AI agents and RAG
Ground your agent in today's news.
Media and publishing
Fill apps, topic pages and related-coverage modules from thousands of sources, tagged by category and topic.
Curate coverage by beat, topic and outlet from one feed.
Newsrooms, aggregators, newsletters and publishers.
Topics and people arrive tagged, so stories file themselves.
Objectives
0/3Launch a feed without building or running crawlers.
Research background and see who else is covering it.
Pick the week's stories from one filtered query.
Add related coverage under each article.
One article
CNBCRelated coverage for one article, leaving out your own site.
Illustrative scenario
When an article goes live, the CMS searches for its key people and topics, leaves out the publisher's own domain, and shows the top results under the story.
At a glance
curl -G https://api.newsmesh.co/v1/search \ -d apiKey=$NEWSMESH_API_KEY \ --data-urlencode 'q="David Ellison" Paramount' \ -d category=entertainment \ -d excludeSources=yoursite.com \ -d limit=5
import os, requestsr = requests.get( "https://api.newsmesh.co/v1/search", params={ "apiKey": os.environ["NEWSMESH_API_KEY"], "q": '"David Ellison" Paramount', "category": "entertainment", "excludeSources": "yoursite.com", "limit": "5", },)articles = r.json()["data"]
const params = new URLSearchParams({ apiKey: process.env.NEWSMESH_API_KEY, q: '"David Ellison" Paramount', category: "entertainment", excludeSources: "yoursite.com", limit: "5",});const res = await fetch(`https://api.newsmesh.co/v1/search?${params}`);const { data } = await res.json();
{
"data": [{
"article_id": "77a23e82b24a4074eba193498e25c00f",
"title": "Here's exactly what Paramount promised Hollywood to land WB…",
"source": "CNBC",
"category": "entertainment",
"topics": ["antitrust agreements", "Hollywood"],
"people": ["David Ellison"],
"country_relevance": ["United States"],
"link": "https://www.cnbc.com/2026/09/25/paramount-hollywood…"
}],
"next_cursor": "eyJwIjoi…"
}