Google Trends Tracker — Free & Open Source

Free & open-source Google Trends monitor. Track keyword interest over time, get daily snapshots, spot rising and falling trends — powered by the Pangolinfo SERP / Google Trends API.

Part of the Pangolinfo open-source ecosystem. A tiny Python tool (zero dependencies) that watches a list of keywords and records their Google Trends relative-interest score (0–100) every day with GitHub Actions. Perfect for SEO research, content planning, product trend monitoring, and keyword trend monitoring without paying for an enterprise tool.

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Table of contents

• Why
• Features
• Architecture
• Quick start
• Commands
• Configuration
• How it works
• 🌐 Pangolinfo ecosystem
• FAQ
• Roadmap

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Why

• 🔎 Google Trends API in Python — no browser automation, no scraping. One call returns the full interest-over-time timeline.
• 📈 Daily automated snapshots — GitHub Actions runs every day and commits the latest values, so your repo becomes a living trend dataset.
• ▲▼ Rising / falling detection — each run computes the delta vs the previous point and flags direction.
• 🧩 Sparklines — the Markdown report renders a 14-day ASCII sparkline per keyword.
• 🆓 Free tier — 200 free API calls at tool.pangolinfo.com. No credit card to start.

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Features

• 🌍 Multi-region / multi-language — track US, GB, DE… in en-US, en-GB…
• 📊 Interest-over-time — relative score 0–100 stored per day
• ▲▼ Direction flags — rising / falling / flat vs the previous snapshot
• 📉 Sparklines — 14-day ASCII trend in reports/latest.md
• 🗄️ SQLite history — query trends over any window
• 🤖 Free daily automation — GitHub Actions commits the report every day
• 🧩 Zero dependencies — Python standard library only

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Architecture

        ┌─────────────────────────────────────────────┐
        │  google-trends-tracker (this repo)           │
        │  google_trends_tracker.py · SQLite           │
        └───────────────────┬─────────────────────────┘
                            │  streamable-HTTP (MCP)
                            │  tools/call → keyword_trends
                            ▼
        ┌─────────────────────────────────────────────┐
        │   Pangolinfo MCP server                       │
        │   mcp.pangolinfo.com/mcp  (Bearer JWT)        │
        └───────────────────┬─────────────────────────┘
                            │  proxy → Google Trends
                            ▼
              Google Trends interest timeline (JSON)

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Quick start

# 1. Clone
git clone https://github.com/pangolinfoapi/google-trends-tracker.git
cd google-trends-tracker

# 2. Add your free API key (no secrets in the repo — it's an env var)
export PANGOLIN_TOKEN="your-free-key-from-tool.pangolinfo.com"

# 3. (optional) edit keywords.json to track the keywords you care about
# 4. Run
python google_trends_tracker.py run
python google_trends_tracker.py report

The bundled keywords.json already tracks a few demo keywords so the tool works out of the box — replace them with your own.

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Commands

Command	What it does
python google_trends_tracker.py init	Create keywords.json from the example file
python google_trends_tracker.py run	Fetch trends for every keyword group and store snapshots
python google_trends_tracker.py history	Print tracked keywords and their latest values
python google_trends_tracker.py report	Generate reports/latest.md with deltas + sparklines

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Configuration (keywords.json)

[
  {
    "label": "kitchen",
    "keywords": ["air fryer", "rice cooker"],
    "region": "US",
    "time_range": "today 3-m",
    "language": "en-US"
  }
]

• keywords (list[str], required) — keywords to compare.
• region (str) — e.g. US, GB. Default US.
• time_range (str) — e.g. today 1-m, today 3-m, today 12-m. Default today 12-m.
• language (str) — e.g. en-US, en-GB. Default en-US.

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How it works

The script talks to the Pangolinfo MCP endpoint (mcp.pangolinfo.com/mcp) over streamable HTTP — the same Model Context Protocol server AI assistants use — and calls the keyword_trends tool. Responses are parsed defensively and stored in a local SQLite database (data/trends.db). Everything is Python standard library only; there is nothing to pip install.

Prefer no code? Connect Claude / Cursor / Windsurf / ChatGPT to https://mcp.pangolinfo.com/mcp and call keyword_trends directly.

Full setup + automation guide: docs/SETUP.md.

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🌐 Pangolinfo ecosystem

🛰️ More free tools by @pangolinfoapi

🏠 Hub: All tools, landing pages & tutorials

• amazon-keyword-rank-tracker — track your Amazon keyword rankings daily
• amazon-niche-finder — discover low-competition Amazon niches
• amazon-review-analyzer — Amazon review sentiment + complaint/praise theme mining

🏗️ Built on the official Pangolinfo projects (by @Pangolin-spg)

• amazon-walmart-shopify-scrape-api ⭐ 56 — the underlying Scrape / SERP API (Google Trends via the SERP API)
• openclaw-skill-pangolinfo ⭐ 8 — real-time web scraper skill for OpenClaw & AI agents (Google SERP + AI Overviews)
• openclaw-skills ⭐ 3 — OpenClaw skills: AI SERP + Amazon Scraper
• pangolinfo-amazon-scraper — official Python SDK for the Pangolinfo Scrape API

Full map of official Pangolinfo projects, skills and the live MCP endpoint: related-projects.md.

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FAQ

Do I need a Google account or the Google Trends UI? No. Trends are fetched via the Pangolinfo SERP/Google Trends API — one call returns the full timeline.

Is my API key safe? Yes — it is an env var locally and an encrypted Actions secret on GitHub, never committed.

How many keywords can I track for free? 200 free calls; one run makes ~1 call per keyword group in keywords.json.

Is the score comparable across keywords? Google Trends scores are relative within the requested keyword set and time window (0–100), ideal for comparing co-tracked terms.

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Roadmap

• Multi-keyword comparison charts
• "Related queries" capture (rising / top)
• Alert on breakout (sudden spike)
• Geo map (interest by region)

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Contributing

Ideas and PRs welcome — see CONTRIBUTING.md. Ecosystem map: related-projects.md.

License

MIT © 2026 pangolinfo