# News Stories & Trends for AI Agents: Deduplicated, Multilingual (`nacenempresas/news-stories-for-ai`) Actor

News stories, not duplicate headlines: each story groups every outlet covering it (289 outlets, 55 countries, multilingual), with trends and spike detection. Incremental mode returns only new or growing stories. Built for AI agents and MCP.

- **URL**: https://apify.com/nacenempresas/news-stories-for-ai.md
- **Developed by:** [NacenEmpresas](https://apify.com/nacenempresas) (community)
- **Categories:** AI, News, Agents
- **Security:** Limited permissions
- **Stats:** 2 total users, 1 monthly users, 100.0% 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?

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

## News Stories & Trends for AI Agents — Deduplicated, Multilingual

**Stop paying for 40 copies of the same headline.** This Actor returns **stories**: every news event grouped with *all* the outlets that cover it, in **65 languages**, with **coverage trends, tone and spike detection** — in a short, stable JSON format designed for AI agents, MCP tools, PR monitoring and market/risk analysts.

- 🧩 **One story, not 40 headlines** — articles about the same event are merged across outlets *and languages*, so your agent reads less duplicated text and spends fewer tokens.
- 🌍 **Truly multilingual** — 289 outlets from 55 countries in 26 languages (RSS feeds + 48-hour news sitemaps), multilingual topic matching ("artificial intelligence" also finds *inteligencia artificial*, *intelligence artificielle*…), plus the GDELT global news index (65 languages) when it responds.
- 📈 **Trends included** — articles and outlets per hour/day for each topic, % change vs. baseline, a spike flag and GDELT tone when available.
- 🔁 **Only what's new** — in incremental mode, scheduled runs return (and charge) only stories that are new or were picked up by more outlets since the last run.
- 🤖 **Agent-ready** — compact mode (~150 words per story), extractive summary by default, optional written summary with *your own* OpenAI/Anthropic key. Works out of the box as an **MCP tool** via Apify's MCP server.
- 💸 **$1.50 per 1,000 stories** — and each story contains every article about it.

### What you get

One row per story:

```json
{
  "type": "story",
  "storyId": "3f1c9a0d2b7e4a11",
  "topic": "artificial intelligence",
  "headline": "EU regulators issue first fines under the AI Act",
  "summary": "Regulators in Brussels have issued the first penalties under the AI Act, Europe's landmark artificial intelligence regulation.",
  "firstSeen": "2026-11-05T06:10:00Z",
  "lastSeen": "2026-11-05T11:40:00Z",
  "articleCount": 37,
  "sourceCount": 29,
  "languages": ["en", "fr", "de", "es", "it"],
  "countries": ["US", "FR", "DE", "ES"],
  "tone": -2.1,
  "trend": "rising",
  "articles": [
    {"title": "EU regulators issue first fines under the AI Act", "source": "politico.eu", "url": "https://…", "publishedAt": "2026-11-05T06:10:00Z", "language": "en"},
    {"title": "L'UE inflige les premières amendes au titre de l'AI Act", "source": "lemonde.fr", "url": "https://…", "publishedAt": "2026-11-05T07:02:00Z", "language": "fr"}
  ],
  "change": "new",
  "attribution": "GDELT Project (https://www.gdeltproject.org/)"
}
```

Plus one **trend** row per topic:

```json
{"type": "trend", "topic": "artificial intelligence", "interval": "hour", "totalArticles": 642, "totalOutlets": 187,
 "trend": "rising", "changePct": 78, "spike": false, "avgTone": null,
 "points": [{"t": "2026-11-05T10:00:00Z", "articles": 41, "outlets": 33}, …]}
```

- `trend` (story): `rising` / `stable` / `fading` / `new`, based on how coverage is distributed over the time window.
- `tone`: GDELT tone (negative ≈ negative coverage, positive ≈ positive), `null` when unknown.
- `change`: `new` or `grown` (incremental mode); `previousSourceCount` is added for grown stories.

### Use cases

- **AI agents & assistants** — give your agent a fresh, deduplicated news briefing on any topic in one tool call (MCP or API). Fewer tokens, more signal.
- **PR & communications** — track a brand, CEO or product across 65 languages; schedule hourly with incremental mode and get alerts only when a story is new or spreading.
- **Market, risk & policy analysts** — detect coverage spikes and tone shifts on companies, sectors, countries or regulations.
- **Newsletters & dashboards** — feed "top stories of the day" by outlet count, not by click-bait.

### How to use

1. Enter one or more **topics** (in English; multi-word topics are matched as exact phrases; GDELT boolean syntax like `(openai OR anthropic)` works).
2. Pick a **time window** (`1h`, `24h`, `7d`, up to `3m`).
3. Run. That's it — defaults return stories covered by at least 2 outlets, with trends.

**Scheduled monitoring:** turn on *Only new or growing stories*, create a Schedule (e.g. every hour) and add an integration (Slack, email, webhook, Make, Zapier, n8n). You're only charged for stories you haven't received yet.

**As an MCP tool:** add this Actor to Apify's MCP server (`https://mcp.apify.com`) and your agent (Claude, ChatGPT, Cursor, LangChain…) can call it with `{"topics": ["…"], "timespan": "24h", "compact": true}`.

**API example (Python):**

```python
from apify_client import ApifyClient
client = ApifyClient("<YOUR_APIFY_TOKEN>")
run = client.actor("nacenempresas/news-stories-for-ai").call(run_input={
    "topics": ["nvidia", "\"AI Act\""], "timespan": "24h", "minSources": 3, "compact": True})
for story in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(story.get("sourceCount"), story.get("headline"))
```

### Input highlights

| Field | Default | What it does |
|---|---|---|
| `topics` | `["artificial intelligence"]` | Topics, brands, public figures |
| `timespan` | `24h` | Window back in time (15min – 3 months) |
| `minSources` | `2` | Minimum outlets per story |
| `incremental` | `false` | Only new / growing stories since last run |
| `trends` | `true` | Volume & tone series per topic |
| `compact` | `false` | Short rows for LLM context windows |
| `languages`, `countries`, `domains`, `excludeDomains` | all | Filters |
| `sources` | `gdelt`, `rss` | Add `googlenews` (off by default) |
| `customFeeds` | – | Your own RSS/Atom feeds |
| `llmApiKey` | – | Your OpenAI/Anthropic key for written summaries (optional) |

### Pricing

Pay per event — no subscription:

| Event | Price |
|---|---|
| Story delivered | **$1.50 / 1,000 stories** |
| Trend series (per topic) | $1.00 / 1,000 |
| Run start | $0.01 |

A daily briefing of 50 stories costs about **$0.085**. In incremental mode, unchanged stories are free. Your maximum cost per run is always capped by the limit you set in Apify.

### Data sources & attribution

- **GDELT Project** DOC 2.0 API — global news monitoring in 65 languages, used as an enrichment (extra outlets, tone) when it responds. Used under GDELT's open terms; rows that use it carry attribution: *GDELT Project, https://www.gdeltproject.org/*.
- **Publisher RSS/Atom feeds and Google-News-style news sitemaps** — 289 outlets, 55 countries, auto-discovered and re-validated every month.
- **Google News RSS** — optional and off by default; enable it under your own responsibility.

The Actor delivers **headlines, a short snippet, source, date and link only** — never full article text. Grouping runs on a small multilingual model inside the Actor; no third-party AI service is called unless you add your own key.

### FAQ

**How accurate is the grouping?** On our labelled multilingual test set (same event reported in up to 5 languages) grouping scores 96.7% (B-cubed F1), using a fast multilingual model that runs inside the Actor. Use `similarityThreshold` to make it stricter (e.g. 0.6) or looser (e.g. 0.45).

**Why fewer rows than a headline scraper?** Because 30 articles about one event become 1 story with 30 sources. That's the point.

**Is it real-time?** Feeds and sitemaps are read live at every run (typical run: under a minute).

**Personal data?** Only public news headlines are processed. Nothing is stored outside your own Apify storage.

Questions or feature requests? Open an issue on the Actor's **Issues** tab — we answer quickly.

# Actor input Schema

## `topics` (type: `array`):

Topics, companies, products or public figures to follow. Write them in English: GDELT finds matching coverage in 65 languages. Multi-word topics are searched as exact phrases; you can also use GDELT syntax such as (openai OR anthropic).

## `timespan` (type: `string`):

How far back to look: 15min to 3 months, e.g. 1h, 24h, 7d, 2w, 3m.

## `minSources` (type: `integer`):

Only return stories covered by at least this many different outlets. 1 returns every article group; 3+ keeps only widely covered news.

## `maxStoriesPerTopic` (type: `integer`):

Upper bound of stories returned (and charged) per topic, ranked by number of outlets.

## `incremental` (type: `boolean`):

Remembers stories delivered in previous runs and returns (and charges) only stories that are new or picked up by more outlets since. Ideal for hourly/daily schedules and alerts.

## `trends` (type: `boolean`):

Adds one trend row per topic: article volume and average tone per hour/day, % change and a spike flag.

## `compact` (type: `boolean`):

Shorter rows (about 150 words per story, max 5 sources) to save tokens in agent pipelines.

## `languages` (type: `array`):

ISO 639-1 codes (en, es, de, fr, zh…). Empty = all 65 languages.

## `countries` (type: `array`):

ISO 3166 country codes of the outlets (US, GB, ES, DE…). Empty = all countries.

## `domains` (type: `array`):

Restrict to these domains, e.g. reuters.com, bbc.co.uk.

## `excludeDomains` (type: `array`):

Domains to ignore.

## `sources` (type: `array`):

gdelt = GDELT global news index (65 languages), rss = ~400 publisher RSS feeds and news sitemaps from 40+ countries, googlenews = Google News RSS search (off by default; you enable it under your own responsibility).

## `customFeeds` (type: `array`):

Your own feed URLs to include (blogs, trade press, press rooms).

## `maxArticlesPerStory` (type: `integer`):

One article per outlet, earliest first.

## `clustering` (type: `string`):

multilingual = groups the same event across languages (default). fast = same-language grouping, quicker.

## `semanticMatching` (type: `boolean`):

Also finds non-English headlines about your topic by meaning (e.g. 'inteligencia artificial', 'KI'), not only by keywords.

## `topicSimilarity` (type: `string`):

Leave empty for the tuned default. Higher = only headlines very clearly about the topic.

## `similarityThreshold` (type: `string`):

Leave empty for the tuned default. Higher = stricter grouping (more, smaller stories).

## `preferLanguage` (type: `string`):

ISO code, e.g. en. If a story has an article in this language, its title becomes the headline.

## `minGrowth` (type: `integer`):

In incremental mode, a story already delivered is sent (and charged) again only when at least this many new outlets have picked it up.

## `stateStoreName` (type: `string`):

Named key-value store used to remember delivered stories. Use different names for independent schedules.

## `llmApiKey` (type: `string`):

If set, each story gets a written 1-2 sentence summary generated with your own key. Without it, summaries are extractive (free).

## `llmProvider` (type: `string`):

Which provider your API key belongs to. auto detects it from the key format.

## `llmModel` (type: `string`):

Optional model id. Defaults to a small, cheap model of the provider.

## `summaryLanguage` (type: `string`):

Language for the written summaries (only used with your own LLM API key).

## Actor input object example

```json
{
  "topics": [
    "artificial intelligence"
  ],
  "timespan": "24h",
  "minSources": 2,
  "maxStoriesPerTopic": 50,
  "incremental": false,
  "trends": true,
  "compact": false,
  "sources": [
    "gdelt",
    "rss"
  ],
  "maxArticlesPerStory": 10,
  "clustering": "multilingual",
  "semanticMatching": true,
  "minGrowth": 1,
  "stateStoreName": "news-stories-state",
  "llmProvider": "auto",
  "summaryLanguage": "English"
}
```

# Actor output Schema

## `stories` (type: `string`):

One row per story: headline, summary, outlets, languages, trend, tone and source articles. Trend rows have type=trend.

## `trends` (type: `string`):

Coverage volume per hour/day for each topic, % change and spike flag.

## `runSummary` (type: `string`):

Run statistics (articles read, stories, warnings) and trend series.

# 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 = {
    "topics": [
        "artificial intelligence"
    ],
    "timespan": "24h"
};

// Run the Actor and wait for it to finish
const run = await client.actor("nacenempresas/news-stories-for-ai").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 = {
    "topics": ["artificial intelligence"],
    "timespan": "24h",
}

# Run the Actor and wait for it to finish
run = client.actor("nacenempresas/news-stories-for-ai").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 '{
  "topics": [
    "artificial intelligence"
  ],
  "timespan": "24h"
}' |
apify call nacenempresas/news-stories-for-ai --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,nacenempresas/news-stories-for-ai"
        }
    }
}
```

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/zKWgqWfOHaagA3gTt/builds/xhcU2fl5s72Qd2NrS/openapi.json