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How to Scrape DexScreener — Free No-Code Guide (2026)

#dexscreener #webscraping #api #python

If you need to pull DexScreener data at scale and there's no clean official API, this guide shows how to export it to JSON, CSV or Excel in a couple of minutes — with no scraping code to maintain.

We'll use the DexScreener Scraper - Boosted & Trending Tokens API, a hosted scraper on the Apify platform. You only need a free Apify token — no DexScreener login, no proxies, no reverse-engineering.

What data you get

Each run returns structured records with fields like:

  • source
  • chainId
  • baseTokenSymbol
  • boostTotalAmount
  • priceUsd
  • liquidityUsd
  • volumeH24
  • priceChangeH24
  • marketCap
  • url
  • query
  • dexId
  • tokenAddress
  • pairAddress

Results are paginated automatically — you can pull thousands of rows per run and export them as JSON, CSV, Excel, JSONL or XML.

Option 1 — No code (Apify Console)

  1. Open the actor: https://apify.com/logiover/dexscreener-boosted-tokens-scraper
  2. Click Try for free.
  3. Leave the input empty for a broad sample, or set the fields below.
  4. Run it, then Export the dataset as CSV/JSON/Excel.

Option 2 — Node.js 

npm install dexscreener-boosted-tokens-scraper
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const scrape = require("dexscreener-boosted-tokens-scraper");

(async () => {
  const items = await scrape({}, { token: process.env.APIFY_TOKEN });
  console.log(items.length, "results");
  console.log(items[0]);
})();
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Option 3 — Python 

pip install dexscreener-boosted-tokens-scraper
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from dexscreener_boosted_tokens_scraper import scrape

items = scrape({}, token="YOUR_APIFY_TOKEN")
print(len(items), "results")
print(items[0])
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Grab a free token at https://console.apify.com/account/integrations.

Input options

Field Type What it does
mode string Data mode
sources array Boost sources
minBoostAmount integer Min boost amount
includeOrders boolean Include marketing orders
enrichWithPairData boolean Enrich with pair data
queries array Search queries
matchSymbol string Symbol contains
exactSymbol string Symbol equals
maxPairsPerQuery integer Max pairs per query
tokenAddresses array Token addresses
chainId string Chain (optional, enables batching)
maxPairsPerToken integer Max pairs per token

Common use cases

  • Build a fresh DexScreener dataset for analysis or dashboards
  • Feed DexScreener records into your own app, CRM or spreadsheet
  • Monitor changes on a schedule and get alerts via webhooks
  • Enrich leads or research with up-to-date DexScreener data

Automation & export

Because it runs on Apify, you can schedule it, trigger it via webhook, and pipe results into Google Sheets, S3, Zapier, Make or n8n. Every run's dataset is downloadable as CSV, JSON, JSONL, Excel or XML.

FAQ

Do I need an API key? Only a free Apify token. No DexScreener account needed.

How many results can I get? Thousands per run; raise the limit to pull more.

Is it an official DexScreener API? No — it's an unofficial, maintained alternative when there's no official or affordable API.

Can I export to CSV/Excel? Yes — JSON, CSV, JSONL, Excel and XML.

▶️ Run the DexScreener Scraper - Boosted & Trending Tokens API on Apify: https://apify.com/logiover/dexscreener-boosted-tokens-scraper

📚 Docs & code examples: https://github.com/logiover/dexscreener-boosted-tokens-scraper

🧰 More scrapers: https://apify.com/logiover

Top comments (1)

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topstar_ai profile image
Luis •

I particularly like how the article highlights the flexibility of the DexScreener Scraper - Boosted & Trending Tokens API, allowing users to export data in various formats such as JSON, CSV, and Excel. The fact that it's hosted on the Apify platform and can be scheduled or triggered via webhook makes it a powerful tool for automating data collection. I've had experience with web scraping projects in the past, and I can appreciate the value of having a maintained, unofficial API alternative when official APIs are not available or affordable. Have you considered exploring other potential use cases for this scraper, such as integrating it with data visualization tools or machine learning models to gain deeper insights from the data?