Gumroad Payouts API Just Got a Game-Changing Upgrade for Bookkeepers Your 2025 Transactions Are Now Scrutinizable

The Game-Changing Addition: include_transactions=true

The world of creator finance management just received a significant shot in the arm. On Feb 13, 2026 · 6:52 PM UTC, the ecosystem surrounding Gumroad's financial infrastructure was upgraded with a powerful new parameter for its Payouts API. This development, heralded by @shl, introduces the capability to append include_transactions=true to API calls querying payout data. This seemingly simple addition holds profound weight for financial operations: it mandates the return of all underlying transactions that constitute a specific payout record.

Previously, extracting the granular detail necessary for meticulous reconciliation often required layering multiple API calls or relying on bulky export files. Now, this single parameter flips the script, bringing the necessary depth directly to the automated data stream. It moves the API response from a high-level summary to a comprehensive ledger entry for every aggregated transfer.

Implications for Bookkeepers and Financial Reconciliation

For those tasked with ensuring creator accounts balance to the penny—the bookkeepers, CPAs, and financial analysts—this update represents a monumental leap forward in efficiency and accuracy. The headache of cross-referencing summarized payout totals against individual sales records has been significantly mitigated.

The primary benefit surfaces immediately in Bank Reconciliation Simplified. When a lump sum hits a creator's bank account corresponding to a Gumroad payout, bookkeepers traditionally had to download the Payouts CSV, isolate that payout ID, and then manually (or via complex custom scripting) match the figures against transaction logs. With include_transactions=true, the API response now contains the necessary line-item details—every sale, refund, or fee that built that final sum—directly nested within the payout object. This drastically streamlines the process of validating bank deposits against source activities.

Furthermore, this upgrade champions Data Parity. @shl confirmed that the structure of the data returned when this parameter is utilized now mirrors the established Payouts CSV export format. This is not just a convenience; it is a stabilizer. For organizations that have built robust, decade-old scripts reliant on the CSV schema, integrating this API functionality becomes seamless, requiring minimal structural overhaul to gain real-time data access.

This depth of data naturally leads to improved Accuracy in Auditing. Whether tracking year-over-year trends or conducting spot-checks for specific sales cohorts, having the complete transactional breakdown available programmatically means audit trails are not just easier to access, they are inherently more robust. Scrutinizing historical records becomes less about digging through archives and more about running targeted queries against a single, rich data source.

Accessing Granular Payout Details

The shift is fundamentally one from summary reporting to line-item detail for every payout processed by the platform. While the core API endpoint remains the same, the context of the data retrieved fundamentally changes based on the inclusion of this boolean flag.

Bookkeepers and developers are directed to the official documentation, specifically the section detailing payouts (gumroad.com/api/#payouts), for full parameter definitions and data structure maps. The practical implementation requires nothing more than appending the new query argument, yet the impact on data retrieval architecture is profound. It unlocks the ability to perform immediate decomposition of complex financial events right where the data is first ingested.

Automating 2025 Data Extraction: The Bash Script Breakdown

To illustrate the practical application of this upgrade, @shl provided a powerful, single-line demonstration built in Bash, specifically targeting data extraction for the 2025 fiscal year. This example showcases how modern, lean scripting can leverage the new API capability for historical analysis.

Script Objective and Core Mechanics

The script’s overarching goal is to systematically iterate through all Gumroad payouts generated during the entirety of 2025, capturing the detailed transaction data for each, and then aggregating the results based on transaction type. This entire process must elegantly handle the inherent limitations of API pagination.

The core mechanics rely on a while loop structure designed to traverse every page of payout records.

• Pagination Handling: The script continuously checks for the presence of next_page_key in the JSON response. If a key exists, the loop continues, appending the key to the next request URL (url="$url&page_key=$key"); when the key is absent, the iteration breaks, ensuring all available pages are processed.
• Date Filtering: A crucial piece of logic within the loop filters the returned payouts to strictly adhere to the target year (2025). It uses creation date checks (.created_at >= "2025-01-01T00:00:00Z") combined with an exit condition based on when the last processed payout falls before the start of the target year, providing an efficient exit strategy if the data set spans across multiple years.
• Transaction Inclusion: The moment a payout ID matching the date criteria is identified, the script executes a subsequent, targeted curl request for that specific payout, this time ensuring the critical new parameter is present: --data-urlencode "include_transactions=true".

Final Output Formatting

Once all the raw, detailed transaction objects are collected from the looping requests, the script utilizes the formidable text-processing tool jq to refine and structure the output. It performs several key formatting steps:

1. It filters the results to only include successful transactions (select(.success==true)).
2. It then groups all individual transactions by their designated .type (e.g., sale, fee, refund).
3. For each group, it calculates the sum of the .net_total field (after rounding to two decimal places using a custom r2 function), aggregating the financial impact of each transaction type.
4. The final result is outputted in a clean Tab-Separated Value (TSV) format, which is subsequently piped to the column utility for perfect terminal alignment, presenting the Type and the Total in a highly readable, organized manner.

This script serves as a potent template, showing how data professionals can immediately leverage this API enhancement to build instantaneous, granular financial reports tailored to any specific time frame.

Source: https://x.com/shl/status/2022383485588209964