ToolzyLab
Transform plain text into valid JSON structure or turn JSON back into readable text instantly in your browser. Fast, private, compact, and built with a professional side-by-side workspace for desktop and mobile.
Perfect for developers, students, APIs, configs, debugging, quick data prep, and everyday formatting work. Convert line-based text into JSON arrays, key-value pairs into JSON objects, CSV-like rows into nested arrays, or turn JSON back into readable text in one click.
Paste text below or upload a file to convert it instantly.
📄 .txt .json .csv · ⚡ instant conversion · 📥 download result · 🧠 smart detection · 🔒 no uploadPaste raw text or JSON on the left. The right side shows the converted result inside a compact, scrollable output pane.
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Inspect detected format, line count, objects, arrays, keys, and a compact structural preview before converting.
Use one item per line for clean JSON arrays, or use key:value per line for quick object creation. Turn on type inference to automatically convert values like 12, true, or null into real JSON types.
See quick stats about the current output including direction, detected input type, mode, item count, and overall size.
It can detect whether your input is plain text or JSON, convert between both formats, build arrays or objects from text, flatten JSON back into readable text, and keep everything compact with scrollable workspaces.
Plain text has no universal schema, so conversion requires a rule: lines, delimited values, key-value pairs, or a wrapped string. Choose direction, mode, separator, indentation, trimming, empty-line handling, type inference, key sorting, and root wrapper, then validate duplicate keys, escaped characters, nulls, numbers, and line endings.
Independent lines can become an array of strings, key-value lines can become an object, and an entire document can remain one escaped string. A delimiter inside the data needs escaping or a split-once rule. Duplicate keys cannot coexist reliably in a normal JSON object, so detect them before conversion.
Automatic inference is convenient for controlled datasets but can remove leading zeros, change large IDs, or turn the word null into a null value. Keep inference off when preserving exact text is more important, and define decimal, sign, and locale behavior for numeric data.
Joining array values by lines is straightforward until a value contains a newline. Object key-value output needs a separator and escaping rule. Nested objects and arrays may need to remain serialized JSON rather than being flattened into ambiguous text. Verify the round trip before building an import process.
Test the selected mode with delimiters, empty values, Unicode, and nested examples.
Technical references: RFC 8259 JSON specification
An array of strings is usually the clearest representation. Decide whether blank lines are values or should be ignored.
Split each line with the configured separator, usually at the first valid occurrence, trim according to policy, and reject or resolve duplicate keys.
Only for controlled input with a defined schema. Otherwise IDs, leading-zero values, large integers, and literal words can change meaning.
A simple split can break the value. Use a split-once rule, escaping, quoting, or a structured format that supports embedded delimiters.
It can be serialized or flattened under explicit rules, but plain text cannot preserve arbitrary nesting without a convention. Avoid an ambiguous output.
Only when the chosen text format preserves types, separators, nesting, empty values, and escaping. Test the round trip with edge cases.