trendspyg 0.9.0

Free, open-source Python library for Google Trends data: trending now plus keyword interest over time, related queries, and interest by region. A maintained pytrends alternative with CLI and API.

trendspyg

Python library for Google Trends data — real-time trending topics and keyword analysis over time (interest over time, related queries, interest by region). A modern, actively-maintained alternative to the archived pytrends.

Using this library from a coding agent? See AGENTS.md for a concise, agent-ready reference.

Installation

pip install trendspyg

# With async support
pip install trendspyg[async]

# With CLI
pip install trendspyg[cli]

# With the MCP server (use trendspyg from Claude & other AI agents; Python 3.10+)
pip install trendspyg[mcp]

# All features
pip install trendspyg[all]

Quick Start

RSS Feed (Fast - 0.2s)

from trendspyg import download_google_trends_rss

# Get current trends with news articles
trends = download_google_trends_rss(geo='US')

for trend in trends[:3]:
    print(f"{trend['trend']} - {trend['traffic']}")
    if trend['news_articles']:
        print(f"  {trend['news_articles'][0]['headline']}")

CSV Export (Comprehensive - 10s)

from trendspyg import download_google_trends_csv

# Get 480+ trends with filtering (requires Chrome)
df = download_google_trends_csv(
    geo='US',
    hours=168,            # Past 7 days
    category='sports',
    output_format='dataframe'
)

Explore — interest over time (the pytrends use case)

from trendspyg import download_google_trends_interest_over_time

# Google's 0-100 relative-interest time series for a keyword (requires Chrome)
series = download_google_trends_interest_over_time("bitcoin", geo="US", timeframe="today 12-m")
for point in series[-3:]:
    print(point["date"], point["value"])   # {'date': '2026-05-31T00:00:00+00:00', 'value': 57, 'is_partial': True}

from trendspyg import download_google_trends_explore

# Full picture in one call: interest over time + related queries + interest by region
env = download_google_trends_explore("bitcoin", geo="US")
print(env["interest_over_time"][-1])
print(env["related_queries"]["rising"][0])     # {'query': '...', 'formatted_value': 'Breakout', ...}
print(env["interest_by_region"][0])            # {'geo_code': 'US-..', 'geo_name': '..', 'value': 100}

The Explore path drives a real browser against Google's Explore page and is rate-limit sensitive (~10–90s per call, with retries). Use it for analysis, not high-frequency polling — use the RSS path for fast, frequent real-time checks.

Watch — real-time monitoring (new in 0.7.0)

from trendspyg import watch_google_trends_rss

# Stream changes between RSS snapshots (safe for continuous polling — RSS only)
for change in watch_google_trends_rss(geo="US", interval=60, events=["new", "volume_up"]):
    print(change["event"], change["keyword"], change["volume_min"])
    # {'event': 'new', 'keyword': '...', 'rank': 3, 'prev_rank': None, 'volume_min': 50000, ...}

Monitoring is built on the fast RSS path, so it is safe to poll continuously (the CSV and Explore paths are not). The pure diff_trends(old, new) helper is also exported if you manage snapshots yourself.

Async (Parallel Fetching)

import asyncio
from trendspyg import download_google_trends_rss_batch_async

async def main():
    results = await download_google_trends_rss_batch_async(
        ['US', 'GB', 'CA', 'DE', 'JP'],
        max_concurrent=5
    )
    for country, trends in results.items():
        print(f"{country}: {len(trends)} trends")

asyncio.run(main())

CLI

trendspyg rss --geo US
trendspyg csv --geo US-CA --category sports --hours 168
trendspyg explore --keyword bitcoin --output csv
trendspyg watch --geo US --interval 60 --events new,volume_up
trendspyg list --type countries

MCP server — use trendspyg from Claude & AI agents (new in 0.8.0)

Give any MCP client (Claude Desktop, Claude Code, Cursor, ...) live Google Trends tools — free, local, no API key. Requires Python 3.10+.

pip install trendspyg[mcp]

# Claude Code — one command:
claude mcp add trendspyg -- trendspyg-mcp

Claude Desktop (claude_desktop_config.json):

{
  "mcpServers": {
    "trendspyg": { "command": "trendspyg-mcp" }
  }
}

Six tools: get_trending_now, compare_trending, get_trend_changes (what changed since the last check), list_supported_options — all fast and browser-free — plus get_interest_over_time and get_trending_full (drive Chrome; slower, described honestly to the agent).

Data Sources

	RSS	CSV	Explore
Answers	"what's trending now?"	"what's trending now?"	"how is interest in X moving?"
Speed	0.2s	~10s	~10–90s (rate-limit sensitive)
Output	10–20 current trends	480+ current trends	interest over time, related queries, regions
News articles	Yes	No	No
Time filtering	No	Yes (4h/24h/48h/7d)	Yes (any timeframe)
Category filter	No	Yes (20 categories)	Yes
Requires Chrome	No	Yes	Yes

Monitoring: trendspyg watch / watch_google_trends_rss(...) polls the RSS path and streams changes (new / dropped / volume / rank) as they happen — built on RSS, so it is safe for continuous polling.

Features

• Real-time trending topics (RSS + CSV paths) and keyword analysis over time (Explore path)
• Real-time monitoring — watch streams trend changes as NDJSON (RSS-only, poll-safe)
• Interest over time, related queries, and interest by region for any keyword — the core pytrends use case
• 125 countries + 51 US states, 20 categories, 4 trending time periods (4h, 24h, 48h, 7 days)
• Output formats: dict, DataFrame, JSON, CSV (+ Parquet on the CSV path)
• Async support for parallel fetching
• Built-in caching (5-min TTL)
• Agent-ready: typed shapes, normalize=True, and a JSON-native Explore schema
• MCP server — trendspyg-mcp exposes 6 tools to Claude and any MCP client (no API key)
• CLI for terminal access

Normalized output (for agents & pipelines)

Pass normalize=True to get one unified, JSON-native schema that is identical for both the RSS and CSV paths — no need to learn two different shapes.

from trendspyg import download_google_trends_rss

env = download_google_trends_rss(geo='US', normalize=True)
# {'schema_version': '1.0', 'source': 'rss', 'geo': 'US',
#  'fetched_at': '2026-05-22T...Z', 'count': 10, 'trends': [...]}

for t in env['trends']:
    print(t['rank'], t['keyword'], t['volume_min'])  # volume_min is a real int

Every trend has a fixed, JSON-safe shape: keyword, rank, volume_text, volume_min (int), started_at / ended_at (ISO 8601 or None), is_active, related_queries (list), news (list), image, explore_url. normalize=True works on every entry point — RSS, CSV, async, and the batch functions (each geo then maps to its own envelope) — and on the CLI (trendspyg rss --geo US --normalize). It is opt-in — default output is unchanged.

Caching

from trendspyg import clear_rss_cache, get_rss_cache_stats

# Results are cached for 5 minutes by default
trends = download_google_trends_rss(geo='US')  # Network call
trends = download_google_trends_rss(geo='US')  # From cache

# Bypass cache
trends = download_google_trends_rss(geo='US', cache=False)

# Check cache stats
print(get_rss_cache_stats())

# Clear cache
clear_rss_cache()

Documentation

• API Reference
• CLI Documentation
• Coding-agent quick reference
• Changelog
• Examples

Requirements

• Python 3.8+
• Chrome browser (for the CSV and Explore paths; the RSS path needs no browser)

License

MIT License - see LICENSE for details.

Links

• GitHub
• PyPI
• Issues