The cryptocurrency market moves fast, and staying ahead of trends can make all the difference. Whether you're building a trading bot, portfolio tracker, or market analysis tool, having access to real-time token data is crucial. Today, I'll walk you through the GMGN.AI API - a powerful tool for tracking trending tokens across multiple blockchains.
What is GMGN.AI API?
GMGN.AI provides a comprehensive API that allows developers to access trending token data across major blockchains including Ethereum, Solana, Base, BSC, and Tron. What sets it apart is its focus on smart money tracking and safety filters to help identify legitimate opportunities while avoiding honeypots and scams.
Important Implementation Considerations
⚠️ Network Protection Notice: The GMGN.AI API is protected by advanced security measures including Cloudflare protection. For production applications, you'll need to implement appropriate request handling solutions that can work with protected endpoints. Consider using proxy services, rotating user agents, or specialized HTTP clients that can handle modern web security measures.
Key Features
🔍 Multi-chain Support: Ethereum, Solana, Base, BSC, and Tron
📊 Multiple Sorting Criteria: Volume, market cap, holder count, smart money activity, and more
🛡️ Safety Filters: Built-in honeypot detection, verification status, and ownership renouncement
⏱️ Flexible Time Periods: From 1-minute to 24-hour trending data
💡 Smart Money Insights: Track what experienced traders are buying and selling
Getting Started
The GMGN.AI API uses a RESTful endpoint structure that's easy to understand and implement:
https://gmgn.ai/defi/quotation/v1/rank/{chain}/swaps/{time_period}?orderby={criteria}&direction={direction}&filters[]={safety_filters}
Basic Parameters
Supported Chains {chain}:
-
eth- Ethereum -
sol- Solana -
base- Base -
bsc- Binance Smart Chain -
tron- Tron
Time Periods {time_period}:
-
1m,5m,1h,6h,24h
Sorting Criteria {criteria}:
-
volume- 24h trading volume -
marketcap- Market capitalization -
swaps- Number of transactions -
holder_count- Number of holders -
smartmoney- Smart money activity -
liquidity- Available liquidity - And many more...
Direction {direction}:
-
asc: Ascending order -
desc: Descending order
Practical Examples
Let's dive into some real-world use cases with code examples.
Level 1: Basic Token Discovery
High-Volume Ethereum Token Scanner
async function getHighVolumeTokens() {
const url = 'https://gmgn.ai/defi/quotation/v1/rank/eth/swaps/6h?orderby=volume&direction=desc&filters[]=not_honeypot&filters[]=verified&filters[]=renounced';
// Note: For production use, implement appropriate request handling
// that can work with protected endpoints
const requestOptions = {
method: 'GET',
headers: {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36',
'Accept': 'application/json',
'Referer': 'https://gmgn.ai/',
}
};
try {
const response = await fetch(url, requestOptions);
const data = await response.json();
if (data.code === 0) {
const topTokens = data.data.rank.slice(0, 10);
topTokens.forEach((token, index) => {
console.log(`${index + 1}. ${token.symbol}`);
console.log(` Volume: ${token.volume.toLocaleString()}`);
console.log(` Price: ${token.price}`);
console.log(` Market Cap: ${token.market_cap.toLocaleString()}`);
console.log(` Holders: ${token.holder_count}`);
console.log('---');
});
}
} catch (error) {
console.error('Error:', error);
// Consider implementing retry logic with exponential backoff
}
}
getHighVolumeTokens();
Level 2: Advanced Smart Money Analytics
Intelligent Smart Money Tracker
import requests
import pandas as pd
import time
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
def create_session_with_retries():
"""Create a requests session with retry strategy and proper headers"""
session = requests.Session()
# Configure retry strategy
retry_strategy = Retry(
total=3,
backoff_factor=1,
status_forcelist=[429, 500, 502, 503, 504],
)
adapter = HTTPAdapter(max_retries=retry_strategy)
session.mount("http://", adapter)
session.mount("https://", adapter)
# Set headers that work better with protected endpoints
session.headers.update({
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36',
'Accept': 'application/json, text/plain, */*',
'Accept-Language': 'en-US,en;q=0.9',
'Referer': 'https://gmgn.ai/',
'Origin': 'https://gmgn.ai'
})
return session
def track_smart_money(chain='sol', time_period='24h'):
url = f"https://gmgn.ai/defi/quotation/v1/rank/{chain}/swaps/{time_period}"
params = {
'orderby': 'smartmoney',
'direction': 'desc',
'filters[]': ['not_honeypot', 'verified', 'renounced']
}
session = create_session_with_retries()
try:
response = session.get(url, params=params, timeout=30)
data = response.json()
if data['code'] == 0:
tokens = data['data']['rank']
# Create DataFrame for analysis
df = pd.DataFrame([{
'symbol': token['symbol'],
'price': token['price'],
'smart_buys': token['smart_buy_24h'],
'smart_sells': token['smart_sell_24h'],
'smart_ratio': token['smart_buy_24h'] / max(token['smart_sell_24h'], 1),
'volume': token['volume'],
'holders': token['holder_count']
} for token in tokens[:20]])
# Filter for tokens with strong smart money interest
hot_tokens = df[df['smart_ratio'] > 3] # More smart buys than sells
print("🔥 Tokens with Strong Smart Money Interest:")
print(hot_tokens.to_string(index=False))
return hot_tokens
except requests.exceptions.RequestException as e:
print(f"Request failed: {e}")
# In production, consider implementing alternative data sources
return None
# Usage
smart_tokens = track_smart_money('sol', '6h')
Level 3: Enterprise-Grade API Client
Production-Ready GMGN Client
class GMGNApiClient {
constructor() {
this.baseUrl = 'https://gmgn.ai/defi/quotation/v1/rank';
this.defaultHeaders = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36',
'Accept': 'application/json',
'Accept-Language': 'en-US,en;q=0.9',
'Referer': 'https://gmgn.ai/',
'Cache-Control': 'no-cache'
};
this.requestQueue = [];
this.isProcessing = false;
}
async makeRequest(url, options = {}) {
return new Promise((resolve, reject) => {
this.requestQueue.push({ url, options, resolve, reject });
this.processQueue();
});
}
async processQueue() {
if (this.isProcessing || this.requestQueue.length === 0) return;
this.isProcessing = true;
while (this.requestQueue.length > 0) {
const { url, options, resolve, reject } = this.requestQueue.shift();
try {
// Add delay between requests to avoid rate limiting
await this.delay(1000);
const response = await fetch(url, {
...options,
headers: { ...this.defaultHeaders, ...options.headers }
});
if (!response.ok) {
throw new Error(`HTTP ${response.status}: ${response.statusText}`);
}
const data = await response.json();
resolve(data);
} catch (error) {
console.warn(`Request failed, retrying: ${error.message}`);
// Implement exponential backoff for retries
await this.delay(2000);
try {
const retryResponse = await fetch(url, {
...options,
headers: { ...this.defaultHeaders, ...options.headers }
});
if (retryResponse.ok) {
const data = await retryResponse.json();
resolve(data);
} else {
reject(new Error(`Retry failed: ${retryResponse.status}`));
}
} catch (retryError) {
reject(retryError);
}
}
}
this.isProcessing = false;
}
delay(ms) {
return new Promise(resolve => setTimeout(resolve, ms));
}
async getTrendingTokens(chain, timePeriod, orderBy = 'volume', filters = ['not_honeypot']) {
const params = new URLSearchParams({
orderby: orderBy,
direction: 'desc'
});
filters.forEach(filter => params.append('filters[]', filter));
const url = `${this.baseUrl}/${chain}/swaps/${timePeriod}?${params}`;
return this.makeRequest(url);
}
}
// Usage example
const apiClient = new GMGNApiClient();
async function findNewGems(chain = 'base') {
try {
const data = await apiClient.getTrendingTokens(
chain,
'1h',
'open_timestamp',
['not_honeypot', 'verified', 'renounced']
);
if (data.code === 0) {
const currentTime = Math.floor(Date.now() / 1000);
const newTokens = data.data.rank.filter(token => {
const tokenAge = currentTime - token.open_timestamp;
return tokenAge <= 3600 && // Less than 1 hour old
token.holder_count > 50 &&
token.volume > 10000;
});
console.log('💎 New Gems Found:', newTokens.length);
return newTokens;
}
} catch (error) {
console.error('Failed to fetch new gems:', error);
return [];
}
}
Best Practices for Production Use
1. Implement Robust Error Handling
class APIErrorHandler {
static async handleRequest(requestFn, maxRetries = 3) {
for (let attempt = 1; attempt <= maxRetries; attempt++) {
try {
return await requestFn();
} catch (error) {
console.log(`Attempt ${attempt} failed:`, error.message);
if (attempt === maxRetries) {
throw new Error(`All ${maxRetries} attempts failed: ${error.message}`);
}
// Exponential backoff with jitter
const delay = Math.min(1000 * Math.pow(2, attempt) + Math.random() * 1000, 30000);
await new Promise(resolve => setTimeout(resolve, delay));
}
}
}
}
2. Request Rate Limiting
class RateLimiter {
constructor(requestsPerMinute = 30) {
this.requests = [];
this.limit = requestsPerMinute;
}
async waitIfNeeded() {
const now = Date.now();
this.requests = this.requests.filter(time => now - time < 60000);
if (this.requests.length >= this.limit) {
const oldestRequest = Math.min(...this.requests);
const waitTime = 60000 - (now - oldestRequest) + 1000; // Add 1s buffer
console.log(`Rate limit reached, waiting ${waitTime}ms`);
await new Promise(resolve => setTimeout(resolve, waitTime));
}
this.requests.push(now);
}
}
3. Data Caching Strategy
class CacheManager {
constructor(ttl = 60000) { // 1 minute default TTL
this.cache = new Map();
this.ttl = ttl;
}
set(key, value) {
this.cache.set(key, {
value,
timestamp: Date.now()
});
}
get(key) {
const item = this.cache.get(key);
if (!item) return null;
if (Date.now() - item.timestamp > this.ttl) {
this.cache.delete(key);
return null;
}
return item.value;
}
clear() {
this.cache.clear();
}
}
Conclusion
The GMGN.AI API is a powerful tool for cryptocurrency developers and traders. Its combination of multi-chain support, safety filters, and smart money insights makes it invaluable for building sophisticated trading tools and market analysis applications.
Key takeaways:
- ✅ Always use safety filters to avoid honeypots
- ✅ Pay attention to smart money metrics for better insights
- ✅ Implement proper error handling and rate limiting
- ✅ Combine multiple sorting criteria for comprehensive analysis
- ✅ Use liquidity lock information to assess token safety
Whether you're building a trading bot, portfolio tracker, or market analysis tool, the GMGN.AI API provides the real-time data you need to stay ahead in the fast-moving crypto market.
Have you used the GMGN.AI API in your projects? Share your experiences and use cases in the comments below!
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