Can I export data from multiple sources into a single file?

Yes. The agent can merge data from multiple connected sources by joining or appending records. Define the merge logic in your transformation rules, specifying which columns to join on or how to stack records from different sources.

What happens if an export fails?

Failed exports are logged with error details in the Automations panel. The agent does not send partial or corrupted data to the destination. You can review the error, fix the issue, and re-run the export manually or wait for the next scheduled run.

Is there a size limit for exports?

There is no hard limit, but very large exports may take longer to process. For datasets with millions of rows, consider using incremental exports or splitting the data into smaller batches. The agent will warn you if a single export is approaching practical limits.

Data

Data Export and Transformation

Extract data from your connected sources, reshape it with custom transformations, and export the results to CSV, JSON, APIs, or other destinations on a schedule.

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What You Will Get

By the end of this guide, your OpenClaw agent will handle complete data pipelines: pulling data from one or more sources, applying transformations like filtering, aggregating, and reformatting, then exporting the results to your chosen destination. This eliminates manual data wrangling and ensures your exports are consistent every time.

Transformations include column renaming, data type conversion, value mapping, deduplication, and aggregation. You describe what you need in plain language or provide specific mapping rules, and the agent builds the pipeline accordingly. The agent can also merge data from multiple sources into a single export.

Exports can be one-time or scheduled. Common destinations include CSV files, JSON files, Google Sheets, webhooks, and API endpoints. Each export run is logged with row counts and status, so you always know whether your pipeline succeeded and how much data was processed.

Step-by-Step Setup

Build a data export pipeline from source to destination.

Identify Your Source Data

Determine which data you need to export and where it lives. This could be a database table, an API endpoint, a Google Sheet, or a combination of sources. Make note of the key columns, filters, and date ranges you want to include in the export.

Connect the Data Sources

If not already connected, add each data source in the Data Sources panel on RunTheAgent. Verify the connections by running a test query or fetching a sample. The agent needs reliable access to pull data during export runs.

Define the Transformation Rules

Tell your agent how to transform the data. You can describe transformations in plain language, like 'Rename the column user_id to Customer ID, convert dates to YYYY-MM-DD format, and remove rows where status is cancelled.' The agent translates these instructions into a repeatable pipeline configuration.

Choose the Export Format

Select the output format for your export. CSV works well for spreadsheet consumption, JSON for API integrations, and direct API calls for pushing data into other systems. Specify any formatting requirements like delimiter characters, encoding, or header rows.

Set the Export Destination

Configure where the export file or data should be delivered. Options include downloading to your local machine, saving to a connected cloud storage service, writing to a Google Sheet, or sending to a webhook URL. The agent handles authentication and delivery automatically.

Schedule Recurring Exports

If this export needs to run regularly, create a scheduled automation in the Automations panel. Set the frequency, time, and any dynamic parameters like date ranges that should update automatically with each run. The agent will execute the full pipeline on schedule.

Verify the Output

Run the export manually for the first time and inspect the output. Check that columns are named correctly, data types are as expected, and row counts match your source data. Review the Logs tab for any warnings or errors. Adjust the transformation rules if anything needs correction.

Tips and Best Practices

Use Incremental Exports for Large Datasets

Instead of exporting all data every time, configure incremental exports that only include new or updated records since the last run. This reduces processing time and keeps export files manageable. Use a timestamp or ID column as the watermark for incremental tracking.

Validate Data Before Exporting

Add validation rules to your pipeline that check for null values, invalid formats, or unexpected duplicates before the data reaches its destination. Catching issues early prevents downstream problems in the systems that consume your exports.

Keep a Log of All Exports

The Logs tab on RunTheAgent records every export run with timestamps, row counts, and success or failure status. Review these logs regularly to catch silent failures and track export volume trends over time.

Version Your Transformation Rules

When you change a transformation, note the date and reason. If a downstream system breaks after a change, you can quickly identify what was modified and revert if needed. Keeping this history in your agent's saved queries or notes makes troubleshooting faster.

Frequently Asked Questions

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