Reshape JSON with JavaScript recipes, custom scripts, and AI-drafted transformations. Filter, map, rename, flatten, group, preview, and apply everything locally with no login required.
The most effective way to use JSON Transformer is as the structure-change layer in your workflow. Start with JSON that already parses, decide whether to begin from a built-in recipe or an AI-drafted script, verify the result in preview, then apply, export, or continue to the table editor, formatter, and schema tools.
From raw payload to target shape
In real work, the job is usually not changing one value. It is turning a noisy source payload into a cleaner structure that fits a frontend list, report, or downstream API.
This kind of filter + rename + deep-field extraction flow is a good case for starting with AI Transform, then validating locally in Preview.
These examples cover cleanup, field reshaping, and value extraction, which are some of the most common tasks when working with APIs, exports, and config payloads.
Useful when you want to remove archived users, failed records, or test entries before exporting, analyzing, or loading the data elsewhere.
Filtering first usually makes every later step cleaner and faster.
Useful when you want a simpler structure for frontend tables, reports, CSV export, or typed models.
A flatter output is usually easier to inspect in the table editor and easier to turn into schema or code.
Useful when you need an allowlist of IDs, a list of SKUs, or any one-column output without manually copying values.
Column extraction is one of the fastest wins from either a recipe template or an AI-drafted script.
Tutorial Step
Step 1 - Start with JSON that already parses cleanly
Before transforming anything, make sure the input is valid JSON. Transformer is not a repair tool. It works best once the data is parseable and the remaining problem is the structure, not the syntax.
Tutorial Step
Step 2 - Choose a recipe or let AI Transform draft the first script
This is where you decide whether a built-in recipe is enough or whether AI should generate a stronger starting point. Recipes are fast for common patterns, while AI helps when the rule is more specific to your business data.
Tutorial Step
Step 3 - Use Preview to validate the output before you overwrite anything
The most important safety feature on this page is Preview. It lets you confirm what the script actually produces before you replace the input or export the result.
Tutorial Step
Step 4 - Apply the result and move it into the rest of your JSON workflow
Once the preview looks correct, apply the result back into the input editor or copy and export it. From there, you can continue into visual review, formatting, schema generation, or downstream integration work.
A more reliable JSON Transformer workflow
Use Validator or Formatter first so the input is parseable and easy to inspect before you start transforming it.
Start from a recipe or an AI Transform draft instead of writing every script from a blank file.
After every script change, run Preview and inspect representative rows before you click Apply.
Once the transformation is stable, copy, export, or move the result into the table editor, formatter, schema generator, or compare tool.
Save successful scripts for reuse. Similar payloads become much faster to handle the next time.
JSON Transformer is most valuable between formatting and visual editing: it is faster than manual text edits, but lighter and easier to iterate on than writing a full standalone data-processing program.
Practical tips
Open the transformed JSON in a table to inspect rows, filter data, and make final manual edits.
Format the input or output JSON so structural changes are easier to read and review.
Validate the payload before transforming it, or re-check the output to confirm it is still valid JSON.
Repair broken JSON syntax first when the original payload cannot be parsed cleanly.
It is best for reshaping valid JSON into a structure that is easier to use elsewhere. Typical jobs include filtering rows, renaming fields, filling defaults, extracting one field as a list, flattening nested objects, or regrouping records for downstream APIs, tables, or reports.
Not necessarily. The page includes ready-made recipes for common operations such as filtering, mapping, flattening, deduping, and renaming keys. If you do not want to start from scratch, AI Transform can draft a script for you from a plain-language goal.
No. AI Transform drafts the script, but it does not bypass your review. You still inspect the generated script, run a preview, confirm the output, and choose whether to apply it to the current JSON. That keeps the workflow fast without removing control.
Script execution, preview, apply, and export all run locally in your browser. The AI assistant may help draft transformation logic from your prompt, but the resulting JSON transformation still runs locally, which is better suited to internal payloads, config objects, and sensitive samples.
Table Editor is better for visual inspection and manual edits, while Formatter is better for readability, minifying, and basic cleanup. JSON Transformer is better when you need to reshape the data itself, such as renaming fields, extracting columns, flattening nested structures, or producing a new output shape.
Use Validator or Repair first when the JSON is not even valid yet. Transformer works best after the payload can already be parsed and you are focused on restructuring the data instead of fixing syntax errors.
