See llms.txt for all machine-readable content.
This workflow runs every 15 minutes to fetch recent blockchain transactions, matches them against whale and exchange address lists in Airtable, uses OpenAI to generate a cautious market interpretation for high-value events, then logs the result to Airtable and posts an alert to Slack.
The existing workflow can be extended with additional functionality.
Add different alert levels for different transaction values, such as $100,000, $500,000, and $1 million.
Extend the classification logic with more movement types when suitable reference data is available.
Use the logged Airtable events to identify repeated whale activity and historical movement patterns.
Add a separate reporting process to summarize whale activity over a selected period.
Use the AI confidence value to route events differently based on confidence levels.
Extend the notification stage to deliver qualifying events through additional communication channels.
Replace the current configured transaction endpoint with a production transaction source while retaining the existing normalization, classification, validation, AI, logging, and notification structure.
These features are possible extensions and are not part of the current workflow configuration.
A tracked whale sends funds to a known exchange wallet. The workflow classifies the movement as an Exchange Deposit, calculates its USD value, and generates an alert when the transaction meets the configured threshold and passes duplicate validation.
A known exchange wallet sends funds to a tracked whale. The workflow identifies the transaction as an Exchange Withdrawal and sends the resulting analysis and transaction information to Slack when the event qualifies.
A tracked whale sends funds to an address that is not listed as another tracked whale or known exchange. The workflow classifies the transaction as Wallet-to-Wallet and processes it when it meets the configured threshold.
Teams can use the $100,000 threshold to focus the workflow on high-value whale movements rather than sending alerts for every transaction returned by the transaction source.
Qualifying events are stored in Airtable with transaction information, classification, USD value, AI interpretation, confidence, and alert metadata, providing a central event history.
There can be many more use cases depending on the transaction data source, wallet registries, business rules, and notification requirements.
| Issue | Possible Cause | Solution |
|---|---|---|
| No transactions are processed | The HTTP response does not contain a transactions array or the array is empty |
Inspect the HTTP Request output and verify the response structure |
| HTTP Request fails | The configured endpoint cannot be reached from n8n | Verify the URL and network accessibility |
| Transactions disappear after normalization | The transaction collection is missing or empty | Check the HTTP response and confirm the transactions property |
| Whale movement is not classified | The wallet is not present as an active whale or exchange address | Check the Airtable wallet registries and address values |
| Exchange movement is classified incorrectly | The wallet is listed in the wrong registry or has an unexpected active value | Review the Tracked Whales and Exchange Addresses records |
| USD value is incorrect | Amount or USD price is missing or not numeric | Verify amount and price_usd in the transaction response |
| Transaction does not reach the AI step | The USD value is below the configured threshold or the event is considered a duplicate | Inspect Check Alert Threshold and Confirm New Whale Event |
| Duplicate event is processed | The duplicate lookup identifier does not match the stored event identifier | Review the duplicate filter and the event_id mapping |
| OpenAI does not return an interpretation | OpenAI credentials or node configuration are incorrect | Check the OpenAI credential and inspect the node execution output |
| AI response is not parsed | The returned response does not match the expected JSON structure | Verify that the response contains interpretation and confidence |
| AI confidence is incorrect | Confidence is missing, invalid, or outside the expected range | Inspect the AI response and normalization logic |
| Airtable logging fails | Credential, table, or field mapping is incorrect | Verify the Airtable credential, table, and mapped fields |
| Slack notification fails | Slack credential or channel configuration is incorrect | Verify the Slack credential and selected channel |
| Workflow runs but no Slack alert appears | The transaction was filtered by threshold or duplicate validation | Review the execution path through the IF nodes |
The current workflow contains an identifier mismatch that should be reviewed before production use.
The duplicate-check node searches for:
event_id = transaction_hash
However, the Log Whale Movement Event node currently writes event_id using:
sender-timestamp
These values are different.
As a result, the duplicate lookup may not reliably find an event that was previously logged.
If the transaction hash is intended to be the unique event identifier, the event logging mapping should use the transaction hash consistently. Alternatively, the duplicate-check expression should use the same identifier that is stored in Airtable.
Setting up an n8n workflow for production use may require adjustments to data sources, Airtable structures, business rules, AI prompts, notification formats and error handling.
WeblineIndia can help with:
If you need help setting up, customizing, extending, or deploying this workflow, contact WeblineIndia for professional n8n automation development and customization services.