# News Intelligence Scraper ā€” AI Agent Real-Time News API (`logiover/news-intelligence-scraper`) Actor Multi-source real-time news aggregator for AI agents: Google News, Bing News and DuckDuckGo News merged, deduplicated, source-ranked and sentiment-scored. One topic or company to clean structured news feed. No API key, no browser. - **URL**: https://apify.com/logiover/news-intelligence-scraper.md - **Developed by:** [Logiover](https://apify.com/logiover) (community) - **Categories:** News, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $3.50 / 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/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use 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 ## News Intelligence Scraper ā€” AI Agent Real-Time News API > **Multi-source real-time news aggregator for AI agents.** Drop in a topic, company name or keyword and get back a clean, deduplicated, sentiment-scored news feed merged from Google News, Bing News and DuckDuckGo News ā€” all in one Apify Actor run. No API key, no headless browser, no per-source scrapers to maintain. Built for the new wave of **AI agents that need fresh, grounded information** ā€” analyst agents tracking a market, brand-monitoring agents watching sentiment, research agents summarizing "what's happening with X this week", and RAG pipelines that must cite current sources instead of relying on training-data knowledge with a cutoff date. --- ### šŸŽÆ What this Actor is for Large language models don't know what happened yesterday. When an AI agent is asked *"what's the latest on OpenAI?"* or *"summarize this week's electric-vehicle news"*, it needs **structured, current, multi-source news** ā€” not a single publisher's RSS or a raw HTML page to re-parse. `news-intelligence-scraper` is that real-time grounding layer: - **One topic ā†’ many sources.** A single query is fanned out to Google News, Bing News and DuckDuckGo News RSS feeds in parallel, then merged. - **Deduplicated.** The same story syndicated across outlets (or the same wire story on multiple aggregators) is collapsed into one row with a `duplicateCount` and the list of source feeds that carried it. - **Sentiment-scored.** A lightweight lexicon model tags each headline + snippet with a `-1..+1` score and a `positive`/`negative`/`neutral` label ā€” no ML dependencies, fast, and good enough for trend signals. - **Time-filtered.** Keep only the last N days; sort newest-first. - **AI-agent friendly schema.** Predictable fields, ISO dates, nullable values, per-item source attribution. Drop straight into a prompt or a vector store. - **Batch by default.** Feed 50 topics and get 50 merged feeds back in one run ā€” perfect for monitoring dashboards and trend reports. - **No keys, no browser.** Pure HTTP + RSS parsing on a small Node 20 container. Cheap, fast, resilient. --- ### āœØ Key features - **šŸŒ Three source feeds** ā€” Google News RSS, Bing News RSS, DuckDuckGo News RSS, fetched in parallel per query. Pick any subset. - **šŸ”€ Cross-source deduplication** ā€” exact key dedup (source-domain + normalized title) plus fuzzy token-Jaccard title similarity (>0.72) to catch syndicated/wire copies across outlets. - **šŸ“ˆ Sentiment scoring** ā€” AFINN-style lexicon (~250 weighted terms + negation handling) producing a normalized `-1..+1` score and `positive`/`negative`/`neutral` label on every item. - **šŸ“… Time filtering** ā€” `daysBack` keeps only items published within a window (0 = no filter). Items without a parseable date are kept (sorted last). - **šŸ·ļø Source attribution** ā€” every item carries the outlet name, the source domain, and the list of feeds (`sourceFeeds`) that surfaced it. `duplicateCount` shows how many raw copies were merged. - **šŸŒ Localization** ā€” Google News `hl`/`gl` params for language + country targeting (en-US, tr-TR, de-DE, fr-FR, ā€¦). - **šŸ“° Company mode** ā€” pass a company name or domain to track brand news specifically. - **šŸ“š Bulk mode** ā€” many topics in one run, each producing its own merged feed, tagged with the originating `query`. - **šŸŒ Proxy-aware** ā€” Apify datacenter proxy by default to avoid per-IP rate limits on news RSS endpoints. - **šŸ’° Pay-per-result** ā€” charged per saved news item, not per run. Empty results (no matches) are free. --- ### šŸ¤– Why AI agents need this News is one of the highest-value grounding tasks for agentic systems. The reasons are simple: news is **time-sensitive** (yesterday's answer is wrong today), **fragmented** (no single source has everything), and **noisy** (the same story is republished dozens of times). An agent that browses one publisher gets a biased, partial view. An agent that hits a single news API gets rate-limited or charged per call. `news-intelligence-scraper` solves all three at once: 1. **Brand & reputation monitoring.** A comms agent watches a company name across three feeds, deduplicates syndications, and surfaces the sentiment trend over 30 days. 2. **Market intelligence.** An analyst agent queries a basket of 20 industry keywords weekly and builds a sentiment-weighted news index. 3. **Event grounding.** A research agent answering *"why did X stock move?"* pulls this week's deduped news for the ticker's company, sorted by sentiment, and summarizes the negative cluster. 4. **Competitor tracking.** A GTM agent monitors competitor names and surfaces only the genuinely new items (dedup kills the wire echo chamber). 5. **RAG freshness.** A support/analyst agent embeds the latest N news items per topic into a vector store so its answers cite current events instead of stale training data. 6. **Crisis detection.** A monitoring agent runs every hour on a watchlist and alerts when the negative-sentiment item count crosses a threshold. Each of these is one Actor call (or a scheduled run). The output is a clean table of articles ready for an LLM to read, summarize, or cite. --- ### šŸ“¦ What you get (output schema) Every run streams **one news article per row** to the default dataset. An item looks like: ```json { "query": "openai", "title": "OpenAI announces new reasoning model", "url": "https://techcrunch.com/2026/07/01/openai-...", "snippet": "The company said the new model improves on... (first 500 chars)", "source": "TechCrunch", "sourceDomain": "techcrunch.com", "sourceFeeds": ["googleNews", "bingNews"], "publishedAt": "Tue, 01 Jul 2026 14:30:00 GMT", "publishedDate": "2026-07-01", "language": "en-US", "sentimentScore": 0.42, "sentimentLabel": "positive", "duplicateCount": 3, "scrapedAt": "2026-07-02T12:00:00.000Z" } ```` Use the **Overview** view to scan all items newest-first with sentiment, or the **By query** view to pivot on the originating topic. *** ### šŸš€ How to use #### 1. Aggregate news for one topic ```json { "mode": "topic", "query": "openai", "sources": ["googleNews", "bingNews", "duckduckgoNews"], "maxPerSource": 50, "maxResults": 100, "daysBack": 7, "sentiment": true } ``` #### 2. Track a company's news ```json { "mode": "company", "query": "stripe.com", "daysBack": 30, "maxResults": 200 } ``` #### 3. Bulk: many topics in one run ```json { "mode": "bulk", "queries": ["openai", "anthropic", "mistral ai", "electric vehicles", "AI regulation"], "daysBack": 7, "maxResults": 40 } ``` #### From code (Apify SDK) ```js import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: process.env.APIFY_TOKEN }); const run = await client.actor('logiover/news-intelligence-scraper').call({ mode: 'topic', query: 'openai', daysBack: 7, sentiment: true, }); const { items } = await client.dataset(run.defaultDatasetId).listItems(); const positive = items.filter(i => i.sentimentLabel === 'positive'); console.log(`${positive.length} positive items of ${items.length}`); ``` #### As an MCP tool for AI agents Wrap this Actor in an MCP server. An agent calls the tool with a topic and receives a clean, deduplicated, sentiment-tagged news feed in its context ā€” no browsing, no HTML parsing, no per-source API juggling on the agent side. *** ### šŸ”§ Input fields | Field | Type | Default | Description | |---|---|---|---| | `mode` | enum | `topic` | `topic` (one topic), `company` (company news), `bulk` (many topics). | | `query` | string | ā€” | Topic/keyword/company for `topic` & `company` modes. Quoted phrases respected. | | `queries` | array | ā€” | Topics for `bulk` mode. | | `sources` | array | all | Which feeds to aggregate: `googleNews`, `bingNews`, `duckduckgoNews`. | | `maxPerSource` | int | 50 | Cap pulled from each source per query (1ā€“200). | | `maxResults` | int | 200 | Final cap on deduplicated items saved per query (1ā€“2000). | | `daysBack` | int | 30 | Keep only items within N days. 0 = no filter (0ā€“365). | | `language` | string | `en-US` | Google News `hl` (e.g. `tr-TR`, `de-DE`). | | `country` | string | `US` | Google News `gl` (e.g. `GB`, `DE`). | | `dedup` | bool | true | Merge near-duplicates across sources (URL + title similarity). | | `sentiment` | bool | true | Run lexicon sentiment on title+snippet. | | `useApifyProxy` | bool | true | Route through Apify datacenter proxy. | *** ### šŸ§© How it works 1. **Build feed URLs.** For the query, construct the RSS URL for each enabled source: Google News (`/rss/search?q=ā€¦&hl=ā€¦&gl=ā€¦&ceid=ā€¦`), Bing News (`/news/search?q=ā€¦&format=rss`), DuckDuckGo News (`/?q=ā€¦&iar=news&format=rss`). 2. **Fetch in parallel.** All sources for one query are fetched concurrently over the Apify proxy with a browser-like User-Agent and a retry/backoff policy for transient errors. 3. **Parse RSS.** A source-agnostic regex parser extracts `` blocks and reads `title`, `link`, `description`, `pubDate`/`dc:date`, and `` (name + url). HTML entities and tags are stripped. 4. **Normalize.** Each item is mapped to a flat record with `title`, `url`, `snippet`, `source` (outlet name), `sourceDomain`, `sourceFeed`, `publishedAt`. 5. **Exact dedup.** Items are keyed by `sourceDomain + normalized-title-prefix`. Collisions merge: `duplicateCount` increments, `sourceFeeds` unions, the richer snippet/earliest date wins. 6. **Fuzzy dedup.** Across different domains, a token-Jaccard similarity on normalized titles (>0.72) collapses syndicated/wire copies (e.g. the same AP story on 12 outlets) into one row. 7. **Time filter.** If `daysBack > 0`, items with a parseable date older than the cutoff are dropped; items without a date are kept (sorted last). 8. **Sort.** Newest-first by `publishedAt`. 9. **Sentiment.** The title + snippet are tokenized; each token is looked up in the lexicon (with negation handling), and the score is normalized to `-1..+1` and bucketed into `positive`/`negative`/`neutral`. 10. **Stream.** Each item is pushed to the dataset and one `result` event is charged. *** ### šŸ’” Tips & best practices - **Use all three sources for coverage.** Google News is broadest; Bing and DuckDuckGo catch outlets Google deprioritizes. The dedup step makes more sources strictly better (up to your `maxResults`). - **Set `daysBack` for freshness.** For dashboards, 7 days; for trend reports, 30; for historical deep-dives, raise `maxResults` and widen the window. - **Bulk mode for watchlists.** Pass 20ā€“50 topics and let the Actor loop. Each topic's items are tagged with `query` so you can pivot downstream. - **Sentiment is a signal, not a verdict.** Lexicon sentiment is fast and cheap but misses sarcasm and context. Use it to *rank* and *filter*, not to make final judgments ā€” let the LLM read the items for nuance. - **Localize for non-English markets.** Set `language: "de-DE"`, `country: "DE"` for German news; `tr-TR`/`TR` for Turkish, etc. The sentiment lexicon is English-centric, so consider disabling `sentiment` for non-English or treating labels as approximate. - **Schedule recurring runs.** News changes hourly. Schedule a run every few hours over your watchlist and diff datasets to detect new items. - **Combine with related Actors.** Pair with `company-deep-research-scraper` (for company context), `discussion-intelligence-scraper` (for social/forum opinion), and `bulk-rss-feed-reader` (for direct publisher feeds). *** ### ā“ FAQ #### Does this Actor need any API keys? No. It reads public RSS feeds from Google News, Bing News and DuckDuckGo News. Just an Apify account. #### Why three sources instead of just Google News? Single-source news is biased and incomplete. Different aggregators surface different outlets and different rankings. Merging three and deduplicating gives broader coverage and a `duplicateCount` signal (how widely a story was syndicated) that's itself useful. #### How does deduplication work? Two stages: (1) exact key dedup on `source-domain + normalized-title-prefix` catches the same article republished; (2) fuzzy token-Jaccard title similarity (>0.72) catches wire/syndicated stories phrased slightly differently across outlets. `duplicateCount` records how many raw copies merged into the saved row. #### Is the sentiment accurate? It's a fast lexicon model (~250 weighted terms + negation), not a transformer. It's good for *trends and ranking* (e.g. "show me the most negative items"), less reliable on sarcasm or domain-specific jargon. For production-grade sentiment, post-process the items with an LLM. #### How far back can I get news? The RSS feeds return recent items (typically the last few days to weeks depending on the source and query volume). `daysBack` filters within that window. For months/years of history, combine with `wayback-machine-url-extractor` or a dedicated archive Actor. #### Why do some items have no `publishedDate`? Some feeds omit ``. Those items are kept (they may still be relevant) but sorted last. The `publishedAt` raw string is always preserved when available. #### Can I get the full article text? This Actor returns title + snippet (the RSS ``). For full article bodies, pass the `url` field into a content extractor like `website-text-markdown-crawler`. #### How is this priced? Pay-per-result: one `result` event per saved (deduplicated) news item. Runs that yield zero items (no matches) are free. #### Will I get rate-limited? The Actor uses the Apify datacenter proxy and polite delays. News RSS endpoints are lenient. For very high-frequency runs, lower `maxPerSource` and increase the delay between bulk queries. #### Can AI agents call this directly? Yes. Expose it through an MCP server or Apify tool integration; the agent passes a topic and gets a clean JSON news feed back. This is the primary design target. *** ### šŸ”— Related Actors - **company-deep-research-scraper** ā€” company dossier (tech stack, socials, contacts) for context. - **discussion-intelligence-scraper** ā€” Reddit + Hacker News + Product Hunt + Stack Exchange opinion. - **bulk-rss-feed-reader** ā€” read specific publisher RSS feeds directly. - **substack-newsletter-scraper** ā€” Substack newsletter posts. - **google-news-scraper** ā€” single-source Google News. - **website-text-markdown-crawler** ā€” extract full article body from a news URL. - **hacker-news-search-scraper** ā€” HN-specific search. - **reddit-subreddit-scraper** / **reddit-search-scraper** ā€” Reddit-specific. *** ### šŸ“ Changelog #### 2026-07-02 ā€” v1.0 - Initial release. - 3 modes: `topic`, `company`, `bulk`. - 3 sources: Google News, Bing News, DuckDuckGo News (any subset). - Two-stage dedup (exact key + fuzzy title Jaccard). - Lexicon sentiment (-1..+1, positive/negative/neutral). - Time filtering (`daysBack`), localization (`hl`/`gl`). - Apify datacenter proxy default. - Pay-per-result (`result` event per saved item). *** ### āš–ļø Disclaimer This Actor reads publicly available RSS feeds. It does not authenticate, bypass access controls, or scrape behind paywalls. News content is owned by the respective publishers; respect their Terms of Service. Use for monitoring, research and AI-agent grounding on data that is already public. # Actor input Schema ## `mode` (type: `string`): How to run. ā€¢ **topic** ā€” aggregate news for one or many topics/keywords (highest volume) ā€¢ **company** ā€” news about a specific company (by name or domain) ā€¢ **bulk** ā€” batch many topics into one run, each producing a merged feed ## `query` (type: `string`): Free-text query for **topic** or **company** mode, e.g. `openai`, `"electric vehicles"`, `AI regulation`. Quoted phrases are respected. ## `queries` (type: `array`): Array of topics/keywords for **bulk** mode. ## `sources` (type: `array`): Which RSS feeds to aggregate. More sources = more coverage + dedup benefit. Defaults to all. ## `maxPerSource` (type: `integer`): Cap pulled from each source per query. Higher = more coverage before dedup. ## `maxResults` (type: `integer`): Final cap on deduplicated items saved per query. ## `daysBack` (type: `integer`): Only keep items published within this many days. 0 = no time filter. ## `language` (type: `string`): BCP-47 language for Google News, e.g. `en-US`, `tr-TR`, `de-DE`, `fr-FR`. ## `country` (type: `string`): ISO 3166-1 alpha-2 country for Google News, e.g. `US`, `GB`, `DE`. ## `dedup` (type: `boolean`): Merge near-duplicate articles across sources (URL + title similarity). Recommended. ## `sentiment` (type: `boolean`): Run a lightweight lexicon sentiment score (-1..+1) on each title+snippet. Cheap, no ML deps. ## `useApifyProxy` (type: `boolean`): Route through Apify datacenter proxy. Recommended to avoid per-IP rate limits on news RSS. ## Actor input object example ```json { "mode": "topic", "query": "openai", "queries": [ "openai", "anthropic" ], "sources": [ "googleNews", "bingNews" ], "maxPerSource": 30, "maxResults": 60, "daysBack": 7, "language": "en-US", "country": "US", "dedup": true, "sentiment": true, "useApifyProxy": true } ``` # Actor output Schema ## `results` (type: `string`): Full dataset of merged, deduplicated, sentiment-scored news items. # 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 '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "mode": "topic", "query": "openai", "queries": [ "openai", "anthropic" ], "sources": [ "googleNews", "bingNews" ], "maxPerSource": 30, "maxResults": 60, "daysBack": 7, "language": "en-US", "country": "US", "dedup": true, "sentiment": true, "useApifyProxy": true }; // Run the Actor and wait for it to finish const run = await client.actor("logiover/news-intelligence-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`šŸ’¾ 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); }); // šŸ“š Want to learn more šŸ“–? Go to ā†’ 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 '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "mode": "topic", "query": "openai", "queries": [ "openai", "anthropic", ], "sources": [ "googleNews", "bingNews", ], "maxPerSource": 30, "maxResults": 60, "daysBack": 7, "language": "en-US", "country": "US", "dedup": True, "sentiment": True, "useApifyProxy": True, } # Run the Actor and wait for it to finish run = client.actor("logiover/news-intelligence-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("šŸ’¾ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # šŸ“š Want to learn more šŸ“–? Go to ā†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "mode": "topic", "query": "openai", "queries": [ "openai", "anthropic" ], "sources": [ "googleNews", "bingNews" ], "maxPerSource": 30, "maxResults": 60, "daysBack": 7, "language": "en-US", "country": "US", "dedup": true, "sentiment": true, "useApifyProxy": true }' | apify call logiover/news-intelligence-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=logiover/news-intelligence-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "News Intelligence Scraper ā€” AI Agent Real-Time News API", "description": "Multi-source real-time news aggregator for AI agents: Google News, Bing News and DuckDuckGo News merged, deduplicated, source-ranked and sentiment-scored. One topic or company to clean structured news feed. No API key, no browser.", "version": "1.0", "x-build-id": "gcbEYS10kIbYNtnGj" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/logiover~news-intelligence-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-logiover-news-intelligence-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/logiover~news-intelligence-scraper/runs": { "post": { "operationId": "runs-sync-logiover-news-intelligence-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/logiover~news-intelligence-scraper/run-sync": { "post": { "operationId": "run-sync-logiover-news-intelligence-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "mode" ], "properties": { "mode": { "title": "Mode", "enum": [ "topic", "company", "bulk" ], "type": "string", "description": "How to run.\n\nā€¢ **topic** ā€” aggregate news for one or many topics/keywords (highest volume)\nā€¢ **company** ā€” news about a specific company (by name or domain)\nā€¢ **bulk** ā€” batch many topics into one run, each producing a merged feed", "default": "topic" }, "query": { "title": "Topic / keyword (topic & company)", "type": "string", "description": "Free-text query for **topic** or **company** mode, e.g. `openai`, `\"electric vehicles\"`, `AI regulation`. Quoted phrases are respected." }, "queries": { "title": "Topics (bulk mode)", "type": "array", "description": "Array of topics/keywords for **bulk** mode.", "items": { "type": "string" }, "default": [] }, "sources": { "title": "News sources", "type": "array", "description": "Which RSS feeds to aggregate. More sources = more coverage + dedup benefit. Defaults to all.", "items": { "type": "string" }, "default": [ "googleNews", "bingNews" ] }, "maxPerSource": { "title": "Max items per source", "minimum": 1, "maximum": 200, "type": "integer", "description": "Cap pulled from each source per query. Higher = more coverage before dedup.", "default": 50 }, "maxResults": { "title": "Max results (after dedup)", "minimum": 1, "maximum": 2000, "type": "integer", "description": "Final cap on deduplicated items saved per query.", "default": 200 }, "daysBack": { "title": "Days back", "minimum": 0, "maximum": 365, "type": "integer", "description": "Only keep items published within this many days. 0 = no time filter.", "default": 30 }, "language": { "title": "Language (hl)", "type": "string", "description": "BCP-47 language for Google News, e.g. `en-US`, `tr-TR`, `de-DE`, `fr-FR`.", "default": "en-US" }, "country": { "title": "Country (gl)", "type": "string", "description": "ISO 3166-1 alpha-2 country for Google News, e.g. `US`, `GB`, `DE`.", "default": "US" }, "dedup": { "title": "Deduplicate", "type": "boolean", "description": "Merge near-duplicate articles across sources (URL + title similarity). Recommended.", "default": true }, "sentiment": { "title": "Score sentiment", "type": "boolean", "description": "Run a lightweight lexicon sentiment score (-1..+1) on each title+snippet. Cheap, no ML deps.", "default": true }, "useApifyProxy": { "title": "Use Apify datacenter proxy", "type": "boolean", "description": "Route through Apify datacenter proxy. Recommended to avoid per-IP rate limits on news RSS.", "default": true } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```