npm install trendflow

import { Client, Region, Timeframe, Resolution, SearchProperty, ExportFormat } from "trendflow";

// Initialize client (optional config)
const tf = new Client({ language: "en", timeout: 10_000 });

// --- Const objects for type safety ---
// Region.US, Region.GB, Region.DE ...           (or any code: "US-CA", "807")
// Timeframe.PAST_HOUR ... PAST_5_YEARS, ALL_TIME (or "2023-01-01 2023-06-30")
// Resolution.COUNTRY, Resolution.REGION, Resolution.CITY
// SearchProperty.WEB, IMAGES, NEWS, YOUTUBE, SHOPPING

// Fetch interest over time
const data = await tf.interestOverTime(
  ["Python", "JavaScript", "Rust"],
  Timeframe.PAST_YEAR,
  Region.US,
);

console.log(data.keywords);    // ["Python", "JavaScript", "Rust"]
console.log(data.granularity); // "weekly"
console.log(data.points);      // TrendPoint[] — { date: Date, scores: Record<string, number> }

// Regional breakdown (region defaults to Region.US)
const regional = await tf.interestByRegion("Python", Resolution.COUNTRY);
for (const row of regional.rows) {
  console.log(row.label, row.value);
}

// Trending searches right now (any country code, or omit for worldwide)
const trending = await tf.trendingNow(Region.US);
for (const item of trending.results) {
  console.log(item.title, item.growth, item.volume, item.traffic);
  // "fifa world cup 2026"  3650  6  "+3,650%"
}

// Related queries (region defaults to worldwide)
const related = await tf.relatedQueries("machine learning", { region: Region.GB });
for (const query of related.top) console.log(query.term, query.value);
for (const query of related.rising) console.log(query.term, query.breakout);

// --- Narrowing a query ---
// Every query method takes an optional category and search property, and any of them
// accepts a custom date range and a sub-region or metro code in place of the named values.

// "jaguar" the car, on YouTube, in California, over the first half of 2023
const jaguar = await tf.interestOverTime(["jaguar"], "2023-01-01 2023-06-30", "US-CA", {
  category: 47, // Autos & Vehicles — disambiguates without needing a topic id
  searchProperty: SearchProperty.YOUTUBE,
});

// --- Exports ---
data.toArray();  // [{ date: Date, Python: 80, ... }] — plain objects, the JS answer to DataFrames
data.toJSON();   // same rows with ISO 8601 date strings (also drives JSON.stringify)
data.toCSV();    // CSV text

// Node.js only — writes UTF-8 to disk
await data.export(ExportFormat.CSV, "trends.csv");
await data.export(ExportFormat.JSON, "trends.json");

import { TooManyRequestsError } from "trendflow";

try {
  await tf.interestOverTime(["Python"], Timeframe.PAST_YEAR, Region.US);
} catch (error) {
  if (error instanceof TooManyRequestsError) {
    // Google is rate-limiting this IP — back off and retry later.
  }
}

const rss = await tf.trendingNow(Region.US, { backend: "rss" });
rss.source; // "rss"
rss.results[0].articles;
// [{ title: "...", url: "https://...", source: "Buffalo News", picture: "https://..." }]

const topics = await tf.suggestions("artificial intelligence");
// [{ mid: "/m/0mkz", title: "Artificial intelligence", type: "Professional field" }]

const data = await tf.interestOverTime(
  [topics[0].mid, "artificial intelligence"],
  Timeframe.PAST_YEAR,
  Region.US,
);
// { "/m/0mkz": 62, "artificial intelligence": 1 }

import { Client, Region, Timeframe } from "trendflow";

const tf = new Client({
  proxies: [
    "http://user:pass@gate.decodo.com:7000",
    "http://user:pass@gate.decodo.com:7000",
  ],
  maxProxyAttempts: 3, // defaults to the pool size, capped at 5
  onProxyRotate: ({ attempt, error }) => console.warn(`rotated after ${attempt}:`, error),
});

const data = await tf.interestOverTime(["Python"], Timeframe.PAST_YEAR, Region.US);
console.log(tf.currentProxy); // the proxy that answered

const tf = new Client({
  proxies: ["http://user:pass@gate.decodo.com:7000"],
});