DevToolsForYou

JSON & Data Format Tools

7 free browser-based tools

JSON has become the default interchange format for APIs, configuration files, and data pipelines. These tools help you work with JSON and related formats without leaving the browser: format and validate raw JSON responses from APIs, convert between JSON and CSV for spreadsheet workflows, switch between JSON and YAML for configuration files, and generate typed code from a JSON schema. All transforms are client-side — paste your data, get the result, move on.

Free online JSON and data format tools — JSON formatter and validator, JSON to CSV converter, JSON to YAML converter, and JSON code generator. Browser-based with no data sent to a server.

Frequently asked questions

Why does the JSON formatter show a syntax error?

Common causes are trailing commas after the last item in an object or array (not allowed in JSON), single quotes instead of double quotes around keys or strings, unquoted keys, and comments (JSON has no comment syntax). The formatter reports the line number where parsing failed so you can locate the issue quickly.

What is the difference between JSON and YAML?

Both represent structured data, but YAML uses indentation and minimal punctuation to express the same structure that JSON expresses with braces, brackets, and commas. YAML supports comments and multiline strings natively. JSON is a strict subset of JavaScript object notation and is easier to parse programmatically. Configuration files often use YAML; API responses almost always use JSON.

What happens to nested JSON when converting to CSV?

CSV is a flat, two-dimensional format. The JSON to CSV converter expects a top-level array of objects with scalar values. Nested objects and arrays are serialised as strings in the CSV output. If your JSON has deep nesting, flatten it first or use the JSON formatter to inspect the structure before converting.

What does the JSON code generator produce?

The JSON code generator takes a JSON object or array as input and outputs typed code representations in languages such as TypeScript interfaces, Go structs, and Python dataclasses. This speeds up the common task of writing data models to match an API response you have already seen.