dataset

After the run finishes, open your dataset in Apify to inspect the collected records or export them.

- **JSON:** Use structured results in data-processing applications.
- **CSV:** Filter and analyze search results in spreadsheet software.
- **Excel:** Build summaries, charts, and engagement reports.
- **Other supported formats:** Choose from the dataset export formats available in Apify.

### Important Notes

- Search results depend on the query, selected mode, and optional filters.
- The `maxItems` setting limits the number of records collected.
- Some fields are specific to stories and may not be present for comments.
- Story and comment counts can change over time.
- Search results represent the data available when the actor runs.

### FAQ

**Who is this actor for?**

It is designed for developers, analysts, researchers, and technology enthusiasts who want searchable Hacker News data in a structured dataset.

**What is the difference between the search modes?**

Relevance-based search prioritizes results according to their relevance to your query. Date-based search orders matching results with newer items first.

**Can I search only for stories or comments?**

Yes. Use the `tags` parameter to narrow the results to supported content types, such as `story` or `comment`.

**Can I filter by points?**

You can use supported numeric filters, such as `points>100`, to focus on items meeting a points threshold.

**What export formats are available?**

You can export collected records as JSON, CSV, Excel, and other formats supported by Apify datasets.

**Can I schedule recurring searches?**

Yes. Configure an Apify schedule to refresh your search results at your preferred interval.

### Disclaimer

This actor is an independent tool and is not affiliated with, endorsed by, sponsored by, or officially associated with Hacker News, Y Combinator, or Algolia. The collected information is provided for research and informational purposes. Users are responsible for ensuring that their collection and use of the data comply with applicable laws and relevant terms.

### Is Web Scraping Legal?

Collecting information that is already public is generally legal. There is no single law that bans all forms of web scraping. The limits are the usual ones: do not break into accounts, do not take private or confidential data, and be careful with personal data and copyrighted works. A site's terms can also restrict how you access it. Facts such as a public name, price, or rating are not the same thing as republishing someone's article or photo.

This actor collects information from public pages. It does not log in and it does not bypass access controls.

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

# Actor input Schema

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

What to scrape from Hacker News Algolia endpoints.

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

Search text.

## `tags` (type: `string`):

Algolia tags filter, for example story, comment, ask_hn, show_hn, or author_x.

## `numericFilters` (type: `string`):

Optional Algolia numericFilters, for example points>100 or created_at_i>1700000000.

## `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": "search",
  "query": "python",
  "tags": "story",
  "numericFilters": "",
  "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 = {};

// Run the Actor and wait for it to finish
const run = await client.actor("zerobreak/hacker-news-search-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 = {}

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

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,zerobreak/hacker-news-search-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/te7Db2j8OjMleqbSK/builds/bnUDb3sD0gGCBbgMP/openapi.json