In this section, we showcase how the AI Agent processes a user query and returns a structured response based on the query.
Which address on Ethereum was the most active in the last 24 hours.
D.A.T.A framework generated query:
D.A.T.A framework query result
User Query:
What is the gas prive average on Ethereum last 3 days.
Which stablecoin had the highest trading volume on Ethereum in the last 24 hours.
Last updated
{
"sql": {
"query": "WITH address_activity AS (\n SELECT\n from_address AS address,\n COUNT(*) AS tx_count\n FROM\n eth.transactions\n WHERE date_parse(date, '%Y-%m-%d') >= date_add('day', -1, current_date)\n GROUP BY\n from_address\n UNION ALL\n SELECT\n to_address AS address,\n COUNT(*) AS tx_count\n FROM\n eth.transactions\n WHERE\n date_parse(date, '%Y-%m-%d') >= date_add('day', -1, current_date)\n GROUP BY\n to_address\n)\nSELECT\n address,\n SUM(tx_count) AS total_transactions\nFROM\n address_activity\nGROUP BY\n address\nORDER BY\n total_transactions DESC\nLIMIT 1;",
"explanation": "This query identifies the most active Ethereum address in the last 24 hours by counting transactions where the address was either the sender (from_address) or the receiver (to_address). The results are aggregated and ordered by the total number of transactions, with the most active address returned.",
"timeRange": "Last 24 hours"
},
"analysis": {
"overview": {
"totalTransactions": "Number of transactions involving the most active address",
"timeSpan": "Last 24 hours",
"keyMetrics": ["Total transactions involving the address", "Address activity rank"]
},
"patterns": {
"transactionPatterns": ["High frequency of transactions", "Potential bot or automated activity"],
"addressBehavior": ["Possible exchange or DeFi protocol address", "High volume of incoming/outgoing transactions"],
"temporalTrends": ["Peak activity during specific hours", "Consistent transaction frequency"]
},
"statistics": {
"averages": {
"transactionsPerHour": "Average transactions per hour for the address"
},
"distributions": {
"transactionTypes": ["Incoming vs outgoing transactions", "Smart contract interactions"]
},
"anomalies": ["Unusual spikes in transaction volume", "Sudden changes in activity patterns"]
},
"insights": [
"The address may belong to a high-frequency trading bot or a centralized exchange.",
"High transaction volume could indicate market-making activity or arbitrage opportunities."
],
"recommendations": [
"Monitor the address for continued high activity to identify potential market trends.",
"Investigate the nature of transactions (e.g., DeFi interactions, NFT trades) for deeper insights."
]
}
} %%%% D.A.T.A. Generated SQL query:
WITH address_activity AS (
SELECT
from_address AS address,
COUNT(*) AS tx_count
FROM
eth.transactions
WHERE date_parse(date, '%Y-%m-%d') >= date_add('day', -1, current_date)
GROUP BY
from_address
UNION ALL
SELECT
to_address AS address,
COUNT(*) AS tx_count
FROM
eth.transactions
WHERE
date_parse(date, '%Y-%m-%d') >= date_add('day', -1, current_date)
GROUP BY
to_address
)
SELECT
address,
SUM(tx_count) AS total_transactions
FROM
address_activity
GROUP BY
address
ORDER BY
total_transactions DESC
LIMIT 1;
◎ LOGS
%%%% D.A.T.A. queryResult
{
"success": true,
"data": [
{
"address": "0xdac17f958d2ee523a2206206994597c13d831ec7",
"total_transactions": "118841"
}
],
"metadata": {
"total": 1,
"queryTime": "2025-01-15T08:04:54.644Z",
"queryType": "aggregate",
"executionTime": 0,
"cached": false
}
}